Donations accepted with Liberapay

If you would like to contribute to the continued development of Irucka Embry’s R packages and/or Irucka Embry’s R Examples, please feel free to donate via the link below:

Please feel free to review Irucka Embry (iaembry)’s profile on Liberapay.

Irucka Embry’s R Contributions [Questions and Answers on Stack Overflow, R Packages, & R Contributions to Existing Projects}

Irucka Embry’s Useful R Resources — [Requires PDF Software]
Stack Overflow: User iembry
Irucka Embry (iembry) GitLab project Repositories (includes all of the projects created by Irucka Embry at GitHub under iembry-USGS)
CRAN - Package iemisc: Irucka Embry’s Miscellaneous Functions
Irucka Embry (iembry) GitLab iemisc Repository: R package that contains Irucka Embry’s miscellaneous functions:

statistical analysis

root-mean-square (RMS),
coefficient of variation (CV),
approximate and relative error,
range,
harmonic mean,
geometric mean,
maximum mean relative error (MAXRE),
and mean relative error (MRE);

engineering economics

benefit-cost,
future value,
present value,
annual value,
gradients,
interest,
periods,
etc.;

geometry

polygon area,
sphere volume,
and right triangle validation;

civil & environmental/water resources engineering

Concrete Mix Design for Normal Strength & Structural Lightweight Concrete,
Construction Measurements with and without Fractions,
Engineering Surveying Calculations,
Air Stripping,
Saturated Vapor Pressure for Ice and Water,
Density of Water,
Dynamic and Kinematic Viscosity of Water,
Specific Gravity and Volume,
Unit Weight,
Surface Tension of Water,
Weighted C Factor,
Weighted Curve Number,
Composite Curve Number (Connected and Unconnected),
Darcy friction factor (f),
the Reynolds number,
Manning’s n,
Gauckler-Manning-Strickler equations for open channel flow,
Modified Rational Method Equation,
Linear Surface Area calculation,
and Rain Garden sizing based on driveways;

convert a fraction (or mixed number) to a decimal (numeric vector);
negation of chin from data.table and negation of in from base R (%notchin%);
quick search (%qsin%);
return character vectors in order (%inorder%);
mortality rate calculations;
proportion solver;
sum of all digits in a vector to a single integer;
string manipulation (splitcomma and splitremove);
a version of linear interpolation for use with NAs;
Python compatible floor division function (%//%);
and GNU Octave/MATLAB compatible functions

trigonometric functions in degrees,
section properties (secprop),
remainder (Rem),
modulus (Mod_octave),
number of dimensions (ndims),
row vector (isrow) and column vector (iscolumn) tests,
fractional differences (fractdiff),
size,
numel,
and length functions.

CRAN - Package iemiscdata: Irucka Embry’s Miscellaneous Data Collection
Irucka Embry (iembry) GitLab iemiscdata Repository: R data package with miscellaneous data sets [Chemistry, Engineering Economics, Environmental/Water Resources Engineering, Nuclear Accidents, US Presidential Elections, and US Continental Congress Presidents].

Chemistry

Table of Plant Nutrients,
27 Human Cell Salts,
Total Number of Atoms of Elements From the 27 Human Cell Salts Table,
Periodic Table of the Elements,
Gaseous Compounds and Elements,
Water Vapor Pressure;

Engineering Economics

Nominal Interest Rate,
Effective Interest Rate;

Environmental/Water Resources Engineering

Exceptional Tennessee Waters,
Greenhouse Gas Contributions,
Manning’s n Roughness Coefficients,
East Palestine
Stormwater-related Tables From NRCS,
Primary and Secondary Drinking Water Contaminants,
Tennessee Department of Transportation (TDOT) Machined riprap;

Nuclear Accidents

Civilian,

Fukushima Daiichi Nuclear Power Plant Explosions

Military;

US Presidential Elections;
and US Continental Congress Presidents

CRAN - Package chem.databases: Collection of 3 Chemical Databases from Public Sources
Irucka Embry (iembry) GitLab chem.databases Repository: R data package that contains 3 Chemical Databases from Public Sources. Contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) combined with the Toxic Substances Control Act (TSCA) Inventory and the Agency for Toxic Substances and Disease Registry (ATSDR) Database in 2 data sets. One data set has a focus on the latter 2 databases and one data set focuses on the former database. Also contains the collection of chemical data from Wikipedia compiled in the US EPA CompTox Chemicals Dashboard.
CRAN - Package install.load: Check, Install and Load CRAN & USGS GRAN Packages
Irucka Embry (iembry) GitLab install.load Repository: R package containing 2 functions dealing with installing and loading or simply loading packages. The function install_load checks the local R library(ies) to see if the required package(s) is/are installed or not. If the package(s) is/are not installed, then the package(s) will be installed along with the required dependency(ies). This function pulls source or binary packages from the Rstudio-sponsored CRAN mirror and/or the USGS GRAN Repository. Lastly, the chosen package(s) is/are loaded. The function load_package simply loads the provided packages.
CRAN - Package ie2misc: Irucka Embry’s Miscellaneous USGS Functions
Irucka Embry (iembry) GitLab ie2misc Repository: R package containing a collection of Irucka Embry’s miscellaneous USGS functions:

processing .exp and .psf files,
statistical error functions,
“+” dyadic operator for use with NA,
creating ADAPS and QW spreadsheet files,
calculating saturated enthalpy.

CRAN - Package ie2miscdata: Irucka Embry’s Miscellaneous USGS Data Collection
Irucka Embry (iembry) GitLab ie2miscdata Repository: R data package with miscellaneous USGS data [USGS Parameter codes with fixed values, USGS global time zone codes, and US Air Force Global Engineering Weather Data].
Irucka Embry (iembry) GitLab FEWSR Repository: R package containing FEWS river, lake, and pond models used to obtain the evapotranspiration from surface waters in power plants.
CRAN - Package USA.state.boundaries: WGS84 Datum Map of the USA, Including Puerto Rico and the U.S. Virgin Islands
Irucka Embry (iembry) GitLab USA.state.boundaries.data Repository: R data package with United States of America (USA) State Boundaries, Including Puerto Rico and the U.S. Virgin Islands.
CRAN - Package iemisctext: Irucka Embry’s Miscellaneous Text Collection
Irucka Embry (iembry) GitLab iemisctext Repository: R data package with an eclectic collection of short stories and poetry with topics on climate strange, connecting the geopolitical dots, the myth of us versus them, and the idiocy of war.
CRAN - Package jab.adverse.reactions: Possible Adverse Events/Reactions from the Vaccinations/Experimental Gene Therapies
Irucka Embry (iembry) GitLab jab.adverse.reactions.full Repository: R data package with data about the possible adverse events/reactions resulting from being injected with a vaccine/experimental gene therapy. Currently, this data set only includes information from six reference sources.
CRAN - Package vaersvax: US Vaccine Adverse Event Reporting System (VAERS) Vaccine Data for Present
Irucka Embry (iembry) GitLab vaers Repository: R data package with US VAERS data for 1990 - Present. “VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA).”
CRAN - Package vaersNDvax: Non-Domestic Vaccine Adverse Event Reporting System (VAERS) Vaccine Data for Present
Irucka Embry (iembry) GitLab vaersND Repository: R data package with Non-Domestic VAERS data for 1990 - Present. “VAERS is a national vaccine safety surveillance program co-sponsored by the US Centers for Disease Control and Prevention (CDC) and the US Food and Drug Administration (FDA).”
Irucka Embry Bitbucket project Repository [This repository includes the R scripts and functions written to download and harmonize the variables from the United Nations (UN) Multiple Indicator Cluster Survey (MICS) data sets.}
Irucka Embry (iembry) GitLab curvenumber Repository: Soil Conservation Service Curve Number method (R-package) — Irucka contributed the weighted_CN function & is now the package maintainer.

Irucka Embry’s R Examples

Other examples can be found within Irucka’s R packages

iemiscdata R Examples

iemiscdata: Total Number of Atoms of Elements in the 33 Human Cell Salts — [Requires PDF Software]

iemisc R Examples

iemisc: Comparing Other Hydraulic Software Output to iemisc’s Manningtrap for Critical Conditions — [Requires PDF Software]

Civil, Environmental, and Water Resources Engineering & Mathematics R Examples

Data Analysis R Examples

Irucka Embry’s Previous R Trainings for his Tennessee State University (TSU) & USGS Colleagues And TSU Freshman Engineering Students

A Practical Introduction to the R Programming Language Part 6: Basic Operations Using R, 28 August 2015
A Practical Introduction to the R Programming Language Part 5: Data Visualization with R, 27 March 2015
A Practical Introduction to the R Programming Language Part 4, 12 March 2015 — [Can be viewed in a Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]
A Practical Introduction to the R Programming Language Part 3 (continuation of Part 2), 9 September 2014 — [Can be viewed in a Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]
A Practical Introduction to the R Programming Language Part 2, 24 June 2014 — [Can be viewed in a Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]
A Practical Introduction to the R Programming Language: Extended Version, 21 August 2013 — [Requires PDF Software]
A Practical Introduction to the R Programming Language, 21 August 2013 — [Requires PDF Software]
A Practical Introduction to the R Programming Language R code, 21 August 2013 — [Can be viewed in a Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 4: Problem Solving — [Requires PDF Software]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 5a: Computer Problem Solving — [Requires PDF Software]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 5a R Commands — [Requires PDF Software]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 5b: Computer Problem Solving — [Requires PDF Software]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 6: Statistics & Probability — [Requires PDF Software]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 6 R Commands — [Requires PDF Software]
Tennessee State University (TSU) Freshman Engineering Seminar (ENGR 1020) Lecture Notes: Lecture 7: Engineering Economics — [Requires PDF Software]

R Applications

The R programming language can be used in a variety of ways, such as, but not limited to:

working with foreign data sets
- OpenDocument Format (ODF) spreadsheets (.ods),
- Microsoft Excel spreadsheets (.xls, .xlsx),
- other statistical software application documents (SAS, SPSS, STATA, etc.),
- databases (.dbf, .sql, etc.),
- MATLAB/GNU Octave files (.dat, .mat),
- text documents (.csv, .exp, .psf, .rdb, .txt),
- shapefiles (.shp),
- etc.;
replacement for Microsoft Access and Excel [calculator, column/row operations, data sorting, graphing/plotting (data visualization), percentiles, pivot tables, reports, search/replace with wildcards and other regular expressions, tables, using SQL commands on data, vertical lookup (vlookup), etc.];
data visualization (graphics/plotting);
string manipulation;
time series analysis;
data mining;
machine learning;
statistics, including Regression Analysis, Principal Component Analysis, Bayesian, etc.;
(eco)-hydrology, including retrieval & analysis of climate and weather data;
modeling and simulations;
mathematical optimization, solving differential equations, working with matrices (linear algebra), etc.;
GIS;
etc.

R Resources

R Introduction and Tutorials

The R Project for Statistical Computing
The R Project for Statistical Computing: Getting Help with R
ProgrammingR is your one-stop source for learning the R programming language, finding R jobs, hiring an R programmer or R consultant, and getting R related help. This page will give you a two-minute overview of everything this site offers to help you find what you’re looking for. The navigation links below have been classified by whether you are looking for help with the R programming language, looking for an R job, or looking to hire individuals with R expertise.
National Ecological Observatory Network (NEON) — Boulder, Colorado: Self Paced Data Tutorial Series
USGS Office of Water Information (OWI): Introduction to R Curriculum
2019 USEPA R User Group Workshop Materials (GitHub page)
USEPA Repository for Introduction to R Class (GitHub page)
US EPA: Introductory R Training materials for workshop given Jan 14-15, 2015 at USEPA Gulf Ecology Division and Jan 7-8, 2015 and Jan 22-23, 2015 at USEPA Atlantic Ecology Division by Jeffrey W. Hollister [Recovered with the Internet Archive: Wayback Machine]
R course material by Paul Hiemstra (S&T)
New Energy Research: R-Programming: A knowledge base of things you need to know about R
R-bloggers: Learning R: Index of Online R Courses By Joseph Rickert, October 8, 2015
DataCamp: Learn R & Become a Data Analyst
‪Learn R in Y Minutes
R-bloggers: How to Learn R By Tal Galili, December 10, 2015
R for Excel Users: Resources for Learning R
Stack Overflow: Learning R. Where does one Start?
Cross Validated: Resources for learning R
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): Resources to help you learn and use R
R-bloggers: Why use R? Five reasons. By Francis Smart, March 27, 2014
R-bloggers: Why you should learn R first for data science By Sharp Sight Labs, January 26, 2015
R-bloggers: Now’s a great time to learn R. Here’s how. By David Smith, January 28, 2015
swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!
Posit Cloud (formerly RStudio Cloud) lets you access Posit’s powerful set of data science tools right in your browser — no installation or complex configuration required.
Snippets: Run any R code you like. There are over three thousand R packages preloaded.
JDoodle: R Online Editor - R Online IDE - R Coding Online
Code School: Try R
R Bootcamp for Education by Jared Knowles on behalf of the Wisconsin Department of Public Instruction
DataScience+: Project-based tutorials with step-by-step instructions and examples. Practice your skills from the basic to advanced R programming, across different topics including statistics, data manipulation and visualization!
The Analysis of Data Project provides educational material in the area of data analysis.
R Programming (Part of the Data Science Specialization): Learn how to program in R and how to use R for effective data analysis. This is the second course in the Johns Hopkins Data Science Specialization.
Revolution R Open Managed R Archive Network (MRAN): Getting Started with R
Radford Neal’s blog: Opiate for the masses: Data is our religion
Milano R net is a users group dedicated to bringing together area practitioners of the popular open source R statistics language to exchange knowledge, learn and share tricks and techniques, provide R beginners with an opportunity to meet more experienced users. Our aims is to spur the adoption of R for innovative research and commercial applications, see case studies of real-life R applications in industry and government.
R project overview By Alastair Sanderson
Using R for computing with data By Alastair Sanderson
Radford Neal’s blog: R Programming
The central mission of the R Consortium is to work with and provide support to the R Foundation and to the key organizations developing, maintaining, distributing and using R software through the identification, development and implementation of infrastructure projects.
The R Project for Statistical Computing [R is ‘GNU S,’ a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc.]
The R Manuals
R Graphical Manual
R FAQ: Frequently Asked Questions on R
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Frequently Asked Questions
R for Windows FAQ by B. D. Ripley and D. J. Murdoch
R for Mac OS X FAQ by Stefano M. Iacus, Simon Urbanek, Rob J. Goedman, and Brian Ripley
The R Journal
R Books (A curated list of #rstats books)
Books related to R
R Contributed Documentation
R Reference Card 2.0 by Matt Baggott — [Requires PDF Software]
R reference card by Jonathan Baron — [Requires PDF Software]
Kickstarting R
An Introduction to R
An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics by W. N. Venables, D. M. Smith and the R Core Team — [Requires PDF Software]
A Guide for the Unwilling S User by Patrick Burns, 23rd February 2003 — [Requires PDF Software]
R for Beginners by Emmanuel Paradis, Institut des Sciences de l’Évolution Université Montpellier II — [Requires PDF Software]
The R Guide by W. J. Owen, Department of Mathematics and Computer Science, University of Richmond — [Requires PDF Software]
The R language — a short companion: This companion is essentially based on the documents “An Introduction to R” and “R language definition”, both version 1.7.1, available on the R website https://www.r-project.org/. Graphical and statistical functionalities are not considered. Version 1.2. Marc Vandemeulebroecke, July 14th, 2003 — [Requires PDF Software]
Rolling Your Rs: Learning R by Imitation
R Views: How to Teach R: Common mistakes by Garrett Grolemund, 2017-02-22
R-BAR: Learning and Teaching R: Get to the Plot By Kenith Grey, April 8, 2019
Teach Yourself R by Chuck Anderson, January 23, 2008, Colorado State University Department of Computer Science
R-bloggers: Free R Book Collection By Richard O. Legendi, September 6, 2011
Internet Archive: The R Book by Michael J. Crawley
World News Inc.: R Programming Language (Videos, etc.)
Princeton LibGuides at Princeton University: R - Getting Started in Data Analysis (Stata, R, SPSS, Excel): A self-guided tour to help you find and analyze data using Stata, R, Excel and SPSS. The goal is to provide basic learning tools for classes, research and/or professional development
Computerworld: 60+ R resources to improve your data skills by Sharon Machlis (Computerworld (US)), 16 January, 2014
R-bloggers: 21 R navigation tools By Patrick Burns, August 17, 2014
ST2137 - Computer Aided Data Analysis: Chapter 3 [Lecture 3]: R: An Introduction by Dr. Zhang Jin-Ting, National University of Singapore Department of Statistics and Applied Probability Faculty of Science — [Requires PDF Software]
R-bloggers: R Programming Notes By atmathew, February 17, 2016
CSIRO Research: R workshop notes
University of California, Davis California Soil Resource Lab: R: advanced statistical package
Statistical Consulting Group, UCLA Institute for Digital Research & Education: Introducing R, Created 2012
Introduction to the R Project for Statistical Computing by D G Rossiter, University of Twente, August 10, 2010 — [Requires PDF Software]
R and S-Plus: Basic Instructions by Murray Logan, School of Biological Sciences, Monash University, July 25, 2005 — [Requires PDF Software]
R by example by Ajay Shah, 2005
R Commands Summary — [Requires PDF Software]
The Personality Project: Using R for psychological research: A short list of the most useful R commands by William Revelle, Department of Psychology, Northwestern University. A summary of the most important commands with minimal examples. See the relevant part of the guide for better examples. For all of these commands, using the help(function) or ? function is the most useful source of information. Unfortunately, knowing what to ask for help about is the hardest problem.
Bio3D wiki: Resources for learning and using R
David’s Statistics: ‪Using R
Teach Yourself R by Chuck Anderson, Department of Computer Science, Colorado State University, January 23, 2008 — [Requires PDF Software]
R and S-Plus: Basic Instructions by Murray Logan, July 25, 2005 — [Requires PDF Software]
Academia.edu: R Basics by Ashok Chandra
Academia.edu: An Introduction to R by The Spring 2007 Math 441/442 Class and Roger Johnson: This is a work in progress based on R version 2.8.1 (December 22, 2008) — [Requires word processor software (.doc file)]
Introduction to R (presentation) by Phil Spector, Department of Statistics, University of California — [Requires PDF Software]
An Introduction to R by Phil Spector, Department of Statistics, University of California, September 24, 2004 — [Requires PDF Software]
Material for R courses by Longhow Lam
Revolutions (R Blog): 20 free R tutorials (and one reference card), April 25, 2012
R-Uni: (A List of Free R Tutorials and Resources in University webpages) by Pairach on February 26, 2012
R Tutorial: R Programming - Introduction To R - R Statistics Tutorial - R Help - STATS4STEM.ORG
Materials from Past Mini-Courses (R & LaTeX), Statistical Consulting Center, UCLA Department of Statistics
RStudio R Training
R Tutorial by Kelly Black, Department of Mathematics, University of Georgia
R Tutorials by William B. King, Ph.D., Coastal Carolina University
R Tutorial Series By John M Quick: The R Tutorial Series provides a collection of user-friendly guides to researchers, students, and others who want to learn how to use R for their statistical analyses.
R-bloggers: Computerworld’s Beginners Guide to R By David Smith, June 17, 2013
Cerebral Mastication: R - Resources
R-bloggers: R Tutorial Series: R Beginner’s Guide and R Bloggers Updates
S Plus and R Statistics and Statistical Graphics Resources by Michael Friendly, Statistical Consulting Service and Psychology Department, York University
Stat 133 Class Notes - Spring, 2011 by Phil Spector, May 31, 2011 — [Requires PDF Software]
stattutorials.com: Statistics Tutorials using R Software
Rtips. Revival 2014! by Paul E. Johnson
Statistical Computing with R: A tutorial by Dong-Yun Kim
The R language - a short companion This companion is essentially based on the documents “An Introduction to R” and “R language definition”, both version 1.7.1, available on the R website https://www.r-project.org/. Graphical and statistical functionalities are not considered. Version 1.2. Marc Vandemeulebroecke, July 14th, 2003 — [Requires PDF Software]
Introduction to the R Project for Statistical Computing by D G Rossiter, University of Twente, August 10, 2010 — [Requires PDF Software]
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): Resources to help you learn and use R
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Library by Matt Baggott — [Requires PDF Software]
Introducing R by Germán Rodríguez, Office of Population Research, Princeton University
Quick-R: The Workspace
Quick-R: The R Interface: Overview
Quick-R: Data Input
R Tutorial: Data Import
National Park Service (NPS): Data Import & Export in R
Saving and Loading Objects in R: Tutorials by William B. King, Ph.D., Coastal Carolina University
Reading and Examining Data by Germán Rodríguez, Office of Population Research, Princeton University
Introduction to Data Manipulation in R by Ryan Womack, Data and Economics Librarian, Rutgers University, May 30, 2010 — [Requires PDF Software]
National Park Service (NPS): Data Manipulation in R by Dr. Tom Philippi
Grokbase: [R] location of maximum
Stack Overflow: Replacing nth line in a text file in R
R-bloggers: Using R: reading tables that need a little cleaning By mrtnj, March 24, 2013
The Endeavour: Five kinds of subscripts in R by John D. Cook, 23 October 2008
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R FAQ: How does R handle missing values?
Quick-R: Data Types
R Tutorial: Basic Data Types
Data Perspective: Basic Data Types in r
How to Create an Array in R By Joris Meys and Andrie de Vries from R For Dummies
R Tutorial: Matrix
Quick-R: Matrix Algebra
Quick Review of Matrix Algebra in R By John Myles White on 12.16.2009
The Personality Project: Using R for psychological research: Appendix E: A Review of Matrices from An introduction to psychometric theory with applications in R by William Revelle and the Personality Project — [Requires PDF Software]
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Library: Matrices and matrix computations in R
R help: Summing over specific columns in a matrix
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Code Fragments: Examples of Singular Value Decomposition
R-statistics blog: Transpose, January 9, 2012
Quick-R: Aggregating Data
R, R bloggers, R programming: Aggregation and Restructuring data (from “R in Action”): This is a guest article by Dr. Robert I. Kabacoff
R-bloggers: Data Aggregation in R: plyr, sqldf and data.table By hayward, April 28, 2011
Stack Overflow: Newest ‘r dplyr’ Questions
R, R bloggers, R programming: data.frame objects in R (via “R in Action”): This is a guest article by Dr. Robert I. Kabacoff
Data Frames in R: Department of Mathematics, College of the Redwoods
Data Frame: R-statistics blog
R Tutorial: Data Frame: An R Introduction to Statistics: R Tutorial
R ProgrammingWorking with data frames: From Wikibooks, open books for an open world
R-bloggers: Select operations on R data frames By Chris, July 26, 2009
Cookbook for R: Renaming columns in a data frame
R-bloggers: Efficiecy of Extracting Rows from A Data Frame in R By statcompute, January 1, 2013
Quick-R: Sorting Data
R-bloggers: sort.data.frame By mark, August 15, 2014
One R Tip A Day: Sorting/ordering a data.frame according specific columns, venerdì 3 agosto 2007
Stack Overflow: sorting - How to sort a dataframe by column(s) in R
Stack Overflow: sorting - How to group columns by sum in R
Stack Overflow: r - Compare two data.frames to find the rows in data.frame 1 that are not present in data.frame 2
Stack Overflow: r - Create dataframe from a input values
Stack Overflow: r - What is the right way to multiply data.frame by vector?
R help: Re : Adding column sum to new row in data frame
R help: Finding the position of a variable in a data.frame
Stack Overflow: r - Adding row to data frame while maintaining the class of each column
Stack Overflow: data.frame - R count number of zero’s per row, and remove every row more than 5 zero’s
Stack Overflow: r - Replacing just ‘0’ (single zeros) in a column, without replacing the zeros in larger numbers (e.g. 10, 20, 30 etc.)
Stack Overflow: How do I copy and paste data into R
How to Use the Clipboard to Copy and Paste Data in R By Joris Meys and Andrie de Vries from R For Dummies
moving from for to foreach By Henry Scharf, May 15, 2015, Department of Statistics at Colorado State University (CSU)
DataCamp: A Tutorial on Loops in R - Usage and Alternatives by Carlo Fanara, September 28th, 2016
Writing a for-loop in R By Patrick on 23 March, 2013 in Patrick’s Ponderables
R-bloggers: For loops (and how to avoid them) By Slawa Rokicki, January 14, 2013
Statistics, R, Graphics and Fun: On the Gory Loops in R By Yihui Xie, 2010-10-17
Stack Overflow: r - Avoid for loop in string replacement?
Stack Overflow: how to start a for loop in R programming
R-bloggers: The R apply function - a tutorial with examples By axiomOfChoice, July 2, 2011
What You’re Doing Is Rather Desperate: Notes from the life of a bioinformatician: A brief introduction to “apply” in R, August 19, 2010
Nice R Code: Repeating things: looping and the apply family, 18 March 2013
R-bloggers: Using apply, sapply, lapply in R By Pete, December 18, 2012
Stack Overflow: plyr - R Grouping functions: sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate
Stack Overflow: r - sum multiple columns by group with tapply

Applications built with R as a back-end

iNZight: a free, easy to use software for statistical data analysis
jamovi is a new “3rd generation” statistical spreadsheet. designed from the ground up to be easy to use, jamovi is a compelling alternative to costly statistical products such as SPSS and SAS. jamovi is built on top of the R statistical language, giving you access to the best the statistics community has to offer. would you like the R code for your analyses? jamovi can provide that too.

Other R Resources

RPubs (brought to you by RStudio): Easy web publishing from R
ResearchGate: R Statistical Package - Science topic
ResearchGate: RStudio - Science topic
R Language Collective: A collective where data scientists and AI researchers gather to find, share, and learn about R and other subtags like knitr and dplyr.
Stack Overflow: R Tagged Questions
Stack Overflow: How to search for “R” materials?
Stack Overflow: r faq - How to get help in R?
R - Search
Help for R: A language and environment for statistical computing and graphics
RSeek.org: R-project Search Engine
R Site Search by Jonathan Baron, Department of Psychology, School of Arts and Sciences, University of Pennsylvania. This search will allow you to search the contents of the R functions, package vignettes, and task views.
Search the R Statistical Language
Summary of “r-project” Messages
R: Mailing Lists
R-help — Main R Mailing List: Primary help
R-help at Nabble.com [Recovered with the Internet Archive: Wayback Machine]
Quick-R
R Views: An R community blog edited by RStudio
RWeekly.org: Blogs to Learn R from the Community
From the bottom of the heap: Category: r
Project MOSAIC is a community of educators working to develop a new way to introduce mathematics, statistics, computation and modeling to students in colleges and universities.
Stochastic Nonsense
R-statistics blog: Writing about statistics with R, and open source stuff (software, data, community)
poissonisfish: Statistics & Mathematics with R
Julia Silge Blog
Thinking inside the box Blog: Dirk Eddelbuettel
We think therefore we R
“R” you ready?: My advances in R - a learner’s diary
Data Steve: R
Stat Bandit: Musings on statistics, computation and data research
Scripts & Statistics
Eight to Late: Sensemaking and Analytics for Organizations
Fun with R
R tips pages By D. Schluter, University of BC
One R Tip A Day
R: Remember’s Blog
R HEAD
Cookbook for R
R Club: Uncovering the mystical world of R
Dean Attali
Econometrics by Simulation: Simulations in *Stata* and #R# - github.com/EconometricsBySimulation/
Shirin’s playgRound
Enhance Data
The Devil is in the Data: Practical Data science using R
Educate-R
Rcrastinate
Environmental Science and Data Analytics with R: Posts on environmental science and general R-based data analysis
Ilya Kashnitsky: demographeR’s notes
Quality and Innovation: exploring quality, productivity & innovation in socio-technical systems (usually, with R!)
mages’ blog: R
ZevRoss Spatial Analysis Blog: Technical Tidbits From Spatial Analysis & Data Science
inside-R: A Community Site for R —Sponsored by Revolution Analytics
R-bloggers: R-bloggers: R news and tutorials contributed by R bloggers
Revolutions (R Blog): Revolutions (R Blog): Learn more about using open source R for big data analysis, predictive modeling, data science and more from the staff of Revolution Analytics.
Heuristic Andrew: R, SAS, machine learning, and statistics
Heuristic Andrew: R posts
Seasonal adjustment: R
District Data Labs: Hands-on data science tutorials, lessons, and other awesome content: R
Data Analysis and Machine Learning with R by Fisseha Berhane, PhD
Econometrics by Simulation: Simulations in *Stata* and #R#
R Tutorial: An R Introduction to Statistics
R Resources by J. C. Wang, Department of Statistics, Western Michigan University
The Personality Project: Using R for psychological research: A simple guide to an elegant package
The Analysis Factor: Statistical Consulting, Resources, and Statistics Workshops for Researchers in Psychology, Sociology, and other Social and Biological Sciences: R
Parse pdf files with R (on a Mac) by Felix Schönbrodt, 4. October, 2012

CRAN Task Views

CRAN Task Views
CRAN Package: ctv (CRAN Task Views): Server-side and client-side tools for task views to CRAN-style repositories
CRAN Task View: Open Data
CRAN Task View: Package Development
CRAN Task View: Official Statistics & Survey Methodology
CRAN Task View: Robust Statistical Methods
CRAN Task View: Multivariate Statistics
CRAN Task View: Analysis of Ecological and Environmental Data
CRAN Task View: Hydrological Data and Modeling
CRAN Task View: Cluster Analysis & Finite Mixture Models
CRAN Task View: Chemometrics and Computational Physics
CRAN Task View: Time Series Analysis
CRAN Task View: Analysis of Spatial Data
CRAN Task View: Handling and Analyzing Spatio-Temporal Data
CRAN Task View: Meta-Analysis
CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data
CRAN Task View: Statistical Genetics
CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
CRAN Task View: gRaphical Models in R
CRAN Task View: Reproducible Research
CRAN Task View: Probability Distributions
CRAN Task View: Bayesian Inference
CRAN Task View: Optimization and Mathematical Programming
CRAN Task View: Differential Equations
CRAN Task View: High-Performance and Parallel Computing with R

R GUIs (Graphical User Interface) — Using R with a GUI

R GUI Projects
SciViews: R GUI Projects Overview
RStudio™ is a free and open source integrated development environment (IDE) for R. You can run it on your desktop (Windows, Mac, or Linux) or even over the web using RStudio Server.
Posit Support (formerly RStudio Support): Working Directories and Workspaces by Josh Paulson, October 12, 2013
Posit Support (formerly RStudio Support): Using Projects by Josh Paulson, October 11, 2013
SAS and R: managing projects using RStudio Posted by Nick Horton, Friday, February 10, 2012
Bio7 is an integrated development environment for ecological modelling and contains powerful tools for model creation, scientific image analysis and statistical analysis. The application itself is based on an RCP-Eclipse-Environment (Rich-Client-Platform) which offers a huge flexibility in configuration and extensibility because of its plug-in structure and the possibility of customization.
The Tinn-R is an editor/word processor ASCII/UNICODE generic for the Windows operating system, very well integrated into the R, with characteristics of Graphical User Interface (GUI) and Integrated Development Environment (IDE).
Red-R is a open source visual programming interface for R designed to bring the power of the R statistical environment to a broader audience. The goal of this project is to provide access to the massive library of packages in R (and even non-R packages) without any programming expertise. The Red-R framework uses concepts of data-flow programming to make data the center of attention while hiding all the programming complexity.
R commander (Rcmdr): a graphical interface for R
JGR (speak ‘Jaguar’) is a universal and unified Graphical User Interface for R (it actually abbreviates Java Gui for R). JGR was introduced at the useR! meeting in 2004 and there is an introductory article in the Statistical Computing and Graphics Newsletter Vol 16 nr 2 p 9-12
Deducer Manual: An R Graphical User Interface (GUI) for Everyone: Deducer is designed to be a free easy to use alternative to proprietary data analysis software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is two fold. Provide an intuitive graphical user interface (GUI) for R, encouraging non-technical users to learn and perform analyses without programming getting in their way. Increase the efficiency of expert R users when performing common tasks by replacing hundreds of keystrokes with a few mouse clicks. Also, as much as possible the GUI should not get in their way if they just want to do some programming. Deducer is designed to be used with the Java based R console JGR, though it supports a number of other R environments (e.g. Windows RGUI and RTerm).
STATS4STEM.ORG: R, R statistical programming language, R tutorial, R help: Rweb
Vim-R-Tmux: An Integrated Working Environment for R by Thomas Girke, University of California Riverside

R for Users of other Statistical Software

Importing from other statistical systems: R Data Import/Export
ENVS 291 Transition to R - Fall 2013: Gregory S. Gilbert Research Group, Environmental Studies Department, University of California Santa Cruz
sasCommunity.org Planet: R
R for Stata Users
r4stats.com: R for SAS, SPSS and Stata Users [Recovered with the Internet Archive: Wayback Machine]
r4stats.com: R Package Equivalents to SAS & SPSS [Recovered with the Internet Archive: Wayback Machine]
Quick-R: R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for both current R users, and experienced users of other statistical packages (e.g., SAS, SPSS, Stata) who would like to transition to R. My goal is to help you quickly access this language in your work.
Migrating to R for SAS/SPSS/Stata Users by Vivian Lew, UCLA Department of Statistics Statistical Consulting Center, April 19, 2009 — [Requires PDF Software]
From spss to R, part 1 By Roel M. Hogervorst, February 20, 2016
From spss to R, part 2 By Roel M. Hogervorst, February 22, 2016
R-bloggers: translate2R: Easy switch from SPSS to R by using common concepts like temporary and column wise missing values By eoda GmbH, June 25, 2015
R-bloggers: translate2R and translateSPSS2R: Implanting SPSS functionality into R By eoda GmbH, February 12, 2015
“R” you ready?: QQ-plots in R vs. SPSS - A look at the differences
R-bloggers: Switch from SAS, SPSS or STATA to R with our latest course By DataCamp, December 8, 2014
Estimating Multilevel Models using SPSS, Stata, SAS, and R by Jeremy J.Albright and Dani M. Marinova, July 14, 2010 — [Requires PDF Software]
Introduction to R for SAS and SPSS Users Webinar Presented by Bob Muenchen, University of Tennessee, Author of R for SAS and SPSS Users, creater of r4stats.com; Presented on Wednesday, October 5th, 2011
R for SAS and SPSS users by Bob Muenchen — [Requires PDF Software]
StatSoftEquivs: The free SAS/R/Stata multilingual dictionary that anyone can edit
MineQuest: Bridge to R for WPS
MineQuest: Bridge to R Study Edition for WPS
IBM: Calling R from SPSS: An introduction to the R plug-in for SPSS by Catherine Dalzell, Published on October 31, 2013
Revolutions: Call R and Python from base SAS by David Smith, May 04, 2015
sasCommunity: A Bridge to R for SAS Users
R Interface Now Available in SAS/IML® Studio: SAS Customer Support
The DO Loop: Statistical programming in SAS with an emphasis on SAS/IML programs: Calling R from SAS/IML software by Rick Wicklin, May 13, 2011
SAS to R Tutorial Part 1 By AzureML Team for Microsoft, September 28, 2016
SAS to R Tutorial Part 2 By AzureML Team for Microsoft, September 28, 2016
SAS to R Tutorial Part 3 By AzureML Team for Microsoft, September 28, 2016
Paper CC03: SAS to R to SAS by Philip R Holland, Holland Numerics Limited, Royston, Herts, UK — [Requires PDF Software]
SAS and R: Data Management, Statistical Analysis, and Graphics by Ken Kleinman and Nicholas J. Horton
Examples for SAS and R: Data Management, Statistical Analysis, and Graphics by Ken Kleinman and Nicholas J. Horton
SAS and R: Examples of tasks replicated in SAS and R: Posted by Ken Kleinman
Stack Overflow: Working with Data.frames in R (Using SAS code to describe what I want)
SAS vs. R: Introduction and request By Peter Flom, April 17, 2010
r4stats.com: Analyzing the World of Analytics (Includes information on using R for SAS users)
sasCommunity.org is an ongoing global community effort-created by SAS users for SAS users. This site is a place to find answers, share technical knowledge, collaborate on ideas and connect with others in the worldwide SAS Community.
Statistics/Numerical Methods/Numerical Comparison of Statistical Software: From Wikibooks, open books for an open world

R for Users of other Applications & Programming Languages (GNU Octave/MATLAB®, Python, Databases, Spreadsheet Applications, etc.)

R language for programmers by John D. Cook
R for Programmers by Norman Matloff, University of California, Davis, Date: November 27, 2008
Stack Overflow: ‘r java’ Questions
Stack Overflow: ‘r octave’ Questions
Stack Overflow: ‘r matlab’ Questions
R for Octave Users version 0.4 by Robin Hankin
Mathesaurus: R for MATLAB users
Mathepi.com: Matlab and R
BioMed Central: Recommendations for translating code from MATLAB to R
R-MATLAB-IGOR Cheat Sheet: This is a short guide to translate commands in three different numerical analysis software packages: R, MATLAB and IGOR Pro. Equivalents are given for commands, gathered under a set of headings. This work is mainly by Vidar Bronken Gundersen and Ben Gallarda.
MATLAB® / R Reference by David Hiebeler
Ajay Shankar Bidyarthy’s blog: MATLAB, R, and Julia: Languages for data analysis
Hyperpolyglot: Numerical Analysis Software: Fortran, MATLAB, R, NumPy: a side-by-side reference sheet
Hyperpolyglot: Numerical Analysis & Statistics: MATLAB, R, NumPy: a side-by-side reference sheet
R-bloggers: Evaluating Optimization Algorithms in MATLAB, Python, and R By Nathan Lemoine, June 18, 2013
Stack Overflow: ‘r python’ Questions
National Ecological Observatory Network (NEON) -- Boulder, Colorado: R & Python Cheatsheets
R-bloggers: Python & R vs. SPSS & SAS By Jeroen Kromme, March 18, 2017
Giga thoughts …: R vs Python: Different similarities and similar differences by Tinniam V Ganesh, May 22, 2017
Mathesaurus: NumPy for R (and S-Plus) users
Statisfactions: Interactive R Notebooks with Jupyter and SageMathCloud Posted on June 20, 2015 by Ethan Brown
Enchufa2: Load a Python/pandas data frame from an HDF5 file into R por Iñaki Úcar, Publicado el 9 Mayo 2017
R-bloggers: Pipelining R and Python in Notebooks By Joseph Rickert, January 26, 2016
KDnuggets: Using Python and R together: 3 main approaches By Ajay Ohri, Decision Stats
Decision Stats: How to use R and Python together by Ajay Ohri, December 7, 2015
Mango Solutions: Integrating Python and R into a Data Analysis Pipeline — Part 1 By Chris Musselle and Kate Ross-Smith, 07 Oct 15
Mango Solutions: Integrating Python and R Part II — Executing R from Python and Vice Versa By Chris Musselle, 26 Oct 15
Mango Solutions: Integrating Python and R Part III: An Extended Example By Chris Musselle, 18 Dec 15
Stack Overflow: ‘r sql’ Questions
Quick-R: Database Access
Burns Statistics: R database interfaces
DataScience+: Working with databases in R by Fisseha Berhane on April 2, 2016
Databases: dplyr Vignette
SQL in R By Josh Errickson, University of Michigan College of Literature, Science, and the Arts, Statistics 701: Special Topics in Applied Statistics II
DataScience+: Efficient data management and sql data selection in R By Kristian Larsen, Published on September 18, 2018
R-bloggers: Using PostgreSQL in R: A quick how-to By Nina Zumel, February 1, 2016
Statistical-Research.com: Connect to MySQL or Microsoft SQL Server using R — [Requires PDF Software]
useR Vignette: Accessing Databases from R by Jeffrey Breen, Greater Boston useR Group, May 4, 2011
R-bloggers: Getting Access data into R By Sandy Muspratt, January 9, 2013
R-bloggers: RDBL — manipulate data in-database with R code only By David Kun, August 29, 2016
DataScience+: Working on Data-Warehouse (SQL) with R by Barath Ravichander on July 13, 2016
DataScience+: Performing SQL selects on R data frames by Fisseha Berhane on March 13, 2016
R for Excel Users: Using SQL in R Posted on June 6, 2014 by John
sqldf: SQL select on R data frames
R-bloggers: Turning Data Into Awesome With sqldf and pandasql By atmathew, April 29, 2015
Getting Genetics Done: Use SQL queries to manipulate data frames in R with sqldf package by Stephen Turner, May 25, 2010
Burns Statistics: Translating between R and SQL: the basics
R-bloggers: How to select and merge R data frames with SQL By David Smith, December 17, 2012
ZevRoss Spatial Analysis Blog: Overlap joins in R: a speed comparison with packages sqldf and data.table Posted on July 9, 2015 by zev@zevross.com
Stack Overflow: How to join data frames in R (inner, outer, left, right)?
R-bloggers: Excel and R
Stack Overflow: ‘r excel’ Questions
Stack Overflow: Running R scripts from VBA
>The University of Sheffield Data Visualisation Hub Blog: Moving from Excel to R By Yu Liang Weng, 08 October 2020
>Appsilon Data Science: How to Switch from Excel to R Shiny: First Steps Posted by Marcin Dubel, 30 September 2020
>Appsilon Data Science: Forget about Excel, Use these R Shiny Packages Instead Posted by Marcin Dubel 3 September, 2019
R-bloggers: Writing from R to Excel with xlsx By Peter Carl, May 1, 2013
The R Trader: A million ways to connect R and Excel, Published on February 11, 2014
R-bloggers: Read Excel files from R By Nicola Sturaro Sommacal, July 21, 2013
Stack Overflow: How to read all worksheets in an Excel Workbook into an R list with data.frame elements using XLConnect?
R for Excel Users By Julie Lowndes & Allison Horst, 2021-01-08
R for Excel Users: How to Learn R
R for Excel Users by John Taveras
Dataquest: 9 Reasons Excel Users Should Consider Learning Programming by Charlie Custer, December 27, 2018
The Yhat Blog: R for Excel Users by Gordon Shotwell, February 14, 2017
Burns Statistics: A first step towards R from spreadsheets: Move your data analysis to a computing environment specifically designed for it, 16 Oct 2013
Burns Statistics: From spreadsheet thinking to R thinking: Towards the basic R mindset, 07 Jan 2014
RStudio: Three Tips for Training Excel Users in R by Merav Yuravlivker, Data Society
District Data Labs: How to Transition from Excel to R: An Intro to R for Microsoft Excel Users by Tony Ojeda
Climate Charts & Graphs II: LearnR for Excel users
Climate Charts & Graphs II: Why I Switched from Excel to R?
TrendCT: R for beginners: How to transition from Excel to R by Andrew Ba Tran, June 12, 2015
Climate Charts & Graphs I: My R and Climate Change Learning Curve: Compare R and Excel Worlds (PPT only includes PPT & Embedded Video) — [Requires presentation software (.ppt file)]
Climate Charts & Graphs I: My R and Climate Change Learning Curve: Compare R and Excel Worlds (Zip includes: PPT, Videos, Example Script & Data Files) — [Requires data decompression software (.zip file)]
R for Excel Users Archives: Marco Ghislanzoni’s Blog (Videos)
Excel Archives: Marco Ghislanzoni’s Blog (Videos)
National Institute of Environmental Health Sciences Introduction to the R Programming and Graphing Environment for Advanced Excel Users: Biostatistics and Bioinformatics Short Course, June 14, 2013
Data manipulation tricks: Even better in R: Anything Excel can do, R can do -- at least as well. By Sharon Machlis, Computerworld, Apr 25, 2014
hselab.org: Using R instead of Excel for analyzing recovery room data
Stack Overflow: string - What’s the equivalent to Excel’s LEFT() and RIGHT() in R?
hselab.org: Getting started with R (with plyr and ggplot2) for group by analysis
joy of data: Pivoting Data in R Excel-style by Raffael Vogler, Posted on 2014/01/02
R-bloggers: Good riddance to Excel pivot tables By Scott Chamberlain, January 30, 2011
R help: Equivalent of Excel pivot tables in R
R-bloggers: Pivot tables in R By Chris, January 9, 2010
Marco Ghislanzoni’s Blog: Pivot Tables in R with aggregate
Marco Ghislanzoni’s Blog: Pivot Tables in R with melt and cast
Marco Ghislanzoni’s Blog: Pivot Tables in R with dplyr
Marco Ghislanzoni’s Blog: Pivot Tables for R: Try sqldf By Alistair Leak, May 21, 2013
Stack Overflow: how to realize countifs function (excel) in R
Stack Overflow: data.frame - Excel like column operations in R dataframe
R-bloggers: FillIn: a function for filling in missing data in one data frame with info from another
R for Excel Users: How to do VLOOKUP in R Posted on January 7, 2015 by John Taveras
Stack Overflow: Excel vlook up function in R for data frame
Stack Overflow: match - VLookup type method in R
Stack Overflow: How to do vlookup and fill down (like in excel) in R?
Stack Overflow: Equivalence of ’vlookup’ in R for multiple columns?
Stack Overflow: statistics - VLookup two values in R and match 10% difference of value

Why you should ditch Microsoft Excel

AriaLab, LLC: 5 Common Shortcomings of Your Spreadsheets By Chris Torres, Mar 20, 2023
Layer Blog: Excel Limitations: Problems, Issues, Disadvantages By Hady ElHady, Dec 9 2022
Five Common Spreadsheet Risks and Ways to Control Them By Larry R. Metz, CIA, CCSA, CGAP, CPA IIA/ITAudit Vol. 10, October 10, 2007 — [Requires PDF Software]
Trunao LLC: 10 Challenges of Spreadsheets or Excel, March 30, 2022
Econometrics Beat: Dave Giles’ Blog: Spreadsheet Errors, September 9, 2016
The Economist: Excel errors and science papers by The Data Team, Sep 7th 2016
Racing Tadpole: Monte Carlo cashflow modelling in R with dplyr by Arthur Street, 26 April 2014 [includes reasons to leave Microsoft Excel behind]
Hyndsight: R: Forget about Excel. It is hopeless for any serious work. (If you need convincing, see Bruce McCullough’s articles on Excel [Google Scholar search].) Any research student in the quantitative sciences should be using a matrix language for computation. I recommend students use R. It is free, has a big user base, and has zillions of add-on packages.
ResearchGate: Is it a good idea to use Microsoft Excel as a statistical software package for analyzing the data? Is it acceptable for the journals? asked by Brijesh Sathian
R Views: Enterprise Dashboards with R Markdown by Nathan Stephens, 2018-05-16
Revolutions (R Blog): Why I migrated from Excel to R by Outsider Data Science, Posted on June 11, 2019
Betterment: Modern Data Analysis: Don’t Trust Your Spreadsheet: To conduct research in business, you need statistical computing that you easily reproduce, scale, and make accessible to many stakeholders. by Sam Swift
Fantasy Football Analytics: Why R is Better Than Excel for Fantasy Football (and most other) Data Analysis By Isaac Petersen, 13 Jan, 2014
Revolutions (R Blog): More reasons not to use Excel for modeling Posted by David Smith, April 17, 2013
Roosevelt Institute: Researchers Finally Replicated Reinhart-Rogoff, and There Are Serious Problems. By Mike Konczal, 04.16.13
‪Ars Technica: Microsoft Excel: The ruiner of global economies?: A paper used to justify austerity economics appears to contain an Excel error. by Peter Bright, Apr 16, 2013
Microsoft Community (2011): Excel R squared is Incorrect
Microsoft Support (2011): Chart trendline formula is inaccurate in Excel (Article ID: 211967)
Practical Statistics: Is Microsoft Excel an Adequate Statistics Package?
On the Accuracy of Statistical Distributions in Microsoft Excel 2010 by Leo Knüsel, University of Munich, Germany — [Requires PDF Software]
Using Excel for Data Manipulation and Statistical Analysis: How-to’s and Cautions: Stanford University Social Science Data and Software, 2010 — [Requires PDF Software]
Hyndsight: Why R is better than Excel for teaching statistics by Rob J Hyndman, 5 October 2010
On the Numerical Accuracy of Spreadsheets by Marcelo G. Almiron, Bruno Lopes, Alyson L. C. Oliveira, Antonio C. Medeiros, and Alejandro C. Frery, Journal of Statistical Software, April 2010, Volume 34, Issue 4
Burns Statistics: 3.5 Reasons to Switch from Excel to R: Presented 2009 March at LondonR
On the accuracy of statistical procedures in Microsoft Excel 2007 by B.D. McCullough and David A. Heiser, Computational Statistics & Data Analysis, Volume 52, Issue 10, 15 June 2008, Pages 4570-4578
The accuracy of statistical distributions in Microsoft® Excel 2007 by A. Talha Yalta, Computational Statistics & Data Analysis, Volume 52, Issue 10, 15 June 2008, Pages 4579-4586
It’s easy to produce chartjunk using Microsoft® Excel 2007 but hard to make good graphs by Yu-Sung Su, Computational Statistics & Data Analysis, Volume 52, Issue 10, 15 June 2008, Pages 4594-4601
CRMguru: Excel 2007 calculation bug displays apparently wrong numbers, September 26, 2007
CRMguru: Excel 2007 bug shows wrong answers to simple multiplications, October 2, 2007
Hyndsight: Problems With Using Microsoft Excel for Statistics by Jonathan D. Cryer, Department of Statistics and Actuarial Science, University of Iowa, Iowa City, Iowa, Joint Statistical Meetings, August 2001, Atlanta, GA
Vanderbilt University Department of Biostatistics Wiki: Problems with Excel
Statistical Engineering: Excel’s Checkered Statistical Past by Charles Annis, P.E.
Burns Statistics: Spreadsheet Addiction
Statistics/Numerical Methods/Numerics in Excel: From Wikibooks, open books for an open world

Connecting R to and from other Programming Languages & Applications

The Comprehensive R Archive Network (CRAN): Writing R Extensions
A Comparative Evaluation of Matlab, Octave, FreeMat, Scilab, R, and IDL on Tara by Ecaterina Coman, Matthew W. Brewster, Sai K. Popuri, and Andrew M. Raim, and Matthias K. Gobbert, Department of Mathematics and Statistics, University of Maryland, Baltimore County — [Requires PDF Software]
R-bloggers: Fortran and R — Speed Things Up By steve, April 11, 2014
Using C/C++ in R, Statistics 506, Fall 2017
R’s C interface, from Advanced R by Hadley Wickham
R-bloggers: Using R — Calling C code ‘Hello World!’ By jonathanatmazamascience, Posted on July 16, 2021
R-bloggers: Using R — .Call(“hello”) By jonathanatmazamascience, Posted on July 16, 2021
R-bloggers: Using R — Calling C code with Rcpp By jonathanatmazamascience, Posted on July 16, 2021
R-bloggers: Using R — Packaging a C library in 15 minutes By jonathanatmazamascience, Posted on July 16, 2021
R Views: R Package Integration with Modern Reusable C++ Code Using Rcpp by Daniel Hanson, 2020-07-08
R Views: R Package Integration with Modern Reusable C++ Code Using Rcpp - Part 2 by Daniel Hanson, 2020-07-14
R Views: R Package Integration with Modern Reusable C++ Code Using Rcpp - Part 3 by Daniel Hanson, 2020-07-31
R Views: R Package Integration with Modern Reusable C++ Code Using Rcpp - Part 4 by Daniel Hanson, 2020-08-18
R Views: R Package Integration with Modern Reusable C++ Code Using Rcpp - Part 5 by Daniel Hanson, 2020-08-24
CXXR: Refactorising R into C++ by Andrew Runnalls, University of Kent Computing
Rcpp package has become the most widely used language extension for R, the powerful environment and language for computing with data. As of May 2015, 379 packages on CRAN and a further 57 on BioConductor deploy Rcpp to extend R, to accelerate computations and to connect to other C++ projects.
Department of Statistics, University of California ~ Berkeley, CA: Using C++, calling C++ from R, and Creating R packages
Rqt is a library for calling R functions within C++/Qt4 applications. The argument interface uses QVariant for flexible exchange of data to and from R. This allows R calls as if they were part of the local Qt environment.
rjulia provides an interface between R and Julia. It allows a user to run a script in Julia from R, and maps objects between the two languages. It currently supports use on Linux and Windows (both R and RGUI), but build on Windows only for advance users.
CRAN Package: PythonInR: Interact with Python from within R.
CRAN Package: rPython: This package permits calls to Python from R
R/SPlus - Python Interface
Stack Overflow: Python interface for R Programming Language
pyRServe is a library for connecting Python to an R process running under Rserve. Through such a connection variables can be get and set in R from Python, and also R-functions can be called remotely.
rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface to R from Python, a proposed high-level interface, including wrappers to graphical libraries, as well as R-like structures and functions.
PypeR (PYthon-piPE-R, originally names as Rinpy - R in Python) allows people to use R in Python through PIPE.
CRAN Package: rJava: Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
JAMSIM: Java & R Based Microsimulation
CRAN Package: RMariaDB: Implements a DBI-compliant interface to MariaDB (https://mariadb.org/) and MySQL (https://www.mysql.com/) databases.
CRAN Package: mongolite: High-level, high-performance MongoDB client based on libmongoc and jsonlite. Includes support for aggregation, indexing, map-reduce, streaming, SSL encryption and SASL authentication. The vignette gives a brief overview of the available methods in the package.
CRAN Package: RSQLite: This package embeds the SQLite database engine in R and provides an interface compliant with the DBI package. The source for the SQLite engine (version 3.8.6) is included.
CRAN Package: RPostgreSQL: Database interface and PostgreSQL driver for R This package provides a Database Interface (DBI) compliant driver for R to access PostgreSQL database systems. In order to build and install this package from source, PostgreSQL itself must be present your system to provide PostgreSQL functionality via its libraries and header files. These files are provided as postgresql-devel package under some Linux distributions. On Microsoft Windows system the attached libpq library source will be used. A wiki and issue tracking system for the package are available at Google Code at https://code.google.com/p/rpostgresql.
CRAN Package: rgrass: Interface Between ‘GRASS’ Geographical Information System and ‘R’. An interface between the ‘GRASS’ geographical information system (‘GIS’) and ‘R’, based on starting ‘R’ from within the ‘GRASS’ ‘GIS’ environment, or running a free-standing ‘R’ session in a temporary ‘GRASS’ location; the package provides facilities for using all ‘GRASS’ commands from the ‘R’ command line. The original interface package for ‘GRASS 5’ (2000-2010) is described in Bivand (2000) doi:10.1016/S0098-3004(00)00057-1 and Bivand (2001) https://www.r-project.org/conferences/DSC-2001/Proceedings/Bivand.pdf. This was succeeded by ‘spgrass6’ for ‘GRASS 6’ (2006-2016) and ‘rgrass7’ for ‘GRASS 7’ (2015-present). The ‘rgrass’ package modernizes the interface for ‘GRASS 8’ while still permitting the use of ‘GRASS 7’. — [Requires PDF Software]
R-ArcGIS
The R — ArcGIS Community is a community driven collection of free, open source projects making it easier and faster for R users to work with ArcGIS data, and ArcGIS users to leverage the analysis capabilities of R.
ArcGIS Blog: Put the ‘R’ in ArcGIS by mpobuda on July 21, 2016
Using R in ArcGIS (Versions 10 and 10.1)
New in Mathematica 9: Built-in Integration with R
CRAN Package: R.matlab: Methods readMat() and writeMat() for reading and writing MAT files. For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
CRAN Package: matlabr: Provides users to call MATLAB from using the “system” command. Allows users to submit lines of code or MATLAB m files. This is in comparison to ‘R.matlab’, which creates a MATLAB server.
R for SAS, SPSS and Stata Users: Calling R from Other Software by Robert A. Muenchen
Statistics::R: Statistics::R is a (Perl) module to controls the R interpreter. It lets you start R, pass commands to it and retrieve their output. A shared mode allows several instances of Statistics::R to talk to the same R process.
CRAN Package: excel.link: Allows access to data in running instance of Microsoft Excel (e. g. ‘xl[a1] = xl[b2]*3’ and so on). Graphics can be transferred with ‘xl[a1] = current.graphics()’. There is an Excel workbook with examples of calling R from Excel in the ‘doc’ folder. It tries to keep things as simple as possible - there are no needs in any additional installations besides R, only ‘VBA’ code in the Excel workbook. Microsoft Excel is required for this package.
CRAN Package: XLConnect: Provides comprehensive functionality to read, write and format Excel data.

R and GNU/Linux / *NIX Command Line Tools (R can process system commands too)

Rcrastinate: Do basic R operations much faster in bash [Slightly off-topic] Posted 25th January 2016 by Sascha W.
Speed comparison using R, awk and Perl by Il-Youp Kwak, 2016-10-20
littler: a scripting front-end for GNU R
littler examples: how to use a scripting front-end for GNU R by Dirk Eddelbuettel
Musings from an unlikely candidate: A small list of command line tips by Dave Tang Posted on February 19, 2014 and Updated 2014 May 14th
Git and GitHub from R packages by Hadley Wickham
Using Git and GitHub with R, RStudio, and R Markdown by Jenny Bryan, The R User Conference 2016, June 27 - June 30 2016
A Million Text Files And A Single Laptop by Randy Zwitch, January 28, 2016
Analytics Vidhya Blog: Tutorial — Data Science at Command Line with R & Python (Scikit Learn) by Sandeep Karkhanis, August 1, 2016
explainshell.com: match command-line arguments to their help text
LinuxCommand.org: The Linux Command Line By William Shotts
A Bioinformatician’s UNIX Toolbox by Heng Li
Useful bash one-liners for bioinformatics by Stephen Turner
Awk, Sed, and Bash-Command-line file Editing and Processing, North Carolina State College of Natural Resources Department of Forestry & Environmental Resources
Linux: Text Processing: grep, cat, awk, uniq By Xah Lee. Date: 2007-03-30 and Last updated: 2016-01-30
grep, awk and sed — three VERY useful command-line utilities by Matt Probert, Uni of York — [Requires PDF Software]
sed & awk, Second Edition by Dale Dougherty and Arnold Robbins
AWK: From Wikibooks, open books for an open world
An Awk Primer: From Wikibooks, open books for an open world
Online Training for NetApp Storage and Linux: awk scripting explained with 10 practical examples by Ankam Ravi Kumar
UrFix’s Blog: 25 Best AWK Commands / Tricks by Isaias Irizarry, November 26, 2010
AWK one-liner collection by Guido Socher
Handy One-Line Scripts for Awk Compiled by Eric Pement, 30 April 2008
Informatica, Oracle, Netezza, Unix, Hadoop Tutorials and Examples: Top Examples of Awk Command in Unix
Informatica, Oracle, Netezza, Unix, Hadoop Tutorials and Examples: Examples of Awk Command in Unix - Part 2
Using AWK to Filter Rows by Tim Dennis, 09 Aug 2016
The Linux Rain: A Pivot Table In AWK By Bob Mesibov, published 23/05/2014
Informatica, Oracle, Netezza, Unix, Hadoop Tutorials and Examples: Sed Command in Unix and Linux Examples
Informatica, Oracle, Netezza, Unix, Hadoop Tutorials and Examples: Cut Command in Unix (Linux) Examples
Informatica, Oracle, Netezza, Unix, Hadoop Tutorials and Examples: Find Command in Unix and Linux Examples
Finding and replacing character strings in multiple files with the Linux command line by Robin Lovelace, 18 Feb 2014

Data Analysis and Large Data with R

Keeping Users Safe While Collecting Data by hrbrmstr on 2017-06-13
Quick-R: Data Management
R for Data Science by Garrett Grolemund and Hadley Wickham
Data Science Heroes Blog: Data analysis with R
Data Science: An Introduction (Wikibooks, open books for an open world)
Irregularly Scheduled Programming: Data Munging With R Preview — Storing Values (Assigning) By Jonathan Carroll, June 26, 2017
Computerworld: 4 data wrangling tasks in R for advanced beginners: Learn how to add columns, get summaries, sort your results and reshape your data. By Sharon Machlis, Apr 14, 2015
Data Science: An Introduction/Appendix 2: 250 R Commands (From Wikibooks, open books for an open world)
Sharp Sight Labs: Why R is the best data science language to learn today
R (and S-PLUS) Manual to Accompany Agresti’s Categorical Data Analysis (2002) 2nd edition by Laura A. Thompson, 2009 — [Requires PDF Software]
DataScience+: Graphical Presentation of Missing Data; VIM Package by Klodian Dhana, May 8, 2017
RDataMining.com: R and Data Mining
R Reference Card for Data Mining by Yanchang Zhao, January 3, 2013 — [Requires PDF Software]
Data Mining Algorithms in R: From Wikibooks, open books for an open world
R-bloggers: Datasets to Practice Your Data Mining By Yanchang Zhao, September 16, 2011
Data Mining: Large Databases and Methods or … by Brian D. Ripley — [Requires PDF Software]
R and Data Mining: Examples and Case Studies 1 by Yanchang Zhao, April 26, 2013 — [Requires PDF Software]
ProgrammingR: Examples of Web Scraping With R
ZevRoss Spatial Analysis Blog: Scrape website data with the new R package rvest (+ a postscript on interacting with web pages with RSelenium) Posted on May 19, 2015 by hollie@zevross.com
Reading Web Pages with R: Originally for Statistics 133, by Phil Spector, University of California, Berkeley Department of Statistics
Win-Vector Blog: What is vtreat? By John Mount, August 14, 2019
How to Automate EDA with DataExplorer in R for flash-fast Data Analysis By Abdul Majed Raja, 13 Aug 2019
The University of British Columbia (UBC) Vancouver Campus STAT 545A and 547M: Data wrangling, exploration, and analysis with R
The ultimate reference guide to data wrangling with Python and R
Musings from an unlikely candidate: Equivalents in R, Python and Perl Last update 2015 September 9th
Analytics Vidhya: LeaRning Path on R — Step by Step Guide to Learn Data Science on R
Revolutions (R Blog): Free e-book on Data Science with R, February 22, 2013
Mad (Data) Scientist: Musings, useful code etc. on R and data science
R-bloggers: A two-hour introduction to data analysis in R By mine, November 9, 2015
Win-Vector Blog: Practical Data Science with R by Nina Zumel and John Mount
R-bloggers: Machine Learning in R for beginners By DataCamp, March 25, 2015
Toptal: Boost Your Data Munging with R by Jan Gorecki
DataScience+: Best packages for data manipulation in R by Fisseha Berhane on May 17, 2016
R-bloggers: Data manipulation with tidyr By Teja Kodali, January 6, 2016
Win-Vector Blog: dplyr in Context by John Mount, May 6, 2017
Speed Up Your Code: Parallel Processing with multidplyr By mattdancho.com - Articles, Written on December 18, 2016
data.table
Rolling Your Rs: Intro to The data.table Package, Posted June 14, 2016
data.table: Benchmarks: Grouping
Stack Overflow: r - data.table vs dplyr: can one do something well the other can’t or does poorly?
No THIS Is How You Dplyr and Data.Table! by Jeffrey Horner, May 28, 2015
DataScience+: Efficient aggregation (and more) using data.table by David Kun on November 27, 2015
R-bloggers: Parallel Vectorized Operations By Christopher Mooney, November 7, 2016
DataScience+: Strategies to Speedup R Code by Selva Prabhakaran on January 30, 2016
A guide to parallelism in R by Florian Privé, Written on September 5, 2017
R-bloggers: A gentle introduction to parallel computing in R By John Mount, January 18, 2016
G-Forge: How-to go parallel in R — basics + tips by Max Gordon, Posted on February 16, 2015
The “Programming with Big Data in R” project (pbdR) enables highndash;level distributed data parallelism in R, so that it can easily utilize large HPC platforms with thousands of cores, making the R language scale to unparalleled heights. We interpret big data quite literally to mean that its size requires parallel processing either because it does not fit in the memory of a single multicore machine or because we need to make its processing time tolerable.
Stack Overflow: ‘r bigdata’ Questions
Stack Overflow: ‘r data.table’ Questions
Revolutions (R Blog): big data
R-bloggers: Big Data
Win-Vector Blog: Tag: R and big data
Computerworld: Learn how to crunch big data with R: Get started using the open-source R programming language to do statistical computing and graphics on large data sets By Martin Heller, Feb 11, 2015
me nugget: Working with hdf files in R - Example: Pathfinder SST data, November 8, 2013
R-bloggers: Hadley’s guide to high-performance R with Rcpp By David Smith, November 28, 2012
Stack Overflow: import - Quickly reading very large tables as dataframes in R
Revolutions (R Blog): R tip: Save time and space by compressing data files, December 03, 2009
Stack Overflow: gzip - Read gzipped csv directly from a url in R
Analytics Vidhya Blog: data.table() vs data.frame() — Learn to work on large data sets in R by Manish Saraswat, May 3, 2016
Robinlovelace GitHub: Teaching materials for handling large datasets in R
Learn to crunch big data with R: Get started using the open source R programming language to do statistical computing and graphics on large data sets By Martin Heller, InfoWorld, Feb 11, 2015
Freakonometrics: Working with “large” datasets, with dplyr and data.table by Arthur Charpentier, 04/05/2015
data.table wiki: Fast subset, fast grouping, fast assign, fast ordered joins and list columns in a short and flexible syntax, for faster development.
data.table wiki Articles: Fast subset, fast grouping, fast assign, fast ordered joins and list columns in a short and flexible syntax, for faster development.
data.table: Advanced 1hr Tutorial by Matthew Dowle, R/Finance, Chicago, May 2013 — [Requires PDF Software]
Stack Overflow: csv - R fread from the data.table package column names
Stack Overflow: r - Adding columns to a data table
Stack Overflow: r - Assign value to specific data.table columns and rows

Data Visualization (Plotting/Graphing) with R

The R graph Gallery: Inspiration and Help concerning R graphics
R Graphical Manual
R Graph Catalog: This catalog is a complement to Creating More Effective Graphs by Naomi Robbins. All graphs were produced using the R language and the add-on package ggplot2, written by Hadley Wickham. The gallery is maintained by Joanna Zhao and Jennifer Bryan.
ggplot2 extensions - gallery
rCharts Gallery: This is a gallery of examples, tutorials and applications that make use of rCharts, an R package that helps create interactive charts using multiple javascript libraries.
plotly: R Graphing Library: Documentation and Examples
R graph gallery: The blog is a collection of script examples with example data and output plots. R produce excellent quality graphs for data analysis, science and business presentation, publications and other purposes. Self-help codes and examples are provided.
Statistics Globe: Graphics in R (Gallery with Examples)
R Programming/Publication quality output: From Wikibooks, open books for an open world
Environmental Informatics Marburg: Creating publication quality graphs in R
Revolutions (R Blog): 10 tips for making your R graphics look their best Posted by David Smith, January 30, 2009
Cookbook for R: Graphs
R Graphics: A project from the Center for Limnology - UW Madison
R Programming/Graphics: From Wikibooks, open books for an open world
Revolutions (R Blog): graphics
Revolutions (R Blog): Some basics for base graphics by Joseph Rickert, January 15, 2015
LISA (Virginia Tech’s Laboratory for Interdisciplinary Statistical Analysis): Graphics in R Short Course (Videos)
Producing Simple Graphs with R by Frank McCown, Computer Science Department, Harding University
Quick-R: CSSS 569: Visualizing Data: Graphical Programming, Part I.: Using R graphics functions by Christopher Adolph, Department of Political Science and Center for Statistics and the Social Sciences, University of Washington, Seattle — [Requires PDF Software]
Quick-R: Basic Graphs
Quick-R: Advanced Graphs
Quick-R: Scatterplots
USGS Office of Water Information (OWI) Blog: R: Calculating Moving Averages and Historical Flow Quantiles by Laura DeCicco
R-bloggers: Box-plot with R — Tutorial By Fabio Veronesi, June 6, 2013
Stochastic Nonsense: Plotting in R, A Series
Climate Charts & Graphs II: Data Visualization
FlowingData: Getting Started with Charts in R By Nathan Yau
More and Fancier Graphics: Tutorials by William B. King, Ph.D., Coastal Carolina University
R-bloggers: Basics of Histograms By Slawa Rokicki, December 22, 2012
Lattice: trellis graphics for R
latticeExtra is an R package (i.e. a package for the R statistical computing environment), providing functions for generating statistical graphics. It extends the Lattice framework (lattice package), which is an implementation of Trellis graphics in R. Many of the functions and datasets in latticeExtra are described in Deepayan Sarkar’s book, Lattice: Multivariate Data Visualization with R. The book web site also includes figures and code. This site serves as a reference for the R functions and datasets in the package.
Lattice: Multivariate Data Visualization with R - Figures and Code by Deepayan Sarkar
directlabels: This package is an attempt to make direct labeling a reality in everyday statistical practice by making available a body of useful functions that make direct labeling of common plots easy to do with high-level plotting systems such as lattice and ggplot2. The main function that the package provides is direct.label(p), which takes a lattice or ggplot2 plot p and adds direct labels.
ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics.
ggplot2 Help topics
Introduction to graphing with the ggplot2 package
ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
ggplot2 explorer
Using Loops with ggplot2 By Rich Majerus, Reed College
Data Visualization — Part 1: Introduction to Data Visualization — Theory, R & ggplot2 By Scott Stoltzman, March 14, 2017
Data Visualization — Part 2: A Quick Overview of the ggplot2 Package in R By Scott Stoltzman, March 22, 2017
Data Visualization — Part 3: What Type of Data Visualization Do You Choose (if any)?
R Club: Basic Data Visualization with ggplot2 by Joe Hoover, May 1, 2014
Basel Institute for Clinical Epidemiology and Biostatistics (ceb): Tutorial: ggplot2 — [Requires PDF Software]
ZevRoss Spatial Analysis Blog: Beautiful plotting in R: A ggplot2 cheatsheet Posted on August 4, 2014 by zev@zevross.com
directlabels: Some notes on extending ggplot2 using custom grid grobs
R-bloggers: Producing grids of plots in R with ggplot2: A journey of discovery By Robin Wilson, August 26, 2010
hselab.org: Create a sequence of plots with R and ggplot2 and save to PDFs
R-bloggers: Pie Charts in ggplot2 By C, July 29, 2010
R-bloggers: Annotated Facets with ggplot2 By Abhijit, October 20, 2016
Stack Overflow: r - tiny pie charts to represent each point in an scatterplot using ggplot2
Stack Overflow: R + ggplot2 => add labels on facet pie chart
R-bloggers: Creating scatter plots using ggplot2 By Ralph, November 6, 2009
Saving Plots in R: Concepts in Computing with Data by Phil Spector, Statistics 133, Spring 2011, Department of Statistics, University of California, Berkeley
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R FAQ: How can I add features or dimensions to my bar plot?
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R FAQ: How can I generate a Venn diagram in R?
Revolutions (R Blog): 3D Plots in R by Joseph Rickert, February 13, 2014
Fun with R: Network visualization — part 2: Gephi Posted on July 26, 2013 by Vessy
Katya Ognyanova: Sunbelt network visualization: R and Gephi tutorials, 04-6-2016
Eight to Late: A gentle introduction to network graphs using R and Gephi
Stack Overflow Plot of a correlation matrix in R like in Excel example
Stack Overflow: r - How can I plot with 2 different y-axes?
R help: log scale y axis ticks control on boxplots
R-bloggers: Visualizing obesity across United States by using data from Wikipedia by Klodian Dhana on June 16, 2016
R-bloggers: Visualization series: Insight from Cleveland and Tufte on plotting numeric data by groups By Solomon, March 4, 2012
The Personality Project: Using R for psychological research: Plotting temporal data using R, Posted on January 25, 2010 by earl
The Personality Project: Using R for psychological research: Adding time to a plot and adventures in smoothing
Stack Overflow: r - Adding text to a grid.table plot
Stack Overflow: r - tableGrob: set the height and width of a grid.table
Displayr: The magic trick that highlights interesting results on any table by Tim Bock, 12 June 2017
Stack Overflow: r - How to print (to paper) a nicely-formatted data frame
Quick-R: Interactive Graphics
R-bloggers: Interactive, Illustrator-quality graphics with R By David Smith, August 5, 2016
Revolutions (R Blog): Interactive 2D & 3D Plots with Plotly and ggplot2 by Matt Sundquist, December 16, 2014
R-project: Code snippets in R, a small contribution, offset from my activity: Interactive 4-dimensional contour plots by Thanos Tsakonas, July 19, 2013
Stochastic Nonsense: Interactive Plotting in R, Posted on January 25, 2010 by earl
University of California, Davis California Soil Resource Lab: Interactive 3D plots with the rgl package: Submitted by dylan on Sun, 2006-12-17
iPlots: High Interaction Graphics for R by Simon Urbanek and Martin Theus, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), Edited by Kurt Hornik, Friedrich Leisch & Achim Zeileis, March 20-22, Vienna, Austria — [Requires PDF Software]
GGobi is an open source visualization program for exploring high-dimensional data. It provides highly dynamic and interactive graphics such as tours, as well as familiar graphics such as the scatterplot, barchart and parallel coordinates plots. Plots are interactive and linked with brushing and identification.
RStudio: ggvis: The goal is to combine the best of R (e.g. every modelling function you can imagine) and the best of the web (everyone has a web browser). Data manipulation and transformation are done in R, and the graphics are rendered in a web browser, using Vega.
RStudio: ggvis Interactivity
rCharts is an R package to create, customize and publish interactive javascript visualizations from R using a familiar lattice style plotting interface.
Revolutions (R Blog): Using Plotly with R for Project Workflows by Matt Sundquist, May 27, 2014
clickme is an R package that lets you create interactive visualizations in the browser, directly from your R session.
R Interactive Graphics with SVG
R-bloggers: 52Vis Week #3 — Waste Not, Want Not By hrbrmstr, April 13, 2016
sweissblaug: Dynamic Networks: Visualizing Arms Trades Over Time Posted by Sam Weiss, June 14, 2017
Econometrics by Simulation: Cause of Death: Melanin: Evaluating Death-by-Police Data, December 10, 2015
R-bloggers: Shiny: Officer Involved Shootings By Steve Wells, May 1, 2015
R-bloggers: Police militarization in the US, over time By David Smith, September 26, 2014

GIS and R Resources

Geocomputation with R By Robin Lovelace, Jakub Nowosad, and Jannes Muenchow
Intro to GIS and Spatial Analysis By Manuel Gimond, Last edited on 2023-12-15
Displaying time series, spatial and space-time data with R: This is the accompanying website of the book published with Chapman&Hall/CRC, a project created and maintained by Oscar Perpiñán Lamigueiro
CRAN Task View: Analysis of Spatial Data
CRAN Task View: Handling and Analyzing Spatio-Temporal Data
U.S. Department of Agriculture - U.S. Forest Service Rocky Mountain Research Station Air, Water, & Aquatic Environments Program: SSN & STARS: Tools for Spatial Statistical Modeling on Stream Networks: Spatial statistical models for streams provide a new set of analytical tools that can be used to improve predictions of physical, chemical, and biological characteristics on stream networks. These models are unique because they account for patterns of spatial autocorrelation among locations based on both Euclidean and in-stream distances. They also have practical applications for the design of monitoring strategies and the derivation of information from databases with non-random sample locations. Generating the spatial data needed to fit these statistical models requires practical skills in multiple disciplines including ecology, geospatial science, and statistics. This is the home page for two sets of tools that have been developed to make the methodology more accessible to users: the STARS ArcGIS toolset and the SSN package for R statistical software. These models were developed by researchers at NOAA and CSIRO.
Geographic Information Systems Stack Exchange: Newest ‘r’ Questions
Stack Overflow: Newest ‘r geo’ Questions
Stack Overflow: Newest ‘r gis’ Questions
R-sig-geo: R Special Interest Group on using Geographical data and Mapping
amsantac.co: Integrating QGIS and R: A stratified sampling example, Oct 31, 2015
r-spatial
Leaflet for R
Research Institute for Nature and Forest (INBO) Tutorials: Tag: gis
US Department of the Interior (US DOI) US Geological Survey (USGS): Water Data For The Nation Blog: Beyond Basic R - Mapping: Basic mapping in R with the maps and ggmap package By David Watkins, Last Updated July 20, 2023
Plotly Open Source Graphing Libraries: Simple Features in Maps in ggplot2: How to use Simple Features in Maps with Plotly
Interactive web-based data visualization with R, plotly, and shiny: 4 Maps By Carson Sievert, 2019-12-19
Medium: How to map data with R: A hands-on tutorial to get you to start creating maps with R By Abhinav Malasi, Jun 29, 2021
Push the limits of interactive mapping in R with vector tiles: A tutorial on mapping with vector tiles in R By Miles McBain, April 9, 2022
United States Environmental Protection Agency (EPA): Introduction to GIS with R: This 1/2 day workshop will provide attendees with an introduction to doing basic GIS analyses using the R Language for Statistical Computing.
GEOSTAT courses: “Applied Spatio-temporal Data Analysis with FOSS: R+OSGeo”
GEOSTAT courses: Get training in R + OSGeo
The CASA Blog Network: R Spatial Tips
Spatial.ly: R Spatial
GRASS Wiki: R statistics
Research Institute for Nature and Forest (INBO) Tutorials: Using WFS services in R: How to use WFS (vectors/features) GIS services within R scripts Posted on 2022, Apr 19 by Thierry Onkelinx, Hans Van Calster, Floris Vanderhaeghe
Research Institute for Nature and Forest (INBO) Tutorials: Using WMS services in R: How to use WMS (raster) GIS services within R scripts Posted on 2022, Apr 08 by Thierry Onkelinx, Floris Vanderhaeghe
r-spatial: Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 1: Basics By Mel Moreno and Mathieu Basille, Oct 25, 2018
r-spatial: Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 2: Layers By Mel Moreno and Mathieu Basille, Oct 25, 2018
r-spatial: Drawing beautiful maps programmatically with R, sf and ggplot2 — Part 3: Layouts By Mel Moreno and Mathieu Basille, Oct 25, 2018
Computerworld: Create maps in R in 10 (fairly) easy steps: Use the R programming language to turn location-based data into interactive maps By Sharon Machlis, Oct 31, 2017
seascapemodels: ArcGIS to R spatial cheat sheet
An Introduction to Spatial Analyses in R: Comparisons to ArcGIS by Shannon E. Albeke, Ph.D., University of Wyoming Wyoming Geographic Information Science Center, NCTC Training: June 17 - 19 2014
amsantac.co: The R-Spatialist Blog
Robinlovelace GitHub
Ilya Kashnitsky: demographeR’s notes
jannesm: R
GIS in R by Nick Eubank
Real Data: Using R as a GIS by Kiefer, April 22, 2017
Using R as a GIS: This repository provides the code used to create a set of tutorial files developed collaboratively by Alex Singleton, Chris Brunsdon and Nick Bearman
Spatial Data Analysis and Modeling with R
Introduction to visualising spatial data in R by Robin Lovelace
Intro to GIS and Spatial Analysis by Manuel Gimond
Spatial Data Analysis and Modeling with R: 8. Point pattern analysis
Intro to GIS and Spatial Analysis: Point pattern analysis in R by Manuel Gimond
Point Pattern analysis and spatial interpolation with R by Robin Lovelace
R-bloggers: R, GIS, and fuzzyjoin to reconstruct demographic data for NUTS regions of Denmark By Ilya Kashnitsky, March 15, 2017
culture of insight: Building Dot Density Maps with UK Census Data in R, 6 June 2017
Subplots in maps with ggplot2 by Ilya Kashnitsky
R-bloggers: R, an Integrated Statistical Programming Environment and GIS By Robin Lovelace - R, November 27, 2014
R-bloggers: R for spatial analysis tutorial + video By Robin Lovelace - R, January 30, 2014
R-bloggers: Basic mapping and attribute joins in R By Robin Lovelace - R, November 9, 2014
Clipping spatial data in R by Robin Lovelace, 29 Jul 2014
R-bloggers: Spatial clipping and aggregation in R By Robin Lovelace - R, January 10, 2014
Geospatial Data in R and Beyond
Geospatial Data in R and Beyond: Spatial Cheatsheet: This cheatsheet is an attempt to supply you with the key functions and manipulations of spatial vector and raster data. It does not have examples for you to cut and paste, its intention is to provoke the “Oh yes, that’s how you do it“ thought when stuck.
Applied Spatial Data Analysis with R by Roger S. Bivand, Edzer J. Pebesma and Virgilio Gómez-Rubio
A Practical Guide to Geostatistical Mapping by Tomislav Hengl
VMERGE & EDENext Data Portal: Shapefiles and Projections in R (R-Project) Submitted by edenext-admin on 08/11/2011
Overview of Coordinate Reference Systems (CRS) in R by Melanie Frazier, National Center for Ecological Analysis and Synthesis, The Regents of the University of California, UC Santa Barbara — [Requires PDF Software]
Frank Davenport’s Research Blog: Notes from A Recent Spatial R Class I Gave, June 19, 2012
Notes on Spatial Data Operations in R by Frank Davenport, March 1, 2013 (DropBox) — [Requires PDF Software]
RPubs: Working with PostGIS from R by Duncan Golicher
R-bloggers: Modis R: Package tutorial By Steven Mosher, November 24, 2012
Center for Spatially Integrated Social Science (CSISS) R Spatial Analysis Tools
spatial-analyst.net
The Berkeley R Language Beginner Study Group: Geospatial Data Processing and Analysis in R by Andy Lyons, August 19, 2014
GeoDa Center for Geospatial Analysis and Computation: R Spatial Projects
Open Source Tools for Spatial Ecological Modeling: The R Project for Statistical Computing
Working With Data: Using R — Working with Geospatial Data Posted on April 14, 2013 by Steven Brey
Spatstat: Open source software for spatial statistics
GIS Workshops - Spatial R: University of Washington Center for Studies in Demography and Ecology
GIS Workshops - Exploratory Spatial Data Analysis: University of Washington Center for Studies in Demography and Ecology

GIS Workshops - Geographically Weighted Regression: University of Washington Center for Studies in Demography and Ecology
GIS Workshops - Spatial Regression, GIS Workshops - Geographically Weighted Regression: University of Washington Center for Studies in Demography and Ecology
Data Steve: Global Temp: Geo-Spatial Records over Time By Steve Simpson October 14, 2016
Data Science with R: Cartography with R Posted on 14 October 2014 by slipiste
GeogR: Geographic Data Analysis Using R: Department of Geography, University of Oregon
GeogR: Geographic Data Analysis Using R: Geographic Data Analysis and Visualization: Topics and Examples: Department of Geography, University of Oregon
GeogR: Geographic Data Analysis Using R: Maps in R — Examples: Department of Geography, University of Oregon
The R graph Gallery: How to draw connecting routes on map with R and great circles by Holtz, June 14, 2017
R-bloggers: Plot maps like a boss By Ian, March 5, 2012
MilanoR: Maps in R: Introduction - Drawing the map of Europe Posted by Max Marchi
MilanoR: Maps in R: Plotting data points on a map Posted on January 10, 2013 by Max Marchi
sample(ECOLOGY): Mapping in R, Part I, February 12, 2013
sample(ECOLOGY): Mapping in R, Part II, February 14, 2013
The Molecular Ecologist: Making Maps with R Posted on 18 September, 2012 by kimgilbert
EcoPress/Natural Resource Ecology Laboratory at Colorado State University: This is how I did it… Mapping in R with ‘ggplot2’ by Andrew Tredennick
ZevRoss Spatial Analysis Blog: Map and analyze raster data in R Posted on March 30, 2015 by hollie@zevross.com
ZevRoss Spatial Analysis Blog: Mapping in R using the ggplot2 package Posted on July 16, 2014 by zev@zevross.com
Plot_geographical_data: This is a little example of making maps in R using ggplot2. The Markdown file, plot_precip_africa.md is the one to take a look at. Working on adding geographic coordinates to the plot.
R-bloggers: R and GIS — working with shapefiles By R - StudyTrails, April 18, 2016
R-bloggers: Converting shapefiles to rasters in R By Amy Whitehead, May 1, 2014
R-bloggers: Using R — Working with Geospatial Data By Steven Brey, April 14, 2013
Revolutions (R Blog): Cartography with complex survey data Posted by David Smith, December 15, 2014
marmap: A Package for Importing, Plotting and Analyzing Bathymetric and Topographic Data in R by Eric Pante and Benoit Simon-Bouhet, PLOS ONE, 1 September 2013, Volume 8, Issue 9
R-bloggers: Creating Inset Map with ggplot2 By Arnold Salvacion, June 14, 2014
R-bloggers: How the Ghana Floods animation was created By David Quartey, November 7, 2016
R-bloggers: Animate maps with mapmate: R package for map- and globe-based still image sequences By Matt Leonawicz, October 10, 2016
R-bloggers: 4 tricks for working with R, Leaflet and Shiny By Holtz, March 14, 2017
DataScience+: Building Interactive Maps with Leaflet by Teja Kodali, November 7, 2015
RPubs: Using RStudio as an interactive GIS with Leaflet by Kyle Walker, February 17, 2015
rMaps makes it easy to create, customize and share interactive maps from R, with a few lines of code. It supports several javascript based mapping libraries like Leaflet, DataMaps and Crosslet, with many more to be added.
Hacking maps with ggplot2 by Ilya Kashnitsky, Updated April 24, 2017
R-bloggers: Overcoming D3 Cartographic Envy With R + ggplot By hrbrmstr, September 25, 2014
Marco Ghislanzoni’s Blog: Interactive map of world population with R
R-bloggers: Making Static & Interactive Maps With ggvis (+ using ggvis maps w/shiny) By hrbrmstr, December 29, 2014
Interactive Mapping in R by Luba Gloukhov — [Requires PDF Software]
ggmap: Spatial Visualization with ggplot2 by David Kahle and Hadley Wickham, The R Journal Vol. 5/1, June — [Requires PDF Software]
Obscure Analytics: Visualizing Baltimore with R and Ggplot2: Crime Data by Rob Mealey, December 7, 2012
rtop: An R package for interpolation of data with a variable spatial support, with an example from river networks By J.O. Skøien, G. Blöschl, G. Laaha, E. Pebesma, J. Parajka, A. Viglione, Computers & Geosciences, Volume 67, June 2014, Pages 180-190
guides on generating map plot with R1 by Mao Jianfeng, 2012/1/
Question & Answer System: Fortifying re-projected shapefile for ggplot2 in R
Visualising World Values Survey on a Map in R by Markus Kainu, 14 Nov 2013
R-bloggers: Visualising F1 Telemetry Data and Plotting Latitude and Longitude with ggplot Map Projections in R By Tony Hirst, March 14, 2012
R-bloggers: Equidistant points on a map By arthur charpentier, October 17, 2013
me nugget: A ridiculous proof of concept: xyz interpolation, March 14, 2012
me nugget: map.xyz(): interpolation of XYZ data and projection onto a map, May 30, 2011
me nugget: XYZ geographic data interpolation, part 2, February 29, 2012
me nugget: XYZ geographic data interpolation, part 3, March 12, 2012
Analysing spatial point patterns in ‘R’: A detailed set of workshop notes on analysing spatial point patterns using the statistical software package ‘R’. (232 pages), 28 April 2011
National Park Service (NPS): Topic 2(a): Spatial Data in R: Using R Statistical and Graphics Tools for Natural Resource Stewardship Science
spatial-analyst.net: R + Open Source Desktop GIS (OSGeo) + Google Earth bundle that can be used to run analysis of various spatio-temporal data
Using R, spsurvey, and Tinn-R for Monitoring Design and Analysis: R for Aquatic Monitoring Survey Design and Analysis, Monitoring Design and Analysis Team, US EPA ORD, National Health and Environmental Effects Research Laboratory, Western Ecology Division
Spatial data in R: Using R as a GIS: A tutorial to perform basic operations with spatial data in R, such as importing and exporting data (both vectorial and raster), plotting, analysing and making maps. by Francisco Rodriguez-Sanchez, 27-01-2015
R and GIS practice series: Shapefile introduction and map shows
R-bloggers: Converting shapefiles to rasters in R By Amy Whitehead, May 1, 2014
Introducing the GWmodel R and python packages for modelling spatial heterogeneity by Binbin Lu, Paul Harris, Isabella Gollini, Martin Charlton, Chris Brunsdon — [Requires PDF Software]
Application of the AMBUR R package for spatio-temporal analysis of shoreline change: Jekyll Island, Georgia, USA by Chester W. Jackson Jr., Clark R. Alexander, David M. Bush, Computers & Geosciences, Volume 41, April 2012, Pages 199-207
R-sig-geo-help at Nabble.com: Plotting shapefiles in R
Geographic Information Systems Stack Exchange: Tutorials to handle spatial data in R?
Geographic Information Systems Stack Exchange: use proj4 to specify Robinson projection with R ggmap and ggplot2 packages?
R-bloggers: The FBI’s aerial surveillance program, visualized with R By David Smith, April 11, 2016
Stack Overflow: vector - reading and plotting an esri shape file in R
Stack Overflow: How keep information from shapefile after fortify()
Stack Overflow: Creating maps in R just like the way rworldmap does but for specific country with provinces
Stack Overflow: R: ggplot2 fill polygons in shapefile by coords
Stack Overflow: Adding NAs to a continuous scale in ggplot2
Stack Overflow: Proportionally sized symbols in ggplot
Stack Overflow: Coordinate points appropriately sized in ggplot2

Creating Documents, Presentations, and Reports with R and friends

CommonMark: A strongly defined, highly compatible specification of Markdown
The Markdown Guide is a free and open-source reference guide that explains how to use Markdown, the simple and easy-to-use markup language you can use to format virtually any document.
Pandoc: Pandoc Markdown
W3schools Learn free Programming full stack development Tutorials: Markdown - Tutorials
GitHub: Marker converts PDF, EPUB, and MOBI to markdown. It’s 10x faster than nougat, more accurate on most documents, and has low hallucination risk.
Quarto® is an open-source scientific and technical publishing system built on Pandoc
RStudio: R Markdown
RStudio: R Markdown Document Templates
Scientific RMarkdown by Marco Sciaini, 01 Jun 2016
Assemble Markdown Cheatsheet
Make Tech Easier: Markdown Cheatsheet
Tutorial R Markdown by Jacolien van Rij
RStudio: Shiny: Introduction to R Markdown By Garrett Grolemund, 16 Jul 2014
knitr in a knutshell: Knitr with R Markdown
Stack Overflow: Insert picture/table in R Markdown
G-Forge: Fast-track publishing using the new R markdown — a tutorial and a quick look behind the scenes by Max Gordon Posted on July 29, 2014
Locke Data: R Quick Tip: Table parameters for rmarkdown reports by Steph, 19/04/2017
Using R Markdown for Class Reports by Cosma Shalizi, First version 7 January 2016, revision of 22 August 2016
Educate-R: Use CSS to format markdown or HTML files
R-bloggers: Creating a Quick Report with knitr, xtable, R Markdown, Pandoc (and some OpenBLAS Benchmark Results) By Jo-fai Chow, August 15, 2013
Locke Data: R Quick Tip: parameter re-use within rmarkdown YAML Posted by Steph, 08/05/2017
Stack Overflow: r - Internal links in rmarkdown don’t work
CRAN Package: rmarkdown: Convert R Markdown documents into a variety of formats.
CRAN Package: knitr: Provides a general-purpose tool for dynamic report generation in R using Literate Programming techniques.
CRAN Package: ReporteRs: Create ‘Microsoft Word’ document (>=2007), ‘Microsoft PowerPoint’ document (>=2007) and ‘HTML’ documents from R. There are several features to let you format and present R outputs ; e.g. Editable Vector Graphics, functions for complex tables reporting, reuse of corporate template document. You can use the package as a tool for fast reporting and as a tool for reporting automation. The package does not require any installation of Microsoft product to be able to write Microsoft files.
CRAN Package: R.rsp: The RSP markup language makes any text-based document come alive. RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. ‘Today’s date is <%=Sys.Date()%>’. Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries. RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things. Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile(‘report.tex.rsp’) generates ‘report.pdf’ and rfile(‘report.md.rsp’) generates ‘report.html’. RSP is ideal for self-contained scientific reports and R package vignettes. It’s easy to use - if you know how to write an R script, you'll be up and running within minutes.
CRAN Package: papeR: A toolbox for writing ‘knitr’, ‘Sweave’ or other ‘LaTeX’- or ‘markdown’-based reports and to prettify the output of various estimated models.
CRAN Package: exreport: Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
CRAN Package: rapport: Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.
Stat Bandit: A quick exploration of the ReporteRs package, October 28, 2016
Mango Solutions: Why I would rather use ReporteRs than RMarkdown by Aimee Gott, 18 Oct 16
R-bloggers: ReporteRs: Manager-friendly Word or Powerpoint documents created with R By David Smith, October 27, 2016

R and Reproducible Research

Migrating from GitHub to GitLab with RStudio (Tutorial) by Shirin Glander, 04 September 2017
R for Researchers: Introduction, The Social Science Computing Cooperative: Providing Computer Services for the Social Sciences at University of Wisconsin-Madison
Reproducible Research @ GIUZ (Geographisches Institut Universität Zürich)
rOpenSci Project: a collaborative effort to develop R-based tools for facilitating Open Science.
R-bloggers: The “Ten Simple Rules for Reproducible Computational Research” are easy to reach for R users By Joris Muller’s blog - Posts about R, January 27, 2017
rOpenSci Project: Reproducibility in Science: A Guide to enhancing reproducibility in scientific results and writing
rOpenSci Project Blog: How rOpenSci uses Code Review to Promote Reproducible Science by Noam Ross, Scott Chamberlain, Karthik Ram, and Maëlle Salmon, September 1, 2017
rrrpkg: Use of an R package to facilitate reproducible research
revisit: a “Statistical Audit” for Statistical Reproducibility and Alternate Analysis
A Partial Remedy to the Reproducibility Problem by matloff, May 31, 2017
R-bloggers: How Reproducible Data Analysis Scripts Can Help You Route Around Data Sharing Blockers By Tony Hirst, March 14, 2017
R-bloggers: Reproducible Data Science with R By David Smith, April 21, 2017
R-posts.com: Composing reproducible manuscripts using R Markdown Posted by Emily Packer, April 17, 2017
R-bloggers: The Next Era of Research Communication By Aman Tsegai, March 21, 2017
R-statistics blog: The reproducibility crisis in science and prospects for R by Tal Galili Posted on July 26, 2016
A Case Study in Reproducible Model Building by Jason C Fisher (USGS), Published: 2016-08-04
Evidence for a limit to effective peer review By Neil Saunders, December 18, 2016
NRC.nl: ‘Top-tier journals like Nature knowingly take risks’ by Lucas Brouwers, 9 december 2016
The Guardian: How journals like Nature, Cell and Science are damaging science: The incentives offered by top journals distort science, just as big bonuses distort banking by Randy Schekman, 9 December 2013
Nature: Challenges in irreproducible research
Nature Announcement: Reducing our irreproducibility, 24 April 2013

Programming with R

Why?: Security: the dangers of copying and pasting R code, June 7, 2017
R Programming: From Wikibooks, open books for an open world
How to use R: From Wikiversity
R (programming language): From Wikipedia, the free encyclopedia
R Programming/Documentation: From Wikibooks, open books for an open world
For Dummies: Programming in R
RProgramming.net: R Programming Help, How To’s, and Examples
Programming in R
Ramarro: R for Developers by Andrea Spanò
Efficient R programming by Colin Gillespie and Robin Lovelace
Irregularly Scheduled Programming: R
Style guide, from Advanced R by Hadley Wickham
R-bloggers: R Best Practices: R you writing the R way! By Milind Paradkar, April 13, 2017
Advanced R by Hadley Wickham
DataScience+: Programming: Learn how to program in R, starting with making simple loops and functions in R and then continuing with building Shiny Apps and R packages for an effective data analysis or data visualization.
The Chemical Statistician: R programming
Programming in R by Thomas Girke, University of California Riverside
R-bloggers: Some programming language theory in R By John Mount, January 1, 2016
DataScience+: R Programming — Pitfalls to avoid (Part 1) By Anup Nair, September 5, 2016
QuantInsti: Common R Programming Errors Faced by Beginners, June 6, 2016
r4stats.com: Why R is Hard to Learn by Robert A. Muenchen
The R Inferno by Patrick Burns, 30th April 2011 — [Requires PDF Software]
A Simple Guide to S3 Methods by Nicholas Tierney, 06 Nov 2016
Scheduling R scripts and processes on Windows and Unix/Linux by BNOSAC
R: Batch Execution of R
Revolutions (R Blog): Running scripts with R CMD BATCH
Burns Statistics: ‪21 R navigation tools: Navigation gets you from where you are to where you want to be, 17 Aug 2014
R-bloggers: How R Searches and Finds Stuff By Suraj Gupta, April 4, 2012
Quick-R: Control Structures
Functions from Advanced R by Hadley Wickham
Quick-R: User-written Functions
DataCamp: A Tutorial on Using Functions in R! by Carlo Fanara, August 20th, 2015
R for Public Health: How to write and debug an R function Posted by Slawa Rokicki, June 8, 2014
R-bloggers: How to Code Something “New” in R By Francis Smart, May 1, 2014
R HEAD: Custom Function
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Library: Advanced functions by Matt Baggott
R-bloggers: Work on lists of datasets instead of individual datasets by using functional programming By Bruno Rodrigues, December 20, 2016
Stack Overflow: r - function returning more than one value
Darren Wilkinson’s research blog: Lexical scope and function closures in R
Simpler R coding with pipes > the present and future of the magrittr package by Tal Galili, August 5, 2014

Regular Expressions (Advanced Find/Replace)

paulvanderlaken.com: Curated Regular Expression Resources
RegexOne: Learn Regular Expressions
Online regex tester and debugger: JavaScript, Python, PHP, and PCRE
RegExr: Learn, Build, & Test RegEx
txt2re: headache relief for programmers :: regular expression generator/li>
Make Tech Easier: Regular Expressions Cheatsheet
gsubfn: Regular Expression Links
Regular Expressions: From Wikibooks, open books for an open world
Regular expression: From Wikipedia, the free encyclopedia
Regular Expressions: The Complete Tutorial by Jan Goyvaerts, July 2007 — [Requires PDF Software]
Regular-Expressions.info: Regex Tutorial, Examples and Reference - Regexp Patterns
Tech Stuff: Regular Expressions: A Gentle User Guide and Tutorial
EuGuide: Regular Expressions: A regular expression is a way of describing, in a cleverly coded way, a fragment of text that you may search for in a larger string of text. It is all about pattern matching. Regular expressions is a coding system, and it’s akin to learning a new language.
Regular-Expressions.info: Regular Expressions Reference
Regular-Expressions.info: Regex Tutorial - Backreferences To Match The Same Text Again
Regular-Expressions.info: Regex Capture Groups and Back-References
PCRE (Perl Compatible Regular Expressions)
grep: From Wikipedia, the free encyclopedia
grep: The Open Group Base Specifications Issue 7
Stack Overflow: Newest “grep” Questions
Informatica, Oracle, Netezza, Unix, Hadoop Tutorials and Examples: Grep Command in Unix and Linux Examples
explain {qdapRegex}: Visualize Regular Expressions
R Programming/Text Processing: Regular Expressions: From Wikibooks, open books for an open world
Introduction to String Matching and Modification in R Using Regular Expressions by Svetlana Eden, March 6, 2007 — [Requires PDF Software]
R-bloggers: Regular expressions in R vs RStudio By Robin Lovelace - R, April 14, 2014
Regular expressions in R by John D. Cook
grep {base}: inside-R
Regular-Expressions.info: Regular Expressions with grep, regexp and sub in the R Language
From the bottom of the heap: Processing sample labels using regular expressions in R By Gavin Simpson, 15 August 2012
Jeromy Anglim’s Blog: Psychology and Statistics: Using Regular Expressions in R: Case Study in Cleaning a BibTeX Database, March 10, 2010
Stack Overflow: Extract location data using regex in R
Stack Overflow: regex - forming and using Regular expressions in R
Stack Overflow: regex - Removing parentheses as unwanted text in R using gsub
Stack Overflow: R regex: remove times from character string
Stack Overflow: regex - inverse of gsub
Stack Overflow: regex - Split text based on dot in R

Develop R (G)UIs [(Graphical) User Interface]

OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R. You can either use the public servers or host your own.
Shiny by RStudio
ShinyProxy is your favourite way to deploy Shiny apps in an enterprise context. It has built-in functionality for LDAP authentication and authorization, makes securing Shiny traffic (over TLS) a breeze and has no limits on concurrent usage of a Shiny app.
RStudio: Shiny User Showcase
RStudio: Teach yourself Shiny
R Views: Productionizing Shiny and Plumber with Pins by Alex Gold, 2019-10-17
DataScience+: Programming: Learn how to program in R, starting with making simple loops and functions in R and then continuing with building Shiny Apps and R packages for an effective data analysis or data visualization.
R-bloggers: Shiny tips & tricks for improving your apps and solving common problems By Dean Attali’s R Blog, August 29, 2016
Show Me Shiny - Gallery of R Web Apps
SNAP tech blog: Assorted Shiny apps collection, full code and data by Matt Leonawicz, April 26, 2017
FrissAnalytics: shiny-js-tutorials: this repo contains the code and documentation for the shiny javascript tutorials at the Shiny Development Center offered by RSTUDIO. The series consist of 6 in depth lessons for the intermediate shiny enthusiast with limited experience in HTML, CSS and/or JavaScript, who wants to learn how to extend shiny.
R-bloggers: Shiny: data presentation with an extra By Pablo Bernabeu, May 31, 2017
R-bloggers: R Quick Tip: Upload multiple files in shiny and consolidate into a dataset By Steph, April 28, 2017
Mango Solutions: Dynamically generated Shiny UI by Gábor Csárdi and Cheng, 15 Dec 16
Enhance data science: Three R Shiny tricks to make your Shiny app shines (1/3) by guillotantoine, 15th February 2017
Enhance data science: Three R Shiny tricks to make your Shiny app shines (2/3): Semi-collapsible sidebar by guillotantoine, 21st February 2017
Enhance data science: Three R Shiny tricks to make your Shiny app shines (3/3): Buttons to delete, edit and compare Datatable rows by guillotantoine, 1st March 2017
ZevRoss Spatial Analysis Blog: R powered web applications with Shiny (a tutorial and cheat sheet with 40 example apps) Posted on April 19, 2016
Building Shiny apps - an interactive tutorial by Dean Attali Posted on December 7, 2015
R-bloggers: shinyData — GUI for data analysis and reporting By Jan Górecki - R, March 18, 2015
R-bloggers: Useful tools for shiny developers in the new version of shinyjs By Dean Attali’s R Blog, November 7, 2016
Stack Overflow: dealing with an input dataset in R Shiny
CRAN Package: gWidgets2: Rewrite of gWidgets API for Simplified GUI Construction
CRAN Package: gWidgets: gWidgets API for building toolkit-independent, interactive GUIs
CRAN Package: gWidgetsRGtk2: Toolkit implementation of gWidgets for RGtk2
CRAN Package: gWidgetstcltk: Toolkit implementation of gWidgets for tcltk package
CRAN Package: pmg: Poor Man’s GUI
CRAN Package: PBSmodelling: GUI Tools Made Easy: Interact with Models and Explore Data
SciViews: R Tcl/Tk recipes
CRAN Package: SciViews: SciViews GUI API - Main package
CRAN Package: ProgGUIinR: support package for “Programming Graphical User Interfaces in R”
CRAN Package: traitr: An interface for creating GUIs modeled in part after traits UI module for python

R Packages

R: Install Packages from Repositories or Local Files
R Installation and Administration by the R Core Team
Phoxis: Get list of installed packages and their details in R
R Installation and Administration Appendix D: The Windows toolset
Rtools: Building R for Windows

Creating R Packages

CRAN Task View: Package Development
CRAN Package: CRAN Repository Policy
Writing R Extensions
The r-hub builder
Checklist for R package submission to CRAN By Kamil Wais, Last updated on 24 January, 2019
Revolutions (R Blog): Syberia: A development framework for R code in production by David Smith, June 13, 2017
Automatic tools for improving R packages by Maëlle, 17 Jun 2017
R packages 2nd Edition by Hadley Wickham
Quick-R: R Packages
R-bloggers: How to write your own R package and publish it on CRAN By R on Methods Bites, July 15, 2020
R-bloggers: Submitting packages to CRAN By Françoisn, March 23, 2016
DataScience+: How to make and share an R package in 3 steps by Emelie Hofland, Published on June 14, 2017
RStudio: Painless package development for R
The University of British Columbia (UBC) Vancouver Campus STAT 545A and 547M: Write your own R package
MIT Step by Step Instructions for Creating Your Own R Package Posted by Data Scientist PakinJa, May 6, 2017
Instructions for Creating Your Own R Package by In Song Kim, Phil Martin, Nina McMurry, February 23, 2016
Analytics Vidhya Blog: How I created a package in R & published it on CRAN / GitHub (and you can too)? by Saurav Kaushik, March 22, 2017
R-bloggers: Writing and Publishing my first R package By Edwin Thoen, September 4, 2017
R-bloggers: Submitting your first package to CRAN, my experience By Roel M. Hogervorst, July 8, 2016
Revolutions (R Blog): Good R Packages by Joseph Rickert, May 12, 2016
Posit Support (formerly RStudio Support): Building, Testing, and Distributing Packages by Josh Paulson, September 13, 2015
Introduction to R Package Development by Dirk Eddelbuettel, Big Data and Open Science with R, Warren Center for Network and Data Sciences, University of Pennsylvania, Philadelphia, PA, 21 November 2014 — [Requires PDF Software]
R-bloggers: SMART Hackathon: Day 2: Writing Packages in RStudio By strictlystat, May 6, 2014
R-bloggers: Writing an R package from scratch By hilaryparker, April 29, 2014
Making an R package by R.M. Ripley, Department of Statistics, University of Oxford, 2012/13
Creating R Packages: A Tutorial by Friedrich Leisch, Department of Statistics, Ludwig-Maximilians-Universität München, and R Development Core Team, September 14, 2009
Steven Mosher’s Blog: Ten Steps to Building an R package under Windows
Building Microsoft Windows Versions of R and R packages under Intel Linux: A Package Developer’s Tool by Jun Yan and A.J. Rossini — [Requires PDF Software]
Building and checking R source packages for Windows
Creating R Packages, Using CRAN, R-Forge, and Local R Archive Networks and Subversion (SVN) Repositories by Spencer Graves and Sundar Dorai-Raj — [Requires PDF Software]
Posit Support (formerly RStudio Support): Writing Package Documentation by Josh Paulson, July 13, 2015
Sweave, Part II: Package Vignettes, R News Vol. 3/2, October 2003 21 — [Requires PDF Software]
technistema: Building a Corporate R Package for Pleasure and Profit By Bradley Lindblad, October 20, 2019
R-hub blog: How to distribute data with your R package By Maëlle Salmon, 2020/05/29

R Package Repositories

Revolutions (R Blog): Introducing miniCRAN: an R package to create a private CRAN repository by Andrie deVries, October 03, 2014
R Documentation: R Documentation and manuals
R Packages: A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge.
R Package Documentation: A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge.
METACRAN: Search and browse all CRAN/R packages
List of R package on github Created by Atsushi Hayakawa
awesome-r: A curated list of awesome R frameworks, libraries and software.
GitHub: Search - R language
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, 671 software packages, and an active user community. Bioconductor is also available as an Amazon Machine Image (AMI).
Bioconductor Software Packages
R-Forge offers a central platform for the development of R packages, R-related software and further projects. It is based on FusionForge offering easy access to the best in SVN, daily built and checked packages, mailing lists, bug tracking, message boards/forums, site hosting, permanent file archival, full backups, and total web-based administration.
RForge strives to provide a collaborative environment for R package developers. The ultimate goal is to offer SourceForge-like services (such as SVN repository, place for documentation, downloads, mailing lists, bugzilla, wiki etc.) without the annoying look and feel but with additional features specific to R package development, such as make check on-commit, nightly builds of packages, testing on various platforms and full CRAN-like repository access. The focus is on R-specific features that are not offered by SourceForge or GForge.
USGS-R at GitHub: Collection of USGS R codes and packages
rOpenGov: R packages for open government data analytics
At rOpenSci we are creating packages that allow access to data repositories through the R statistical programming environment that is already a familiar part of the workflow of many scientists. Our tools not only facilitate drawing data into an environment where it can readily be manipulated, but also one in which those analyses and methods can be easily shared, replicated, and extended by other researchers. We develop open source R packages that provide programmatic access to a variety of scientific data, full-text of journal articles, and repositories that provide real-time metrics of scholarly impact. Visit our packages section for a full list of production and development versions of packages.
seascapemodels: Popular R resources
Dirk Eddelbuettel Code
CINC (CINC Is Not CRAN)
John Verzani’s projects related to R
R Packages, Course Notes, and Code Snippets: Dr. Bret A. Collier, School of Renewable Natural Resources at Louisiana State University
Julien Moeys: Software development
Jason.Bryer.org: R Packages and Resources by Jason Bryer
Educate-R: R Packages
Dr. Robert Erhardt, Assistant Professor of Statistics, Wake Forest University
Claudia Vitolo: Scientist working on natural hazard risk modelling
Dr. Tom August, Centre for Ecology & Hydrology
Anne Chao’s Software Download
Example R scripts Maintained by Andrew Turpin, the University of Melbourne School of Engineering
CRAN Rstudio-sponsored 0-Cloud mirror
Managed R Archive Network (‪MRAN) - Explore Packages
CRANberries aggregates information about new, updated and removed packages from the CRAN network for R available as this html version and a corresponding RSS feed. Created and provided by Dirk Eddelbuettel.
crantastic, a community site for R packages where you can search for, review and tag CRAN packages
R Package Documentation: A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R-Forge.
Rdocumentation is a tool that helps you easily find and browse the documentation of all current and some past packages on CRAN.
CRAN Mirrors
CRAN Rstudio-sponsored 0-Cloud mirror Contributed Packages
CRAN Rstudio-sponsored 0-Cloud mirror Packages By Name
CRAN Rstudio-sponsored 0-Cloud mirror Other Software on CRAN
R-bloggers: Update all user installed R packages — again By rmkrug, November 10, 2014

Software Testing and Debugging

Software Testing: Algorithm Design & Software Engineering by Stefan Feuerriegel, February 10, 2016, Albert-Ludwigs-Universität Freiburg Information Systems Research — [Requires PDF Software]
Win-Vector Blog: Unit Tests in R by John Mount, March 13, 2019
Testing from R packages by Hadley Wickham
R package primer: a minimal tutorial: Writing tests by Karl Broman
Bioconductor: Unit Testing Guidelines
Inattentional Coffee: Intro to unit testing in R by Katherine Wood, Jun 26, 2017
R-bloggers: Unit Testing in R By blogisr, March 14, 2017
Testing and debugging: Tools for Reproducible Research by Karl Broman, Biostatistics & Medical Informatics, UW-Madison — [Requires PDF Software]
Advanced R by Hadley Wickham: Debugging, condition handling, and defensive programming
Debugging with RStudio by Jonathan McPherson, December 20, 2018
Debugging in R By Duncan Murdoch, Department of Statistical & Actuarial Sciences, University of Western Ontario
Posit Support (formerly RStudio Support): Building, Testing, and Distributing Packages by Josh Paulson, September 13, 2015

STEM (Science, Technology, Engineering, and Mathematics) and R

ResearchGate: What are the software programs I need in Civil and Environmental Engineering (Water Engineering)? asked by Ahmed Tarek
CRAN Task View: Chemometrics and Computational Physics
CRAN Task View: Optimization and Mathematical Programming
CRAN Task View: Differential Equations
CRAN Task View: Numerical Mathematics
SciViews - Reproductible research with R
Data Science for Production & Logistics By Thomas Kirschstein, 2022-07-10
R for Science: Computing is an essential tool for scientists that want to extract the maximum of information out of their data. At the NIOZ Department of Estuarine & Delta Systems in Yerseke, we use R as the problem solving environment for our visualisation, statistical analysis, our scientific computing and environmental modelling.
Exploring Modeling with Data and Differential Equations Using R By John M. Zobitz
Statistics and Analytics for the Social and Computing Sciences By Desmond C. Ong
Notes of a Dabbler: Wandering through the beautiful world of math, computations and visualizations
Symbolic math with julia (through SymPy)
Symbolic Computation in R by João Neto, October 2014
Econometrics by Simulation: Symbolic Math - Differentiation in R, August 21, 2012
Stack Overflow: Solving equations in R similar to the Excel solver parameters function
University of California, Davis California Soil Resource Lab: Numerical Integration/Differentiation in R: FTIR Spectra, Posted: February 23rd, 2010
Use R to Compute Numerical Integrals by Jie Yang, November 2, 2011, Stat401: Introduction to Probability, University of Illinois at Chicago — [Requires PDF Software]
How to solve a constraint optimization problem in R: Nonlinear Constraint Optimization in R using nloptr By Rahul Bhadani, Jan 8, 2021
deSolve contains functions that solve initial value problems of a system of first-order ordinary differential equations (ODE), of partial differential equations (PDE), of differential algebraic equations (DAE), and of delay differential equations (DDE). The functions provide an interface to the FORTRAN functions lsoda, lsodar, lsode, lsodes of the ODEPACK collection, to the FORTRAN functions dvode, zvode, daspk and radau5, and a C-implementation of solvers of the Runge-Kutta family with fixed or variable time steps. The package contains also routines designed for solving ODEs resulting from 1-D, 2-D and 3-D partial differential equations (PDE) that have been converted to ODEs by numerical differencing.
Solving ODEs, DAEs, DDEs and PDEs in R by Karline Soetaert and Thomas Petzoldt, Journal of Numerical Analysis, Industrial and Applied Mathematics (JNAIAM) vol. 6, no. 1-2, 2011, pp. 51-65
phaseR: An R Package for Phase Plane Analysis of Autonomous ODE Systems by Michael J. Grayling, The R Journal Vol. 6/2, December 2014 — [Requires PDF Software]
R Tutorial for First Cource in Applied Differential Equations by Vladimir Dobrushkin, Fluids at Brown University
Differential Equations in R by Karline Soetaert & Thomas Petzoldt, Tutorial useR conference 2011, September 15, 2011 — [Requires PDF Software]
Using R for mathematical modelling (the environment) by Karline Soetaert, Netherlands Institute of Ecology — [Requires PDF Software]
R-bloggers: Learning R: Parameter Fitting for Models Involving Differential Equations By rdabbler, June 30, 2013
Learn R On Your Own (formerly CSE 8091: Advanced Scientific Computing with R), Lyle Computer Science and Engineering Department - Southern Methodist University (SMU)
Introducing Monte Carlo Methods with R by Christian P. Robert and George Casella — [Requires PDF Software]
Racing Tadpole: Monte Carlo cashflow modelling in R with dplyr by Arthur Street, 26 April 2014
Project MOSAIC: Start R in Calculus by Daniel Kaplan, 2013 — [Requires PDF Software]
Using R for Introductory Calculus and Statistics by Daniel Kaplan, August 9, 2007, Macalester College — [Requires PDF Software]
Fourier Transform: A R Tutorial By João Neto, March 2013
CRAN Package: linkR: Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
Revolutions (R Blog): R and Signal Processing by Joseph Rickert, October 10, 2013
CRAN Package: GeoLight: Provides basic functions for global positioning based on light intensity measurements over time. Positioning process includes the determination of sun events, a discrimination of residency and movement periods, the calibration of period-specific data and, finally, the calculation of positions.
CRAN Package: opentraj uses the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) for computing simple air parcel trajectories. The functions in this package allow users to run HYSPLIT for trajectory calculations, as well as get its results, directly from R without using any GUI interface.
Scientific computing in R by Karline Soetaert and Filip Meysman, Royal Netherlands Institute of Sea Research (NIOZ), May 2013
Computational Physics with Maxima or R by Edwin L. (Ted) Woollett, Professor Emeritus, Department of Physics and Astronomy, California State University, Long Beach (CSULB)
The Chemical Statistician: R programming
Experimental Particle Physics Analysis with R by Adam L. Lyon, Fermi National Accelerator Laboratory, useR! 2007, Iowa State University
R-bloggers: How to Calculate a Partial Correlation Coefficient in R: An Example with Oxidizing Ammonia to Make Nitric Acid By Eric Cai - The Chemical Statistician, May 5, 2013
Notes of a Dabbler: Learning R using a Chemical Reaction Engineering Book: Part 1 Posted on January 26, 2013
R-bloggers: Learning R using a Chemical Reaction Engineering Book: Part 2 By rdabbler, January 26, 2013
R-bloggers: Learning R using a Chemical Reaction Engineering Book: Part 3 By rdabbler, January 26, 2013
R-bloggers: Learning R Using a Chemical Reaction Engineering Book: Part 4 By rdabbler, February 8, 2013
R Programming for Environmental Engineers by Jeff Walker, PhD
CRAN Package: openair: Tools to analyse, interpret and understand air pollution data. Data are typically hourly time series and both monitoring data and dispersion model output can be analysed. Many functions can also be applied to other data, including meteorological and traffic data.
human exposure research (heR) software project: The heR Project is officially part of the Inhalation Exposure Simulation Modeling project, because it includes sophisticated tools for modeling individual and population exposures. However, it also contains many tools, sub-models, and data sets that are likely to be useful in many different areas of human exposure research. For example, it contains subroutines for manipulating and statistically analyzing activity pattern data, and it also contains routines for executing advanced indoor air quality models.
R-bloggers: R and other open source tools for Civil Engineering By Robin Lovelace - R, October 21, 2015
CRAN Package: geotech: A compilation of functions for performing calculations and creating plots that commonly arise in geotechnical engineering and soil mechanics. The types of calculations that are currently included are: (1) phase diagrams and index parameters, (2) grain-size distributions, (3) plasticity, (4) soil classification, (5) compaction, (6) groundwater, (7) subsurface stresses (geostatic and induced), (8) Mohr circle analyses, (9) consolidation settlement and rate, (10) shear strength, (11) bearing capacity, (12) lateral earth pressures, (13) slope stability, and (14) subsurface explorations. Geotechnical engineering students, educators, researchers, and practitioners will find this package useful.
CRAN Package: SDAR: Stratigraphic Data Analysis: A fast, consistent tool for plotting and facilitating the analysis of stratigraphic and sedimentological data. Taking advantage of the flexible plotting tools available in R, ‘SDAR’ uses stratigraphic and sedimentological data to produce detailed graphic logs for outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness, lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, electrical logs) (Johnson, 1992, ISSN 0037-0738).
The Propensity to Cycle Tool: An on-line interactive web tool for transport planning by Robin Lovelace, University of Leeds, 19th Nov. 2015, UCL Geospatial Science Seminar Series
This is a package for sustainable transport planning with R (stplanr). It brings together a range of tools for transport planning practitioners and researchers to better understand transport systems and inform policy.
R-bloggers: R vs QGIS for sustainable transport planning By Robin Lovelace - R, April 19, 2015
How to Create an Array in R By Joris Meys and Andrie de Vries from R For Dummies
The Matrix Cheatsheet for MATLAB, Python NumPy, R, and Julia by Sebastian Raschka (last updated: June 18, 2014)
R Tutorial: Matrix
Quick-R: Matrix Algebra
R-bloggers: A Hack to Create Matrices in R, Matlab style By Rasmus Bååth, March 7, 2014
Econometrics by Simulation: Matrix operations in R, September 7, 2012
Quick Review of Matrix Algebra in R By John Myles White on 12.16.2009
The Personality Project: Using R for psychological research: Appendix E: A Review of Matrices from An introduction to psychometric theory with applications in R by William Revelle and the Personality Project — [Requires PDF Software]
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Library: Matrices and matrix computations in R
R help: Summing over specific columns in a matrix
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Code Fragments: Examples of Singular Value Decomposition

Time Series Analysis and R

Little Book of R for Time Series! By Avril Coghlan, Parasite Genomics Group, Wellcome Trust Sanger Institute, Cambridge, U.K.
R by example: Reading a file involving dates
R Manual: DateTimeClasses {base}
The R Trader: Date formating in R, Published on April 18, 2014
R-bloggers: Preparing Datetime Data for Analysis with padr and dplyr By That’s so Random, March 19, 2017
R help: How to sum and group data by DATE in data frame
Stack Overflow: Converting Date/Time stamp into correct R Format using chron()
Revolutions (R Blog): Converting time zones in R: tips, tricks and pitfalls, June 02, 2009
lukemiller.org» Blog Archive »: Converting MATLAB and R date and time values Luke Miller, February 20, 2011
R-bloggers: Timeseries forecasting using extreme gradient boosting By Peter’s stats stuff - R, November 5, 2016
Working with Financial Time Series Data in R by Eric Zivot, Department of Economics, University of Washington, June 30, 2014 — [Requires PDF Software]
Time Series Analysis and Its Applications: With R Examples (Third Edition) by Robert Shunway and David Stoffer
R functions for time series analysis by Vito Ricci, 26/11/04 — [Requires PDF Software]

Probability, Statistics, and R

easystats
poissonisfish: Statistics & Mathematics with R
Set Theory by Aaron Schlegel
The Mrs. White probability puzzle By Antoine R, April 28, 2019
R Tutorial: Introduction to Probability and Statistics Using R by G. Jay Kerns — [Requires PDF Software]
Stat401: Introduction to Probability: Handout — 08, November 2, 2011 — [Requires PDF Software]
easystats: How to easily generate a perfectly normal distribution, Apr 24, 2019
ouR data generation: Copulas and correlated data generation: getting beyond the normal distribution, Posted on June 19, 2017
Raccoon: Statistical Models with R free web-book by Quantide srl, Oct 6, 2016
Massachusetts Institute of Technology (MIT) OpenCourseWare: Mathematics: Statistics for Applications, Spring 2015
The Personality Project: Using R for psychological research: Statistics in Research Methods: Using R: This is a very brief guide to help students in a research methods course make use of the R statistical language to analyze some of the data they have collected.
R Tutorial: Elementary Statistics with R
Quick-R: Basic Statistics
Quick-R: Advanced Statistics
R help: quantiles and dataframe
An R Companion for the Handbook of Biological Statistics, Version 1.09i By Salvatore S. Mangiafico
R packages for The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman
R-bloggers: Principal curves example (Elements of Statistical Learning) By BioStatMatt, April 21, 2016
Software for Exploratory Data Analysis and Statistical Modelling: Statistical Modelling with R
Software for Exploratory Data Analysis and Statistical Modelling: Statistical Modelling with R R Environment
Software for Exploratory Data Analysis and Statistical Modelling: Statistical Modelling with R Supplementary Material
Statistical Computing with R: A tutorial: Department of Mathematics, Illinois State University
Learning Statistics with R by Daniel Navarro
Mr. Simoneau’s BLS Website: Statistics with R
Using R for Your Basic Statistical Needs: LISA Short-Course by Nels Johnson, LISA Collaborator, Department of Statistics, Virginia Tech, November 15 and 16, 2010 — [Can be viewed in a Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]
Using R for statistical analyses by Dr. Mark Gardener. This page is intended to be a help in getting to grips with the powerful statistical program called R. It is not intended as a course in statistics. If you have an analysis to perform I hope that you will be able to find the commands you need here and copy/paste them into R to get going. On this page learn how to create data files, read them into R and generally get ready to perform analyses. Also find out about getting further help and documentation.
Exploring Data and Descriptive Statistics (using R) by Oscar Torres-Reyna, Data Analysis 101 Workshops — [Requires PDF Software]
Statistical Analysis: an Introduction using R: This book aims to introduce the principles of statistics and modern statistical analysis for a non-mathematical audience, using the free statistical package R. (From Wikibooks, open books for an open world)
Using R in Statistics: Department of Mathematics, College of the Redwoods
Transforming Data in R: Department of Mathematics, College of the Redwoods
USGS Office of Water Information (OWI) Blog: R: Calculating Moving Averages and Historical Flow Quantiles by Laura DeCicco
Theory meets practice…: Better Confidence Intervals for Quantiles, Oct 23, 2016
Smooth Tests of Goodness of Fit Using R, Second Edition, by J.C.W. Rayner, O. Thas and D.J. Best
Win-Vector Blog: Be careful evaluating model predictions by John Mount, December 2, 2016
poissonisfish: Partial least squares in R by Francisco Lima, June 17, 2017
R Tutorial Series: Graphic Analysis of Regression Assumptions By John M Quick
Quick-R: Regression Diagnostics
R-bloggers: Regression Models, It’s Not Only About Interpretation By arthur charpentier, March 22, 2015
R Functions For Regression Analysis by Vito Ricci, 14/10/05 — [Requires PDF Software]
The Personality Project: Using R for psychological research: Chapter 4 Covariance, Regression, and Correlation from An introduction to psychometric theory with applications in R by William Revelle and the Personality Project — [Requires PDF Software]
Analytical and Numerical Solutions, with R, to Linear Regression Problems by Fisseha Berhane, PhD
Linear Regression in R: Department of Mathematics, College of the Redwoods
Using R for Linear Regression — [Requires PDF Software]
R-bloggers: My Own R Function and Script for Simple Linear Regression — An Illustration with Exponential Decay of DDT in Trout
Regression in R: Part I: Simple Linear Regression by Denise Ferrari & Tiffany Head, UCLA Department of Statistics Statistical Consulting Center, Feb 10, 2010 — [Requires PDF Software]
RStudio™: Automatic or Manual: A regression analysis using the mtcars dataset by rcquan, June 21, 2014
R-bloggers: Can We do Better than R-squared? By Thomas Hopper, May 16, 2014
Win-Vector Blog: An easy way to accidentally inflate reported R-squared in linear regression models by John Mount, June 15, 2017
Statistical tools for high-throughput data analysis (STHDA): Add P-values and Significance Levels to ggplots
Stack Overflow: R print equation of linear regression on the plot itself
Stack Overflow: How to add RMSE, slope, intercept, r^2 to R plot?
Stack Overflow: r - ggplot2: Adding Regression Line Equation and R2 on graph
Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics by Lopaka Lee and Dennis Helsel, Computers & Geosciences, Volume 31, Issue 10, December 2005, Pages 1241-1248
Linear Regression and ANOVA shaken and stirred (Part 1) by Mauricio Vargas S. 帕夏, 20 March 2017
Linear Regression and ANOVA shaken and stirred (Part 2) by Mauricio Vargas S. 帕夏, 21 March 2017
Practical Regression and Anova using R by Julian J. Faraway, July 2002 — [Requires PDF Software]
Repeated measures ANOVA with R (functions and tutorials) by Tal Galili, April 13, 2010
Quick-R: Multiple (Linear) Regression
A Handbook of Statistical Analyses Using R: Chapter 5 Multiple Linear Regression: Cloud Seeding by Brian S. Everitt and Torsten Hothorn — [Requires PDF Software]
A Handbook of Statistical Analyses Using R: Chapter 6 Logistic Regression and Generalised Linear Models: Blood Screening, Women’s Role in Society, and Colonic Polyps by Brian S. Everitt and Torsten Hothorn — [Requires PDF Software]
Nonlinear Regression and Nonlinear Least Squares: Appendix to An R and S-PLUS Companion to Applied Regression by John Fox, January 2002 — [Requires PDF Software]
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Library: Introduction to bootstrapping
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R FAQ: How can I generate boostrap statistics in R?
Bootstrapping Regression Models: Appendix to An R and S-PLUS Companion to Applied Regression by John Fox, January 2002 — [Requires PDF Software]
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): How can I estimate the standard error of transformed regression parameter in R?
Stack Overflow: In R, how to find the standard error of the mean?
SAS/STAT(R) 9.22 User’s Guide: The SURVEYMEANS Procedure: Variance and Standard Error of the Mean
BayesFactor: Numerical pitfalls in computing variance, May 3, 2016
Heuristic Andrew: Confidence interval diagram in R, October 19, 2011
A Handbook of Statistical Analyses Using R: Chapter 9 Survival Analysis: Glioma Treatment and Breast Cancer Survival by Brian S. Everitt and Torsten Hothorn — [Requires PDF Software]
Quick-R: Power Analysis
Heuristic Andrew: Calculate RMSE and MAE in R and SAS, July 12, 2013

Multivariate Statistics and R

A curated list of awesome network analysis resources
Little Book of R for Bayesian Statistics! By Avril Coghlan, Wellcome Trust Sanger Institute, Cambridge
BayesFactor: Software for Bayesian inference
An Introduction to Bayesian Inference using R Interfaces to Stan, Part I by Ben Goodrich, June 27, 2016
Revolutions (R Blog): R and Bayesian Statistics By Joseph Rickert, November 21, 2013
Little Book of R for Multivariate Analysis! By Avril Coghlan, Wellcome Trust Sanger Institute, Cambridge, U.K.
R tips pages: Multivariate By D. Schluter, University of BC
R-bloggers: Getting Started with Markov Chains By Joseph Rickert, January 7, 2016
Stack Overflow: Calculate marginal probabilities from dataframe in R
Quick-R: Cluster Analysis
Cluster analysis in R: York University Department of Mathematics and Statistics WIKI service
A Handbook of Statistical Analyses Using R: Chapter 15 Cluster Analysis: Classifying the Exoplanets by Brian S. Everitt and Torsten Hothorn — [Requires PDF Software]
R-statistics blog: Clustergram: visualization and diagnostics for cluster analysis (R code) Posted by Tal Galili on June 15, 2010
University of California at Los Angeles (UCLA) Institute for Digital Research and Education (IDRE): R Data Analysis Examples: Canonical Correlation Analysis
Jeromy Anglim’s Blog: Psychology and Statistics: Canonical Correlation: Getting Started with R or SPSS by Jeromy Anglim, June 17, 2010
Canonical Correlation Analysis by J. C. Wang, Department of Statistics, Western Michigan University
An R Package to Extend Canonical Correlation Analysis by Ignacio González, Sébastien Déjean, Pascal G. P. Martin, Alain Baccini, Journal of Statistical Software, Vol. 23, Issue 12, Jan 2008
Lab 12 - Canonical Correspondence Analysis: R Labs for Vegetation Ecologists, Department of Ecology, Montana State University
R-posts.com: Naive Principal Component Analysis (using R) Post from Pablo Bernabeu’s blog, September 7, 2017
A layman’s introduction to principal component analysis by James X. Li (YouTube video)
GUide to STasitical Analysis in Microbial Ecology (GUSTA ME): Principal Components Analysis
A Tutorial on Principal Component Analysis Submitted by Jonathon Shlens on 3 Apr 2014, Cornell University Library
Principal component analysis: From Wikipedia, the free encyclopedia
poissonisfish: Principal Component Analysis in R by Francisco Lima, January 23, 2017
An introduction to Principal Component Analysis & Factor Analysis: Using SPSS 19 and R (psych package) by Robin Beaumont, Monday, 23 April 2012 — [Requires word processor software (.docx file)]
A tutorial on Principal Components Analysis by Lindsay I Smith, February 26, 2002 (uses uses Scilab) — [Requires PDF Software]
Dave’s Wiki: Principal component analysis
Cross Validated: pca - Using principal components in a linear discriminant analysis for a diagnostic test
Cross Validated: How can I interpret what I get out of PCA?
R-bloggers: PCA and R
R-bloggers: Computing and visualizing PCA in R By thiagogm, November 28, 2013
Stack Overflow: Newest ’r pca’ Questions
Stack Overflow: About PCA in R?
Stack Overflow: r - Plotting pca biplot with ggplot2
Stack Overflow: r - how to use (after a pca) just one vector in an object?
Stack Overflow: cluster analysis - R - ‘princomp’ can only be used with more units than variables
Stack Overflow: r - PCA FactoMineR plot data
Stack Overflow: r - PCA -how are the principal components mapped?
Stack Overflow: Principal Components Analysis - how to get the contribution (%) of each parameter to a Prin.Comp.?
Win-Vector Blog: Why you should read Nina Zumel’s 3 part series on principal components analysis and regression by John Mount, June 9, 2016
Win-Vector Blog: Principal Components Regression, Pt.1: The Standard Method by Nina Zumel, May 16, 2016
Win-Vector Blog: Principal Components Regression, Pt. 2: Y-Aware Methods by Nina Zumel, May 23, 2016
Win-Vector Blog: Principal Components Regression, Pt. 3: Picking the Number of Components by Nina Zumel, May 30, 2016
poissonisfish: Principal Component Analysis in R by Francisco Lima. January 23, 2017
Data Analysis Visually Enforced: 5 functions to do Principal Components Analysis in R
R: Principal Component Analysis
R: Principal Components Analysis: stats package
The Personality Project: Using R for psychological research: R: Principal components analysis (PCA): psych package
Quick-R: Principal Components and Factor Analysis
Principal Components Analysis: A How-To Manual for R by Emily Mankin — [Requires PDF Software]
Principal Component Analysis — [Requires PDF Software]
Data Mining Algorithms In R/Dimensionality Reduction/Principal Component Analysis: From Wikibooks, open books for an open world
A Handbook of Statistical Analyses Using R Chapter 13: Principal Component Analysis: The Olympic Heptathlon by Brian S. Everitt and Torsten Hothorn — [Requires PDF Software]
R & Bioconductor Manual Principal Component Analysis (PCA) by Thomas Girke, University of California Riverside
Three tips for Principal Component Analysis by Karen, The Analysis Factor
R-bloggers: PCA to PLS modeling analysis strategy for WIDE DATA By dgrapov, March 2, 2013
Lab 7 -Principal Components Analysis and Redundancy Analysis: R Labs for Vegetation Ecologists, Department of Ecology, Montana State University
Utah State University — Spring 2014: STAT 5570: Statistical Bioinformatics: Visualization and PCA with Gene Expression Data: Notes 2.4 — [Requires PDF Software]
R-bloggers: PCA with “ChemoSpec” - 001 By jrcuesta, October 20, 2012
CRAN Package: Multivariate Statistical Analysis using the R package chemometrics by Heide Garcia and Peter Filzmoser, Department of Statistics and Probability Theory, Vienna University of Technology, Austria, November 7, 2011 — [Requires PDF Software]
Multivariate Data Analysis in Microbial Ecology: New Skin for the old Ceremony by Jean Thioulouse, UMR 5558 CNRS, CNRS — University of Lyon - France — [Requires PDF Software]
Introduction to multivariate analysis for bacterial GWAS using R by Thibaut Jombart, Imperial College London — [Requires PDF Software]
R-bloggers: Looking to the PCA scores with GGobi By jrcuesta, October 21, 2012
R-bloggers: Plotting principal component analysis with ggplot #rstats By Daniel, July 8, 2013
martins bioblogg: Using R: Two plots of principal component analysis
R-bloggers: Reconstructing Principal Component Analysis Matrix By mintgene, April 5, 2013
We think therefore we R: Principal component analysis: Use extended to Financial economics: Part 1

USGS and R

USGS Office of Water Information (OWI) Blog
USGS Office of Water Information (OWI) Blog: R
USGS Office of Water Information (OWI): Introduction to R Lessons
USGS-R is a community of support for users of the R scientific programming language. USGS-R resources include R training materials, R tools for the retrieval and analysis of USGS data, and support for a growing group of USGS-R developers. R is a flexible programming language that provides researchers with access to state-of-the-science analytical methods. Moreover, R is open-source and free to use for all. The USGS-R builds on the existing R community and provides resources for technical skill-building within USGS for all levels of expertise.
USGS-R at GitHub: Collection of USGS R codes and packages

EGRET/WRTDS

Exploration and Graphics for RivEr Trends (EGRET): An R-package for the analysis of long-term changes in water quality and streamflow, including the water-quality method Weighted Regressions on Time, Discharge, and Season (WRTDS) [U.S. Geological Survey · GitHub]
Exploration and Graphics for RivEr Trends (EGRET)
dataRetrieval: This R package is designed to obtain USGS or EPA water quality sample data, streamflow data, and metadata directly from web services. [U.S. Geological Survey · GitHub]
User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data By Robert M. Hirsch and Laura De Cicco, U.S. Geological Survey Techniques and Methods book 4, chap. A10
New Tools for Water Quality Data Access, Trend and Load Analysis: An overview of the USGS R Packages: dataRetrieval and EGRET by Robert M. Hirsch, USGS, 2014-11-20 — [Requires PDF Software]
Science Summary-Determining Nutrient and Sediment Loads and Trends in the Chesapeake Bay Watershed by Using an Enhanced Statistical Technique Prepared by Robert M. Hirsch, Douglas L. Moyer, and Scott W. Phillips, U.S. Geological Survey
Comparison of Two Regression-Based Approaches for Determining Nutrient and Sediment Fluxes and Trends in the Chesapeake Bay Watershed By Douglas L. Moyer, Robert M. Hirsch, and Kenneth E. Hyer, Scientific Investigations Report 2012-5244
Nitrogen, Phosphorus, and Suspended Sediment fluxes from the Susquehanna River to the Bay in Tropical Storm Lee, 2011 — results and implications by Robert M. Hirsch, Research Hydrologist, USGS, August 13, 2012 — [Requires PDF Software]
Flux of Nitrogen, Phosphorus, and Suspended Sediment from the Susquehanna River Basin to the Chesapeake Bay during Tropical Storm Lee, September 2011, as an Indicator of the Effects of Reservoir Sedimentation on Water Quality By Robert M. Hirsch, Scientific Investigations Report 2012-5185
Nitrate in the Mississippi River and Its Tributaries, 1980 to 2008: Are We Making Progress? by Lori A. Sprague, Robert M. Hirsch, and Brent T. Aulenbach, Environ. Sci. Technol., 2011, 45 (17), pp 7209-7216
Weighted Regressions on Time, Discharge, and Season (WRTDS), with an Application to Chesapeake Bay River Inputs by Robert M. Hirsch, Douglas L. Moyer, Stacey A. Archfield, JAWRA Journal of the American Water Resources Association, Volume 46, Issue 5, pages 857-880, October 2010
Spatial and Temporal Trends in Runoff at Long-Term Streamgages within and near the Chesapeake Bay Watershed By Karen C. Rice and Robert M. Hirsch, Scientific Investigations Report 2012-5151

(Eco)Hydrology Applications with R

CRAN Task View: Hydrological Data and Modeling
CRAN Task View: Analysis of Ecological and Environmental Data
R for Water Resources Data Science (R 4 WRDS) course
Hydraulics and Water Resources: Examples Using R By Ed Maurer
Environmental Science and Data Analytics with R: Posts on environmental science and general R-based data analysis
swmmr - an R package to interface SWMM By Dominik Leutnant, Anneke Döring & Mathias Uhl, Urban Water Journal, Volume 16, 2019 - Issue 1
Water Underground - AGU Blogosphere: Doing Hydrogeology in R By Sam Zipper, Posted by larryohanlon on June 25, 2019
R Resources for Hydrologists: A collection of R packages and other helpful R resources for hydrological sciences, which can be used for different stages of data analysis and modelling in hydrology. By Javed Ali, Jul 21, 2020
Using R in Hydrology: recent developments and future directions By Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prodoscimi, Claudia Vitolo, Katie Smith, and Paul Astagneau — [Requires PDF Software]
Using R in hydrology: a review of recent developments and future directions By Louise J. Slater, Guillaume Thirel, Shaun Harrigan, Olivier Delaigue, Alexander Hurley, Abdou Khouakhi, Ilaria Prodoscimi, Claudia Vitolo, and Katie Smith, Hydrol. Earth Syst. Sci. Discuss., 23, 2939-2963, 2019
Comparison of hydrological modelling R packages By Paul Astagneau — [Requires PDF Software]
AboutHydrology: R resources for Hydrologists
r4hydrology ~ Learning hydrology with R
r-sig-ecology: Using R for hydrology: short course material available
State of R in Hydrological Modelling by Jairo A. Torres and Edzer Pebesma, 2nd OpenWater symposium and workshops, September 16th, 2013 — [Requires PDF Software]
R in Hydrological Modelling: Why we should try it? by Mauricio Zambrano Bigiarini — [Requires PDF Software]
AboutHydrology: R resources for Hydrologists
National Park Service (NPS): Using R Statistical and Graphics Tools for Natural Resource Stewardship Science
Fabricating data: How substituting values for nondetects can ruin results, and what can be done about it by Dennis R. Helsel, Chemosphere: Environmental Chemistry, Volume 65, Issue 11, December 2006, Pages 2434-2439
Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing by Lopaka Lee and Dennis Helsel, Computers & Geosciences, Volume 33, Issue 5, May 2007, Pages 696-704
R-bloggers: SCADA spikes in Water Treatment Data By Peter Prevos, January 29, 2017
The Devil is in the Data: Percentile Calculations in Water Quality Regulations By Peter Prevos, 10 February 2017
SAS Blog: Quantiles and the Flint water crisis By Rick Wicklin on The DO Loop May 17, 2017
Theory meets practice…: Beware the Argument: The Flint Water Crisis and Quantiles, Jun 18, 2017
The Data Science Tribune: An Analysis of the Flint Michigan Water Crisis: Part 1 Initial Corrosivity, March 2, 2016
Split long time series into (hydrological) years in R by Claudia Vitolo, November 30, 2014
R-bloggers: Using R and satellite data to identify marine bioregions By Christian Marchese, January 30, 2017
Using R to Conduct Hydrostatistical Analysis at the Virginia Department of Environmental Quality by Robert Burgholzer and Lindsay Carr — [Requires PDF Software]
An R-based Web Application to Search, Analyze and Display Water Quality Data in Oregon State, USA By Peter Bryant, May 4, 2016, National Water Quality Monitoring Council 10th National Monitoring Conference — [Requires PDF Software]
Hydrology of Jacob’s Well Spring: A tutorial for using HydroDesktop to discover and access water data Prepared by Tim Whiteaker and David Tarboton, November 30, 2010 — [Requires PDF Software]
Using R in Water Resources Education by Milan Cisty and Lubomir Celar, International Journal for Innovation Education and Research, Vol-3 No-10, October 2015, p. 97 - 117
Support Of Teaching and Research in Hydroinformatics with R by Milan Cisty, City University of New York (CUNY) Academic Works International Conference on Hydroinformatics, 8-1-2014
SlidePlayer: Powerful and Free: Open-source Software that Water Resources Professionals can use for Data Analysis and Visualization by Kenneth R. Odom, PhD, PE, Hydro Services - Reservoir Management Section, Southern Company, 2012 Alabama Water Resources Conference
R-bloggers: Parse NOAA Integrated Surface Data Files By Scott Chamberlain, November 3, 2016
Open-Channel Computation with R by Michael C. Koohafkan and Bassam A. Younis, The R Journal Vol. 7/2, December 2015
Convert Manning’s Equation Input to Multiplier Exponent Expressions: An app for fitting muliplier and exponent values for V=aQ^b and Depth=cQ^d using Mannings equation as input. By John Yagecic, P.E.
AdventuresInData: Monte Carlo Analysis of Manning’s Equation: A Shiny App By John Yagecic, P.E.
petertbryant GitHub Projects (Oregon DEQ)
Jason C Fisher Repositories, USGS Idaho National Laboratory Proj.
tonyladson: Hydrology, Natural Resources and R
R code - Handy routines for hydrologists
AboutHydrology: A few R scripts useful for hydrologists, April 2, 2016
National Park Service (NPS): R Packages for Natural Resources
loadflex: Models and tools for watershed flux estimates
EcoHydRology project: A collection of R packages for Community Environmental Modeling in R
Watersheds: Spatial Watershed Aggregation and Spatial Drainage Network Analysis: Methods for watersheds aggregation and spatial drainage network analysis.
The Nature Conservancy’s Indicators of Hydrologic Alteration software in R
RGLUEANN provides an R implementation of the coupling between general likelihood uncertainty estimation (GLUE) and artificial neural networks (ANN).
The RTOOLZ package provides different useful functions for performing a variety of tasks, not available in base R.
The RMODFLOW package provides a set of tools for groundwater flow modelling with MODFLOW.
The RMT3DMS package provides a set of tools for solute transport modelling with MT3DMS.
The RGROUNDWATER package provides a set of functions useful for performing groundwater research.
airGR: a suite of lumped hydrological models in an R package: poster presented by Guillaume Thirel — [Requires PDF Software]
RHydro: Hydrological models and tools to represent and analyze hydrological data
Groundwater flow and solute transport modelling from within R: The RMODFLOW and RMT3DMS packages by Rogiers Bart, Institute for Environment, Health and Safety, Belgian Nuclear Research Centre (SCK·CEN)
This package allows to load, parameterize, run and analyze OpenFLUID simulations from the GNU R environment
hydromad is an R package (i.e. a software package for the R statistical computing environment). It provides a modelling framework for environmental hydrology: water balance accounting and flow routing in spatially aggregated catchments. It supports simulation, estimation, assessment and visualisation of flow response to time series of rainfall and other drivers.
Short communication: An R package for reading EPANET files by Bradley J. Eck, Environmental Modelling & Software Volume 84, October 2016, Pages 149-154
CRAN Package: epanetReader: Reads water network simulation data in Epanet’s text-based ‘.inp’ and ‘.rpt’ formats into R. Also reads results from Epanet-msx. Provides basic summary information and plots. The README file has a quick introduction. See http://www2.epa.gov/water-research/epanet for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems.
CRAN Package: dynatopmodel: A native R implementation and enhancement of the Dynamic TOPMODEL semi-distributed hydrological model. Includes some pre-processsing and output routines.
CRAN Package: reservoir: Measure single-storage water supply system performance using resilience, reliability, and vulnerability metrics; assess storage-yield-reliability relationships; determine no-fail storage with sequent peak analysis; optimize release decisions for water supply, hydropower, and multi-objective reservoirs using deterministic and stochastic dynamic programming; evaluate inflow persistence using the Hurst coefficient.
CRAN Package: weirs: Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
CRAN Package: rivr: A tool for undergraduate and graduate courses in open-channel hydraulics. Provides functions for computing normal and critical depths, steady-state water surface profiles (e.g. backwater curves) and unsteady flow computations (e.g. flood wave routing)
CRAN Package: rivernet: Functions for reading, analysing and plotting river networks. For this package, river networks consist of sections and nodes with associated attributes, e.g. to characterise their morphological, chemical and biological state. The package provides functions to read this data from text files, to analyse the network structure and network paths and regions consisting of sections and nodes that fulfill prescribed criteria, and to plot the river network and associated properties.
CRAN Package: rivervis: This R package is a flexible and efficient tool to visualise both quantitative and qualitative data from river surveys. It can be used to produce diagrams with the topological structure of the river network.
RNWIS: This R package provides access to water-resources data stored on the National Water Information System (NWIS). A graphical user interface (GUI) is provided and requires R operate as an SDI application, using multiple top-level windows for the console, graphics, and pager.
ObsNetwork: This R package evaluates and optimizes long-term monitoring networks using a kriging-based genetic algorithm method.
Trends: This R package is for identifying trends in data from multiple observation sites in a monitoring network. A parametric survival regression model is used to fit the observed data, both censored and uncensored.
Irucka Embry (iembry) GitLab project Repositories (includes all of the projects created by Irucka Embry at GitHub under iembry-USGS)
Irucka Embry (iembry) GitLab iemisc Repository: R package that contains Irucka Embry’s miscellaneous functions: statistical analysis [RMS, coefficient of variation (CV), approximate and relative error, range, harmonic mean, geometric mean], engineering economics (benefit-cost, future value, present value, annual value, gradients, interest, periods, etc.), geometry (sphere volume and right triangle), environmental/water resources engineering (Manning’s n, Gauckler-Manning-Strickler equation), a version of linear interpolation for use with NAs, & GNU Octave/MATLAB compatible size, numel, and length functions.
Irucka Embry (iembry) GitLab iemiscdata Repository: R data package with miscellaneous data sets [Chemistry, Engineering Economics, Environmental/Water Resources Engineering, Nuclear Accidents, US Presidential Elections, and US Continental Congress Presidents].
Irucka Embry (iembry) GitLab ie2misc Repository: R package containing a collection of Irucka Embry’s miscellaneous USGS functions (processing .exp and .psf files, statistical error functions, “+” dyadic operator for use with NA, creating ADAPS and QW spreadsheet files, calculating saturated enthalpy).
Irucka Embry (iembry) GitLab ie2miscdata Repository: R data package with miscellaneous USGS data.
Irucka Embry (iembry) GitLab FEWSR Repository: R package containing FEWS river, lake, and pond models used to obtain the evapotranspiration from surface waters in power plants.
Irucka Embry (iembry) GitLab curvenumber Repository: Soil Conservation Service Curve Number method (R-package) — Irucka contributed the weighted_CN function & is now the package maintainer
R-sig-ecology: R for use in ecological and environmental data analysis
R-sig-ecology help at Nabble.com [Recovered with the Internet Archive: Wayback Machine]
National Ecological Observatory Network (NEON) — Boulder, Colorado: Articles by tag: R
National Ecological Observatory Network (NEON) — Boulder, Colorado: Data Tutorials & Workshop Groupings
Recology: code and such: Scott Chamberlain
Montana State University Department of Ecology: R for Ecologists
Montana State University Department of Ecology: R Labs for Vegetation Ecologists
GUide to STatistical Analysis in Microbial Ecology (GUSTA ME)
phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data by Paul J. McMurdie and Susan Holmes, PLoS ONE 8(4): e61217, Published: April 22, 2013
Statistics Using R with Biological Examples by Kim Seefeld, MS, M.Ed. and Ernst Linder, Ph.D., Department of Mathematics & Statistics, University of New Hampshire, Durham, NH, May 2007 — [Requires PDF Software]
R & Bioconductor Manual by Thomas Girke, University of California Riverside
ChemmineR Manual by Eddie Cao, Tyler Backman, Kevin Horan and Thomas Girke, University of California Riverside
EMBOSS Manual by Thomas Girke, University of California Riverside
Statistics Using R with Biological Examples by Kim Seefeld, MS, M.Ed. and Ernst Linder, Ph.D., Department of Mathematics & Statistics, University of New Hampshire, Durham, NH — [Requires PDF Software]
OneMap Tutorial: Software for constructing genetic maps in experimental crosses: full-sib, RILs, F2 and backcrosses by Gabriel R A Margarido, Marcelo Mollinari and A Augusto F Garcia — [Requires PDF Software]
Open Source Software Tools for Soil Scientists
The Devil is in the Data: Analysing soil moisture data in NetCDF format with the ncdf4 library Written by Peter Prevos on 7 September 2017
Tutorial: An example of statistical data analysis using the R environment for statistical computing by D G Rossiter, Version 1.3; March 8, 2014, Cornell University College of Agriculture and Life Sciences, School of Integrative Plant Science, Soil and Crop Sciences Section — [Requires PDF Software]
USGS-R at GitHub: Collection of USGS R codes and packages

Non-R software and resources

EcoC²S [Irucka Embry): The Study of Water (Hydrology): Modeling, Software, and Toolkits
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Online File Storage and Sharing Alternatives to WeTransfer, Dropbox, OneDrive, Google Drive, etc.
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Collaborative Editing (Google, Microsoft, etc. Collaborative Editing Alternatives)
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Collaborative Editing / Online Spreadsheets (Google, Microsoft, etc. Collaborative Editing Alternatives)
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Collaborative Notebook Code Sharing and/or Editing (Alternatives to Google Colab, Deepnote, etc.)
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Business Intelligence (BI)
EcoC²S [Irucka Embry): EcoC²S Online Resources: Freeing and Opening Architects, Computer Programmers/Developers, Designers, Engineers, Mathematicians, Scientists, and Statisticians with Ethics and Integrity
EcoC²S [Irucka Embry): EcoC²S Online Resources: Free and Open Source Software for Architects, Designers, Engineers, Mathematicians, Scientists, and Statisticians
SlidePlayer: Powerful and Free: Open-source Software that Water Resources Professionals can use for Data Analysis and Visualization by Kenneth R. Odom, PhD, PE, Hydro Services - Reservoir Management Section, Southern Company, 2012 Alabama Water Resources Conference
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Knowledge Databases (Alternatives to myBase, Confluence, etc.)
Zettlr: Your One-Stop Publication Workbench. From idea to publication in one app: Zettlr accompanies you while writing your blog post, newspaper article, term paper, thesis, or entire book.
Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share research.
JabRef: Free Reference Manager - Stay on top of your Literature
PEST: Model-Independent Parameter Estimation and Uncertainty Analysis. PEST is the industry standard software package for parameter estimation and uncertainty analysis of complex environmental and other computer models.
OpenFLUID is a fully featured environment for spatial modelling of landscapes dynamics, designed to be extensible by modelers. Models can be implemented in OpenFLUID using C++ code, then used for simulations executions and analyses.
Community Surface Dynamics Modeling System (CSDMS)
Data Science Toolkit
The National Network of Reference Watersheds is a collaborative and multipurpose network of minimally disturbed watersheds and monitoring sites. The purpose of this website is to allow users to search the NNRW database of reference watersheds, to identify watersheds of interest, and download watershed information and water quality data. The current scope of the network is limited to freshwater streams. Membership in the network is voluntary and open to individuals, agencies, and institutions interested in participating in monitoring and (or) research in minimally disturbed and pristine watersheds.
The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics: Global Hydrologic Soil Groups (HYSOGs250m) for Curve Number-Based Runoff Modeling, Documentation Revision Date: 2020-04-22. The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics is a NASA Earth Observing System Data and Information System (EOSDIS) data center managed by the Earth Science Data and Information System (ESDIS) Project. The ORNL DAAC is operated by Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee, and is a member of the Remote Sensing and Environmental Informatics Group of the Environmental Sciences Division (ESD).
University of California, Davis California Soil Resource Lab: SoilWeb products can be used to access USDA-NCSS detailed soil survey data (SSURGO) for most of the United States.
NASA Open Source Software
NASA Goddard Institute for Space Studies (GISS): Software Tools
US Environmental Protection Agency (EPA): Storm Water Management Model (SWMM): Helps predict runoff quantity and quality from drainage systems
Open SWMM: EPA SWMM Knowledge Base and Code Viewer
US Environmental Protection Agency (EPA): Water Quality Analysis Simulation Program (WASP)
GitHub: U.S. Environmental Protection Agency
The MHYDAS model (Moussa et al., 2002, Hydrol. Process. 16) allows to simulate the water production and transfer on surface, the surface-subsurface simplified exchanges and the water balance at different scales - from plot to watershed, and from rain event to annual or pluri-annual simulations. It also can be extended by using extra thematic modules such as pollutant fate and transport or erosive transfer.
AGricultural Non-Point Source Pollution Model (AGNPS) is a joint USDA - Agricultural Research Service (ARS) and - Natural Resources Conservation Service system of computer models developed to predict non point source pollutant loadings within agricultural watersheds. It contains a continuous simulation surface runoff model designed to assist with determining BMPs, the setting of TMDLs, and for risk & cost/benefit analyses.
USGS Software
U.S. Geological Survey: Software
GitHub: U.S. Geological Survey
USGS Water Software: Explore all water resources applications software to include general use, water quality and chemistry, groundwater, statistics and graphics, and surface water.
USGS Water Resources: Applications Software
PyFlo is an open-source library written in Python for performing hydraulic and hydrology stormwater analysis. Features include network hydraulic grade analysis and time/iteration based storage and flood routing simulations.
Precipitation Runoff Modeling System (PRMS) is a deterministic, distributed-parameter, physical process based modeling system developed to evaluate the response of various combinations of climate and land use on streamflow and general watershed hydrology.
Aquatic Informatics: Water Monitoring & Analysis Software
Aquatic Informatics: AQUARIUS lets you understand the hydrology hiding within your data by providing powerful tools that are easy to deploy and use. By allowing you to manage all of your environmental monitoring data in one place, AQUARIUS enables higher data integrity and defensibility, greater confidence in your data and decisions, ongoing open and flexible control of your data and customized reporting and publishing that matches your requirements.
Kentucky Water Science Center: Water Availability Tool for Environmental Resources (WATER) is a spatially distributed, object oriented, decision support system that combines the expertise of numerous hydrologists, pedologists, computer scientists, and other discipline experts into a user-friendly computer application for managing water resources. The application is an expandable and flexible platform that allows future modifications or extensions, to be added (the hydrologic model “TOPMODEL” is the first of these); thereby, continually increasing the power and utility of the application. WATER was developed within the USGS Center for Applied Hydrologic Solutions (CAHS).
Bayesian hydraulic rating curve programs
British Geological Survey (BGS): AquiMod is a simple, lumped-catchment groundwater model. It simulates groundwater level time-series at a point by linking simple algorithms of soil drainage, unsaturated zone flow and groundwater flow. It takes time-series of rainfall and potential evapotranspiration as input, and produces a time-series of groundwater level. Hydrographs of flows through the outlets of the groundwater store are also generated, which can potentially be related to river flow measurements. The software is easy to use and should be accessible to users who are new to the field of groundwater/hydrological modelling. The model is configured using a series of text files and run through the command line.
Notepad++ is a free (as in “free speech” and also as in “free beer”) source code editor and Notepad replacement that supports several languages. Running in the MS Windows environment, its use is governed by GPL License.
Vim is a highly configurable text editor built to enable efficient text editing. It is an improved version of the vi editor distributed with most UNIX systems. Vim is distributed free as charityware. If you find Vim a useful addition to your life please consider helping needy children in Uganda.
Vimdoc: the online source for Vim documentation
The GNU Operating System and the Free Software Movement: GNU Emacs is an extensible, customizable text editor—and more. At its core is an interpreter for Emacs Lisp, a dialect of the Lisp programming language with extensions to support text editing.
Crimson Editor is a professional source code editor for Windows. (It is no longer developed, but it is still very useful.)
Emerald Editor is (being) designed to be an open-source multi-purpose, functional text editor, inspired heavily by Crimson Editor. It is available under the GNU General Public License. (It is no longer developed, but it is still very useful.)
Antiword is a free MS Word reader for Linux and RISC OS. There are ports to FreeBSD, BeOS, OS/2, Mac OS X, Amiga, VMS, NetWare, Plan9, EPOC, Zaurus PDA, MorphOS, Tru64/OSF, Minix, Solaris and DOS. Antiword converts the binary files from Word 2, 6, 7, 97, 2000, 2002 and 2003 to plain text and to PostScript™.
Pandoc: a universal document converter
LaTeX is a high-quality typesetting system; it includes features designed for the production of technical and scientific documentation. LaTeX is the de facto standard for the communication and publication of scientific documents. LaTeX is available as free software.
MiKTeX is an up-to-date implementation of TeX/LaTeX and related programs for Windows (all current variants). TeX is a typesetting system written by Donald Ervin Knuth who says that it is “intended for the creation of beautiful books - and especially for books that contain a lot of mathematics”.
Circos is a software package for visualizing data and information. It visualizes data in a circular layout — this makes Circos ideal for exploring relationships between objects or positions. There are other reasons why a circular layout is advantageous, not the least being the fact that it is attractive. Circos is ideal for creating publication-quality infographics and illustrations with a high data-to-ink ratio, richly layered data and pleasant symmetries. You have fine control each element in the figure to tailor its focus points and detail to your audience.
Scribus is an Open Source program that brings professional page layout to Linux, BSD UNIX, Solaris, OpenIndiana, GNU/Hurd, Mac OS X, OS/2 Warp 4, eComStation, Haiku and Windows desktops with a combination of press-ready output and new approaches to page design. Underneath a modern and user-friendly interface, Scribus supports professional publishing features, such as color separations, CMYK and spot colors, ICC color management, and versatile PDF creation.
Inkscape: An Open Source vector graphics editor, with capabilities similar to Illustrator, CorelDraw, or Xara X, using the W3C standard Scalable Vector Graphics (SVG) file format. Inkscape supports many advanced SVG features (markers, clones, alpha blending, etc.) and great care is taken in designing a streamlined interface. It is very easy to edit nodes, perform complex path operations, trace bitmaps and much more. We also aim to maintain a thriving user and developer community by using open, community-oriented development.
GIMP is an acronym for GNU Image Manipulation Program. It is a freely distributed program for such tasks as photo retouching, image composition and image authoring. It has many capabilities. It can be used as a simple paint program, an expert quality photo retouching program, an online batch processing system, a mass production image renderer, an image format converter, etc. GIMP is expandable and extensible. It is designed to be augmented with plug-ins and extensions to do just about anything. The advanced scripting interface allows everything from the simplest task to the most complex image manipulation procedures to be easily scripted.
Hierarchical Data Format: From Wikipedia, the free encyclopedia
The HDF Group: Information, Support, and Software
The HDF Group: Why use HDF?
The HDF Group: HDF5 is a data model, library, and file format for storing and managing data. It supports an unlimited variety of datatypes, and is designed for flexible and efficient I/O and for high volume and complex data. HDF5 is portable and is extensible, allowing applications to evolve in their use of HDF5. The HDF5 Technology suite includes tools and applications for managing, manipulating, viewing, and analyzing data in the HDF5 format.
NetCDF: From Wikipedia, the free encyclopedia
NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data.
NoSQL Databases
Structured Query Language (SQL): From Wikibooks, open books for an open world
SQLCourse.com: This unique introductory SQL tutorial not only provides easy-to-understand SQL instructions, but it allows you to practice what you learn using the on-line SQL interpreter. You will receive immediate results after submitting your SQL commands. You will be able to create your own unique tables as well as perform selects, inserts, updates, deletes, and drops on your tables. This SQL tutorial currently supports a subset of ANSI SQL. The basics of each SQL command will be covered in this introductory tutorial. Unless otherwise stated, the interpreter will support everything covered in this course. If you’re already familar with the basics of SQL, you can still use this as a refresher, and practice some SQL statements.
SQLCourse2.com: This unique SQL Tutorial is the “sequel” to the highly successful SQLCourse.com site and will provide you with more advanced easy-to-follow SQL Instruction and the ability to practice what you learn on-line with immediate feedback! You will receive immediate results on a web page after submitting your SQL Commands. This continuation course will provide you with critical need-to-know advanced features and clauses of the SELECT statement that weren’t supported in the previous SQLCourse.com site. Everything you learn here will be ANSI SQL compliant and should work with most SQL databases such as Oracle, SQL Server, mySQL, MS Access, Informix, Sybase, or any other ANSI SQL compliant database. If you’re already familar with the basics of SQL, you can still use this as a refresher, and practice some SQL statements.
PostgreSQL: The world’s most advanced open source database
PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.
Converting MySQL to PostgreSQL: From Wikibooks, open books for an open world
DuckDB is a fast in-process analytical database. DuckDB supports a feature-rich SQL dialect complemented with deep integrations into client APIs
MariaDB: An enhanced, drop-in replacement for MySQL.
MySQL: From Wikibooks, open books for an open world
MariaDB: AskMonty KnowledgeBase
MariaDB Crash Course by Ben Forta
MariaDB: From Wikibooks, open books for an open world
H2 is a Java SQL database
HSQLDB (HyperSQL DataBase) is the leading SQL relational database engine written in Java. It offers a small, fast multithreaded and transactional database engine with in-memory and disk-based tables and supports embedded and server modes. It includes a powerful command line SQL tool and simple GUI query tools. HSQLDB supports the widest range of SQL Standard features seen in any open source database engine: SQL:2011 core language features and an extensive list of SQL:2011 optional features. It supports nearly full Advanced ANSI-92 SQL (BNF format). Many extensions to the Standard, including syntax compatibility and features of other popular database engines, are also supported.
Poppler is a PDF rendering library based on the xpdf-3.0 code base.
Extract tables from PDF files. tabula-extractor is the table extraction engine that powers Tabula, now available as a library and command line program.
Tabula: Extract Tables from PDFs
Tesseract is probably the most accurate open source OCR engine available. Combined with the Leptonica Image Processing Library it can read a wide variety of image formats and convert them to text in over 60 languages. It was one of the top 3 engines in the 1995 UNLV Accuracy test. Between 1995 and 2006 it had little work done on it, but since then it has been improved extensively by Google. It is released under the Apache License 2.0.
YAGF is a graphical front-end for cuneiform and tesseract OCR tools. With YAGF you can open already scanned image files or obtain new images via XSane (scanning results are automatically passed to YAGF). Once you have a scanned image you can prepare it for recognition, select particular image areas for recognition, set the recognition language and so no. Recognized text is displayed in a editor window where it can be corrected, saved to disk or copied to clipboard. YAGF also provides some facilities for a multi-page recognition.
FreeOCR is a free Optical Character Recognition Software for Windows and supports scanning from most Twain scanners and can also open most scanned PDF’s and multi page Tiff images as well as popular image file formats. FreeOCR outputs plain text and can export directly to Microsoft Word format.
OCRopus™ is an OCR system written in Python, NumPy, and SciPy focusing on the use of large scale machine learning for addressing problems in document analysis.
GOCR is an OCR (Optical Character Recognition) program, developed under the GNU Public License. It converts scanned images of text back to text files. Joerg Schulenburg started the program, and now leads a team of developers. GOCR can be used with different front-ends, which makes it very easy to port to different OSes and architectures. It can open many different image formats, and its quality have been improving in a daily basis.
PDF OCR Wrapper: This is a wrapper written in Java that allows to recursively iterate a directory structure and call an OCR engine on each found PDF on the condition that it hat not yet been called for that PDF. It works well with the ABBYY OCR Engine for Linux.
Perl 5 is a highly capable, feature-rich programming language with over 27 years of development. Perl 5 runs on over 100 platforms from portables to mainframes and is suitable for both rapid prototyping and large scale development projects.
PDL (“Perl Data Language”) gives standard Perl the ability to compactly store and speedily manipulate the large N-dimensional data arrays which are the bread and butter of scientific computing. PDL turns Perl in to a free, array-oriented, numerical language similar to (but, we believe, better than) such commerical packages as IDL and MatLab. One can write simple perl expressions to manipulate entire numerical arrays all at once. A simple interactive shell (perldl) is provided for use from the command line and a module (PDL) for use in Perl scripts.
Python is a programming language that lets you work quickly and integrate systems more effectively
SageMath is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more. Access their combined power through a common, Python-based language or directly via interfaces or wrappers.
GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable.
Scilab: The Free Software for Numerical Computation
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. Julia’s Base library, largely written in Julia itself, also integrates mature, best-of-breed open source C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace. IJulia, a collaboration between the Jupyter and Julia communities, provides a powerful browser-based graphical notebook interface to Julia.
Julia for R programmers by Douglas Bates, U. of Wisconsin-Madison, July 18, 2013 — [Requires PDF Software]
Maxima is a system for the manipulation of symbolic and numerical expressions, including differentiation, integration, Taylor series, Laplace transforms, ordinary differential equations, systems of linear equations, polynomials, sets, lists, vectors, matrices and tensors. Maxima yields high precision numerical results by using exact fractions, arbitrary-precision integers and variable-precision floating-point numbers. Maxima can plot functions and data in two and three dimensions.
The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, machine learning and much more.
Beaker is a notebook-style development environment for working interactively with large and complex datasets. Its plugin-based architecture allows you to switch between languages or add new ones with ease, ensuring that you always have the right tool for any of your analysis and visualization needs.
The FEniCS Project is a collection of free software with an extensive list of features for automated, efficient solution of differential equations.
MATLAB program for plotting a Simplified Psychrometric Chart
Psychropy: A repository of Psychrometric calculation and plotting tools: This is currently a translated Excel VB calculator that I wanted to be able to use in python code with an added funtion to back out Dry bulb temps using humidity ratio and enthalpy. the original calculator can calculate any of 9 psychrometric variables based on two input variables and the pressure
CoolProp is an open-source, cross-platform, free property database based in C++ that includes pure fluids, pseudo-pure fluids, and humid air properties. CoolProp is an alternative to NIST REFPROP. Based on reference-accuracy equations of state and transport property correlations for refrigerants Water, CO2, R134a, Nitrogen, Argon, Ammonia, Air, R404a, R410a, Propane and many others. A selection of secondary working fluid properties are also available. Can also make use of REFPROP when available. In addition, calculations for Humid Air Properties based on ASHRAE RP-1485 are provided.
jamovi is a new “3rd generation” statistical spreadsheet. designed from the ground up to be easy to use, jamovi is a compelling alternative to costly statistical products such as SPSS and SAS. jamovi is built on top of the R statistical language, giving you access to the best the statistics community has to offer. would you like the R code for your analyses? jamovi can provide that too.
Stixbox, developed by Anders Holtsberg, is a statistics toolbox for Matlab, Octave, and Matcom/Mideva.
Mastrave is a free software library written to perform vectorized computation and to be as compatible as possible with both GNU Octave and Matlab computing frameworks, offering general purpose, portable and freely available features for the scientific community. Mastrave is mostly oriented to ease complex modeling tasks such as those typically needed within environmental models, even when involving irregular and heterogeneous data series. The Mastrave project attempts to allow a more effective, quick interoperability between GNU Octave and Matlab users by using a reasonably well documented wrap around the main incompatibilities between those computing environments and by promoting a reasonably general idiom based on their common, stable syntagms. There are a couple of underlying ideas: library design is language design and vice versa (Bell labs); language notation is definitely a “tool of thought” (Iverson), in the sense that there is a feedback between programming/mathematical notation and the ability to think new scientific insights. And perhaps ethic ones.
Namazu is a full-text search engine intended for easy use. Not only does it work as a small or medium scale Web search engine, but also as a personal search system for email or other files.
Linux Basics by Jordan Hayes, Grant Brady & Thomas Girke, UC Riverside
EMBOSS (The European Molecular Biology Open Software Suite) is a free Open Source software analysis package specially developed for the needs of the molecular biology (e.g. EMBnet) user community. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is a platform to allow other scientists to develop and release software in true open source spirit. EMBOSS also integrates a range of currently available packages and tools for sequence analysis into a seamless whole. EMBOSS breaks the historical trend towards commercial software packages.
Plotly Open Source Graphing Libraries
Gephi is the leading visualization and exploration software for all kinds of graphs and networks. Gephi is open-source and free.
Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.
Gnuplot is a portable command-line driven graphing utility for Linux, OS/2, MS Windows, OSX, VMS, and many other platforms. The source code is copyrighted but freely distributed (i.e., you don’t have to pay for it). It was originally created to allow scientists and students to visualize mathematical functions and data interactively, but has grown to support many non-interactive uses such as web scripting. It is also used as a plotting engine by third-party applications like Octave. Gnuplot has been supported and under active development since 1986.
pyGtkPlot is a pygtk-based front-end for gnuplot. This project aims to a complete graphical front-end for gnuplot, capable not only of handling all its powerful features, but also of extending them by the introduction of new functionalities (like a LaTeX renderer and such…). It is the project owner’s opinion that gnuplot is a handy tool especially for scientists. This means that any major help to this project can come from experienced (python) programmers that are also physicists and/or mathematicians, because these people really need a program like gnuplot, and nobody better than them know what to require from a project like pyGtkPlot.
Gephi is an interactive visualization and exploration platform for all kinds of networks and complex systems, dynamic and hierarchical graphs.
SWRC Fit: The soil hydraulic parameters for analyzing water movement in variably saturated soil can be determined by fittig soil hydraulic model to a soil water retention curve. SWRC Fit performs nonlinear fitting of 5 soil hydraulic models to measured soil water retention curve; the relationship between the soil water potential and volumetric water content.
CesiumJS is an open source JavaScript library for creating world-class 3D globes and maps with the best possible performance, precision, visual quality, and ease of use. Developers across industries, from aerospace to smart cities to drones, use CesiumJS to create interactive web apps for sharing dynamic geospatial data.
USGS Global Visualization Viewer (GloVis)
USGS EarthExplorer
USGS National Map Viewer
NASA Goddard Institute for Space Studies (GISS): Panoply is a cross-platform application that plots geo-gridded and other arrays from netCDF, HDF, GRIB, and other datasets.
National Aeronautics and Space Administration (NASA): EOSDIS Worldview
National Aeronautics and Space Administration (NASA): WorldWind is a planetary globe 3D engine. WorldWind allows application developers to quickly and easily create interactive visualizations of geographical information in a 3D planetary context. Organizations around the world use WorldWind to monitor weather patterns, visualize cities and terrain, track vehicle movements, analyze geospatial data and educate humanity about the Earth.
National Aeronautics and Space Administration (NASA): Web WorldWind is a free, open-source virtual globe for web pages. Written in JavaScript, Web WorldWind enables web page and application builders to quickly create interactive visualizations of geographic information on an interactive 3D globe or 2D map. Web WorldWind provides an API that enables JavaScript programs to control every detail of visualization and interaction. Use it to embed a globe in your web page or create your own application. Web WorldWind runs on all major operating systems, desktop and mobile devices, and web browsers.
The Open Source Geospatial Foundation (OSGeo)
OSGeo4W: This is the web site, wiki and issue tracking database for the OSGeo4W project. OSGeo4W is a binary distribution of a broad set of open source geospatial software for Win32 environments (Windows XP, Vista, etc).
OSGeo-Live is a self-contained bootable DVD, USB thumb drive or Virtual Machine based on Lubuntu, that allows you to try a wide variety of open source geospatial software without installing anything. It is composed entirely of free software, allowing it to be freely distributed, duplicated and passed around. It provides pre-configured applications for a range of geospatial use cases, including storage, publishing, viewing, analysis and manipulation of data. It also contains sample datasets and documentation.
The MapLibre Organization is an umbrella for open-source mapping libraries.
GeoPackage is an open, standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information.
FWTools is a set of Open Source GIS binaries for Windows (win32) and Linux (x86 32bit) systems produced by me, Frank Warmerdam (ie. FW). The kits are intended to be easy for end users to install and get going with. No fudzing with building from source, or having to collect lots of interrelated packages. FWTools includes OpenEV, GDAL, MapServer, PROJ.4 and OGDI as well as some supporting components. The FWTools kits also aims to track the latest development versions of the packages included as opposed to official releases. While this may mean the packages are less stable, it is intended to give folks a chance to use the latest and greatest. FWTools releases also are a means by which I make recent development version bug fixes available to a wider audience than would be prepared to build them from the source. With FWTools releases, I also endeavor to build in as many optional components as possible. Thus, I include support for ECW, JPEG2000, HDF and other file formats that require extra libraries. Linux FWTools releases are intended to be distribution and packaging system agnostic. They should install on pretty much any x86 style Linux system released within the last few years.
EcoC²S [Irucka Embry): Alternatives to Proprietary, Freedom- and Privacy-Deleting Technologies: Alternatives to Google Maps, etc. (Online and Offline)
USGS Maps: Our programs produce accurate geologic maps and 3-D geologic frameworks that provide critical data for sustaining and improving the quality of life and economic vitality of the Nation. They also organize, maintain, and publish the geospatial baseline of the Nation’s topography, natural landscape, built environment and more.
QGIS: A Free and Open Source Geographic Information System
QGIS Plugins Repository
QGIS Application - Hydrology and Hydraulic modelling: QGIS Issue Tracking
Geographic Resources Analysis Support System (GRASS) is a free Geographic Information System (GIS) software used for geospatial data management and analysis, image processing, graphics/maps production, spatial modeling, and visualization. GRASS is currently used in academic and commercial settings around the world, as well as by many governmental agencies and environmental consulting companies. GRASS is an official project of the Open Source Geospatial Foundation.
GRASS and Sextante: GRASS-Wiki
R statistics: GRASS (Geographic Resources Analysis Support System) GIS-Wiki
Hydrological Sciences: GRASS GIS Users Wiki
GRASS and SAGA: GRASS GIS Users Wiki
System for Automated Geoscientific Analyses (SAGA)
Utah Water Research Laboratory, Civil and Environmental Engineering, Utah State University: Terrain Analysis Using Digital Elevation Models (TauDEM) is a suite of Digital Elevation Model (DEM) tools for the extraction and analysis of hydrologic information from topography as represented by a DEM.
gvSIG Desktop is designed to be an easily extensible solution, allowing thus continually improving the software application and developing tailor made solutions.
gvSIG Mobile is a powerful, easy-to-use and interoperable solution for data collection on Android devices. It is a cornerstone of the gvSIG Suite and it’s integrated directly with gvSIG Desktop and gvSIG Online. It is oriented to the data field collection and recommended for inventory projects, census, revisions, inspections…
uDig is an open source (EPL and BSD) desktop application framework, built with Eclipse Rich Client (RCP) technology
OpenJUMP GIS: The free, cross-platform and open source GIS for the World
Kalypso is an open source application for geospatial modelling and simulation. It is primarily developed to be a user friendly tool for GIS-based modelling and simulation of hydrological and hydraulic numerical models.
KalypsoBASE is a Desktop-GIS built on Eclipse. It’s focus lies on modelling gis data using GML Application-Schemata. Features contain generic dialogs based on GML, maps based on OGC’s SLD, and other tools like a charting framework or API for timeseries.
British Geological Survey (BGS) Groundhog Desktop GSIS (desktop geoscientific information system) is a graphical software tool developed by the environmental modelling directorate of BGS for the display of geological and geospatial information such as interpreted (correlated) geological cross sections, maps and boreholes. Groundhog Desktop is intended as a basic geoscientific information system (GSIS) — a software tool that facilitates the collation, display, filtering and editing of a range of data relevant to subsurface interpretation and modelling. You can use Groundhog Desktop to load and display certain types of borehole data, geological map linework, interpreted (correlated) cross sections and faults. It also supports reference data such as elevation models and images and has basic editing capabilities.
Coordinate Conversions and Transformations including Formulas: IOGP Publication 373-7-2 — Geomatics Guidance Note number 7, part 2 — September 2019 — [Requires PDF Software]
HYdrological Predictions for the Environment (HYPE) Open Source Community (OSC) is an open source initiative under the Lesser GNU Public License taken by SMHI to strengthen international collaboration in hydrological modelling and hydrological data prodution. The hypothesis is that more brains and more testing will result in better models and better code.
The nofdp Information and Decision Support System (IDSS) is an open source application for the interactive development of flood risk strategies and 1D hydrodynamic flood simulation. Additional modules for ecological and spatial analysis, multicriteria evaluation, flood risk maps, flood frequency, flood duration and communication are included.
ANUGA [Australian National University (ANU) and Geoscience Australia (GA)] is a Free & Open Source Software (FOSS) package capable of modelling the impact of hydrological disasters such as dam breaks, riverine flooding, storm-surge or tsunamis. ANUGA is based on the Shallow Water Wave Equation discretised to unstructured triangular meshes using a finite-volumes numerical scheme. A major capability of ANUGA is that it can model the process of wetting and drying as water enters and leaves an area. This means that it is suitable for simulating water flow onto a beach or dry land and around structures such as buildings. ANUGA is also capable of modelling difficult flows involving shock waves and rapidly changing flow speed regimes (transitions from sub critical to super critical flows).
The Penn State Integrated Hydrologic Model (PIHM) is a multiprocess, multi-scale hydrologic model where the major hydrological processes are fully coupled using the semi-discrete finite volume method. The model itself is “tightly-coupled” with PIHMgis, an open-source Geographical Information System designed for PIHM. The PIHMgis provides the interface to PIHM, access to the digital data sets (terrain, forcing and parameters) and tools necessary to drive the model, as well as a collection of GIS-based pre- and post-processing tools. Collectively the system is referred to as the Penn State Integrated Hydrologic Modeling System. The modeling system has been written in C/C++, while the GIS interface is supported by Qt. The Penn State Hydrologic Modeling System is open source software, freely available for download at this site along with installation and user guides.
HydroDesktop is a free and open source GIS enabled desktop application that helps you search for, download, visualize, and analyze hydrologic and climate data registered with the CUAHSI Hydrologic Information System.
HydroPy is a Python library for computational hydrology. It aims to become a full-featured computational hydrology while keeping the code as simple as possible in order to be comprehensible and easily extensible. HydroPy is written entirely in Python and require some external libraries.
OpenEV is a software library and application for viewing and analysing raster and vector geospatial data.
spatial-analyst.net is a non-commercial website intended for users interested in advanced use of geocomputational tools. The topics discussed generally belong to spatio-temporal data analysis sciences, digital cartography, geomorphometry, geostatistics, geovisualization, GPS tracking and navigation, raster-based GIS modelling and similar. Most of the articles presented are only supplementary materials to various research publications. Visitors of the website are kindly asked to refer to the peer-reviewed publications, when citing some of the materials, instead of referring to the URL of an article. All materials are prepared on an informative basis only. This is a wiki project, which obviously means that you can edit, extend and modify much of its content.
Software: spatial-analyst.net
University of California Davis Soil Resource Laboratory: Open Source Software Tools for Soil Scientists
GeographicLib is a small set of C++ classes for performing conversions between geographic, UTM, UPS, MGRS, geocentric, and local cartesian coordinates, for gravity (e.g., EGM2008), geoid height and geomagnetic field (e.g., WMM2015) calculations, and for solving geodesic problems. The emphasis is on returning accurate results with errors close to round-off (about 5-15 nanometers). New accurate algorithms for Geodesics on an ellipsoid of revolution and Transverse Mercator projection have been developed for this library. The functionality of the library can be accessed from user code, from the Utility programs provided, or via the Implementations in other languages.
libLAS is a C/C++ library for reading and writing the very common LAS LiDAR format. The ASPRS LAS format is a sequential binary format used to store data from LiDAR sensors and by LiDAR processing software for data interchange and archival.
rapidlasso GmbH: creators of LAStools, LASzip, and PulseWaves: Our LiDAR processing tools are widely known for their blazing speeds and high productivity. Our software combines robust algorithms with efficient I/O and clever memory management to achieve high throughput for data sets containing billions of points.
GeoDa Center for Geospatial Analysis and Computation: School of Geographical Sciences and Urban Planning, College of Liberal Arts and Sciences, Arizona State University (ASU)
PCRaster: Software for environmental modelling
GDAL (Geospatial Data Abstraction Library): ESRI Shapefile
Shapefile C Library provides the ability to write simple C programs for reading, writing and updating (to a limited extent) ESRI Shapefiles, and the associated attribute file (.dbf).
Python Shapefile Library (pyshp) library reads and writes ESRI Shapefiles in pure Python. You can read and write shp, shx, and dbf files with all types of geometry. Everything in the public ESRI shapefile specification is implemented. This library is compatible with Python versions 2.4 to 3.x.
Geo::ShapeFile: Perl extension for handling ESRI GIS Shapefiles.
GDAL is a translator library for raster geospatial data formats that is released under an X/MIT style Open Source license by the Open Source Geospatial Foundation. As a library, it presents a single abstract data model to the calling application for all supported formats. It also comes with a variety of useful commandline utilities for data translation and processing. The related OGR library (which lives within the GDAL source tree) provides a similar capability for simple features vector data.
The OGR Simple Features Library is a C++ open source library (and commandline tools) providing read (and sometimes write) access to a variety of vector file formats including ESRI Shapefiles, S-57, SDTS, PostGIS, Oracle Spatial, and Mapinfo mid/mif and TAB formats. OGR is a part of the GDAL library.
PROJ.4 Cartographic Projections library originally written by Gerald Evenden then of the USGS.
STK++ is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an API Eigen-like), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment: rtkore.
AD Model Builder, or ADMB, is a powerful software package for the development of state-of-the-art nonlinear statistical models. ADMB is built around the AUTODIF Library, a C++ language extension which implements reverse mode automatic differentiation. A closely related software package, ADMB-RE, implements random effects in nonlinear models. ADMB was created by David Fournier and now continues to be developed by the ADMB Project, a creation of the non-profit ADMB Foundation. ADMB is free, open source, and available for Windows, Linux, MacOS, and Sun/SPARC.
Bayes Net Toolbox for Matlab
OpenBUGS (Bayesian inference Using Gibbs Sampling)
JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS.
MATJAGS: This interface allows JAGS (“Just Another Gibbs Sampler”) to be used in combination with Matlab. JAGS is a program for Bayesian hierarchical models using Markov chain Monte Carlo (MCMC) inference methods. JAGS is similar to the OpenBUGS and WinBUGS programs but can operate on a number of platforms including Windows and Max OS X.
MATBUGS is a Matlab interface for WinBugs and OpenBugs, which are programs for Gibbs sampling applied to hierarchical Bayesian models.
PMTK is a collection of Matlab/Octave functions, written by Matt Dunham, Kevin Murphy and various other people. The toolkit is primarily designed to accompany Kevin Murphy’s textbook, Machine learning: a probabilistic perspective, but can also be used independently of this book. The goal is to provide a unified conceptual and software framework encompassing machine learning, graphical models, and Bayesian statistics (hence the logo). (Some methods from frequentist statistics, such as cross validation, are also supported.) The toolbox is currently (December 2011) in maintenance mode, meaning that bugs will be fixed, but no new features will be added (at least not by Kevin or Matt). PMTK supports a large variety of probabilistic models, including linear and logistic regression models (optionally with kernels), SVMs and gaussian processes, directed and undirected graphical models, various kinds of latent variable models (mixtures, PCA, HMMs, etc), etc. Several kinds of prior are supported, including Gaussian (L2 regularization), Laplace (L1 regularization), Dirichlet, etc. Many algorithms are supported, for both Bayesian inference (including dynamic programming, variational Bayes and MCMC) and MAP/ML estimation (including EM, conjugate and projected gradient methods, etc.) PMTK builds on top of several existing packages, available from pmtksupport, and provides a unified interface to them. In addition, it provides readable “reference” implementations of many common machine learning techniques. The vast majority of the code is written in Matlab. (For a brief discussion of why we chose Matlab, Most of the code also runs on Octave, an open-source Matlab clone.) However, in a few cases we also provide wrappers to implementations written in C, for speed reasons. PMTK currently (October 2010) has over 67,000 lines of code. PMTK contains many demos of different methods applied to many different kinds of data sets. The demos are listed here, and the data is available from pmktdata (but will be downloaded automatically when a demo calls loadData).
pymc: Markov Chain Monte Carlo sampling toolkit. Bayesian estimation, particularly using Markov chain Monte Carlo (MCMC), is an increasingly relevant approach to statistical estimation. However, few statistical software packages implement MCMC samplers, and they are non-trivial to code by hand. pymc is a python package that implements the Metropolis-Hastings algorithm as a python class, and is extremely flexible and applicable to a large suite of problems. pymc includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. pymc only requires NumPy. All other dependencies such as matplotlib, SciPy, pytables, sqlite or mysql are optional.
agate is a Python data analysis library that is optimized for humans instead of machines. It is an alternative to numpy and pandas that solves real-world problems with readable code.
csvkit is a suite of utilities for converting to and working with CSV, the king of tabular file formats.
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
MetaAnalyst: powerful meta-analysis software
Open MetaAnalyst
SimLab provides a free development framework for Sensitivity and Uncertainty Analysis. SimLab is a professional tool for model developers, scientists and professionals, to learn, use and exploit global uncertainty and sensitivity analysis techniques. The SimLab license encourages free non-commercial use.
The GNU Operating System and the Free Software Movement: GNU MCSim is a simulation package, written in C, which allows you to: design your own statistical or simulation models (eventually dynamic, via ODEs), perform Monte Carlo stochastic simulations, do Bayesian inference through Markov Chain Monte Carlo simulations.
MCS-libre Monte-Carlo Simulation Toolkit: Free C++ toolkit to facilitate Monte-Carlo simulation. This is a library covered under the LGPL. “MCS-libre” stands for “Monte Carlo Simulation - libre”.

Statistical Analysis & Hydrology

Practical Statistics
David’s Statistics: Statistics for Geologists, Environmental Scientists, and other Non-Statisticians
Statistical Engineering: Charles Annis, P.E.
Burns Statistics
StatisticsLectures.com
Statistics: From Wikibooks, open books for an open world
The National Institute of Standards and Technology (NIST)/SEMATECH e-Handbook of Statistical Methods
Statistics: Coastal Inlets Research Program (CIRP) Wiki
Massachusetts Institute of Technology (MIT) OpenCourseWare: Mathematics: Introduction to Probability and Statistics
Statistics 401: An Introduction to Statistics for Engineers and Scientists by Michael G. Akritas, Penn State University, Spring 2008 — [Requires PDF Software]
STATS4STEM.ORG: Resources for AP Statistics, Intro to Statistics, and R
Lecture notes on forecasting: What’s the bottom line? How to compare models Robert Nau, Fuqua School of Business, Duke University
ClockBackward Essays: Ordinary Least Squares Linear Regression: Flaws, Problems and Pitfalls: An analysis of the defects of least squares regression Posted on June 18, 2009 by ClockBackward
On the use of dimensioned measures of error to evaluate the performance of spatial interpolators by C. J. Willmott and K. Matsuura, January 2006, International Journal of Geographical Information Science, Vol. 20, No. 1, 89-102 — [Requires PDF Software]
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance by Cort J. Willmott, Kenji Matsuura, 2005, Climate Research, Vol. 30: 79-82 — [Requires PDF Software]
Ambiguities inherent in sums-of-squares-based error statistics by Cort J. Willmott, Kenji Matsuura, Scott M. Robeson, 2009, Atmospheric Environment, vol. 43, no. 3, pp. 749-752
A refined index of model performance by Cort J. Willmott, Scott M. Robeson, Kenji Matsuura, 2012, International Journal of Climatology, vol. 32, no. 13, pp. 2088-2094
Statistics for the Evaluation and Comparison of Models by Cort J. Willmott, Steven G. Ackleson, Robert E. Davis, Johannes J. Feddema, Katherine M. Klink, David R. Legates, James O’Donnell, Clinton M. Rowe, 1985, Journal of Geophysical Research, vol. 32, no. C5, pp. 8995-9005
International Commission on Statistical Hydrology (ICSH)
US Geological Survey (USGS) Water Resources: National Research Program (NRP) Statistical Analysis of Errors in Hydrologic Models
USGS Techniques of Water-Resources Investigations Book 4, Chapter A3: Statistical Methods in Water Resources by D. R. Helsel and R. M. Hirsch
Quantitative Hydrology: WASR 4500/6500: Chapter 4: Statistical Hydrology — [Requires PDF Software]
US Army Corps of Engineers Hydrologic Engineering Center: Statistical Methods in Hydrology
USGS Water Resources Statistics Software
The Earth Resources Observation and Science (EROS) Center’s mission is to document and analyze changes to the Earth’s land areas, across our nation and around the world. To study land change, EROS researchers utilize a vast database of images of the Earth’s surface, including those acquired by Landsat satellites.
US Dept of Commerce National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC)
US Department of Commerce National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) is responsible for preserving, monitoring, assessing, and providing public access to the Nation’s treasure of climate and historical weather data and information.
US Department of Commerce National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory Physical Sciences Division (PSD) Climate and Weather Data
US Dept of Commerce National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC): Climate Data Online (CDO) provides free access to NCDC’s archive of global historical weather and climate data in addition to station history information. These data include quality controlled daily, monthly, seasonal, and yearly measurements of temperature, precipitation, wind, and degree days as well as radar data and 30-year Climate Normals.
US Dept of Commerce National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC): Climate Normals
US Dept of Commerce National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) National Centers for Environmental Information (NCEI): Historical Palmer Drought Indices
US Dept of Commerce National Oceanic and Atmospheric Administration (NOAA) National Weather Service
National Weather Service (NWS) National Climate
National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS)
National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) Precipitation Analysis
USDA Natural Resources Conservation Service (NRCS) National Water and Climate Center: Climate Analysis for Wetlands - Individual Counties
USDA Natural Resources Conservation Service (NRCS) National Water and Climate Center: Community Collaborative Rain, Hail & Snow Network (CoCoRaHS)
Suspended sediment load prediction of river systems: An artificial neural network approach by A. M. Melesse, S. Ahmad, M. E. McClain, X. Wang, Y. H. Lim, March 2011, Agricultural Water Management, Volume 98, Issue 5, Pages 855-866
India National Programme on Technology Enhanced Learning (NPTEL) Civil Engineering Advanced Hydrology
Quantitative Hydrology: WASR 4500/6500: Todd C. Rasmussen, Ph.D., Warnell School of Forestry and Natural Resources, The University of Georgia, Athens
USGS Georgia Water Science Center Water Science School: All about water!
USGS Water Resources of the United States
The National Water Census is a USGS research program on national water availability and use that develops new water accounting tools and assesses water availability at the regional and national scales. Through the Water Census, USGS is integrating diverse research on water availability and use and enhancing the understanding of connection between water quality and water availability. Research is designed to build decision support capacity for water management agencies and other natural resource managers. It is one of six major science directions identified by the USGS in its 2007 Science Plan and is called for in the SECURE Water Act and implemented through the Department of the Interior WaterSMART initiative.
National Water Census - Data Portal
USGS Office of Surface Water (OSW)
USGS OSW Technical Memorandum Summaries for Surface Water
USGS Water Data for the Nation National Water Information System (NWIS)
USGS National Water Information System (NWIS) Mapper
USGS National Streamflow Information Program (NSIP)
USGS WaterWatch -- Streamflow conditions
USGS Water Resources: Maps and GIS Data
USGS National Hydrography Dataset (NHD)
USGS NHD Watershed Boundary Dataset (WBD)
Horizon Systems Corporation NHDPlus
USGS National Elevation Dataset (NED)
Persistent URLs for Some USGS Water Resources Reports
USGS National Water-Quality Assessment (NAWQA) Program
Aquatic Informatics Hydrology Corner Blog: A Rising Tide Floats All Boats - USGS ADAPS Replacement
CARP (Commercial ADAPS Replacement Project): This project will produce a modern, commercial solution -- AQUARIUS, from Aquatic Informatics, Inc -- to the acquisition, processing, and analysis of the US Geological Survey, Water Mission Area (WMA) time-series data. We anticipate AQUARIUS will provide a better tool for both experienced and new hydrographers, leading to reduced gaging and monitoring costs for our streams and wells in addition to opportunities for network growth.
USGS Tennessee Water Science Center
USGS WaterWatch Customized Rating Curve Builder
USGS Tennessee Water Science Center Ratings Depot
TDEC Tennessee Healthy Watershed Initiative
TDEC Interactive Mapping
TDEC Interactive Mapping portal
USGS Kentucky Water Science Center Ratings Depot Information
United States Environmental Protection Agency (EPA): Watershed Assessment of River Stability & Sediment Supply (WARSSS): Excess sediment has been a leading cause of water quality impairment across the Nation for years, but methods to assess sediment problems and plan solutions have been limited. WARSSS is a technical procedure developed by Dr. David L. Rosgen for water quality scientists to use in evaluating streams and rivers impaired by excess sediment.
US EPA: Causal Analysis/Diagnosis Decision Information System (CADDIS) is a website developed to help scientists and engineers in the Regions, States, and Tribes conduct causal assessments in aquatic systems.
US EPA: STORET (short for STOrage and RETrieval) Data Warehouse is a repository for water quality, biological, and physical data and is used by state environmental agencies, EPA and other federal agencies, universities, private citizens, and many others.
US EPA: The Watershed Assessment, Tracking & Environmental Results System (WATERS) unites water quality information previously available only from several independent and unconnected databases.
US EPA — A national copy of the National Hydrography Dataset (NHD) is stored in the WATERS database for use by the Reach Address Database, services, applications and reporting tools.
US EPA Environmental Impact Statement (EIS) Mapper
Substance Registry Services (SRS) is the Environmental Protection Agency’s (EPA) central system for information about substances that are tracked or regulated by EPA or other sources. It is the authoritative resource for basic information about chemicals, biological organisms, and other substances of interest to EPA and its state and tribal partners. The SRS makes it possible to identify which EPA data systems, environmental statutes, or other sources have information about a substance and which synonym is used by that system or statute. It becomes possible therefore to map.
US EPA: CProb: A Tool for Conditional Probability Analysis
US NOAA National Estuarine Research Reserve
US Department of Agriculture (USDA) Agricultural Research Service (ARS) National Sedimentation Laboratory (NSL)
USDA Natural Resources Conservation Service (NRCS) Water
USDA NRCS The National Water Management Center (NWMC)
USDA NRCS Regional Hydraulic Geometry Curves
USDA NRCS Regional Hydraulic Geometry Curves Links
US EPA National Pollutant Discharge Elimination System (NPDES)
US EPA Databases
US EPA Data and Information Technology
USGS Threatened and Endangered Stations
USGS: Science Topics: Stream-gage measurement: Use of specialized instruments designed for observing, checking, and recording the water discharge that occurs in a natural channel.
USGS Water Resources NSDI Node: USGS Streamgages Linked to the Medium Resolution NHD
USGS Water Resources NSDI Node: Base-flow index grid for the conterminous United States
Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) 2001 (NLCD2001)
USGS Water Resources NSDI Node: Hydrologic landscape regions of the United States
USGS: Global GIS Databases
USGS Publications Warehouse
Stochastic Empirical Loading and Dilution Model (SELDM) Version 1.0.0 By Gregory E. Granato, Techniques and Methods 4-C3, Prepared in cooperation with the Department of Transportation Federal Highway Administration, Office of Project Development and Environmental Review
The StreamStats Program
The StreamStats Program Data-Collection Stations
Assessing the National Streamflow Information Program by Committee on Review of the USGS National Streamflow Information Program, National Research Council
Assessing the National Streamflow Information Program 2. Gaging the Nation’s Streams
Everglades Depth Estimation Network (EDEN) for Support of Biological and Ecological Assessments: Explanations of Terms and Methods (for Water-Level Values)
Surface-Water Data and Statistics from U.S. Geological Survey Data-Collection Networks in New Jersey on the World Wide Web, USGS Fact Sheet FS-109-02
The StreamStats Program: Obtaining Streamflow Statistics for Ungages Sites
Metadata (Gaging Stations)
Water Resources of the United States—Annual Water Data Reports
The hydraulic geometry of stream channels and some physiographic implications by L. B. Leopold and Thomas Maddock, Jr., 1953, USGS Professional Paper: 252
USGS Water Resources NSDI Node: GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow
Geometry, basin-characteristics, discharge, and particle-size data from gaged stream-channel sites, Western United States by W. R. Osterkamp, E. R. Hedman, and A. G. Wiseman, 1982, USGS Open-File Report: 82-93
Stage-fall-discharge relations for steady flow in prismatic channels by William D. Mitchell, 1954, USGS Water Supply Paper: 1164
Estimating Basin Lagtime and Hydrograph-Timing Indexes Used to Characterize Stormflows for Runoff-Quality Analysis By Gregory E. Granato, Scientific Investigations Report 2012-5110
Ecological Archives E091-045-D1: GAGES: A stream gage database for evaluating natural and altered flow conditions in the conterminous United States by James A. Falcone, Daren M. Carlisle, David M. Wolock, and Michael R. Meador. 2010. Ecology 91:621.
Elevation Derivatives for National Applications (EDNA) is a multi-layered database derived from a version of the National Elevation Dataset (NED), which has been hydrologically conditioned for improved hydrologic flow representation.
USGS Georgia Water Science Center: How streamflow is measured
USGS Science in Your Watershed General Introduction and Hydrologic Definitions by W. B. Langbein and Kathleen T. Iseri, Manual of Hydrology: Part 1. General Surface-Water Techniques, Geological Survey Water-Supply Paper 1541-A, Methods and practices of the Geological Survey
USGS Water-Science School: How stream height relates to streamflow
Stage-Discharge Relations—Basic Concepts Training Class by K. Michael Nolan, Larry Bohman, Tim Stamey, and Gary Firda, Scientific Investigations Report 2005-5028, USGS Training Class SW1409
Measurement of Stream Discharge by Wading by K. Michael Nolan, Ronald R. Shields, and Michael S. Rehmel, Water Resources Investigation Report 00-4036, USGS Training Class SW1271
Discharge measurements at gaging stations by T. J. Buchanan and W.P. Somers, US Geological Survey, Techniques of Water-Resources Investigations, Book 3, Chapter A8
Aquatic Informatics Hydrology Corner Blog: Best Practice Approach to Stage-Discharge Rating Curve Development By Stu Hamilton, January 14th, 2014
The USGS Water Science School: How Streamflow is Measured: Part 3: The stage-discharge relation
National Weather Service Office of Climate, Water and Weather Services Hydrologic Services Division Service Hydrologist Reference Manual (SHR Manual): Rating Tables and Curves
Bayesian methods for estimating multi-segment discharge rating curves by Trond Reitan and Asgeir Petersen-Øverleir, Stochastic Environmental Research and Risk Assessment, July 2009, Volume 23, Issue 5, pp 627-642
Methods for Adjusting U.S. Geological Survey Rural Regression Peak Discharges in an Urban Setting by Glenn E. Moglen and Dorianne E. Shivers, U.S. Geological Survey Scientific Investigations Report 2006-5270
Floods and Flood Plains: US Geological Survey Open File Report 93-641
Differences in phosphorus and nitrogen delivery to the Gulf of Mexico from the Mississippi River Basin by R. B. Alexander, R. A. Smith, G. E. Schwarz, E. W. Boyer, J. V. Nolan, and J. W. Brakebill, 2008, Environmental Science and Technology, vol. 42, no. 3, p. 822-830. — [Requires PDF Software]
Development of Relations of Stream Stage to Channel Geometry and Discharge for Stream Segments Simulated with Hydrologic Simulation Program-Fortran (HSPF), Chesapeake Bay Watershed and Adjacent Parts of Virginia, Maryland, and Delaware by Douglas L. Moyer and Mark R. Bennett, 2007, USGS Scientific Investigations Report: 2007-5135
Development of regional curves of bankfull-channel geometry and discharge for streams in the non-urban, Piedmont Physiographic Province, Pennsylvania and Maryland by Peter J. Cinotto, 2003, USGS Water-Resources Investigations Report: 2003-4014
Regional Curves of Bankfull Channel Geometry for Non-Urban Streams in the Piedmont Physiographic Province, Virginia by R. Russell Lotspeich, Scientific Investigations Report 2009-5206
Optimizing Bankfull Discharge and Hydraulic Geometry Relations for Streams in New York State by Christiane I. Mulvihill and Barry P. Baldigo, JAWRA Journal of the American Water Resources Association, Volume 48, Issue 3, pages 449-463, June 2012
Regional channel geometry equations: a statistical comparison for physiographic provinces in the eastern US by P. A. Johnson and B. J. Fecko, River Research and Applications, Volume 24, Issue 6, pages 823-834, July 2008
Uncertainty In Estimating Bankfull Conditions by Peggy A. Johnson and Thomas M. Heil, JAWRA Journal of the American Water Resources Association, Volume 32, Issue 6, pages 1283-1291, December 1996
Active Channel Geometry and Discharge Relations of U.S. Piedmont and Midwestern Streams: The Variable Exponent Model Revisited by Frederick J. Kolberg and Alan D. Howard, Water Resources Research, Volume 31, Issue 9, pages 2353-2365, September 1995
Geometry, basin-characteristics, discharge, and particle-size data from gaged stream-channel sites, Western United States by W. R. Osterkamp, E. R. Hedman, and A. G. Wiseman, 1982, USGS Open-File Report: 82-93
Estimates of Mean-Annual Streamflow and Flow Loss for Ephemeral Channels in the Salt Basin, Southeastern New Mexico, 2009, Scientific Investigations Report 2011-5062 — [Requires PDF Software]
Methods For Estimating Streamflow At Mountain Fronts In Southern New Mexico by Scott D. Waltemeyer, Water-Resources Investigations Report 93-4213 — [Requires PDF Software]
Selected Papers in the Hydrologic Sciences 1984, United States Geological Survey Water-Supply Paper 2262 — [Requires PDF Software]
The Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI)
CUAHSI Water Data Center
CUAHSI Hydrologic Information System (HIS) Central Registered Data Services
Hydrology: From Wikipedia, the free encyclopedia
Bankfull Hydraulic Geometry Relationships for North Carolina Streams by William A. Harman, Gregory D. Jennings, Jan M. Patterson, Dan R. Clinton, Louise O. Slate, Angela G. Jessup, J. Richard Everhart and Rachel E. Smith. 1999. AWRA Wildland Hydrology Symposium Proceedings. Edited By: D. S. Olsen and J. P. Potyondy. AWRA Summer Symposium. Bozeman, MT.
Bankfull Regional Curves for North Carolina Mountain Streams by W. A. Harman, D. E. Wise, M. A. Walker, R. Morris, M. A. Cantrell, M. Clemmons, G. D. Jennings, D. Clinton, and J. Patterson
Streamflow of 2011—Water Year Summary by Xiaodong Jian, David M. Wolock, Harry F. Lins, and Steve Bradym, 2012, U.S. Geological Survey Fact Sheet 2012-3085
Journal of Research of the U.S. Geological Survey, May - June 1977, volume 5, issue 3
Sediment and channel-geometry investigations for the Kansas River bank stabilization study, Kansas, Nebraska, and Colorado by W. R. Osterkamp, R. E. Curtis, and H. G. Crowther, 1982, USGS Open-File Report: 81-128
Perennial-streamflow characteristics related to channel geometry and sediment in Missouri River basin by W. R. Osterkamp and E. R. Hedman, 1982, USGS Professional Paper: 1242
Streamflow characteristics related to channel geometry of streams in western United States by E. R. Hedman and 1982, USGS Water Supply Paper: 2193
Fluvial Sediment in the Little Arkansas River Basin Kansas, Geological Survey Water-Supply Paper 1798-B — [Requires PDF Software]
Variation of alluvial-channel width with discharge and character of sediment by W. R. Osterkamp, 1979, USGS Water-Resources Investigations Report: 79-15
Guidelines for the use of structural versus regression analysis in geomorphic studies by W. R. Osterkamp, Jesse M. McNellis, and Paul Robert Jordan, 1978, USGS Water-Resources Investigations Report: 78-135
Particle-size analyses of bed and bank material from channels of the Missouri River basin by W. R. Osterkamp and A. G. Wiseman, 1980, USGS Open-File Report: 80-429
Surface water supply of the United States, 1947, Part VI, Missouri River basin, 1950, USGS Water Supply Paper: 1086
Bankfull Hydraulic Geometry Relationships and Recurrence Intervals for North Carolina’s Coastal Plain by William V. Sweet and Jens W. Geratz, JAWRA Journal of the American Water Resources Association, Volume 39, Issue 4, pages 861-871, August 2003
Hydraulic Geometry Relationships for Urban Streams Throughout the Piedmont of North Carolina by Barbara A. Doll, Dani E. Wise-Frederick, Carolyn M. Buckner, Shawn D. Wilkerson, William A. Harman, Rachel E. Smith, and Jean Spooner, JAWRA Journal of the American Water Resources Association, Volume 38, Issue 3, pages 641-651, June 2002
Variation of width and discharge for natural high-gradient stream channels by W. R. Osterkamp and E. R. Hedman, Water Resources Research, Volume 13, Issue 2, pages 256-258, April 1977
Hydrometry -- Measurement of liquid flow in open channels -- Part 2: Determination of the stage-discharge relationship: ISO 1100-2:2010 specifies methods of determining the stage-discharge relationship for a gauging station. A sufficient number of discharge measurements, complete with corresponding stage measurements, are required to define a stage-discharge relationship to the accuracy required by ISO 1100-2. Stable and unstable channels are considered, including brief descriptions of the effects on the stage-discharge relationship of shifting controls, variable backwater and hysteresis. Methods of determining discharge for twin-gauge stations, ultrasonic velocity-measurement stations, electromagnetic velocity-measurement stations and other complex rating curves are not described in detail. These types of rating curve are described separately in other International Standards, Technical Specifications and Technical Reports, which are listed in ISO 1100-2.
WaterML2 is a new data exchange standard in Hydrology which can basically be used to exchange many kinds of hydro-meteorological observations and measurements. WaterML2 has been initiated and designed over a period of several years by a group of major national and international organizations from public and private sector, such as CSIRO, CUAHSI, USGS, BOM, NOAA, KISTERS and others. WaterML2 has been developed within the OGC Hydrology Domain Working group which has a mandate by the WMO, too.
Open Geospatial Consortium (OGC): WaterML 2.0 Standards Working Group
New Approach for Stage-Discharge Relationship: Gene-Expression Programming by Aytac Guven and Ali Aytek, Journal of Hydrologic Engineering, Volume 14, Issue 8 (August 2009), pages 812-820
Storm water runoff concentration matrix for urban areas by P. Göbel, C. Dierkes and W.G. Coldewey, Journal of Contaminant Hydrology, Volume 91, Issues 1-2, 1 April 2007, Pages 26-42
Performance of a filtration system equipped with filter media for parking lot runoff treatment by Maria Fuerhacker, Tadele Measho Haile, Bernhard Monai and Axel Mentler, Desalination, Volume 275, Issues 1-3, 15 July 2011, Pages 118-125
Understanding heavy metal and suspended solids relationships in urban stormwater using simulated rainfall by Lars Herngren, Ashantha Goonetilleke and Godwin A. Ayoko, Journal of Environmental Management, Volume 76, Issue 2, July 2005, Pages 149-158
Hydrograph and chloride pollutograph analysis of Hobbs Brook reservoir subbasin in eastern Massachusetts by David W. Ostendor, Journal of Hydrology, Volume 503, 30 October 2013, Pages 123-134
Toxicity of urban highway runoff with respect to storm duration by M. Kayhanian, C. Stransky, S. Bay, S.-L. Lau and M.K. Stenstrom, Science of The Total Environment, Volume 389, Issues 2-3, 25 January 2008, Pages 386-406
Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources by Allen P Davis, Mohammad Shokouhian and Shubei Ni, Chemosphere, Volume 44, Issue 5, August 2001, Pages 997-1009
Analysis and detection of outliers in water quality parameters from different automated monitoring stations in the Miño river basin (NW Spain) by J.I. Piñeiro Di Blasi, J. Martínez Torres, P.J. García Nieto, J.R. Alonso Fernández, C. Díaz Muñz and J. Taboada, Ecological Engineering, Volume 60, November 2013, Pages 60-66

SPARROW

USGS SPARROW (SPAtially Referenced Regressions On Watershed attributes) Surface Water-Quality Modeling
SPARROW Watershed Modeling: Estimation of Nutrient and Sediment Transport
SPARROW Decision Support System (online version)
USGS NAWQA SPARROW Model Variables for NHDPlus Catchments: Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States (DS-490)
SPARROW modeling to understand water-quality conditions in major regions of the United States by R. B. Alexander, S. D. Preston, D. Wolock, 2011, Journal of American Water Resources Association Featured Collection, 1172 p. — [Requires PDF Software]
Reply to Discussion — “Nutrient Inputs to the Laurentian Great Lakes by Source and Watershed Estimated Using SPARROW Watershed Models” by R. Peter Richards, Ibrahim Alameddine, J. David Allan, David B. Baker, Nathan S. Bosch, Remegio Confesor, Joseph V. DePinto, David M. Dolan, Jeffrey M. Reutter, and Donald Scavia By Dale M. Robertson and David A. Saad, JAWRA Journal of the American Water Resources Association, Volume 49, Issue 3, pages 725-734, June 2013

Table of Contents for R Trainings and Resources