# Irucka Embry's R Packages

- Irucka Embry (iembry–USGS) GitHub Repositories
- 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), civil & environmental/water resources engineering (Normal Strength Concrete Mix Design, 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 [Engineering Economics, Environmental/Water Resources Engineering, US Presidential Elections].
- 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. - curvenumber: Soil Conservation Service Curve Number method (R–package) — contributed the weighted_CN function

# Irucka Embry's R Examples

- Data Analysis With R Using data.table and dplyr
- Microsoft Excel Examples in R
- Routine Microsoft Excel and SQL Operations with R (using a USGS NWIS data set)
- Routine Microsoft Excel and SQL Operations with R (using the TDEC SWM Dataviewer)
- Open Channel Flow Examples using the Gauckler–Manning–Strickler equation
- Visualizing EPANET files with R
- Sand Gradation Analysis using ASTM C136 with R
- Numerical Methods with R
- Function Plots in R (based on GNU Octave/MATLAB Examples)
- ode45 Plots in R (based on GNU Octave/MATLAB Examples)
- Mesh Plots in R (based on GNU Octave/MATLAB Example)
- Solved Problems given to Freshman Engineering Seminar Students, Fall 2015
- Potential Energy Problem Solved in R (based on GNU Octave/MATLAB Example)
- LU Factorization with R (based on GNU Octave/MATLAB Example)
- Using R to Balance Chemical Reactions (Stoichiometry)

# Irucka Embry's R Trainings

- R_Tutorial Directory
- A Practical Introduction to the R Programming Language Part 6: Basic Operations Using R
- A Practical Introduction to the R Programming Language Part 5: Data Visualization with R
- A Practical Introduction to the R Programming Language Part 4 — [Can be viewed in 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) — [Can be viewed in 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 — [Can be viewed in Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]
- A Practical Introduction to the R Programming Language — [Requires PDF Software]
- A Practical Introduction to the R Programming Language: Extended Version — [Requires PDF Software]
- A Practical Introduction to the R Programming Language R code — [Can be viewed in Web browser, advanced text editor (ex. Notepad++ for Microsoft Windows), and/or RStudio™ {.R file}]

# 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, 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;
- 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

- 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 Lessons
- 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
- 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!
- JDoodle: R Online Editor – R Online IDE – R Coding Online
- Code School: Try R
- R–Fiddle
- 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 http://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
- 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
- R: advanced statistical package, University of California Davis Soil Resource Laboratory
- Introducing R: Statistical Consulting Group, UCLA Institute for Digital Research & Education, 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 http://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* - 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

## Other R Resources

- RPubs (brought to you by RStudio): Easy web publishing from R
- 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
- R Tagged Questions: Stack Overflow
- 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
- 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 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: 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
- CRAN Package: ctv (CRAN Task Views): Server–side and client–side tools for task views to CRAN–style repositories

## 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.
- RStudio Support: Working Directories and Workspaces by Josh Paulson, October 12, 2013
- 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).
- Rweb: R, R statistical programming language, R tutorial, R help – STATS4STEM.ORG
- 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 SAS, SPSS and Stata Users: r4stats.com
- r4stats.com: Add–ons: R has over 5,000 add–on packages, many containing multiple procedures, so it can do most of the things that SAS and SPSS can do and quite a bit more. The table below focuses only on SAS and SPSS products and which of them have counterparts in R. As a result, some categories are extremely broad (e.g. regression) while others are quite narrow (e.g. conjoint analysis). This table does not contain the hundreds of R packages that have no counterparts in the form of SAS or SPSS products. There are many important topics (e.g. mixed models, survival analysis) offered by all three that are not listed because neither SAS Institute nor IBM's SPSS Company sell a product focused just on that.
- 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
- 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
- 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
- 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?
- 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
- R for Excel Users: How to Learn R
*R for Excel Users*by John Taveras- 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

- 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
- 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

- 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
- 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: RMySQL: Implements ‘DBI’ Interface to ‘MySQL’ and ‘MariaDB’ 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: rgrass7: Interpreted interface between GRASS 7 geographical information system and R, based on starting R from within the GRASS GIS environment, or running free–standing R in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. This package may not be used for GRASS 6, for which spgrass6 should be used.
- CRAN Package: spgrass6: Interpreted interface between GRASS 6+ geographical information system and R, based on starting R from within the GRASS environment, or running free–standing R in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. This package may not be used for GRASS 7, for which rgrass7 should be used.
- 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: RcppOctave: Direct interface to Octave. The primary goal is to facilitate the port of Matlab/Octave scripts to R. The package enables to call any Octave functions from R and as well as browsing their documentation, passing variables between R and Octave, using R core RNGs in Octave, which ensures that stochastic computations are also reproducible.
- 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
- 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.
- 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
- 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
- Interactive 3D plots with the rgl package: Submitted by dylan on Sun, 2006–12–17, University of California Davis Soil Resource Laboratory
- 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

- 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
- R–sig–geo help at Nabble.com
- CRAN Package: rgrass7: Interpreted interface between GRASS 7 geographical information system and R, based on starting R from within the GRASS GIS environment, or running free–standing R in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. This package may not be used for GRASS 6, for which spgrass6 should be used.
- CRAN Package: spgrass6: Interpreted interface between GRASS 6+ geographical information system and R, based on starting R from within the GRASS environment, or running free–standing R in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. This package may not be used for GRASS 7, for which rgrass7 should be used.
- R–bloggers: RQGIS 0.1.0 release By jannesm, August 15, 2016
- amsantac.co: Integrating QGIS and R: A stratified sampling example, Oct 31, 2015
- US 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
- 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
- The CASA Blog Network: R Spatial Tips
- Spatial.ly: R Spatial
- GRASS Wiki: R statistics
- 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

- 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
- RStudio: Pandoc Markdown
- CRAN Package: rmarkdown: Convert R Markdown documents into a variety of formats.
- RStudio: R Markdown: Dynamic Documents for R
- 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
- 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

## 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
- R–bloggers: Evidence for a limit to effective peer review By nsaunders, December 18, 2016 NRC.nl:
- ‘Top–tier journals like
*Nature*knowingly take risks’ by Lucas Brouwers, 9 december 2016 - 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,*The Guardian*, 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)

- 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]

- Shiny by RStudio
- RStudio: Teach yourself Shiny
- 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
- 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*by Hadley Wickham- Quick–R: R Packages
- 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
- 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]
- 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]

### R Package Repositories

- Revolutions (R Blog): Introducing miniCRAN: an R package to create a private CRAN repository by Andrie deVries, October 03, 2014
- R Packages: A comprehensive index of R packages and documentation from CRAN, Bioconductor, GitHub and R–Forge.
- All CRAN, Bioconductor, and R–Forge 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.
- The Omega Project for Statistical Computing
- USGS–R at GitHub: Collection of USGS R codes and packages
- rOpenGov: R Ecosystem for Open Government Data and Computational Social Science
- 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
- 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
- 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]
- R–bloggers: Unit Testing in R By blogisr, March 14, 2017
- Testing from
*R packages*by Hadley Wickham - Debugging in R By Duncan Murdoch, Department of Statistical & Actuarial Sciences, University of Western Ontario

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

- CRAN Task View: Chemometrics and Computational Physics
- CRAN Task View: Optimization and Mathematical Programming
- CRAN Task View: Differential Equations
- SciViews – Reproductible research with R
- R for Science: Use R!: Computing is an essential tool for scientists that want to extract the maximum of information out of their data. At the NIOZ department ecosystem studies in Yerseke, we use R as the problem solving environment for our visualisation, statistical analysis, our scientific computing and environmental modelling. Department of Ecosystem Studies is part of the Royal Netherlands Institute for Sea Research (NIOZ).
- Notes of a Dabbler: Wandering through the beautiful world of math, computations and visualizations
- Stack Overflow: Solving equations in R similar to the Excel solver parameters function
- Numerical Integration/Differentiation in R: FTIR Spectra, Posted: February 23rd, 2010, University of California, Davis California Soil Resource Lab
- Use R to Compute Numerical Integrals by Jie Yang, November 2, 2011, Stat401: Introduction to Probability, University of Illinois at Chicago — [Requires PDF Software]
- 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
- 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]
- 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.
- 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

- Set Theory by Aaron Schlegel
- 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]
- 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 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
- 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.?
- 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) [USGS–CIDA/WRTDS – GitHub]
- USGS Professional Pages: Robert Hirsch, Research Hydrologist
- Note to users of earlier versions of EGRET
- Sample Workflows
- dataRetrieval: R package for data retrieval of water quality and hydrology data. This package was designed to integrate with the EGRET packageR package for data retrieval of water quality and hydrology data. This package was designed to integrate with the EGRET package.
- 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]
- 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
- 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

- R4Hydrology Google+ Group
- CRAN Task View: Analysis of Ecological and Environmental Data
- Environmental Science and Data Analytics
- 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
- 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 — [Requires presentation software (.pptx file)]
- 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.
- curvenumber: Soil Conservation Service Curve Number method (R–package)
- 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–USGS) GitHub Repositories
- 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 [Engineering Economics, Environmental/Water Resources Engineering, US Presidential Elections].
- R–sig–ecology: R for use in ecological and environmental data analysis
- R–sig–ecology help at Nabble.com
- National Ecological Observatory Network (NEON) –– Boulder, Colorado: Articles by tag: R
- National Ecological Observatory Network (NEON) –– Boulder, Colorado: Data Tutorials & Workshop Groupings
- R for Ecologists, Department of Ecology, Montana State University
- R Labs for Vegetation Ecologists, Department of Ecology, Montana State University
- 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

- Babun: a windows shell you will love
- 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.
- 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.
- The OpenFLUID project aims at building and distributing a software platform dedicated to modelling of complex landscape systems, mainly focused on fluxes.
- Community Surface Dynamics Modeling System (CSDMS)
- Data Science Toolkit
- NASA Goddard Institute for Space Studies (GISS): Software Tools
- US EPA: Water Quality Analysis Simulation Program (WASP)
- MHYDAS (Modélisation Hydrologique Des AgroSystèmes) is an hydrological model for water exchanges, pollutants and erosion transport in cultivated landscapes
- 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
- 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 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.
- 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
- 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
- MariaDB: An enhanced, drop–in replacement for MySQL.
- MySQL Big Resource Forum
- 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.
- Cognitive OpenOCR (Cuneiform)
- 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.
- Free and Open Source Software for Architects, Designers, Engineers, Mathematicians, Scientists, and Statisticians (Irucka Embry)
- 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.
- 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.
- 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.
- Cesium is a JavaScript library for creating 3D globes and 2D maps in a web browser without a plugin. It uses WebGL for hardware–accelerated graphics, and is cross–platform, cross–browser, and tuned for dynamic–data visualization. Cesium is open source under the Apache 2.0 license. It is free for commercial and non–commercial use.
- USGS Global Visualization 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.
- 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.
- 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.
- QGIS is a user friendly Open Source Geographic Information System (GIS) licensed under the GNU General Public License.
- 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 is a Geographic Information System (GIS), that is, a desktop application designed for capturing, storing, handling, analyzing and deploying any kind of referenced geographic information in order to solve complex management and planning problems. gvSIG is known for having a user–friendly interface, being able to access the most common formats, both vector and raster ones. It features a wide range of tools for working with geographic–like information (query tools, layout creation, geoprocessing, networks, etc.), which turns gvSIG into the ideal tool for users working in the land realm.
- gvSIG Community Edition (CE) is a community driven GIS project fork of gvSIG that will be bundled with SEXTANTE and GRASS GIS. gvSIG CE is a fully functional Open Source Desktop GIS that provides powerful visualization (including thematic maps, advanced symbology and labelling), cartography, raster, vector and geoprocessing in a single, integrated software suite.
- 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.
- 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
- Open Source Software Tools for Soil Scientists: University of California Davis Soil Resource Laboratory
- 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.
- 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.
- 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”.