Programming Languages
22,586 items in 77 lists
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Syntax equivalents: base R vs Tidyverse tavareshugo.github.io
A detailed comparison of base R and tidyverse. Author: Hugo Tavares.
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Data Wrangling Part 4: Summarizing and slicing your data suzan.rbind.io
Author: Suzan Baert.
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Data Wrangling Part 3: Basic and more advanced ways to filter rows suzan.rbind.io
Author: Suzan Baert.
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Data Wrangling Part 2: Transforming your columns into the right shape suzan.rbind.io
Author: Suzan Baert.
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Data Wrangling Part 1: Basic to Advanced Ways to Select Columns suzan.rbind.io
Author: Suzan Baert.
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From base R to stringr stringr.tidyverse.org
This vignette compares stringr functions to their base R equivalents to help users transitioning from using base R to stringr. Author: Sara Stoudt.
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Graphics in R with ggplot2 statsandr.com
A detailed guide for the use of graphics within ggplot2. Author: Antoine Soetewey.
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Descriptive statistics in R statsandr.com
This article explains how to compute the main descriptive statistics in R and how to present them graphically. Author - Antoine Soetewey.
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Stats and R statsandr.com
Through his blog, Antoine Soetewey (PhD in statistics) aims at helping academics and professionals working with data to grasp important statistical concepts, and shows how to apply them in R.
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Statistics Globe statisticsglobe.com
Joachim Schork started this platform to share his statistical know-how and to improve his own statistical skills by discussing with other statisticians and programmers.
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The R Language stat.ethz.ch
An introduction to R written by the authors of the R language.
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stat.berkeley - Introduction to Functions stat.berkeley.edu
An introduction to functions in the R language by the organizers of Integrating Computing into the Statistics Curricula (U.C. Berkeley).
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Supervised Machine Learning for Text Analysis in R smltar.com
This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. Authors: Emil Hvitfeldt and Julia Silge.
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Learn Shiny shiny.rstudio.com
The video and written tutorials on this page are primarily designed for users who are new to Shiny and want a guided introduction. Author: RStudio.
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Shiny Articles shiny.rstudio.com
Various articles covering individual Shiny topics at a more advanced level. Author: RStudio.
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Ryo Nakagawara ryo-n7.github.io
Ryo Nakagawara is a Data Scientist and has been doing work as both a reporting analyst and a software developer in R and SQL to improve ACDI and VOCA data pipelines, create R packages, reproducible reports, dashboards, and Shiny apps to communicate how his projects worldwide…
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rweekly rweekly.org
Weekly Updates from the Entire R Community by Bruce Zhao, Colin Fay, Eric Nantz, Hao Zhu, Jon Calder, Jonathan Carroll, Maëlle Salmon, Ryo Nakagawara, and Wolfram Qin.
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Building Shiny Applications with R Tutorial (Deprecated) rstudio.github.io
Introductory tutorial to Shiny. Note, this tutorial is deprecated. Author: RStudio.
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An Introduction to Choropleth maps in R rstudio-pubs-static.s3.amazonaws.com
Author: Henry Cann.
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Introduction to R & Spatial Data with Raster and Terra rspatial.org
This document provides a concise introduction to R. It emphasizes what you need to know to be able to use the language in any context. Author: Professor Robert Hijmans.
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Joining Data in R with dplyr rpubs.com
Course notes from the Joining Data in R with dplyr course on DataCamp. Topics include mutating joins, filtering joins and set operations, assembling data, advanced joining. Author: William Surles.
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R for Data Science (R4DS) Online Learning Community rfordatasci.com
Founded by Jessie Mostipak (@kierisi) to create a supportive and responsive online space for learners and mentors to gather and work through the R for Data Science book by Garrett Grolemund and Hadley Wickham. Grown into a community of R learners at all skill levels working…
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Subreddit - r/programming - The R Project for Statistical Computing reddit.com
A Reddit subreddit focused on using R for statistical computing.
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Subreddit - r/Rlanguage - R Programming Language reddit.com
A Reddit subreddit focused on implementing the R programming language for statistics and data science.
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Data meets Narrative rebeccabarter.com
Rebecca Barter enjoys making sense of complex, messy and sometimes nonsensical datasets, such as electronic health records, and insurance claims. Her dual passions are explaining “seemingly complicated” concepts to others in plain English, and exploring and uncovering the…
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Descriptive Statistics rcompanion.org
A tutorial of descriptive statistics which are used to summarize data in a way that provides insight into the information contained in the data. Author: Salvatore S. Mangiafico.
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R Cookbook, 2nd Edition rc2e.com
This book is full of how-to recipes, each of which solves a specific problem. The recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. Authors: James (JD) Long and Paul Teetor.
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Introduction to Data Science - Data Analysis and Prediction Algorithms with R rafalab.github.io
This book started out as the class notes used in the HarvardX Data Science Series. It introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine…
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Top 50 ggplot2 Visualizations - The Master List (With Full R Code) r-statistics.co
This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2. Author: Selva Prabhakaran.
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R Packages r-pkgs.had.co.nz
In this book you will learn how to turn your code into packages that others can easily download and use. Author: Hadley Wickham.
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R CODER r-coder.com
The tutorials are grouped into categories (introduction, data structures, data wrangling, programming, import & export, graphics) that cover in-depth all the basic needs for someone starting learning the R programming language.
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Quantitative Politics with R qpolr.com
The aim of this book is to provide an easily accessible introduction to R for the collection, study and presentation of different types of political data. Authors: Erik Gahner Larsen and Zoltán Fazekas.
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Forecasting: Principles and Practice otexts.com
This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. Authors: Rob J Hyndman and George Athanasopoulos.
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Modern Statistics with R modernstatisticswithr.com
From wrangling and exploring data to inference and predictive modelling. The book includes plenty of examples and more than 200 exercises with worked solutions. Author: Måns Thulin.
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Statistical Inference via Data Science moderndive.com
This is intended to be a gentle introduction to the practice of analyzing data and answering questions using data the way data scientists, statisticians, data journalists, and other researchers would. Authors: Chester Ismay and Albert Y. Kim.
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Essential Math for Data Science medium.com
An article discussing the key mathematical topics to master to become a better data scientist. Author: Tirthajyoti Sarkar.
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Mastering Shiny mastering-shiny.org
This is the online version of Mastering Shiny, a book currently under early development and intended for a late 2020 release. This book complements the Shiny online documentation and is intended to help app authors develop a deeper understanding of Shiny. Author: Hadley…
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Musings on R martinctc.github.io
A blog on all things R and Data Science by Martin Chan. Topics covered include comparing dplyr and data.table, Shiny apps, ggplot, data cleaning, using RStudio, interviews with other R users/data scientists, and web scraping.
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Practical Data Science with R manning.com
The intent of this book is to present data science from a pragmatic, practice-oriented viewpoint. The book concentrates on the process of data science, from the planning stages of a project, through the data collection and exploration, to the modeling, and finally to deployment…
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Map Plots Created With R And Ggmap littlemissdata.com
Author: Laura Ellis.
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Time Based Heatmaps in R littlemissdata.com
Author: Laura Ellis.
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Learning Statistics with R learningstatisticswithr.com
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. Author: Danielle Navarro.
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Julia Silge juliasilge.com
Julia Silge is a data scientist and software engineer at RStudio where she work on open source modeling tools. She is passionate about making beautiful charts, the statistical programming language R, Jane Austen, black coffee, and red wine.
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HighlandR johnmackintosh.net
John Mackintosh's blog is a place for him to showcase demonstrations or workshops, notes he's learned at work, chart makeovers, and techniques and technology that he doesn't currently use in his role.
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Introduction to Data Analysis with R jmbuhr.de
This is a lecture series with videos, scripts and exercises introducing R and the tidyverse as well as statistical concepts.
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Gallery of Statistical Distributions itl.nist.gov
Author: NIST/SEMATECH.
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An Introduction to R intro2r.com
The aim of this book is to introduce you to using R, a powerful and flexible interactive environment for statistical computing and research. Authors: Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau
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The ultimate R data.table cheat sheet infoworld.com
Find code for dozens of data tasks in this searchable cheat sheet of R data.table and Tidyverse code. Author: Sharon Machlis.
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The Graphic Continuum informationisbeautifulawards.com
The Graphic Continuum shows the many different types of visualizations available to us when we encode and present data. Authors: Jonathan Schwabish, and Severino Ribecca.
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Happy Git and GitHub for the useR happygitwithr.com
Happy Git provides opinionated instructions on how to install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE, develop a few key workflows that cover your most common tasks, and integrate Git and GitHub into your daily work with R and R…
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