juanfranceschini / learning-r-resources Goto Github PK

Here are my favorite sites to learn R that usually include Data Science, Intro to r, and Stats but don't go too deep in either category.

Beginners 🌱 Intermediate 🌳 Advanced 🌳🌳

** Make sure to visit the wiki page for more resources!!

What is this? Excerpt from site: This free Getting Started with R course is designed to get new users, no matter what’s holding you back, up and running quickly. It takes you step-by-step, helping you download exactly what you need to get started.

In this course, you’ll learn what you need to get up and running with R, including getting R and RStudio set up, working with projects, installing and loading packages, importing and examining data, and more.

1. Free course here: https://rfortherestofus.com/courses/getting-started/

What is this? A beginner's guide to R from Basic R, the world of Tidiverse, to even plotting.

1. Link to OSF files here: https://osf.io/2bfjy/

What is this? The site's purpose is to introduce you to statistics with R but the first module gives you a really concise introduction of R.

1. Link to module: https://tinystats.github.io/teacups-giraffes-and-statistics/01_introToR.html

What is this? Excerpt from course: On this site, you will find materials for a full, 8-week, college-level course focused on learning to use R for Data Science and Statistical Analysis.

This course was created by Dr. Kelly Bodwin. The materials shared here are free and open source, and may be shared and adapted under the Creative Commons license.

1. Link to course here: https://www.adventures-in-r.com/

What is this? Excerpt from site: This post provides a discussion of best practices1 for developing code-based projects and for writing R code in a research setting with an eye toward proactively avoiding common pitfalls.

1. Post here: https://kdestasio.github.io/post/r_best_practices/

What is this?

Excerpt from site: This book is for anyone who wants to make their R code faster to type, faster to run and more scalable. These considerations generally come after learning the very basics of R for data analysis: we assume you are either accustomed to R or proficient at programming in other languages, although this book could still be of use to beginners.

1. Free Web-book here: https://csgillespie.github.io/efficientR/

What is this?

Excerpt from site: This book is meant to introduce you to the basic analytical tools in R, from basic coding and analyses, to data wrangling, plotting, and statistical inference.

1. Web-book is here: https://bookdown.org/ndphillips/YaRrr/

What is this? Excerpt from Site by Navarro: "I’ve set this up as an introductory resource for psychology students taking my Programming with R class at UNSW. There are two goals with this resource. The first is to provide an introduction to R programming that doesn’t assume any previous experience. The second is to provide a brief sketch of some of things that R can be useful for in psychology. Data analysis inevitably forms a large part of this, but I hope to cover a broader range of topics".

1. Resource here: https://psyr.djnavarro.net/

What is this? Excerpt from site: This is the website for “Hands-On Programming with R”. This book will teach you how to program in R, with hands-on examples. I wrote it for non-programmers to provide a friendly introduction to the R language. You’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools.

1. Free web-book here: https://rstudio-education.github.io/hopr/
2. You can buy book here: Link to Amazon

What is this? Excerpt from site: The initial impetus for creating these materials is a two-day hands-on workshop. The in-person workshops are still the primary delivery method for this content, but we’ve begun recording prose versions of this content, in order to make it more widely available and for participants to refer back to. Warning: these materials absolutely do not constitute a self-contained “book”, nor do they capture all workshop content.

1. Web-book here: https://rstats.wtf/index.html

What is this? Excerpt from site: This is a basic overview of using R for data analysis, and of linear statistical modelling. It would be suitable for anyone who has had some stats training but not used R; or has used R a little but not become confident; or has neither had stats training nor used R but is not happy with a reasonably steep learning curve. The emphasis is on rapidly getting to be able to use R for most of the common practical purposes, and knowing that you can take it further should you need to. It is designed for you to be able to work through on your own.

1. Link to pdf for intro: https://www.danielnettle.org.uk/wp-content/uploads/2019/07/funwithR3.0.pdf
2. Data files used for this intro: https://www.dropbox.com/sh/7s14m6ceph3laja/AAAIGT7jBZ3n6aIBR8-IT8R4a?dl=0

What is this?

Excerpt from site: This is the website for 2nd edition of “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it helps you to understand why R works the way it does.

1. Free web-book here: https://adv-r.hadley.nz/ or http://adv-r.had.co.nz/Introduction.html
2. Buy book here: Link to Amazon

What is this? Excerpt from site: Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. This site describes the style used throughout the tidyverse.

1. Guide here: https://style.tidyverse.org/

What is this? Excerpt from site: This lesson is written as an introduction to R, but its real purpose is to introduce the single most important idea in programming: how to solve problems by building functions, each of which can fit in a programmer’s working memory. In order to teach that, we must teach people a little about the mechanics of manipulating data with lists and file I/O so that their functions can do things they actually care about. Our teaching order tries to show practical uses of every idea as soon as it is introduced; instructors should resist the temptation to explain the “other 90%” of the language as well.

1. Course here: https://swcarpentry.github.io/r-novice-inflammation/

What is this? Excerpt from site: This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

1. Web-book here: https://bookdown.org/rdpeng/rprogdatascience/

What is this? Excerpt from site: Have you been meaning to start using R for analysis? Have you been wondering how R can make your analysis easier and your research reproducible? We present a practical introduction to R, demonstrating core analyses in psychology research (e.g., ANOVA, regression). We also explain how to manipulate and clean data in R to maintain a precise record of how the data went from raw to analysis-ready. The ideal audience for this workshop is a researcher who has not used R before, has used it briefly and then gone back to SPSS for the final analyses, or who has used R a few times but wants a better grounding in the basics.

1. Workshop materials here: https://osf.io/a2x7j/
2. Workshop requested examples here: https://osf.io/a2x7j/wiki/Requested%20Examples/

What is this? Description from site: In this three-hour workshop, we introduce you to data science in the R programming language. In particular, we'll show you how to use the "tidyverse" package to transform, visualize and model data. The workshop consists of short introductory presentations followed by elaborate hands-on sessions. At the end of each section, we'll showcase more complex features and functionality. The workshop is designed as an introduction to R. Zero programming experience is required. By the end of the workshop, you'll have a "feel" for what R is and what it can do for you, and we'll provide resources for follow-up self-study.

1. Course materials here: https://osf.io/69gub/

What is this? Excerpt from site: This repository contains editable class materials built by Garrett Grolemund for two separate one-day workshops: Welcome to the Tidyverse A gentle introduction to R and its Tidyverse that focuses on learning to do Exploratory Data Analysis with the ggplot2, dplyr, broom, modelr, and rmarkdown packages. The course focuses on doing data science, not writing code; but by the end of the day students will find that they have gained confidence writing code with R.

Data Wrangling with the Tidyverse An introduction to wrangling lists and tabular data in R with the tidyr, stringr, forcats, lubridate, and purrr packages. The course focuses on creating and using tidy tables and is designed to be a sequel to Welcome to the Tidyverse.

1. Link to repo here: https://github.com/rstudio-education/remaster-the-tidyverse

Whatis this? Excerpt from site: Coding togetheR is a series of collaborative workshops to teach foundational R coding and data science skills at the University of Southampton in 2019. This book contains the materials covered over eight, two-hour sessions.

1. Link to web-book here: https://ab604.github.io/docs/coding-together-2019/

What is this? Excerpt from site: In this tutorial, I'll walk through how to go from a database or tabular data file to an interactive plot with surprisingly little pain (and less code than you'd imagine). My focus will be on introducing a workflow that uses a wide variety of different tools and packages, including readr, dplyr, tidyr, and shiny. I'll assume basic familiarity with R and will use (but not spend too much time teaching) ggplot2. Featuring data from http://wordbank.stanford.edu

1. Link to the tutorial here: https://github.com/mcfrank/tidyverse-tutorial

What is this? Excerpt from ebook: This book is for anyone who wants to make their R code faster to type, faster to run and more scalable. These considerations generally come after learning the very basics of R for data analysis: we assume you are either accustomed to R or proficient at programming in other languages, although this book could still be of use to beginners

1. https://bookdown.org/csgillespie/efficientR/

What is this? Excerpt from site: Wonderful materials to learn and teach R!! Link to site here: https://drive.google.com/drive/folders/0BzCQiZLG7vQmVm9ISndsRzllbTA

What is this? Excerpt from site: Swirl teaches you R programming and data science interactively, at your own pace, and right in the R console!

1. Link to site here: https://swirlstats.com/
2. Link to courses repo here: https://github.com/swirldev/swirl_courses#swirl-courses
3. Link to the Swirl Course Network: https://swirlstats.com/students.html

What is this? Excerpt from the course: The R-Bootcamp is a gentle and gradual introduction to manipulating and visualizing data in R using the tidyverse suite of packages. It is meant for incoming OHSU Bioinformatics and Computational Biomedicine Majors but is appropriate for any beginners wanting to learn elementary data manipulation in R.

1. Link to course: https://r-bootcamp.netlify.app/

What is this? Excerpt from the ebook: This book collects code recipes for doing data science with R’s tidyverse. Each recipe solves a single common task, with a minimum of discussion.

1. Link to the ebook here: https://rstudio-education.github.io/tidyverse-cookbook/

What is this? Wonderful youtube channel by David Robinson.

1. Link to site here: https://www.youtube.com/user/safe4democracy/featured

What is this? Excerpt from site here: This course is designed to give you a sense of all the possibilities from programming in R. It’ll emphasize packages that will help you do data analysis and visualization. Once you see how fun and easy it can be, you’ll be more open to tackling the more challenging task of robust statistical analysis in R, with all its quirks.

1. Link to the course here: https://learn.r-journalism.com/en/

What is this? Excerpt from site here: Here is a list of classes by Justin Post in the Department of Statistics at North Carolina State University.

1. Link for the INTRODUCTION TO R FOR DATA SCIENCE here: https://www4.stat.ncsu.edu/~post/DataScienceR/
2. Link for the INTERMEDIATE PROGRAMMING IN R here: https://www4.stat.ncsu.edu/~post/ProgrammingR/
3. Link for the ADVANCED R: AUTOMATING WORKFLOW & SHARING WORK here: https://www4.stat.ncsu.edu/~post/IntermediateR/ 🌳🌳

What is this? Excerpt from site here: This document introduces you to dplyr’s basic set of tools, and shows you how to apply them to data frames. dplyr also supports databases via the dbplyr package, once you’ve installed, read vignette("dbplyr") to learn more.

1. Link to site: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html

What is this? Excerpt from site here: This vignette introduces the data.table syntax, its general form, how to subset rows, select and compute on columns, and perform aggregations by group. Familiarity with data.frame data structure from base R is useful, but not essential to follow this vignette.

1. Link to site here: https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro.html
2. Link to reference semantics here: https://cran.r-project.org/web/packages/data.table/vignettes/datatable-reference-semantics.html

What is this? Excerpt from e-book here: 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.

1. Link to ebook here: https://otexts.com/fpp2/

What is this? Excerpt from e-book here:R makes it easy to work with and learn from data.

It also happens to be a programmming language, but if you’re reading this, that might not be of interest. That’s OK — the goal here is not to teach programming1. The goal is to teach you just enough R to be confident to explore your data.

This book uses R like any other statistics software: To work-with and visualise data, run statistical analyses, and share our results with others. To do that you don’t need more than the absolute basics of the R language itself.

1. Link to ebook here: https://benwhalley.github.io/just-enough-r/