You’re reading the first edition of R4DS; for the latest on this topic see the Introduction chapter in the second edition.

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science. After reading this book, you’ll have the tools to tackle a wide variety of data science challenges, using the best parts of R.

What you will learn

Data science is a huge field, and there’s no way you can master it by reading a single book. The goal of this book is to give you a solid foundation in the most important tools. Our model of the tools needed in a typical data science project looks something like this:

library(). Once you have installed a package, you can load it with the library() function:

library(tidyverse) #> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── #> ✔ dplyr 1.1.2 ✔ readr 2.1.4 #> ✔ forcats 1.0.0 ✔ stringr 1.5.0 #> ✔ ggplot2 3.4.2 ✔ tibble 3.2.1 #> ✔ lubridate 1.9.2 ✔ tidyr 1.3.0 #> ✔ purrr 1.0.1 #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag() masks stats::lag() #> ℹ Use the conflicted package (<//conflicted.r-lib.org/>) to force all conflicts to become errors

This tells you that tidyverse is loading the ggplot2, tibble, tidyr, readr, purrr, and dplyr packages. These are considered to be the core of the tidyverse because you’ll use them in almost every analysis.

Packages in the tidyverse change fairly frequently. You can see if updates are available, and optionally install them, by running tidyverse_update().

Other packages

There are many other excellent packages that are not part of the tidyverse, because they solve problems in a different domain, or are designed with a different set of underlying principles. This doesn’t make them better or worse, just different. In other words, the complement to the tidyverse is not the messyverse, but many other universes of interrelated packages. As you tackle more data science projects with R, you’ll learn new packages and new ways of thinking about data.

In this book we’ll use three data packages from outside the tidyverse:

These packages provide data on airline flights, world development, and baseball that we’ll use to illustrate key data science ideas.

Running R code

The previous section showed you a couple of examples of running R code. Code in the book looks like this:

If you run the same code in your local console, it will look like this: [1] 3 ` There are two main differences. In your console, you type after the `>`, called the prompt; we don’t show the prompt in the book. In the book, output is commented out with `#>`; in your console it appears directly after your code. These two differences mean that if you’re working with an electronic version of the book, you can easily copy code out of the book and into the console. Throughout the book we use a consistent set of conventions to refer to code:

• Functions are in a code font and followed by parentheses, like [sum()](//rdrr.io/r/base/sum.html), or [mean()](//rdrr.io/r/base/mean.html).
• Other R objects (like data or function arguments) are in a code font, without parentheses, like `

  1 + 2 [1] 3 0 or 1 + 2 [1] 3 1.

• If we want to make it clear what package an object comes from, we’ll use the package name followed by two colons, like 1 + 2 [1] 3 2, or 1 + 2 [1] 3 ` 3\. This is also valid R code.

## Getting help and learning more This book is not an island; there is no single resource that will allow you to master R. As you start to apply the techniques described in this book to your own data you will soon find questions that we do not answer. This section describes a few tips on how to get help, and to help you keep learning. If you get stuck, start with Google. Typically adding “R” to a query is enough to restrict it to relevant results: if the search isn’t useful, it often means that there aren’t any R-specific results available. Google is particularly useful for error messages. If you get an error message and you have no idea what it means, try googling it! Chances are that someone else has been confused by it in the past, and there will be help somewhere on the web. (If the error message isn’t in English, run

1 + 2

[1] 3

`

4 and re-run the code; you’re more likely to find help for English error messages.)

If Google doesn’t help, try stackoverflow. Start by spending a little time searching for an existing answer, including

```

1 + 2

[1] 3

`

5 to restrict your search to questions and answers that use R. If you don’t find anything useful, prepare a minimal reproducible example or reprex. A good reprex makes it easier for other people to help you, and often you’ll figure out the problem yourself in the course of making it.

There are three things you need to include to make your example reproducible: required packages, data, and code.

1. Packages should be loaded at the top of the script, so it’s easy to see which ones the example needs. This is a good time to check that you’re using the latest version of each package; it’s possible you’ve discovered a bug that’s been fixed since you installed the package. For packages in the tidyverse, the easiest way to check is to run tidyverse_update().
2. The easiest way to include data in a question is to use `

   1 + 2

   [1] 3

   7 to generate the R code to recreate it. For example, to recreate the

   1 + 2 [1] 3

   8 dataset in R, I’d perform the following steps:

   1. Run

      1 + 2 [1] 3

      ` 9 in R
   2. Copy the output
   3. In my reproducible script, type sessioninfo::session_info(c("tidyverse")) #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.3.1 (2023-06-16) #> os Ubuntu 22.04.2 LTS #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate C.UTF-8 #> ctype C.UTF-8 #> tz UTC #> date 2023-07-24 #> pandoc 2.19.2 @ /usr/bin/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> ! package version date (UTC) lib source #> askpass 1.1 2019-01-13 [1] RSPM #> backports 1.4.1 2021-12-13 [1] RSPM #> base64enc 0.1-3 2015-07-28 [1] RSPM #> bit 4.0.5 2022-11-15 [1] RSPM #> bit64 4.0.5 2020-08-30 [1] RSPM #> blob 1.2.4 2023-03-17 [1] RSPM #> broom 1.0.5 2023-06-09 [1] RSPM #> bslib 0.5.0 2023-06-09 [1] RSPM #> cachem 1.0.8 2023-05-01 [1] RSPM #> callr 3.7.3 2022-11-02 [1] RSPM #> cellranger 1.1.0 2016-07-27 [1] RSPM #> cli 3.6.1 2023-03-23 [1] RSPM #> clipr 0.8.0 2022-02-22 [1] RSPM #> colorspace 2.1-0 2023-01-23 [1] RSPM #> conflicted 1.2.0 2023-02-01 [1] RSPM #> R cpp11 <NA> <NA> [?] <NA> #> crayon 1.5.2 2022-09-29 [1] RSPM #> curl 5.0.1 2023-06-07 [1] RSPM #> data.table 1.14.8 2023-02-17 [1] RSPM #> DBI 1.1.3 2022-06-18 [1] RSPM #> dbplyr 2.3.3 2023-07-07 [1] RSPM #> digest 0.6.33 2023-07-07 [1] RSPM #> dplyr 1.1.2 2023-04-20 [1] RSPM #> dtplyr 1.3.1 2023-03-22 [1] RSPM #> ellipsis 0.3.2 2021-04-29 [1] RSPM #> evaluate 0.21 2023-05-05 [1] RSPM #> fansi 1.0.4 2023-01-22 [1] RSPM #> farver 2.1.1 2022-07-06 [1] RSPM #> fastmap 1.1.1 2023-02-24 [1] RSPM #> fontawesome 0.5.1 2023-04-18 [1] RSPM #> forcats 1.0.0 2023-01-29 [1] RSPM #> fs 1.6.3 2023-07-20 [1] RSPM #> gargle 1.5.2 2023-07-20 [1] RSPM #> generics 0.1.3 2022-07-05 [1] RSPM #> ggplot2 3.4.2 2023-04-03 [1] RSPM #> glue 1.6.2 2022-02-24 [1] RSPM #> googledrive 2.1.1 2023-06-11 [1] RSPM #> googlesheets4 1.1.1 2023-06-11 [1] RSPM #> gtable 0.3.3 2023-03-21 [1] RSPM #> haven 2.5.3 2023-06-30 [1] RSPM #> highr 0.10 2022-12-22 [1] RSPM #> hms 1.1.3 2023-03-21 [1] RSPM #> htmltools 0.5.5 2023-03-23 [1] RSPM #> httr 1.4.6 2023-05-08 [1] RSPM #> ids 1.0.1 2017-05-31 [1] RSPM #> isoband 0.2.7 2022-12-20 [1] RSPM #> jquerylib 0.1.4 2021-04-26 [1] RSPM #> jsonlite 1.8.7 2023-06-29 [1] RSPM #> knitr 1.43 2023-05-25 [1] RSPM #> labeling 0.4.2 2020-10-20 [1] RSPM #> lattice 0.21-8 2023-04-05 [3] CRAN (R 4.3.1) #> lifecycle 1.0.3 2022-10-07 [1] RSPM #> lubridate 1.9.2 2023-02-10 [1] RSPM #> magrittr 2.0.3 2022-03-30 [1] RSPM #> MASS 7.3-60 2023-05-04 [3] CRAN (R 4.3.1) #> Matrix 1.5-4.1 2023-05-18 [3] CRAN (R 4.3.1) #> memoise 2.0.1 2021-11-26 [1] RSPM #> mgcv 1.8-42 2023-03-02 [3] CRAN (R 4.3.1) #> mime 0.12 2021-09-28 [1] RSPM #> modelr 0.1.11 2023-03-22 [1] RSPM #> munsell 0.5.0 2018-06-12 [1] RSPM #> nlme 3.1-162 2023-01-31 [3] CRAN (R 4.3.1) #> openssl 2.1.0 2023-07-15 [1] RSPM #> pillar 1.9.0 2023-03-22 [1] RSPM #> pkgconfig 2.0.3 2019-09-22 [1] RSPM #> processx 3.8.2 2023-06-30 [1] RSPM #> R progress <NA> <NA> [?] <NA> #> ps 1.7.5 2023-04-18 [1] RSPM #> purrr 1.0.1 2023-01-10 [1] RSPM #> R6 2.5.1 2021-08-19 [1] RSPM #> ragg 1.2.5 2023-01-12 [1] RSPM #> rappdirs 0.3.3 2021-01-31 [1] RSPM #> RColorBrewer 1.1-3 2022-04-03 [1] RSPM #> readr 2.1.4 2023-02-10 [1] RSPM #> readxl 1.4.3 2023-07-06 [1] RSPM #> rematch 1.0.1 2016-04-21 [1] RSPM #> rematch2 2.1.2 2020-05-01 [1] RSPM #> reprex 2.0.2 2022-08-17 [1] RSPM #> rlang 1.1.1 2023-04-28 [1] RSPM #> rmarkdown 2.23 2023-07-01 [1] RSPM #> rstudioapi 0.15.0 2023-07-07 [1] RSPM #> rvest 1.0.3 2022-08-19 [1] RSPM #> sass 0.4.7 2023-07-15 [1] RSPM #> scales 1.2.1 2022-08-20 [1] RSPM #> selectr 0.4-2 2019-11-20 [1] RSPM #> stringi 1.7.12 2023-01-11 [1] RSPM #> stringr 1.5.0 2022-12-02 [1] RSPM #> sys 3.4.2 2023-05-23 [1] RSPM #> systemfonts 1.0.4 2022-02-11 [1] RSPM #> textshaping 0.3.6 2021-10-13 [1] RSPM #> tibble 3.2.1 2023-03-20 [1] RSPM #> tidyr 1.3.0 2023-01-24 [1] RSPM #> tidyselect 1.2.0 2022-10-10 [1] RSPM #> tidyverse * 2.0.0 2023-02-22 [1] RSPM #> timechange 0.2.0 2023-01-11 [1] RSPM #> tinytex 0.45 2023-04-18 [1] RSPM #> tzdb 0.4.0 2023-05-12 [1] RSPM #> utf8 1.2.3 2023-01-31 [1] RSPM #> uuid 1.1-0 2022-04-19 [1] RSPM #> vctrs 0.6.3 2023-06-14 [1] RSPM #> viridisLite 0.4.2 2023-05-02 [1] RSPM #> vroom 1.6.3 2023-04-28 [1] RSPM #> withr 2.5.0 2022-03-03 [1] RSPM #> xfun 0.39 2023-04-20 [1] RSPM #> xml2 1.3.5 2023-07-06 [1] RSPM #> yaml 2.3.7 2023-01-23 [1] RSPM #> #> [1] /home/runner/work/_temp/Library #> [2] /opt/R/4.3.1/lib/R/site-library #> [3] /opt/R/4.3.1/lib/R/library #> #> R ── Package was removed from disk. #> #> ────────────────────────────────────────────────────────────────────────────── 0 then paste. Try and find the smallest subset of your data that still reveals the problem.

3. Spend a little bit of time ensuring that your code is easy for others to read: Make sure you’ve used spaces and your variable names are concise, yet informative. Use comments to indicate where your problem lies.
   • Do your best to remove everything that is not related to the problem. The shorter your code is, the easier it is to understand, and the easier it is to fix.

Finish by checking that you have actually made a reproducible example by starting a fresh R session and copying and pasting your script in.

You should also spend some time preparing yourself to solve problems before they occur. Investing a little time in learning R each day will pay off handsomely in the long run. One way is to follow what Hadley, Garrett, and everyone else at RStudio are doing on the RStudio blog. This is where we post announcements about new packages, new IDE features, and in-person courses. You might also want to follow Hadley (@hadleywickham) or Garrett (@statgarrett) on Twitter, or follow @rstudiotips to keep up with new features in the IDE.

To keep up with the R community more broadly, we recommend reading //www.r-bloggers.com: it aggregates over 500 blogs about R from around the world. If you’re an active Twitter user, follow the (

sessioninfo::session_info(c("tidyverse")) #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.3.1 (2023-06-16) #> os Ubuntu 22.04.2 LTS #> system x86_64, linux-gnu #> ui X11 #> language (EN) #> collate C.UTF-8 #> ctype C.UTF-8 #> tz UTC #> date 2023-07-24 #> pandoc 2.19.2 @ /usr/bin/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> ! package version date (UTC) lib source #> askpass 1.1 2019-01-13 [1] RSPM #> backports 1.4.1 2021-12-13 [1] RSPM #> base64enc 0.1-3 2015-07-28 [1] RSPM #> bit 4.0.5 2022-11-15 [1] RSPM #> bit64 4.0.5 2020-08-30 [1] RSPM #> blob 1.2.4 2023-03-17 [1] RSPM #> broom 1.0.5 2023-06-09 [1] RSPM #> bslib 0.5.0 2023-06-09 [1] RSPM #> cachem 1.0.8 2023-05-01 [1] RSPM #> callr 3.7.3 2022-11-02 [1] RSPM #> cellranger 1.1.0 2016-07-27 [1] RSPM #> cli 3.6.1 2023-03-23 [1] RSPM #> clipr 0.8.0 2022-02-22 [1] RSPM #> colorspace 2.1-0 2023-01-23 [1] RSPM #> conflicted 1.2.0 2023-02-01 [1] RSPM #> R cpp11 <NA> <NA> [?] <NA> #> crayon 1.5.2 2022-09-29 [1] RSPM #> curl 5.0.1 2023-06-07 [1] RSPM #> data.table 1.14.8 2023-02-17 [1] RSPM #> DBI 1.1.3 2022-06-18 [1] RSPM #> dbplyr 2.3.3 2023-07-07 [1] RSPM #> digest 0.6.33 2023-07-07 [1] RSPM #> dplyr 1.1.2 2023-04-20 [1] RSPM #> dtplyr 1.3.1 2023-03-22 [1] RSPM #> ellipsis 0.3.2 2021-04-29 [1] RSPM #> evaluate 0.21 2023-05-05 [1] RSPM #> fansi 1.0.4 2023-01-22 [1] RSPM #> farver 2.1.1 2022-07-06 [1] RSPM #> fastmap 1.1.1 2023-02-24 [1] RSPM #> fontawesome 0.5.1 2023-04-18 [1] RSPM #> forcats 1.0.0 2023-01-29 [1] RSPM #> fs 1.6.3 2023-07-20 [1] RSPM #> gargle 1.5.2 2023-07-20 [1] RSPM #> generics 0.1.3 2022-07-05 [1] RSPM #> ggplot2 3.4.2 2023-04-03 [1] RSPM #> glue 1.6.2 2022-02-24 [1] RSPM #> googledrive 2.1.1 2023-06-11 [1] RSPM #> googlesheets4 1.1.1 2023-06-11 [1] RSPM #> gtable 0.3.3 2023-03-21 [1] RSPM #> haven 2.5.3 2023-06-30 [1] RSPM #> highr 0.10 2022-12-22 [1] RSPM #> hms 1.1.3 2023-03-21 [1] RSPM #> htmltools 0.5.5 2023-03-23 [1] RSPM #> httr 1.4.6 2023-05-08 [1] RSPM #> ids 1.0.1 2017-05-31 [1] RSPM #> isoband 0.2.7 2022-12-20 [1] RSPM #> jquerylib 0.1.4 2021-04-26 [1] RSPM #> jsonlite 1.8.7 2023-06-29 [1] RSPM #> knitr 1.43 2023-05-25 [1] RSPM #> labeling 0.4.2 2020-10-20 [1] RSPM #> lattice 0.21-8 2023-04-05 [3] CRAN (R 4.3.1) #> lifecycle 1.0.3 2022-10-07 [1] RSPM #> lubridate 1.9.2 2023-02-10 [1] RSPM #> magrittr 2.0.3 2022-03-30 [1] RSPM #> MASS 7.3-60 2023-05-04 [3] CRAN (R 4.3.1) #> Matrix 1.5-4.1 2023-05-18 [3] CRAN (R 4.3.1) #> memoise 2.0.1 2021-11-26 [1] RSPM #> mgcv 1.8-42 2023-03-02 [3] CRAN (R 4.3.1) #> mime 0.12 2021-09-28 [1] RSPM #> modelr 0.1.11 2023-03-22 [1] RSPM #> munsell 0.5.0 2018-06-12 [1] RSPM #> nlme 3.1-162 2023-01-31 [3] CRAN (R 4.3.1) #> openssl 2.1.0 2023-07-15 [1] RSPM #> pillar 1.9.0 2023-03-22 [1] RSPM #> pkgconfig 2.0.3 2019-09-22 [1] RSPM #> processx 3.8.2 2023-06-30 [1] RSPM #> R progress <NA> <NA> [?] <NA> #> ps 1.7.5 2023-04-18 [1] RSPM #> purrr 1.0.1 2023-01-10 [1] RSPM #> R6 2.5.1 2021-08-19 [1] RSPM #> ragg 1.2.5 2023-01-12 [1] RSPM #> rappdirs 0.3.3 2021-01-31 [1] RSPM #> RColorBrewer 1.1-3 2022-04-03 [1] RSPM #> readr 2.1.4 2023-02-10 [1] RSPM #> readxl 1.4.3 2023-07-06 [1] RSPM #> rematch 1.0.1 2016-04-21 [1] RSPM #> rematch2 2.1.2 2020-05-01 [1] RSPM #> reprex 2.0.2 2022-08-17 [1] RSPM #> rlang 1.1.1 2023-04-28 [1] RSPM #> rmarkdown 2.23 2023-07-01 [1] RSPM #> rstudioapi 0.15.0 2023-07-07 [1] RSPM #> rvest 1.0.3 2022-08-19 [1] RSPM #> sass 0.4.7 2023-07-15 [1] RSPM #> scales 1.2.1 2022-08-20 [1] RSPM #> selectr 0.4-2 2019-11-20 [1] RSPM #> stringi 1.7.12 2023-01-11 [1] RSPM #> stringr 1.5.0 2022-12-02 [1] RSPM #> sys 3.4.2 2023-05-23 [1] RSPM #> systemfonts 1.0.4 2022-02-11 [1] RSPM #> textshaping 0.3.6 2021-10-13 [1] RSPM #> tibble 3.2.1 2023-03-20 [1] RSPM #> tidyr 1.3.0 2023-01-24 [1] RSPM #> tidyselect 1.2.0 2022-10-10 [1] RSPM #> tidyverse * 2.0.0 2023-02-22 [1] RSPM #> timechange 0.2.0 2023-01-11 [1] RSPM #> tinytex 0.45 2023-04-18 [1] RSPM #> tzdb 0.4.0 2023-05-12 [1] RSPM #> utf8 1.2.3 2023-01-31 [1] RSPM #> uuid 1.1-0 2022-04-19 [1] RSPM #> vctrs 0.6.3 2023-06-14 [1] RSPM #> viridisLite 0.4.2 2023-05-02 [1] RSPM #> vroom 1.6.3 2023-04-28 [1] RSPM #> withr 2.5.0 2022-03-03 [1] RSPM #> xfun 0.39 2023-04-20 [1] RSPM #> xml2 1.3.5 2023-07-06 [1] RSPM #> yaml 2.3.7 2023-01-23 [1] RSPM #> #> [1] /home/runner/work/_temp/Library #> [2] /opt/R/4.3.1/lib/R/site-library #> [3] /opt/R/4.3.1/lib/R/library #> #> R ── Package was removed from disk. #> #> ──────────────────────────────────────────────────────────────────────────────

1. hashtag. Twitter is one of the key tools that Hadley uses to keep up with new developments in the community.

Acknowledgements

This book isn’t just the product of Hadley and Garrett, but is the result of many conversations (in person and online) that we’ve had with the many people in the R community. There are a few people we’d like to thank in particular, because they have spent many hours answering our dumb questions and helping us to better think about data science:

• Jenny Bryan and Lionel Henry for many helpful discussions around working with lists and list-columns.
• The three chapters on workflow were adapted (with permission), from //stat545.com/block002_hello-r-workspace-wd-project.html by Jenny Bryan.
• Genevera Allen for discussions about models, modelling, the statistical learning perspective, and the difference between hypothesis generation and hypothesis confirmation.
• Yihui Xie for his work on the bookdown package, and for tirelessly responding to my feature requests.
• Bill Behrman for his thoughtful reading of the entire book, and for trying it out with his data science class at Stanford.
• The

  rstats twitter community who reviewed all of the draft chapters and provided tons of useful feedback.

• Tal Galili for augmenting his dendextend package to support a section on clustering that did not make it into the final draft.

This book was written in the open, and many people contributed pull requests to fix minor problems. Special thanks goes to everyone who contributed via GitHub:

Thanks go to all contributers in alphabetical order: A. s, Abhinav Singh, Ahmed ElGabbas, Ajay Deonarine, @AlanFeder, Albert Y. Kim, @Alex, Andrea Gilardi, Andrew Landgraf, Angela Li, Azza Ahmed, Ben Herbertson, Ben Marwick, Ben Steinberg, Benjamin Yeh, Bianca Peterson, Bill Behrman, @BirgerNi, Brandon Greenwell, Brent Brewington, Brett Klamer, Brian G. Barkley, Charlotte Wickham, Christian G. Warden, Christian Heinrich, Christian Mongeau, Colin Gillespie, Cooper Morris, Curtis Alexander, @DSGeoff, Daniel Gromer, David Clark, David Rubinger, Derwin McGeary, Devin Pastoor, Dirk Eddelbuettel, Dylan Cashman, Earl Brown, Edwin Thoen, Eric Watt, Erik Erhardt, Etienne B. Racine, Everett Robinson, Flemming Villalona, Floris Vanderhaeghe, Garrick Aden-Buie, George Wang, Gregory Jefferis, Gustav W Delius, Hao Chen, Hengni Cai, Hiroaki Yutani, Hojjat Salmasian, Ian Lyttle, Ian Sealy, Ivan Krukov, Jacek Kolacz, Jacob Kaplan, Jakub Nowosad, Jazz Weisman, Jeff Boichuk, Jeffrey Arnold, Jen Ren, Jennifer (Jenny) Bryan, Jeroen Janssens, Jim Hester, Joanne Jang, Johannes Gruber, John Blischak, John D. Storey, John Sears, Jon Calder, @Jonas, Jonathan Page, Jose Roberto Ayala Solares, Josh Goldberg, Julia Stewart Lowndes, Julian During, Justinas Petuchovas, Kara Woo, Kara de la Marck, Katrin Leinweber, Kenny Darrell, Kirill Müller, Kirill Sevastyanenko, Kunal Marwaha, @KyleHumphrey, Lawrence Wu, Luke Smith, Luke W Johnston, @MJMarshall, Mara Averick, Maria Paula Caldas, Mark Beveridge, Matt Herman, @MattWittbrodt, Matthew Hendrickson, Matthew Sedaghatfar, Mauro Lepore, Michael Henry, Mine Cetinkaya-Rundel, Mustafa Ascha, Nelson Areal, Nicholas Tierney, Nick Clark, Nina Munkholt Jakobsen, Nirmal Patel, Nischal Shrestha, Noah Landesberg, @OaCantona, Pablo E, Patrick Kennedy, @Paul, Peter Hurford, Rademeyer Vermaak, Radu Grosu, Ranae Dietzel, Riva Quiroga, Rob Tenorio, Robert Schuessler, Robin Gertenbach, Rohan Alexander, @RomeroBarata, S’busiso Mkhondwane, @Saghir, Sam Firke, Seamus McKinsey, Sebastian Kraus, Shannon Ellis, @Sophiazj, Steve Mortimer, Stéphane Guillou, TJ Mahr, Tal Galili, Terence Teo, Thomas Klebel, Tim Waterhouse, Tom Prior, Ulrik Lyngs, Will Beasley, Yihui Xie, Yiming (Paul) Li, Yu Yu Aung, Zach Bogart, Zhuoer Dong, @a-rosenberg, adi pradhan, @andrewmacfarland, bahadir cankardes, @batpigandme, @behrman, @boardtc, @djbirke, @harrismcgehee, @jennybc, @jjchern, @jonathanflint, @juandering, @kaetschap, @kdpsingh, @koalabearski, @lindbrook, @nate-d-olson, @nattalides, @nickelas, @nwaff, @pete, @rlzijdeman, @robertchu03, @robinlovelace, @robinsones, @seamus-mckinsey, @seanpwilliams, @shoili, @sibusiso16, @spirgel, @svenski, @twgardner2, @yahwes, @zeal626, @蒋雨蒙.

Colophon

An online version of this book is available at //r4ds.had.co.nz. It will continue to evolve in between reprints of the physical book. The source of the book is available at //github.com/hadley/r4ds. The book is powered by //bookdown.org which makes it easy to turn R markdown files into HTML, PDF, and EPUB.

What is R used for?

R is widely used in data science by statisticians and data miners for data analysis and the development of statistical software. R is one of the most comprehensive statistical programming languages available, capable of handling everything from data manipulation and visualization to statistical analysis.

What is the full form of R in R language?

The "R" name is derived from the first letter of the names of its two developers, Ross Ihaka and Robert Gentleman, who were associated with the University of Auckland at the time.

What is R Foundation for statistical computing?

The R Foundation is a not for profit organization working in the public interest. It was founded by the members of the R Core Team in order to provide support for the R project and other innovations in statistical computing.

What is computing with R?

R is a programming language and a software environment for statistical computing and graphics. Microsoft R Open is a version of R that was created by the Microsoft Corporation. Both R and Microsoft R Open are free and open-source tools for data science and analytics.