R (programming language) explained

R latex
Logo Size:121px
Screenshot Caption:R terminal
Designer:Ross Ihaka and Robert Gentleman
Developer:R Core Team
Typing:Dynamic
Influenced:Julia[1] pandas[2]
Platform:arm64 and x86-64
License:GPL-2.0-or-later
Wikibooks:R Programming
Paradigms:Multi-paradigm

procedural, object-oriented, functional, reflective, imperative, array[3]

Influenced By:

R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis.[5]

The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data.

R software is open-source and free software. It is licensed by the GNU Project and available under the GNU General Public License.[6] It is written primarily in C, Fortran, and R itself. Precompiled executables are provided for various operating systems.

As an interpreted language, R has a native command line interface. Moreover, multiple third-party graphical user interfaces are available, such as RStudio—an integrated development environment—and Jupyter—a notebook interface.

History

R was started by professors Ross Ihaka and Robert Gentleman as a programming language to teach introductory statistics at the University of Auckland.[7] The language was inspired by the S programming language, with most S programs able to run unaltered in R.[4] The language was also inspired by Scheme's lexical scoping, allowing for local variables.[3]

The name of the language, R, comes from being both an S language successor as well as the shared first letter of the authors, Ross and Robert.[8] In August 1993, Ihaka and Gentleman posted a binary of R on StatLib — a data archive website.[9] At the same time, they announced the posting on the s-news mailing list.[10] On December 5, 1997, R became a GNU project when version 0.60 was released.[11] On February 29, 2000, the first official 1.0 version was released.[12]

Packages

See main article: article and R package. R packages are collections of functions, documentation, and data that expand R.[13] For example, packages add report features such as RMarkdown, Quarto,[14] knitr and Sweave. Packages also add the capability to implement various statistical techniques such as linear, generalized linear and nonlinear modeling, classical statistical tests, spatial analysis, time-series analysis, and clustering. Easy package installation and use have contributed to the language's adoption in data science.[15]

Base packages are immediately available when starting R and provide the necessary syntax and commands for programming, computing, graphics production, basic arithmetic, and statistical functionality.[16]

The Comprehensive R Archive Network (CRAN) was founded in 1997 by Kurt Hornik and Friedrich Leisch to host R's source code, executable files, documentation, and user-created packages. Its name and scope mimic the Comprehensive TeX Archive Network and the Comprehensive Perl Archive Network.[17] CRAN originally had three mirrors and 12 contributed packages.[18], it has 99 mirrors[19] and 21,513 contributed packages.[20] Packages are also available on repositories R-Forge, Omegahat, and GitHub.[21] [22] [23]

The Task Views on the CRAN web site list packages in fields such as causal inference, finance, genetics, high-performance computing, machine learning, medical imaging, meta-analysis, social sciences, and spatial statistics.

The Bioconductor project provides packages for genomic data analysis, complementary DNA, microarray, and high-throughput sequencing methods.

The tidyverse package bundles several subsidiary packages that provide a common interface for tasks related to accessing and processing "tidy data",[24] data contained in a two-dimensional table with a single row for each observation and a single column for each variable.[25]

Installing a package occurs only once. For example, to install the tidyverse package:> install.packages("tidyverse")

To load the functions, data, and documentation of a package, one executes the library function. To load tidyverse:> # Package name can be enclosed in quotes> library("tidyverse")

> # But also the package name can be called without quotes> library(tidyverse)

Interfaces

R comes installed with a command line console. Available for installation are various integrated development environments (IDE). IDEs for R include R.app[26] (OSX/macOS only), Rattle GUI, R Commander, RKWard, RStudio, and Tinn-R.[27]

General purpose IDEs that support R include Eclipse via the StatET plugin and Visual Studio via R Tools for Visual Studio.

Editors that support R include Emacs, Vim via the Nvim-R plugin, Kate, LyX via Sweave, WinEdt (website), and Jupyter (website).

Scripting languages that support R include Python (website), Perl (website), Ruby (source code), F# (website), and Julia (source code).

General purpose programming languages that support R include Java via the Rserve socket server, and .NET C# (website).

Statistical frameworks which use R in the background include Jamovi and JASP.

Community

The R Core Team was founded in 1997 to maintain the R source code. The R Foundation for Statistical Computing was founded in April 2003 to provide financial support. The R Consortium is a Linux Foundation project to develop R infrastructure.

The R Journal is an open access, academic journal which features short to medium-length articles on the use and development of R. It includes articles on packages, programming tips, CRAN news, and foundation news.

The R community hosts many conferences and in-person meetups - see the community maintained GitHub list. These groups include:

Implementations

The main R implementation is written primarily in C, Fortran, and R itself. Other implementations include:

Microsoft R Open (MRO) was an R implementation. As of 30 June 2021, Microsoft started to phase out MRO in favor of the CRAN distribution.[30]

Commercial support

Although R is an open-source project, some companies provide commercial support:

Examples

Hello, World!

"Hello, World!" program

> print("Hello, World!")[1] "Hello, World!"

Basic syntax

The following examples illustrate the basic syntax of the language and use of the command-line interface. (An expanded list of standard language features can be found in the R manual, "An Introduction to R".[31])

In R, the generally preferred assignment operator is an arrow made from two characters <-, although = can be used in some cases.[32]

> x <- 1:6 # Create a numeric vector in the current environment> y <- x^2 # Create vector based on the values in x.> print(y) # Print the vector’s contents.[1] 1 4 9 16 25 36

> z <- x + y # Create a new vector that is the sum of x and y> z # Return the contents of z to the current environment.[1] 2 6 12 20 30 42

> z_matrix <- matrix(z, nrow = 3) # Create a new matrix that turns the vector z into a 3x2 matrix object> z_matrix [,1] [,2][1,] 2 20[2,] 6 30[3,] 12 42

> 2 * t(z_matrix) - 2 # Transpose the matrix, multiply every element by 2, subtract 2 from each element in the matrix, and return the results to the terminal. [,1] [,2] [,3][1,] 2 10 22[2,] 38 58 82

> new_df <- data.frame(t(z_matrix), row.names = c("A", "B")) # Create a new data.frame object that contains the data from a transposed z_matrix, with row names 'A' and 'B'> names(new_df) <- c("X", "Y", "Z") # Set the column names of new_df as X, Y, and Z.> print(new_df) # Print the current results. X Y ZA 2 6 12B 20 30 42

> new_df$Z # Output the Z column[1] 12 42

> new_df$Z

new_df['Z'] && new_df[3]

new_df$Z # The data.frame column Z can be accessed using $Z, ['Z'], or [3] syntax and the values are the same. [1] TRUE

> attributes(new_df) # Print attributes information about the new_df object$names[1] "X" "Y" "Z"

$row.names[1] "A" "B"

$class[1] "data.frame"

> attributes(new_df)$row.names <- c("one", "two") # Access and then change the row.names attribute; can also be done using rownames> new_df X Y Zone 2 6 12two 20 30 42

Structure of a function

One of R's strengths is the ease of creating new functions.[33] Objects in the function body remain local to the function, and any data type may be returned. In R, almost all functions and all user-defined functions are closures.[34]

Create a function:

  1. The input parameters are x and y.
  2. The function returns a linear combination of x and y.

f <- function(x, y)

Usage output:> f(1, 2)[1] 11

> f(c(1, 2, 3), c(5, 3, 4))[1] 23 18 25

> f(1:3, 4)[1] 19 22 25

It is possible to define functions to be used as infix operators with the special syntax `%name%` where "name" is the function variable name:> `%sumx2y2%` <- function(e1, e2) > 1:3 %sumx2y2% -(1:3)[1] 2 8 18

Since version 4.1.0 functions can be written in a short notation, which is useful for passing anonymous functions to higher-order functions:[35] > sapply(1:5, \(i) i^2) # here \(i) is the same as function(i) [1] 1 4 9 16 25

Native pipe operator

In R version 4.1.0, a native pipe operator, |>, was introduced.[36] This operator allows users to chain functions together one after another, instead of a nested function call.

> nrow(subset(mtcars, cyl

4)) # Nested without the pipe character[1] 11

> mtcars |> subset(cyl

4) |> nrow # Using the pipe character[1] 11

Another alternative to nested functions, in contrast to using the pipe character, is using intermediate objects:

> mtcars_subset_rows <- subset(mtcars, cyl

4)> num_mtcars_subset <- nrow(mtcars_subset_rows)> print(num_mtcars_subset)[1] 11While the pipe operator can produce code that is easier to read, it has been advised to pipe together at most 10 to 15 lines and chunk code into sub-tasks which are saved into objects with meaningful names.[37] Here is an example with fewer than 10 lines that some readers may still struggle to grasp without intermediate named steps:(\(x, n = 42, key = c(letters, LETTERS, " ", ":", ")")) strsplit(x, "")1 |> (Vectorize(\(chr) which(chr

key) - 1)) |> (`+`)(n) |> (`%%`)(length(key)) |> (\(i) key[i + 1]) |> paste(collapse = ""))("duvFkvFksnvEyLkHAErnqnoyr")

Object-oriented programming

The R language has native support for object-oriented programming. There are two native frameworks, the so-called S3 and S4 systems. The former, being more informal, supports single dispatch on the first argument and objects are assigned to a class by just setting a "class" attribute in each object. The latter is a Common Lisp Object System (CLOS)-like system of formal classes (also derived from S) and generic methods that supports multiple dispatch and multiple inheritance[38]

In the example, summary is a generic function that dispatches to different methods depending on whether its argument is a numeric vector or a "factor":> data <- c("a", "b", "c", "a", NA)> summary(data) Length Class Mode 5 character character > summary(as.factor(data)) a b c NA's 2 1 1 1

Modeling and plotting

The R language has built-in support for data modeling and graphics. The following example shows how R can generate and plot a linear model with residuals.

  1. Create x and y values

x <- 1:6y <- x^2

  1. Linear regression model y = A + B * x

model <- lm(y ~ x)

  1. Display an in-depth summary of the model

summary(model)

  1. Create a 2 by 2 layout for figures

par(mfrow = c(2, 2))

  1. Output diagnostic plots of the model

plot(model)

Output:Residuals: 1 2 3 4 5 6 7 8 9 10 3.3333 -0.6667 -2.6667 -2.6667 -0.6667 3.3333

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -9.3333 2.8441 -3.282 0.030453 * x 7.0000 0.7303 9.585 0.000662 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.055 on 4 degrees of freedomMultiple R-squared: 0.9583, Adjusted R-squared: 0.9478F-statistic: 91.88 on 1 and 4 DF, p-value: 0.000662

Mandelbrot set

This Mandelbrot set example highlights the use of complex numbers. It models the first 20 iterations of the equation z = z<sup>2</sup> + c, where c represents different complex constants.

Install the package that provides the write.gif function beforehand:install.packages("caTools")

R Source code:library(caTools)

jet.colors <- colorRampPalette(c("green", "pink", "#007FFF", "cyan", "#7FFF7F", "white", "#FF7F00", "red", "#7F0000"))

dx <- 1500 # define widthdy <- 1400 # define height

C <- complex(real = rep(seq(-2.2, 1.0, length.out = dx), each = dy), imag = rep(seq(-1.2, 1.2, length.out = dy), times = dx))

  1. reshape as matrix of complex numbers

C <- matrix(C, dy, dx)

  1. initialize output 3D array

X <- array(0, c(dy, dx, 20))

Z <- 0

  1. loop with 20 iterations

for (k in 1:20)

write.gif(X, "Mandelbrot.gif", col = jet.colors, delay = 100)

Version names

All R version releases from 2.14.0 onward have codenames that make reference to Peanuts comics and films.[39] [40]

In 2018, core R developer Peter Dalgaard presented a history of R releases since 1997.[41] Some notable early releases before the named releases include:

The idea of naming R version releases was inspired by the Debian and Ubuntu version naming system. Dalgaard also noted that another reason for the use of Peanuts references for R codenames is because, "everyone in statistics is a P-nut".

R release codenames!Version!Release date!Name!Peanuts reference!Reference
4.4.22024-10-31Pile of Leaves[42] [43]
4.4.12024-06-14Race for Your Life[44] [45]
4.4.02024-04-24Puppy Cup[46] [47]
4.3.32024-02-29Angel Food Cake[48] [49]
4.3.22023-10-31Eye Holes[50] [51]
4.3.12023-06-16Beagle Scouts[52] [53]
4.3.02023-04-21Already Tomorrow[54] [55] [56] [57]
4.2.32023-03-15Shortstop Beagle[58] [59]
4.2.22022-10-31Innocent and Trusting[60] [61]
4.2.12022-06-23Funny-Looking Kid[62] [63] [64] [65] [66] [67] [68]
4.2.02022-04-22Vigorous Calisthenics[69] [70]
4.1.32022-03-10One Push-Up[71]
4.1.22021-11-01Bird Hippie[72] [73]
4.1.12021-08-10Kick Things[74] [75]
4.1.02021-05-18Camp Pontanezen[76] [77]
4.0.52021-03-31Shake and Throw[78] [79]
4.0.42021-02-15Lost Library Book[80] [81] [82] [83]
4.0.32020-10-10Bunny-Wunnies Freak Out[84] [85]
4.0.22020-06-22Taking Off Again[86] [87]
4.0.12020-06-06See Things Now[88] [89]
4.0.02020-04-24Arbor Day[90] [91]
3.6.32020-02-29Holding the Windsock[92] [93]
3.6.22019-12-12Dark and Stormy NightSee It was a dark and stormy night#Literature[94] [95]
3.6.12019-07-05Action of the Toes[96] [97]
3.6.02019-04-26Planting of a Tree[98] [99]
3.5.32019-03-11Great Truth[100] [101]
3.5.22018-12-20Eggshell Igloos[102] [103]
3.5.12018-07-02Feather Spray[104] [105]
3.5.02018-04-23Joy in Playing[106] [107]
3.4.42018-03-15Someone to Lean On[108] [109]
3.4.32017-11-30Kite-Eating TreeSee Kite-Eating Tree[110] [111]
3.4.22017-09-28Short SummerSee It Was a Short Summer, Charlie Brown[112]
3.4.12017-06-30Single Candle[113] [114]
3.4.02017-04-21You Stupid Darkness[115]
3.3.32017-03-06Another Canoe[116] [117]
3.3.22016-10-31Sincere Pumpkin Patch[118] [119]
3.3.12016-06-21Bug in Your Hair[120] [121]
3.3.02016-05-03Supposedly Educational[122] [123]
3.2.52016-04-11Very, Very Secure Dishes[124] [125] [126] [127]
3.2.42016-03-11Very Secure Dishes[128]
3.2.32015-12-10Wooden Christmas-TreeSee A Charlie Brown Christmas[129] [130]
3.2.22015-08-14Fire Safety[131] [132] [133]
3.2.12015-06-18World-Famous Astronaut[134] [135]
3.2.02015-04-16Full of Ingredients[136] [137]
3.1.32015-03-09Smooth Sidewalk[138] [139]
3.1.22014-10-31Pumpkin HelmetSee You're a Good Sport, Charlie Brown[140]
3.1.12014-07-10Sock it to Me[141] [142] [143] [144] [145]
3.1.02014-04-10Spring Dance[146]
3.0.32014-03-06Warm Puppy[147] [148]
3.0.22013-09-25Frisbee Sailing[149] [150]
3.0.12013-05-16Good Sport[151] [152]
3.0.02013-04-03Masked Marvel[153] [154]
2.15.32013-03-01Security Blanket[155] [156]
2.15.22012-10-26Trick or Treat[157] [158]
2.15.12012-06-22Roasted Marshmallows[159] [160]
2.15.02012-03-30Easter Beagle[161] [162]
2.14.22012-02-29Gift-Getting SeasonSee It's the Easter Beagle, Charlie Brown[163] [164]
2.14.12011-12-22December Snowflakes[165] [166]
2.14.02011-10-31Great PumpkinSee It's the Great Pumpkin, Charlie Brown[167] [168]
r-develN/AUnsuffered Consequences[169]

See also

Further reading

External links

Notes and References

  1. Web site: Introduction . The Julia Manual . 2018-08-05 . https://web.archive.org/web/20180620172516/https://docs.julialang.org/en/stable/manual/introduction/#man-introduction-1 . 20 June 2018 . dead . dmy-all .
  2. Web site: Comparison with R. 2024-07-15. pandas Getting started.
  3. Morandat. Frances. Hill. Brandon. Osvald. Leo. Vitek. Jan. 11 June 2012. Evaluating the design of the R language: objects and functions for data analysis. European Conference on Object-Oriented Programming. 2012. 104–131. 10.1007/978-3-642-31057-7_6. 2016-05-17. SpringerLink.
  4. Web site: Hornik . Kurt . The R Core Team . 2022-04-12 . R FAQ . live . https://web.archive.org/web/20221228045640/https://cran.r-project.org/doc/FAQ/R-FAQ.html . 2022-12-28 . 2022-12-27 . . 3.3 What are the differences between R and S?.
  5. Giorgi . Federico M. . Ceraolo . Carmine . Mercatelli . Daniele . 2022-04-27 . The R Language: An Engine for Bioinformatics and Data Science . Life . en . 12 . 5 . 648 . 10.3390/life12050648 . 35629316 . 9148156 . 2022Life...12..648G . free .
  6. Web site: R - Free Software Directory . 2024-01-26 . directory.fsf.org.
  7. Web site: Ihaka . Ross . The R Project: A Brief History and Thoughts About the Future . 12 . live . We set a goal of developing enough of a language to teach introductory statistics courses at Auckland. . https://web.archive.org/web/20221228043824/https://www.stat.auckland.ac.nz/~ihaka/downloads/Otago.pdf . 2022-12-28 . 2022-12-27.
  8. Web site: Hornik . Kurt . The R Core Team . 2022-04-12 . R FAQ . live . https://web.archive.org/web/20221228045640/https://cran.r-project.org/doc/FAQ/R-FAQ.html . 2022-12-28 . 2022-12-28 . . 2.13 What is the R Foundation?.
  9. Web site: Index of /datasets . 2024-09-05 . lib.stat.cmu.edu.
  10. Web site: Ihaka . Ross . R: Past and Future History . live . 4 . https://web.archive.org/web/20221228071311/https://www.stat.auckland.ac.nz/~ihaka/downloads/Interface98.pdf . 2022-12-28 . 2022-12-28.
  11. Web site: Ihaka . Ross . 1997-12-05 . New R Version for Unix . live . https://web.archive.org/web/20230212133422/https://stat.ethz.ch/pipermail/r-announce/1997/000014.html . 2023-02-12 . 2023-02-12 . stat.ethz.ch.
  12. Web site: Ihaka . Ross . The R Project: A Brief History and Thoughts About the Future . 18 . live . https://web.archive.org/web/20221228043824/https://www.stat.auckland.ac.nz/~ihaka/downloads/Otago.pdf . 2022-12-28 . 2022-12-27.
  13. Book: Wickham . Hadley . Cetinkaya-Rundel . Mine . Grolemund . Garrett . R for Data Science, Second Edition . . 2023 . xvii . 978-1-492-09740-2.
  14. Web site: Quarto . 2024-09-05 . Quarto . en.
  15. Chambers . John M. . 2020 . S, R, and Data Science . The R Journal . en . 12 . 1 . 462–476 . 10.32614/RJ-2020-028 . 2073-4859 . free . The R language and related software play a major role in computing for data science. ... R packages provide tools for a wide range of purposes and users..
  16. Book: The Book of R: A First Course in Programming and Statistics. Tilman M.. Davies. 9781593276515. 2016. No Starch Press. San Francisco, California. Installing R and Contributed Packages. 739.
  17. Hornik . Kurt . 2012 . The Comprehensive R Archive Network . WIREs Computational Statistics . en . 4 . 4 . 394–398 . 10.1002/wics.1212 . 1939-5108 . 62231320.
  18. .
  19. Web site: The Status of CRAN Mirrors . 2024-10-16 . cran.r-project.org.
  20. Web site: CRAN - Contributed Packages . 2024-10-16 . cran.r-project.org.
  21. Web site: R-Forge: Welcome . 2024-09-05 . r-forge.r-project.org.
  22. Web site: The Omega Project for Statistical Computing . 2024-09-05 . www.omegahat.net.
  23. Web site: Build software better, together . 2024-09-05 . GitHub . en.
  24. [Hadley Wickham|Wickham, Hadley]
  25. Book: Wickham . Hadley . R for Data Science, Second Edition . Cetinkaya-Rundel . Mine . Grolemund . Garrett . . 2023 . 978-1-492-09740-2.
  26. Web site: R for macOS . 2024-09-05 . cran.r-project.org.
  27. Web site: IDE/Editor para Linguagem R Tinn-R - Home . 2024-09-05 . Tinn-R . pt-br.
  28. Book: Talbot . Justin . DeVito . Zachary . Hanrahan . Pat . Proceedings of the 21st international conference on Parallel architectures and compilation techniques . Riposte: A trace-driven compiler and parallel VM for vector code in R . 1 January 2012 . 43–52 . 10.1145/2370816.2370825 . ACM . 9781450311823 . 1989369.
  29. Jackson, Joab (16 May 2013). TIBCO offers free R to the enterprise. PC World. Retrieved 20 July 2015.
  30. Web site: Looking to the future for R in Azure SQL and SQL Server . 30 June 2021 . November 7, 2021 .
  31. Web site: An Introduction to R. Notes on R: A Programming Environment for Data Analysis and Graphics. 2021-01-03.
  32. Web site: R Development Core Team. Assignments with the = Operator. 2018-09-11.
  33. Web site: Quick-R: User-Defined Functions. Robert. Kabacoff. 2012. 2018-09-28. statmethods.net.
  34. Web site: Advanced R - Functional programming - Closures. adv-r.had.co.nz. Hadley. Wickham.
  35. Web site: NEWS. r-project.org.
  36. Web site: R: R News . 2024-03-14 . cran.r-project.org.
  37. Book: Wickham, Hadley . R for data science: import, tidy, transform, visualize, and model data . Çetinkaya-Rundel . Mine . Grolemund . Garrett . 2023 . O'Reilly . 978-1-4920-9740-2 . 2nd . Beijing ; Sebastopol, CA . 4 Workflow: code style . on1390607935 . https://r4ds.hadley.nz/workflow-style.html.
  38. Web site: Class Methods. 2024-04-25.
  39. Book: Monkman, Martin . Chapter 5 R Release Names Data Science with R: A Resource Compendium.
  40. Web site: McGowan . Lucy D’Agostino . 2017-09-28 . R release names . 2024-04-07 . livefreeordichotomize.com . en.
  41. Web site: Dalgaard . Peter . 2018-07-15 . What's in a name? 20 years of R release management . 2024-04-09 . . video.
  42. Web site: Schulz . Charles . 1957-11-15 . Peanuts by Charles Schulz for November 15, 1957 GoComics.com . 2025-01-06 . GoComics . en.
  43. Web site: [Rd] R 4.4.2 is released ]. 2024-12-26 . stat.ethz.ch.
  44. Web site: 1977-08-03 . Race for Your Life, Charlie Brown . 2024-06-18 . IMDB . en.
  45. Web site: R 4.4.1 is released . 2024-06-18 . stat.ethz.ch.
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  47. Web site: R 4.4.0 is released . 2024-04-24 . stat.ethz.ch.
  48. Web site: Schulz . Charles . 1980-06-29 . Peanuts by Charles Schulz for June 29, 1980 GoComics.com . 2024-04-09 . GoComics . en.
  49. Web site: R 4.3.3 is released . 2024-04-07 . hypatia.math.ethz.ch.
  50. Web site: Schulz . Charles . 1996-10-31 . Peanuts by Charles Schulz for October 31, 1996 GoComics.com . 2024-04-09 . GoComics . en.
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  53. Web site: [Rd] R 4.3.1 is released ]. 2024-04-07 . stat.ethz.ch.
  54. Web site: Schulz . Charles . 1980-06-13 . Peanuts by Charles Schulz for June 13, 1980 GoComics.com . 2024-04-09 . GoComics . en.
  55. Web site: Schulz . Charles . 1980-06-16 . Peanuts by Charles Schulz for June 16, 1980 GoComics.com . 2024-04-09 . GoComics . en.
  56. Web site: Schulz . Charles . 1964-11-26 . Peanuts by Charles Schulz for November 26, 1964 GoComics.com . 2024-04-09 . GoComics . en.
  57. Web site: [Rd] R 4.3.0 is released ]. 2024-04-07 . stat.ethz.ch.
  58. Web site: Schulz . Charles . 2001-03-30 . Peanuts by Charles Schulz for March 30, 2001 GoComics.com . 2024-04-09 . GoComics . en.
  59. Web site: [Rd] R 4.2.3 is released ]. 2024-04-07 . stat.ethz.ch.
  60. Web site: Schulz . Charles . 1962-10-30 . Peanuts by Charles Schulz for October 30, 1962 GoComics.com . 2024-04-09 . GoComics . en.
  61. Web site: [Rd] R 4.2.2 is released ]. 2024-04-07 . stat.ethz.ch.
  62. Web site: Schulz . Charles . 1970-11-22 . Peanuts by Charles Schulz for November 22, 1970 GoComics.com . 2024-04-09 . GoComics . en.
  63. Web site: Schulz . Charles . 1971-07-29 . Peanuts by Charles Schulz for July 29, 1971 GoComics.com . 2024-04-09 . GoComics . en.
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