1975-1976: S (Book: A Brief History of S) grew up in the statistics research departments (John Chambers and others) at Bell Laboratories
1993: Prof. Ross Ihaka and Robert Gentleman from University of Auckland posted first binary file of R to teach introductory statistics
1995: Martin Mächler made an important contribution by convincing Ross and Robert to use the GNU General Public License to make R free software
1997: The Comprehensive R Archive Network (CRAN) was founded by Kurt Hornik and Friedrich Leisch to host R’s source code, executable files, documentation, and user-created packages
2000: the first official 1.0 version of R was released
2024: R ver. 4.2.1
X = 1:5 # A vector of numbers from 1 to 5
X[c(TRUE, TRUE, TRUE, FALSE, FALSE)][1] 1 2 3X[1:3][1] 1 2 3X[-1:3]Error in X[-1:3]: only 0's may be mixed with negative subscriptsX[-(1:3)][1] 4 5X[NULL]integer(0)X[NA][1] NA NA NA NA NAX[][1] 1 2 3 4 5One way of using R is to simply start R console on your computer (PC).
As a quick example, try using the console to calculate a 15% tip on a meal that cost $19.71:
0.15 * 19.71You can download and install R base via r-project.org (currently R-4.4.1)
Then, after the installation of R, you can download RStudio via posit.co (currently)
After installation of R and RStudio, you can open up the RStudio to start your R programming.
however, your R only has the base package
To enhance its utility, most users will install R packages for certain purposes
R packages are uploaded to some platforms (i.e., CRAN or Github) by researchers or companies
Those R packages typically have their version numbers. Some functions may be available for some version (like Ver. 1.1) but not be available in other versions.
Do not upgrade your packages if you code is running well
R users are free to download and use those R packages
To download certain package, you should know package name
For example, if you want to download the latest version of tidyverse package, you can type in following command in the console panel of Rstudio
install.packages("tidyverse")require(devtools)
install_version("tidyverse", version = "1.3.0", repos = "http://cran.us.r-project.org")It contains the up-to-date version of R which may potentially be unstable
You can download the package from Github using pak package
pak::pak("tidyverse/ggplot2")You can update the package and its dependencies
pak::pkg_install("ggplot2", upgrade = TRUE)To operate certain tasks, you need to use functions contained in R packages
There are two ways of using R functions
Direct way: you don’t have to load your package first
Use-after-load way: Package is loaded in your session before you can call the function name without specifying the package name
library("ggplot2")
ggplot() +
geom_point(aes(x = 1:100, y = 100:1), color = "tomato")
How do you know you already load the package or not
You can use sessionInfo function
sessionInfo()It outputs multiple info:
R version, Operations System, Matrix operation package, Locale
Attached packages (you can call the functions of those package)
Loaded package via a namespace (and not attached), which you cannot call functions and need to library or require them
After you finish R script, you have multiple ways of executing the code and output the results on Console:
Method 1: you can click Run button in the top right-head of Rstudio
Method 2: you can select certain code and press Ctrl + ↵ (Win) or Command + Return (Mac)
Method 3: you can Rscript [FILENAME].r to run the whole script
Method 4: you can using R notebook to interactively run R code
| Script file is .R | Script file is .rmd or .qmd | |
|---|---|---|
| Run the whole script |
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| Run the partial script |
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RStudio will be our launching pad for data science projects. It not only provides an editor for us to create and edit our scripts but also provides many other useful tools.
When you start RStudio for the first time, you will see three panes:
The left pane shows the Code editor (will show when you create a new file) and R console.
On the right, the top pane includes tabs such as Environment and History, while the bottom pane shows five tabs: File, Plots, Packages, Help, and Viewer .
To start a new script in Code editor, you can click on File > New File > R Script.
For the efficient coding, we highly recommend that you memorize key bindings for the operations you use most.
RStudio provides a useful cheat sheet with the most widely used commands
To open the cheat sheet, Help > Cheat Sheets > Rstudio IDE Cheat Sheets
You can change the look and functionality of RStudio quite a bit.
To change the global options you click on Tools then Global Options….
As an example we show how to make a change that we highly recommend:
General > Basic > Workspace: Change the Save workspace to .RData on exit to Never .
General > Basic > Workspace: Uncheck the Restore .RData into workspace at startup to Never
Code > Editing: check use the native piper operator, |>
By default, when you exit R saves all the objects you have created into a file called .RData.
This is done so that when you restart the session in the same folder, it will load these objects.
We find that this causes confusion especially when we share code with colleagues and assume they have this .RData file.
For example, to install the dslabs package, you would type the following in your console:
install.packages("dslabs") # DON'T FORGET DOUBLE QUOTEWe can then load the package into our R sessions using the library function in your Rscript file:
library("dslabs")
head(admissions) major gender admitted applicants
1 A men 62 825
2 B men 63 560
3 C men 37 325
4 D men 33 417
5 E men 28 191
6 F men 6 373As you go through this class, you will see that we load packages without installing them. This is because once you install a package, it remains installed and only needs to be loaded with library.
We can install more than one package at once by feeding a character vector to this function:
install.packages(c("dplyr", "dslabs"))You can see all the packages you have installed using the following function:
installed.packages()You can also install user-built package from GitHub
I built a package for this course: link
install.packages("remotes") # install one package called "remotes"
library("remotes") # load the package into your R session
install_github(repo = "JihongZ/ESRM6990V") # install one GitHub package from my GitHub repository
library(ESRM6990V) # load the package into your R session
jihong(details = TRUE) # call one function called "jihong" from the packageFunctions: A set of R functions that perform specific tasks, which are not available in the default R environment.
Data: Some packages include datasets that are useful for demonstrating functions within the package or for use in specific types of analysis.
Documentation: Every package comes with documentation that explains how the functions work, the data included (if any), and examples of how to use the package. This is often accessible via R help pages.
Vignettes: Many packages include vignettes, which are long-form documentation that shows how to use the package functions in a more detailed and contextual way, often in the form of tutorials.
Namespace: A namespace file that manages how functions from the package are imported and exported, helping avoid naming conflicts between different packages.
Meta-information: A DESCRIPTION file containing metadata about the package, such as its name, version, dependencies (other packages it requires to function), author, and license information.
install.packages() can move a package from source/bundled/binary into installed state.library can load a package from installed state into memory (in-memory state)R/ folder containing all .r filesnetworkscore: https://github.com/JihongZ/networkscoreesrm64503: https://github.com/JihongZ/ESRM64503tar.gz file on packages’ CRAN landing page via the “Package source” field (this is the bundled state of the package). Decompressing the tar.gz file will have the source directory including R/ and DESCRIPTION
tar xvf forcats_0.4.0.tar.gzA bundled package is a package that’s been compressed into a single file (this process is called build the package). Bundled state is a compressed form of package with only single file.
By convention, package bundles in R use the extension .tar.gz and are sometimes referred to as “source tarballs”. In computer science, it is called gzipped tar file format.
A “source tarballs” file is not simply compressed file of source directory. When build source directory into bundled (.tar.gz), a few diagnostic checks and cleanups are performed. See more details here.
If you want to distribute your package to an R user who doesn’t have package development tools, you’ll need to provide a binary package. The main distributor of binary package is CRAN.
Like a package bundle, a binary package is a single file. Unlike a bundled package, a binary package is platform specific and there are two basic flavors: Windows and macOS.
CRAN packages are usually available in binary form:
This is, indeed, part of what’s usually going on behind the scenes when you call install.packages().
Uncompressing binary file will give you totally difference file structure than source/bundled package.
An installed package is a binary package that’s been decompressed into a package library
In practice, you don’t need to care about stats if you install popular package, unless you have issues installing R package via install.packages() or you install in-development packages .
library() function, we load installed package into the memory of R.library(somepackage), R looks through the current libraries for an installed package named “somepackage” and, if successful, it makes somepackage available for use.