Surviving in the R Environment

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R is not just a programming language; it also functions as a comprehensive programming shell featuring a read-eval-print loop (REPL). While many users primarily interact with R through this shell, understanding the underlying mechanics can enhance the overall experience. In this tutorial, you will learn about:

  • Managing variables in R
  • Handling packages in R

Let’s get started!

Overview

This tutorial is structured into three main sections:

  1. The REPL in R
  2. Getting Help in R
  3. Package Management in R

The REPL in R

The REPL (Read-Eval-Print Loop) is accessed by executing the R command. For instance, if you create a script named inverse.R containing the following code:

A <- matrix(c(9, 5, 4, 2, -1, 0, 1, 6, -2), ncol=3)
A.inv <- solve(A)
print(A)

Running this script with the command Rscript inverse.R will output the matrix:

     [,1] [,2] [,3]
[1,]    9    2    1
[2,]    5   -1    6
[3,]    4    0   -2

You can launch the R shell by simply typing R in your terminal, which presents you with a prompt (>). From there, you can directly enter R commands. If your command is incomplete, R will display a continuation prompt (+), waiting for more input.

In R, you can create variables like so:

a <- c(1, 2, 3)
b <- c(-2, -4, -6)

To check your defined variables in an environment, use the ls() function:

> ls()
[1] "a" "b"

You can overwrite a variable by assigning it a new value, and to reclaim memory occupied by unwanted variables, you can remove them using:

rm(a, b)

Getting Help in R

R’s syntax can sometimes be challenging, but help is readily available. For instance, Stack Overflow has a dedicated section for R-related questions:

Stack Overflow R Tag

Within the R REPL, you can access help for any function using:

help(ls)
?ls

Both of these will display detailed information on the specified command. If you forget the exact name, you can search for functions related to a keyword:

help.search("regression")
??regression

This returns a list of relevant functions along with brief descriptions. If you need specific examples of function usage, you can run:

example(ls)

This will illustrate the function in action and provide practical examples.

Package Management in R

R is an extensible language with a plethora of packages available for various functionalities. Notably, many packages cater to advanced statistical analysis, reflecting the strong focus on mathematics and statistics within the R community.

R packages are listed in the Comprehensive R Archive Network (CRAN), which features around 20,000 packages for public use. You can browse these packages via:

CRAN Available Packages

To install a package, use:

install.packages("package_name")  # Replace package_name with the actual name

To uninstall a package, you can run:

remove.packages("package_name")

In RStudio, you can effortlessly view all installed packages. In the REPL, you can check using:

installed.packages()

To update your installed packages, simply enter:

update.packages()

R will prompt you to confirm the updates.

While having a package installed is one step, you must also load it to use its functionalities:

library(Matrix)

To explore the functions provided by a specific package, you can use:

help(package = "Matrix")

Conclusion

In this tutorial, you learned how to navigate the R environment effectively. Specifically, you covered:

  • How to manage variables within R.
  • How to seek help on R functions.
  • How to install, remove, and update packages in R.

Further Reading

For additional resources on R programming, consider the following:

Websites:

Books:

  • The Book of R: A First Course in Programming and Statistics

Feel free to ask if you would like more adjustments or additional details!

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