RStudio Guide

To get started, click on the 'workspace' folder in the bottom right pane of RStudio. Click on the file entitled "Step 1...". You may want to drag the console pane to be smaller so that you have more room to work. You'll complete each task for Step 1 in that R Markdown file. Remember, you must run the cells with the play button at the top right of each cell for a task before moving onto the next task in the R Markdown file. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Creating and Binding Values to Environments in R

To review the concepts covered in this step, please refer to the Presenting Environments and Binding Values in R module of the Customizing R Environments course.

Understanding how to create an environment and bind values to it in R is important because it allows you to organize your data and functions in a logical and efficient manner. This is a fundamental concept in R programming and is crucial for data analysis.

Let's put what we've learned into practice! In this step, we'll create a new environment in R and bind some values to it. This will help us understand how R stores and retrieves data in different environments. We'll use the env() function to create a new environment and the <- operator to bind values to it.

Task 1.1: Create a New Environment

Load the rlang package, which is already installed. Create a new environment in R and assign it to a variable named my_env.

library('rlang')
my_env <- env()

Task 1.2: Bind a Value to the Environment

Assign the value 42 to the variable x in the my_env environment.

my_env$x <- 42

Task 1.3: Retrieve the Value from the Environment

Retrieve the value of x from the my_env environment.

my_env$x

Identifying Types of Environments in R

To review the concepts covered in this step, please refer to the Identifying Types of Environments in R module of the Customizing R Environments course.

Knowing the different types of environments in R is important because it helps you understand how R organizes data and functions. This knowledge is crucial when you're working with packages and libraries in R.

Time to dive deeper into the world of R environments! In this step, we'll identify the different types of environments in R, including the base, global, and package environments. We'll use the find() function to find out what environment a function or value is in, and the search() function to list all the environments currently in use.

Task 2.1: Identifying the Environment of a Function

Find out what environment the mean function is in. Use the find() function, which is an updated function similar to the where() function from pryr.

find('mean')

Task 2.2: Identifying the Environment of a Value

Find out what environment the variable pi is in using the find() function.

find('pi')

Task 2.3: Listing All Environments

List all the environments currently in use using the search() function.

search()

Task 2.4: Identifying the Package Environment

Load the ggplot2 package and the devtools package. This will give you access to several new functions.
Identify which package the function aes comes from.

library('ggplot2')
library('devtools')
find('aes')

Working with Libraries and CRAN in R

To review the concepts covered in this step, please refer to the Listing Environments and Presenting Libraries from a CRAN in R module of the Customizing R Environments course.

Understanding how to use libraries and the Comprehensive R Archive Network (CRAN) in R is important because it enables you to access a vast array of pre-built functions and datasets that can greatly enhance your data analysis capabilities.

Let's explore the power of libraries and CRAN in R! In this step, we'll learn how to use the library() function to load a library, and the getCRANmirrors() function to list available CRANs. We'll also use the library() function to find documentation for a package. This will give us a good understanding of how to leverage external resources in R.

Task 3.1: Loading a Library in R

Load the ggplot2 library.

library('ggplot2')

Task 3.2: Listing Available CRANs in R

List all available CRAN mirrors.

getCRANmirrors()

Task 3.3: Finding Documentation for a Package in R

Use the help argument of the library() function to find documentation for the ggplot2 package.

library(help = 'ggplot2')