Load an R Package
There are basically two extremely important functions when it comes down to R packages:
install.packages(), which as you can expect, installs a given package.library()which loads packages, i.e. attaches them to the search list on your R workspace.
To install packages, you need administrator privileges. This means that install.packages() will thus not work in the DataCamp interface. However, almost all CRAN packages are installed on our servers. You can load them with library().
In this exercise, you'll be learning how to load the ggplot2 package, a powerful package for data visualization. You'll use it to create a plot of two variables of the mtcars data frame. The data has already been prepared for you in the workspace.
Before starting, execute the following commands in the console:
search(), to look at the currently attached packages andqplot(mtcars$wt, mtcars$hp), to build a plot of two variables of themtcarsdata frame.
An error should occur, because you haven't loaded the ggplot2 package yet!
This is a part of the course
“Intermediate R”
Exercise instructions
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load the ggplot2 package
# Retry the qplot() function
# Check out the currently attached packages againThis exercise is part of the course
Intermediate R
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Chapter 1: Conditionals and Control Flow
In this chapter, you'll learn about relational operators for comparing R objects, and logical operators like "and" and "or" for combining TRUE and FALSE values. Then, you'll use this knowledge to build conditional statements.
Exercise 1: Relational OperatorsExercise 2: EqualityExercise 3: Greater and less thanExercise 4: Compare vectorsExercise 5: Compare matricesExercise 6: Logical OperatorsExercise 7: & and |Exercise 8: & and | (2)Exercise 9: Reverse the result: !Exercise 10: Blend it all togetherExercise 11: Conditional StatementsExercise 12: The if statementExercise 13: Add an elseExercise 14: Customize further: else ifExercise 15: Else if 2.0Exercise 16: Take control!Chapter 2: Loops
Loops can come in handy on numerous occasions. While loops are like repeated if statements, the for loop is designed to iterate over all elements in a sequence. Learn about them in this chapter.
Exercise 1: While loopExercise 2: Write a while loopExercise 3: Throw in more conditionalsExercise 4: Stop the while loop: breakExercise 5: Build a while loop from scratchExercise 6: For loopExercise 7: Loop over a vectorExercise 8: Loop over a listExercise 9: Loop over a matrixExercise 10: Mix it up with control flowExercise 11: Next, you break itExercise 12: Build a for loop from scratchChapter 3: Functions
Functions are an extremely important concept in almost every programming language, and R is no different. Learn what functions are and how to use them—then take charge by writing your own functions.
Exercise 1: Introduction to FunctionsExercise 2: Function documentationExercise 3: Use a functionExercise 4: Use a function (2)Exercise 5: Use a function (3)Exercise 6: Functions inside functionsExercise 7: Required, or optional?Exercise 8: Writing FunctionsExercise 9: Write your own functionExercise 10: Write your own function (2)Exercise 11: Write your own function (3)Exercise 12: Function scopingExercise 13: R passes arguments by valueExercise 14: R you functional?Exercise 15: R you functional? (2)Exercise 16: R PackagesExercise 17: Load an R PackageExercise 18: Different ways to load a packageChapter 4: The apply family
Whenever you're using a for loop, you may want to revise your code to see whether you can use the lapply function instead. Learn all about this intuitive way of applying a function over a list or a vector, and how to use its variants, sapply and vapply.
Exercise 1: lapplyExercise 2: Use lapply with a built-in R functionExercise 3: Use lapply with your own functionExercise 4: lapply and anonymous functionsExercise 5: Use lapply with additional argumentsExercise 6: Apply functions that return NULLExercise 7: sapplyExercise 8: How to use sapplyExercise 9: sapply with your own functionExercise 10: sapply with function returning vectorExercise 11: sapply can't simplify, now what?Exercise 12: sapply with functions that return NULLExercise 13: Reverse engineering sapplyExercise 14: vapplyExercise 15: Use vapplyExercise 16: Use vapply (2)Exercise 17: From sapply to vapplyChapter 5: Utilities
Mastering R programming is not only about understanding its programming concepts. Having a solid understanding of a wide range of R functions is also important. This chapter introduces you to many useful functions for data structure manipulation, regular expressions, and working with times and dates.
Exercise 1: Useful FunctionsExercise 2: Mathematical utilitiesExercise 3: Find the errorExercise 4: Data UtilitiesExercise 5: Find the error (2)Exercise 6: Beat Gauss using RExercise 7: Regular ExpressionsExercise 8: grepl & grepExercise 9: grepl & grep (2)Exercise 10: sub & gsubExercise 11: sub & gsub (2)Exercise 12: Times & DatesExercise 13: Right here, right nowExercise 14: Create and format datesExercise 15: Create and format timesExercise 16: Calculations with DatesExercise 17: Calculations with TimesExercise 18: Time is of the essenceWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.