# 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 and`qplot(mtcars$wt, mtcars$hp)`

, to build a plot of two variables of the`mtcars`

data 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 again
```

This 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 scratch## Chapter 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 package## Chapter 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 vapply## Chapter 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 essence### What is DataCamp?

Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.