How it works
R makes use of the # sign to add comments, so that you and others can understand what the R code is about. Just like Twitter! Comments are not run as R code, so they will not influence your result. For example, Calculate 3 + 4 in the editor on the right is a comment.
You can also execute R commands straight in the console. This is a good way to experiment with R code, as your submission is not checked for correctness.
# Calculate 3 + 4
3 + 4## [1] 7# Calculate 6 + 12
6 + 12## [1] 18Awesome! See how the console shows the result of the R code you submitted? Now that you’re familiar with the interface, let’s get down to R business!
Arithmetic with R
In its most basic form, R can be used as a simple calculator. Consider the following arithmetic operators:
+-*/^%%The last two might need some explaining:
^ operator raises the number to its left to the power of the number to its right: for example 3^2 is 9.5 %% 3 is 2.With this knowledge, follow the instructions to complete the exercise.
# An addition
5 + 5 ## [1] 10# A subtraction
5 - 5 ## [1] 0# A multiplication
3 * 5## [1] 15 # A division
(5 + 5)/2 ## [1] 5# Exponentiation
2^5## [1] 32# Modulo
28 %% 6## [1] 4Variable assignment
A basic concept in (statistical) programming is called a variable.
A variable allows you to store a value (e.g. 4) or an object (e.g. a function description) in R. You can then later use this variable’s name to easily access the value or the object that is stored within this variable.
You can assign a value 4 to a variable my_var with the command
my_var <- 4# Assign the value 42 to 'x'
x <- 42
# Print out the value of the variable 'x'
x## [1] 42Good job! Have you noticed that R does not print the value of a variable to the console when you did the assignment?x <- 42 did not generate any output, because R assumes that you will be needing this variable in the future. Otherwise you wouldn’t have stored the value in a variable in the first place, right? Proceed to the next exercise!
Variable assignment (2)
Suppose you have a fruit basket with five apples. As a data analyst in training, you want to store the number of apples in a variable with the name my_apples.
# Assign the value 5 to the variable called 'my_apples'
my_apples <- 5
# Print out the value of the variable 'my_apples'
my_apples## [1] 5Variable assignment (3)
Every tasty fruit basket needs oranges, so you decide to add six oranges. As a data analyst, your reflex is to immediately create the variable my_oranges and assign the value 6 to it. Next, you want to calculate how many pieces of fruit you have in total. Since you have given meaningful names to these values, you can now code this in a clear way:
my_apples + my_oranges# Assign a value to the variables called 'my_apples' and 'my_oranges'
my_apples <- 5
my_oranges <-6
# Add these two variables together and print the result
my_apples + my_oranges## [1] 11# Create the variable 'my_fruit'
my_fruit <- my_apples + my_orangesNice one! The great advantage of doing calculations with variables is reusability. If you just change my_apples to equal 12 instead of 5 and rerun the script, my_fruit will automatically update as well. Continue to the next exercise.
Apples and oranges
Common knowledge tells you not to add apples and oranges. But hey, that is what you just did, no :-)? The my_apples and my_oranges variables both contained a number in the previous exercise. The + operator works with numeric variables in R. If you really tried to add “apples” and “oranges”, and assigned a text value to the variable my_oranges (see the editor), you would be trying to assign the addition of a numeric and a character variable to the variable my_fruit. This is not possible.
# Assign a value to the variable called 'my_apples'
my_apples <- 5
# Print out the value of 'my_apples'
my_apples ## [1] 5# Assign a value to the variable 'my_oranges' and print it out
my_oranges <- 6
my_oranges ## [1] 6# New variable that contains the total amount of fruit
my_fruit <- my_apples + my_oranges
my_fruit## [1] 11Basic data types in R
R works with numerous data types. Some of the most basic types to get started are:
4.5 are called numerics.4 are called integers. Integers are also numerics.TRUE or FALSE) are called logical.Note how the quotation marks in the editor indicate that "some text" is a string.
# What is the answer to the universe?
my_numeric <- 42
# The quotation marks indicate that the variable is of type character
my_character <- "universe"
my_logical <- FALSEWhat’s that data type?
Do you remember that when you added 5 + "six", you got an error due to a mismatch in data types? You can avoid such embarrassing situations by checking the data type of a variable beforehand. You can do this with the class() function, as the code in the editor shows.
# Declare variables of different types
my_numeric <- 42
my_character <- "universe"
my_logical <- FALSE
# Check which type these variables have:
class(my_numeric)## [1] "numeric"class(my_character)## [1] "character"class(my_logical)## [1] "logical"Congratulations! This was the last exercise for this chapter. Head over to the next chapter to get immersed in the world of vectors!
Create a vector
Feeling lucky? You better, because this chapter takes you on a trip to the City of Sins, also known as Statisticians Paradise!
Thanks to R and your new data-analytical skills, you will learn how to uplift your performance at the tables and fire off your career as a professional gambler. This chapter will show how you can easily keep track of your betting progress and how you can do some simple analyses on past actions. Next stop, Vegas Baby… VEGAS!!
# Define the variable 'Vegas'
vegas <- "Go!"Create a vector (2)
Let us focus first!
On your way from rags to riches, you will make extensive use of vectors. Vectors are one-dimension arrays that can hold numeric data, character data, or logical data. In other words, a vector is a simple tool to store data. For example, you can store your daily gains and losses in the casinos.
In R, you create a vector with the combine function c(). You place the vector elements separated by a comma between the parentheses. For example:
numeric_vector <- c(1, 2, 3)
character_vector <- c("a", "b", "c")Once you have created these vectors in R, you can use them to do calculations.
numeric_vector <- c(1, 10, 49)
character_vector <- c("a", "b", "c")
# Complete the code for 'boolean_vector'
boolean_vector <- c(TRUE, FALSE, TRUE)Perfect! Notice that adding a space behind the commas in the c() function improves the readability of your code. Let’s practice some more with vector creation in the next exercise.
Create a vector (3)
After one week in Las Vegas and still zero Ferraris in your garage, you decide that it is time to start using your data analytical superpowers.
Before doing a first analysis, you decide to first collect all the winnings and losses for the last week:
For poker_vector:
For roulette_vector:
You only played poker and roulette, since there was a delegation of mediums that occupied the craps tables. To be able to use this data in R, you decide to create the variables poker_vector and roulette_vector.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24,-50, 100,-350, 10)Very good! To check out the contents of your vectors, remember that you can always simply type the variable in the console and hit Enter. Proceed to the next exercise!
Naming a vector
As a data analyst, it is important to have a clear view on the data that you are using. Understanding what each element refers to is therefore essential.
In the previous exercise, we created a vector with your winnings over the week. Each vector element refers to a day of the week but it is hard to tell which element belongs to which day. It would be nice if you could show that in the vector itself.
You can give a name to the elements of a vector with the names() function. Have a look at this example:
some_vector <- c("John Doe", "poker player")
names(some_vector) <- c("Name", "Profession")This code first creates a vector some_vector and then gives the two elements a name. The first element is assigned the name Name, while the second element is labeled Profession. Printing the contents to the console yields following output:
Name Profession
"John Doe" "poker player" # Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Add your code here
names(poker_vector) <- c("Monday","Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- c("Monday","Tuesday", "Wednesday", "Thursday", "Friday")Naming-a-vector-(2)
If you want to become a good statistician, you have to become lazy. (If you are already lazy, chances are high you are one of those exceptional, natural-born statistical talents.)
In the previous exercises you probably experienced that it is boring and frustrating to type and retype information such as the days of the week. However, when you look at it from a higher perspective, there is a more efficient way to do this, namely, to assign the days of the week vector to a variable!
Just like you did with your poker and roulette returns, you can also create a variable that contains the days of the week. This way you can use and re-use it.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Create the variable 'days_vector'
days_vector <- c("Monday","Tuesday","Wednesday","Thursday","Friday")
#Assign the names of the day to 'roulette_vector' and 'poker_vector'
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vectorNice one! A word of advice: try to avoid code duplication at all times. Continue to the next exercise and learn how to do arithmetic with vectors!
Calculating total winnings Now that you have the poker and roulette winnings nicely as named vectors, you can start doing some data analytical magic.
You want to find out the following type of information:
To get the answers, you have to do arithmetic calculations on vectors.
It is important to know that if you sum two vectors in R, it takes the element-wise sum. For example, the following three statements are completely equivalent:
c(1, 2, 3) + c(4, 5, 6)
c(1 + 4, 2 + 5, 3 + 6)
c(5, 7, 9)You can also do the calculations with variables that represent vectors:
a <- c(1, 2, 3)
b <- c(4, 5, 6)
c <- a + bA_vector <- c(1, 2, 3)
B_vector <- c(4, 5, 6)
# Take the sum of 'A_vector' and 'B_vector'
total_vector <- A_vector + B_vector
# Print 'total_vector' to the console
total_vector## [1] 5 7 9Calculating total winnings (2)
Now you understand how R does arithmetic with vectors, it is time to get those Ferraris in your garage! First, you need to understand what the overall profit or loss per day of the week was. The total daily profit is the sum of the profit/loss you realized on poker per day, and the profit/loss you realized on roulette per day.
In R, this is just the sum of roulette_vector and poker_vector.
# Poker winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday:
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Up to you now:
total_daily <- poker_vector + roulette_vectorCalculating total winnings (3)
Based on the previous analysis, it looks like you had a mix of good and bad days. This is not what your ego expected, and you wonder if there may be a very tiny chance you have lost money over the week in total?
A function that helps you to answer this question is sum(). It calculates the sum of all elements of a vector. For example, to calculate the total amount of money you have lost/won with poker you do:
total_poker <- sum(poker_vector)# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Total winnings with poker
total_poker <- sum(poker_vector)
# Total winnings with roulette
total_roulette <- sum(roulette_vector)
# Total winnings overall
total_week <- total_roulette + total_poker
# Print out total_week
total_week## [1] -84Comparing total winnings
Oops, it seems like you are losing money. Time to rethink and adapt your strategy! This will require some deeper analysis…
After a short brainstorm in your hotel’s jacuzzi, you realize that a possible explanation might be that your skills in roulette are not as well developed as your skills in poker. So maybe your total gains in poker are higher (or > ) than in roulette.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
poker_vector## Monday Tuesday Wednesday Thursday Friday
## 140 -50 20 -120 240roulette_vector## Monday Tuesday Wednesday Thursday Friday
## -24 -50 100 -350 10# Calculate total gains for poker and roulette
total_poker <- sum(poker_vector)
total_poker## [1] 230total_roulette <- sum(roulette_vector)
total_roulette## [1] -314# Check if you realized higher total gains in poker than in roulette
answer <- total_poker > total_roulette
answer## [1] TRUEVector selection: the good times
Your hunch seemed to be right. It appears that the poker game is more your cup of tea than roulette.
Another possible route for investigation is your performance at the beginning of the working week compared to the end of it. You did have a couple of Margarita cocktails at the end of the week…
To answer that question, you only want to focus on a selection of the total_vector. In other words, our goal is to select specific elements of the vector. To select elements of a vector (and later matrices, data frames, …), you can use square brackets. Between the square brackets, you indicate what elements to select. For example, to select the first element of the vector, you type poker_vector[1]. To select the second element of the vector, you type poker_vector[2], etc. Notice that the first element in a vector has index 1, not 0 as in many other programming languages.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
# Define a new variable based on a selection
poker_wednesday <- poker_vector[3]Great! R also makes it possible to select multiple elements from a vector at once. Learn how in the next exercise!
Vector selection: the good times (2)
How about analyzing your midweek results?
To select multiple elements from a vector, you can add square brackets at the end of it. You can indicate between the brackets what elements should be selected. For example: suppose you want to select the first and the fifth day of the week: use the vector c(1, 5) between the square brackets. For example, the code below selects the first and fifth element of poker_vector:
poker_vector[c(1, 5)]# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
# Define a new variable based on a selection
poker_midweek <- poker_vector[c(2,3,4)]Well done! Continue to the next exercise to specialize in vector selection some more!
Vector selection: the good times (3)
Selecting multiple elements of poker_vector with c(2, 3, 4) is not very convenient. Many statisticians are lazy people by nature, so they created an easier way to do this: c(2, 3, 4) can be abbreviated to 2:4, which generates a vector with all natural numbers from 2 up to 4.
So, another way to find the mid-week results is poker_vector[2:4]. Notice how the vector 2:4 is placed between the square brackets to select element 2 up to 4.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
# Define a new variable based on a selection
roulette_selection_vector <- roulette_vector[2:5]Awesome! The colon operator is extremely useful and very often used in R programming, so remember it well. Proceed to the next exercise.
Vector selection: the good times (4)
Another way to tackle the previous exercise is by using the names of the vector elements (Monday, Tuesday, …) instead of their numeric positions. For example,
poker_vector["Monday"]will select the first element of poker_vector since “Monday” is the name of that first element.
Just like you did in the previous exercise with numerics, you can also use the element names to select multiple elements, for example:
poker_vector[c("Monday","Tuesday")]# Poker and roulette winnings from Monday to Friday:
poker_vector <- c(140, -50, 20, -120, 240)
roulette_vector <- c(-24, -50, 100, -350, 10)
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(poker_vector) <- days_vector
names(roulette_vector) <- days_vector
# Select poker results for Monday, Tuesday and Wednesday
poker_start <- poker_vector[c("Monday", "Tuesday", "Wednesday")]
# Calculate the average of the elements in poker_start
mean(poker_start)## [1] 36.66667Good job! Apart from subsetting vectors by index or by name, you can also subset vectors by comparison. The next exercises will show you how!
Selection by comparison - Step 1
By making use of comparison operators, we can approach the previous question in a more proactive way.
The (logical) comparison operators known to R are:
< for less than> for greater than<= for less than or equal to>= for greater than or equal to== for equal to each other!= not equal to each otherAs seen in the previous chapter, stating 6 > 5 returns TRUE. The nice thing about R is that you can use these comparison operators also on vectors. For example:
c(4, 5, 6) > 5
[1] FALSE FALSE TRUEThis command tests for every element of the vector if the condition stated by the comparison operator is TRUE or FALSE
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
# What days of the week did you make money on poker?
selection_vector <- poker_vector > 0
selection_vector## Monday Tuesday Wednesday Thursday Friday
## TRUE FALSE TRUE FALSE TRUESelection by comparison - Step 2
Working with comparisons will make your data analytical life easier. Instead of selecting a subset of days to investigate yourself (like before), you can simply ask R to return only those days where you realized a positive return for poker.
In the previous exercises you used selection_vector <- poker_vector > 0 to find the days on which you had a positive poker return. Now, you would like to know not only the days on which you won, but also how much you won on those days.
You can select the desired elements, by putting selection_vector between the square brackets that follow poker_vector:
poker_vector[selection_vector]
R knows what to do when you pass a logical vector in square brackets: it will only select the elements that correspond to TRUE in selection_vector.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
# What days of the week did you make money on poker?
selection_vector <- poker_vector > 0
# Select from poker_vector these days
poker_winning_days <- poker_vector[selection_vector]
poker_winning_days## Monday Wednesday Friday
## 140 20 240Advanced selection
Just like you did for poker, you also want to know those days where you realized a positive return for roulette.
# Poker winnings from Monday to Friday
poker_vector <- c(140, -50, 20, -120, 240)
# Roulette winnings from Monday to Friday
roulette_vector <- c(-24, -50, 100, -350, 10)
# Give names to both 'poker_vector' and 'roulette_vector'
days_vector <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday")
names(roulette_vector) <- days_vector
names(poker_vector) <- days_vector
# What days of the week did you make money on roulette?
selection_vector <- roulette_vector > 0
# Select from roulette_vector these days
roulette_winning_days <- roulette_vector[selection_vector]Great! This exercise concludes the chapter on vectors. The next chapter will introduce you to the two-dimensional version of vectors: matrices.
What’s a matrix?
In R, a matrix is a collection of elements of the same data type (numeric, character, or logical) arranged into a fixed number of rows and columns. Since you are only working with rows and columns, a matrix is called two-dimensional.
You can construct a matrix in R with the matrix() function. Consider the following example:
matrix(1:9, byrow = TRUE, nrow = 3)In the matrix() function:
1:9 which is a shortcut for c(1, 2, 3, 4, 5, 6, 7, 8, 9).byrow indicates that the matrix is filled by the rows. If we want the matrix to be filled by the columns, we just place byrow = FALSE.nrow indicates that the matrix should have three rows.# Construct a matrix with 3 rows that contain the numbers 1 up to 9
matrix(1:9, byrow = TRUE, nrow = 3)## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9Analyze matrices, you shall
It is now time to get your hands dirty. In the following exercises you will analyze the box office numbers of the Star Wars franchise. May the force be with you!
In the editor, three vectors are defined. Each one represents the box office numbers from the first three Star Wars movies. The first element of each vector indicates the US box office revenue, the second element refers to the Non-US box office (source: Wikipedia).
In this exercise, you’ll combine all these figures into a single vector. Next, you’ll build a matrix from this vector.
# Box office Star Wars (in millions!)
new_hope <- c(460.998, 314.4)
empire_strikes <- c(290.475, 247.900)
return_jedi <- c(309.306, 165.8)
# Create box_office
box_office <- c(new_hope, empire_strikes, return_jedi)
# Construct star_wars_matrix
star_wars_matrix <- matrix(box_office, nrow = 3, byrow = TRUE) The force is actually with you! Continue to the next exercise.
Naming a matrix
To help you remember what is stored in star_wars_matrix, you would like to add the names of the movies for the rows. Not only does this help you to read the data, but it is also useful to select certain elements from the matrix.
Similar to vectors, you can add names for the rows and the columns of a matrix
rownames(my_matrix) <- row_names_vector
colnames(my_matrix) <- col_names_vectorWe went ahead and prepared two vectors for you: region, and titles. You will need these vectors to name the columns and rows of star_wars_matrix, respectively.
# Box office Star Wars (in millions!)
new_hope <- c(460.998, 314.4)
empire_strikes <- c(290.475, 247.900)
return_jedi <- c(309.306, 165.8)
# Construct matrix
star_wars_matrix <- matrix(c(new_hope, empire_strikes, return_jedi), nrow = 3, byrow = TRUE)
# Vectors region and titles, used for naming
region <- c("US", "non-US")
titles <- c("A New Hope", "The Empire Strikes Back", "Return of the Jedi")
# Name the columns with region
colnames(star_wars_matrix) <- region
# Name the rows with titles
rownames(star_wars_matrix) <- titles
# Print out star_wars_matrix
star_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8Great! You’re on the way of becoming an R jedi! Continue to the next exercise.
Calculating the worldwide box office
The single most important thing for a movie in order to become an instant legend in Tinseltown is its worldwide box office figures.
To calculate the total box office revenue for the three Star Wars movies, you have to take the sum of the US revenue column and the non-US revenue column.
In R, the function rowSums() conveniently calculates the totals for each row of a matrix. This function creates a new vector:
rowSums(my_matrix)# Construct star_wars_matrix
box_office <- c(460.998, 314.4, 290.475, 247.900, 309.306, 165.8)
region <- c("US", "non-US")
titles <- c("A New Hope",
"The Empire Strikes Back",
"Return of the Jedi")
star_wars_matrix <- matrix(box_office,
nrow = 3, byrow = TRUE,
dimnames = list(titles, region))
# Calculate worldwide box office figures
worldwide_vector <- rowSums(star_wars_matrix)Adding a column for the Worldwide box office
In the previous exercise you calculated the vector that contained the worldwide box office receipt for each of the three Star Wars movies. However, this vector is not yet part of star_wars_matrix.
You can add a column or multiple columns to a matrix with the cbind() function, which merges matrices and/or vectors together by column. For example:
big_matrix <- cbind(matrix1, matrix2, vector1 ...)# Construct star_wars_matrix
box_office <- c(460.998, 314.4, 290.475, 247.900, 309.306, 165.8)
region <- c("US", "non-US")
titles <- c("A New Hope",
"The Empire Strikes Back",
"Return of the Jedi")
star_wars_matrix <- matrix(box_office,
nrow = 3, byrow = TRUE,
dimnames = list(titles, region))
# The worldwide box office figures
worldwide_vector <- rowSums(star_wars_matrix)
# Bind the new variable worldwide_vector as a column to star_wars_matrix
all_wars_matrix <- cbind(star_wars_matrix, worldwide_vector)Nice job! After adding column to a matrix, the logical next step is adding rows. Learn how in the next exercise.
Adding a row
Just like every action has a reaction, every cbind() has an rbind(). (We admit, we are pretty bad with metaphors.)
Your R workspace, where all variables you defined ‘live’ (check out what a workspace is), has already been initialized and contains two matrices:
star_wars_matrix that we have used all along, with data on the original trilogy,star_wars_matrix2, with similar data for the prequels trilogy. Explore these matrices in the console if you want to have a closer look. If you want to check out the contents of the workspace, you can type ls() in the console.# Construct star_wars_matrix2
box_office2 <- c(474.5, 552.5, 310.7, 338.7, 380.3, 468.5)
region2 <- c("US", "non-US")
titles2 <- c("The Phantom Menace",
"Attack of the Clones",
"Revenge of the Sith")
star_wars_matrix2 <- matrix(box_office2,
nrow = 3, byrow = TRUE,
dimnames = list(titles2, region2))
# star_wars_matrix and star_wars_matrix2 are available in your workspace
star_wars_matrix ## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8star_wars_matrix2 ## US non-US
## The Phantom Menace 474.5 552.5
## Attack of the Clones 310.7 338.7
## Revenge of the Sith 380.3 468.5# Combine both Star Wars trilogies in one matrix
all_wars_matrix <- rbind(star_wars_matrix, star_wars_matrix2)Wonderful! Continue with the next exercise and see how you can combine the results of the rbind() function with the colSums() function!
The total box office revenue for the entire saga
Just like cbind() has rbind(), colSums() has rowSums(). Your R workspace already contains the all_wars_matrix that you constructed in the previous exercise; type all_wars_matrix to have another look. Let’s now calculate the total box office revenue for the entire saga.
# all_wars_matrix is available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5# Total revenue for US and non-US
total_revenue_vector <- colSums(all_wars_matrix)
# Print out total_revenue_vector
total_revenue_vector## US non-US
## 2226.279 2087.800Bellissimo! Head over to the next exercise to learn matrix subsetting.
Selection of matrix elements
Similar to vectors, you can use the square brackets [ ] to select one or multiple elements from a matrix. Whereas vectors have one dimension, matrices have two dimensions. You should therefore use a comma to separate the rows you want to select from the columns. For example:
my_matrix[1,2] selects the element at the first row and second column.my_matrix[1:3,2:4] results in a matrix with the data on the rows 1, 2, 3 and columns 2, 3, 4.If you want to select all elements of a row or a column, no number is needed before or after the comma, respectively:
my_matrix[,1] selects all elements of the first column.my_matrix[1,] selects all elements of the first row.Back to Star Wars with this newly acquired knowledge! As in the previous exercise, all_wars_matrix is already available in your workspace.
# all_wars_matrix is available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5# Select the non-US revenue for all movies
non_us_all <- all_wars_matrix[,2]
# Average non-US revenue
mean(non_us_all)## [1] 347.9667# Select the non-US revenue for first two movies
non_us_some <- all_wars_matrix[1:2,2]
# Average non-US revenue for first two movies
mean(non_us_some)## [1] 281.15A little arithmetic with matrices
Similar to what you have learned with vectors, the standard operators like +, -, /,*, etc. work in an element-wise way on matrices in R.
For example, 2 * my_matrix multiplies each element of my_matrix by two.
As a newly-hired data analyst for Lucasfilm, it is your job to find out how many visitors went to each movie for each geographical area. You already have the total revenue figures in all_wars_matrix. Assume that the price of a ticket was 5 dollars. Simply dividing the box office numbers by this ticket price gives you the number of visitors.
# all_wars_matrix is available in your workspace
all_wars_matrix## US non-US
## A New Hope 460.998 314.4
## The Empire Strikes Back 290.475 247.9
## Return of the Jedi 309.306 165.8
## The Phantom Menace 474.500 552.5
## Attack of the Clones 310.700 338.7
## Revenge of the Sith 380.300 468.5# Estimate the visitors
visitors <- all_wars_matrix / 5
# Print the estimate to the console
visitors## US non-US
## A New Hope 92.1996 62.88
## The Empire Strikes Back 58.0950 49.58
## Return of the Jedi 61.8612 33.16
## The Phantom Menace 94.9000 110.50
## Attack of the Clones 62.1400 67.74
## Revenge of the Sith 76.0600 93.70Great! What do these results tell you? A staggering 92 million people went to see A New Hope in US theaters! Continue to the next exercise.
A little arithmetic with matrices (2)
Just like 2 * my_matrix multiplied every element of my_matrix by two,my_matrix1 * my_matrix2 creates a matrix where each element is the product of the corresponding elements in my_matrix1 and my_matrix2.
After looking at the result of the previous exercise, big boss Lucas points out that the ticket prices went up over time. He asks to redo the analysis based on the prices you can find in ticket_prices_matrix (source: imagination).
Those who are familiar with matrices should note that this is not the standard matrix multiplication for which you should use %*% in R.
ticket_prices_matrix <- all_wars_matrix
ticket_prices <- rep(c(5.0, 6.0, 7.0, 4.0, 4.5, 4.9), 2)
ticket_prices_matrix[,] <- ticket_prices
# all_wars_matrix and ticket_prices_matrix are available in your workspace
all_wars_matrix <- round(all_wars_matrix, 1)
all_wars_matrix## US non-US
## A New Hope 461.0 314.4
## The Empire Strikes Back 290.5 247.9
## Return of the Jedi 309.3 165.8
## The Phantom Menace 474.5 552.5
## Attack of the Clones 310.7 338.7
## Revenge of the Sith 380.3 468.5ticket_prices_matrix## US non-US
## A New Hope 5.0 5.0
## The Empire Strikes Back 6.0 6.0
## Return of the Jedi 7.0 7.0
## The Phantom Menace 4.0 4.0
## Attack of the Clones 4.5 4.5
## Revenge of the Sith 4.9 4.9# Estimated number of visitors
visitors <- all_wars_matrix / ticket_prices_matrix
# US visitors
us_visitors <- visitors[ ,1]
# Average number of US visitors
mean(us_visitors)## [1] 75.01401It’s a fact: the R force is with you! This exercise concludes the chapter on matrices. Next stop on your journey through the R language: factors.
What’s a factor and why would you use it?
In this chapter you dive into the wonderful world of factors.
The term factor refers to a statistical data type used to store categorical variables. The difference between a categorical variable and a continuous variable is that a categorical variable can belong to a limited number of categories. A continuous variable, on the other hand, can correspond to an infinite number of values.
It is important that R knows whether it is dealing with a continuous or a categorical variable, as the statistical models you will develop in the future treat both types differently. (You will see later why this is the case.)
A good example of a categorical variable is sex. In many circumstances you can limit the sex categories to “Male” or “Female”. (Sometimes you may need different categories. For example, you may need to consider chromosomal variation, hermaphroditic animals, or different cultural norms, but you will always have a finite number of categories.)
# Assign to the variable theory what this chapter is about!
theory <- "factors"What’s a factor and why would you use it? (2)
To create factors in R, you make use of the function factor(). First thing that you have to do is create a vector that contains all the observations that belong to a limited number of categories. For example, sex_vector contains the sex of 5 different individuals:
sex_vector <- c("Male","Female","Female","Male","Male")It is clear that there are two categories, or in R-terms ‘factor levels’, at work here: “Male” and “Female”.
The function factor() will encode the vector as a factor:
factor_sex_vector <- factor(sex_vector)# Sex vector
sex_vector <- c("Male", "Female", "Female", "Male", "Male")
# Convert sex_vector to a factor
factor_sex_vector <- factor(sex_vector)
# Print out factor_sex_vector
factor_sex_vector## [1] Male Female Female Male Male
## Levels: Female MaleGreat! If you want to find out more about the factor() function, do not hesitate to type ?factor in the console. This will open up a help page. Continue to the next exercise.
What’s a factor and why would you use it? (3)
There are two types of categorical variables: a nominal categorical variable and an ordinal categorical variable.
A nominal variable is a categorical variable without an implied order. This means that it is impossible to say that ‘one is worth more than the other’. For example, think of the categorical variable animals_vector with the categories "Elephant", "Giraffe", "Donkey" and "Horse". Here, it is impossible to say that one stands above or below the other. (Note that some of you might disagree ;-) ).
In contrast, ordinal variables do have a natural ordering. Consider for example the categorical variable temperature_vector with the categories: "Low", "Medium" and "High". Here it is obvious that "Medium" stands above "Low", and "High" stands above "Medium".
# Animals
animals_vector <- c("Elephant", "Giraffe", "Donkey", "Horse")
factor_animals_vector <- factor(animals_vector)
factor_animals_vector## [1] Elephant Giraffe Donkey Horse
## Levels: Donkey Elephant Giraffe Horse# Temperature
temperature_vector <- c("High", "Low", "High","Low", "Medium")
factor_temperature_vector <- factor(temperature_vector, order = TRUE, levels = c("Low", "Medium", "High"))
factor_temperature_vector## [1] High Low High Low Medium
## Levels: Low < Medium < HighCan you already tell what’s happening in this exercise? Awesome! Continue to the next exercise and get into the details of factor levels.
Factor levels
When you first get a data set, you will often notice that it contains factors with specific factor levels. However, sometimes you will want to change the names of these levels for clarity or other reasons. R allows you to do this with the function levels():
levels(factor_vector) <- c("name1", "name2",...)A good illustration is the raw data that is provided to you by a survey. A common question for every questionnaire is the sex of the respondent. Here, for simplicity, just two categories were recorded, "M" and "F". (You usually need more categories for survey data; either way, you use a factor to store the categorical data.)
survey_vector <- c("M", "F", "F", "M", "M")Recording the sex with the abbreviations "M" and "F" can be convenient if you are collecting data with pen and paper, but it can introduce confusion when analyzing the data. At that point, you will often want to change the factor levels to "Male" and "Female" instead of "M" and "F" for clarity.
Watch out: the order with which you assign the levels is important. If you type levels(factor_survey_vector), you’ll see that it outputs [1] "F" "M". If you don’t specify the levels of the factor when creating the vector, R will automatically assign them alphabetically. To correctly map "F" to "Female" and "M" to "Male", the levels should be set to c("Female", "Male"), in this order.
# Code to build factor_survey_vector
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
# Specify the levels of factor_survey_vector
levels(factor_survey_vector) <- c("Female", "Male")
factor_survey_vector## [1] Male Female Female Male Male
## Levels: Female MaleSummarizing a factor
After finishing this course, one of your favorite functions in R will be summary(). This will give you a quick overview of the contents of a variable:
summary(my_var)Going back to our survey, you would like to know how many "Male" responses you have in your study, and how many "Female" responses. The summary() function gives you the answer to this question.
# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")
factor_survey_vector## [1] Male Female Female Male Male
## Levels: Female Male# Generate summary for survey_vector
summary(survey_vector)## Length Class Mode
## 5 character character# Generate summary for factor_survey_vector
summary(factor_survey_vector)## Female Male
## 2 3Nice! Have a look at the output. The fact that you identified "Male" and "Female" as factor levels in factor_survey_vector enables R to show the number of elements for each category.
Battle of the sexes
You might wonder what happens when you try to compare elements of a factor. In factor_survey_vector you have a factor with two levels: "Male" and "Female". But how does R value these relative to each other?
# Build factor_survey_vector with clean levels
survey_vector <- c("M", "F", "F", "M", "M")
factor_survey_vector <- factor(survey_vector)
levels(factor_survey_vector) <- c("Female", "Male")
# Male
male <- factor_survey_vector[1]
# Female
female <- factor_survey_vector[2]
# Battle of the sexes: Male 'larger' than female?
male > female## Warning in Ops.factor(male, female): '>' not meaningful for factors## [1] NAHow interesting! By default, R returns NA when you try to compare values in a factor, since the idea doesn’t make sense. Next you’ll learn about ordered factors, where more meaningful comparisons are possible.
Ordered factors
Since "Male" and "Female" are unordered (or nominal) factor levels, R returns a warning message, telling you that the greater than operator is not meaningful. As seen before, R attaches an equal value to the levels for such factors.
But this is not always the case! Sometimes you will also deal with factors that do have a natural ordering between its categories. If this is the case, we have to make sure that we pass this information to R…
Let us say that you are leading a research team of five data analysts and that you want to evaluate their performance. To do this, you track their speed, evaluate each analyst as "slow", "medium" or "fast", and save the results in speed_vector.
# Create speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")Ordered factors (2)
speed_vector should be converted to an ordinal factor since its categories have a natural ordering. By default, the function factor() transforms speed_vector into an unordered factor. To create an ordered factor, you have to add two additional arguments: ordered and levels.
factor(some_vector,
ordered = TRUE,
levels = c("lev1", "lev2" ...))By setting the argument ordered to TRUE in the function factor(), you indicate that the factor is ordered. With the argument levels you give the values of the factor in the correct order.
# Create speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")
# Add your code below
factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "medium", "fast"))
# Print factor_speed_vector
factor_speed_vector## [1] medium slow slow medium fast
## Levels: slow < medium < fastsummary(factor_speed_vector)## slow medium fast
## 2 2 1Great! Have a look at the console. It is now indicated that the Levels indeed have an order associated, with the < sign. Continue to the next exercise.
Comparing ordered factors
Having a bad day at work, ‘data analyst number two’ enters your office and starts complaining that ‘data analyst number five’ is slowing down the entire project. Since you know that ‘data analyst number two’ has the reputation of being a smarty-pants, you first decide to check if his statement is true.
The fact that factor_speed_vector is now ordered enables us to compare different elements (the data analysts in this case). You can simply do this by using the well-known operators.
# Create factor_speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")
factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "medium", "fast"))
# Factor value for second data analyst
da2 <- factor_speed_vector[2]
# Factor value for fifth data analyst
da5 <- factor_speed_vector[5]
# Is data analyst 2 faster data analyst 5?
da2 > da5## [1] FALSEBellissimo! What does the result tell you? Data analyst two is complaining about the data analyst five while in fact they are the one slowing everything down! This concludes the chapter on factors. With a solid basis in vectors, matrices and factors, you’re ready to dive into the wonderful world of data frames, a very important data structure in R!
What’s a data frame?
You may remember from the chapter about matrices that all the elements that you put in a matrix should be of the same type. Back then, your data set on Star Wars only contained numeric elements.
When doing a market research survey, however, you often have questions such as:
logical)numeric)character)The output, namely the respondents’ answers to the questions formulated above, is a data set of different data types. You will often find yourself working with data sets that contain different data types instead of only one.
A data frame has the variables of a data set as columns and the observations as rows. This will be a familiar concept for those coming from different statistical software packages such as SAS or SPSS.
# Print out built-in R data frame
mtcars ## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Quick, have a look at your data set
Wow, that is a lot of cars!
Working with large data sets is not uncommon in data analysis. When you work with (extremely) large data sets and data frames, your first task as a data analyst is to develop a clear understanding of its structure and main elements. Therefore, it is often useful to show only a small part of the entire data set.
So how to do this in R? Well, the function head() enables you to show the first observations of a data frame. Similarly, the function tail() prints out the last observations in your data set.
Both head() and tail() print a top line called the ‘header’, which contains the names of the different variables in your data set.
# Call head() on mtcars
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Wonderful! So, what do we have in this data set? For example, hp represents the car’s horsepower; the Datsun has the lowest horse power of the 6 cars that are displayed. For a full overview of the variables’ meaning, type ?mtcars in the console and read the help page. Continue to the next exercise!
Have a look at the structure
Another method that is often used to get a rapid overview of your data is the function str(). The function str() shows you the structure of your data set. For a data frame it tells you:
mpg, cyl … )num)Applying the str() function will often be the first thing that you do when receiving a new data set or data frame. It is a great way to get more insight in your data set before diving into the real analysis.
# Investigate the structure of mtcars
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...Creating a data frame
Since using built-in data sets is not even half the fun of creating your own data sets, the rest of this chapter is based on your personally developed data set. Put your jet pack on because it is time for some space exploration!
As a first goal, you want to construct a data frame that describes the main characteristics of eight planets in our solar system. According to your good friend Buzz, the main features of a planet are:
After doing some high-quality research on Wikipedia, you feel confident enough to create the necessary vectors: name, type, diameter, rotation and rings; these vectors have already been coded up in the editor. The first element in each of these vectors correspond to the first observation.
You construct a data frame with the data.frame() function. As arguments, you pass the vectors from before: they will become the different columns of your data frame. Because every column has the same length, the vectors you pass should also have the same length. But don’t forget that it is possible (and likely) that they contain different types of data.
# Definition of vectors
name <- c("Mercury", "Venus", "Earth",
"Mars", "Jupiter", "Saturn",
"Uranus", "Neptune")
type <- c("Terrestrial planet",
"Terrestrial planet",
"Terrestrial planet",
"Terrestrial planet", "Gas giant",
"Gas giant", "Gas giant", "Gas giant")
diameter <- c(0.382, 0.949, 1, 0.532,
11.209, 9.449, 4.007, 3.883)
rotation <- c(58.64, -243.02, 1, 1.03,
0.41, 0.43, -0.72, 0.67)
rings <- c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
# Create a data frame from the vectors
planets_df <- data.frame(name, type, diameter, rotation, rings)Great job! Continue to the next exercise. The logical next step, as you know by now, is inspecting the data frame you just created. Head over to the next exercise.
Creating a data frame (2)
The planets_df data frame should have 8 observations and 5 variables. It has been made available in the workspace, so you can directly use it.
# Check the structure of planets_df
str(planets_df)## 'data.frame': 8 obs. of 5 variables:
## $ name : chr "Mercury" "Venus" "Earth" "Mars" ...
## $ type : chr "Terrestrial planet" "Terrestrial planet" "Terrestrial planet" "Terrestrial planet" ...
## $ diameter: num 0.382 0.949 1 0.532 11.209 ...
## $ rotation: num 58.64 -243.02 1 1.03 0.41 ...
## $ rings : logi FALSE FALSE FALSE FALSE TRUE TRUE ...Awesome! Now that you have a clear understanding of the planets_df data set, it’s time to see how you can select elements from it. Learn all about in the next exercises!
Selection of data frame elements
Similar to vectors and matrices, you select elements from a data frame with the help of square brackets [ ]. By using a comma, you can indicate what to select from the rows and the columns respectively. For example:
my_df[1,2] selects the value at the first row and second column in my_df.my_df[1:3,2:4] selects rows 1, 2, 3 and columns 2, 3, 4 in my_df.Sometimes you want to select all elements of a row or column. For example, my_df[1, ] selects all elements of the first row. Let us now apply this technique on planets_df!
# The planets_df data frame from the previous exercise is pre-loaded
# Print out diameter of Mercury (row 1, column 3)
planets_df[1,3]## [1] 0.382# Print out data for Mars (entire fourth row)
planets_df[4, ]## name type diameter rotation rings
## 4 Mars Terrestrial planet 0.532 1.03 FALSEGreat! Apart from selecting elements from your data frame by index, you can also use the column names. To learn how, head over to the next exercise.
Selection of data frame elements (2)
Instead of using numerics to select elements of a data frame, you can also use the variable names to select columns of a data frame.
Suppose you want to select the first three elements of the type column. One way to do this is
planets_df[1:3,2]A possible disadvantage of this approach is that you have to know (or look up) the column number of type, which gets hard if you have a lot of variables. It is often easier to just make use of the variable name:
planets_df[1:3,"type"]# The planets_df data frame from the previous exercise is pre-loaded
# Select first 5 values of diameter column
planets_df[1:5, "diameter"]## [1] 0.382 0.949 1.000 0.532 11.209Only planets with rings
You will often want to select an entire column, namely one specific variable from a data frame. If you want to select all elements of the variable diameter, for example, both of these will do the trick:
planets_df[,3]
planets_df[,"diameter"]However, there is a short-cut. If your columns have names, you can use the $ sign:
planets_df$diameter# planets_df is pre-loaded in your workspace
# Select the rings variable from planets_df
rings_vector <- planets_df$rings
# Print out rings_vector
rings_vector## [1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUEGreat! Continue to the next exercise and discover yet another way of subsetting!
Only planets with rings (2)
You probably remember from high school that some planets in our solar system have rings and others do not. Unfortunately you can not recall their names. Could R help you out?
If you type rings_vector in the console, you get:
[1] FALSE FALSE FALSE FALSE TRUE TRUE TRUE TRUEThis means that the first four observations (or planets) do not have a ring (FALSE), but the other four do (TRUE). However, you do not get a nice overview of the names of these planets, their diameter, etc. Let’s try to use rings_vector to select the data for the four planets with rings.
# planets_df and rings_vector are pre-loaded in your workspace
# Adapt the code to select all columns for planets with rings
planets_df[rings_vector, ]## name type diameter rotation rings
## 5 Jupiter Gas giant 11.209 0.41 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 8 Neptune Gas giant 3.883 0.67 TRUEWonderful! This is a rather tedious solution. The next exercise will teach you how to do it in a more concise way.
Only planets with rings but shorter
So what exactly did you learn in the previous exercises? You selected a subset from a data frame (planets_df) based on whether or not a certain condition was true (rings or no rings), and you managed to pull out all relevant data. Pretty awesome! By now, NASA is probably already flirting with your CV ;-).
Now, let us move up one level and use the function subset(). You should see the subset() function as a short-cut to do exactly the same as what you did in the previous exercises.
subset(my_df, subset = some_condition)The first argument of subset() specifies the data set for which you want a subset. By adding the second argument, you give R the necessary information and conditions to select the correct subset.
The code below will give the exact same result as you got in the previous exercise, but this time, you didn’t need the rings_vector!
subset(planets_df, subset = rings)# planets_df is pre-loaded in your workspace
# Select planets with diameter < 1
subset(planets_df, subset = diameter < 1)## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSEGreat! Not only is the subset() function more concise, it is probably also more understandable for people who read your code. Continue to the next exercise.
Sorting
Making and creating rankings is one of mankind’s favorite affairs. These rankings can be useful (best universities in the world), entertaining (most influential movie stars) or pointless (best 007 look-a-like).
In data analysis you can sort your data according to a certain variable in the data set. In R, this is done with the help of the function order().
order() is a function that gives you the ranked position of each element when it is applied on a variable, such as a vector for example:
a <- c(100, 10, 1000)
order(a)
[1] 2 1 310, which is the second element in a, is the smallest element, so 2 comes first in the output of order(a). 100, which is the first element in a is the second smallest element, so 1 comes second in the output of order(a).
This means we can use the output of order(a) to reshuffle a:
a[order(a)]
[1] 10 100 1000# Play around with the order function in the consoleGreat! Now let’s use the order() function to sort your data frame!
Sorting your data frame
Alright, now that you understand the order() function, let us do something useful with it. You would like to rearrange your data frame such that it starts with the smallest planet and ends with the largest one. A sort on the diameter column.
# planets_df is pre-loaded in your workspace
# Use order() to create positions
positions <- order(planets_df$diameter)
# Use positions to sort planets_df
planets_df[positions, ]## name type diameter rotation rings
## 1 Mercury Terrestrial planet 0.382 58.64 FALSE
## 4 Mars Terrestrial planet 0.532 1.03 FALSE
## 2 Venus Terrestrial planet 0.949 -243.02 FALSE
## 3 Earth Terrestrial planet 1.000 1.00 FALSE
## 8 Neptune Gas giant 3.883 0.67 TRUE
## 7 Uranus Gas giant 4.007 -0.72 TRUE
## 6 Saturn Gas giant 9.449 0.43 TRUE
## 5 Jupiter Gas giant 11.209 0.41 TRUEWonderful! This exercise concludes the chapter on data frames. Remember that data frames are extremely important in R, you will need them all the time. Another very often used data structure is the list. This will be the subject of the next chapter!
Lists, why would you need them?
Congratulations! At this point in the course you are already familiar with:
Pretty sweet for an R newbie, right? ;-)
# Just submit the answer.Lists, why would you need them? (2)
A list in R is similar to your to-do list at work or school: the different items on that list most likely differ in length, characteristic, and type of activity that has to be done.
A list in R allows you to gather a variety of objects under one name (that is, the name of the list) in an ordered way. These objects can be matrices, vectors, data frames, even other lists, etc. It is not even required that these objects are related to each other in any way.
You could say that a list is some kind super data type: you can store practically any piece of information in it!
# Just submit the answer to start the first exercise on lists.Creating a list
Let us create our first list! To construct a list you use the function list():
my_list <- list(comp1, comp2 ...)The arguments to the list function are the list components. Remember, these components can be matrices, vectors, other lists, …
# Vector with numerics from 1 up to 10
my_vector <- 1:10
# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)
# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]
# Construct list with these different elements:
my_list <- list(my_vector, my_matrix, my_df)Creating a named list
Well done, you’re on a roll!
Just like on your to-do list, you want to avoid not knowing or remembering what the components of your list stand for. That is why you should give names to them:
my_list <- list(name1 = your_comp1,
name2 = your_comp2)This creates a list with components that are named name1, name2, and so on. If you want to name your lists after you’ve created them, you can use the names() function as you did with vectors. The following commands are fully equivalent to the assignment above:
my_list <- list(your_comp1, your_comp2)
names(my_list) <- c("name1", "name2")# Vector with numerics from 1 up to 10
my_vector <- 1:10
# Matrix with numerics from 1 up to 9
my_matrix <- matrix(1:9, ncol = 3)
# First 10 elements of the built-in data frame mtcars
my_df <- mtcars[1:10,]
# Adapt list() call to give the components names
my_list <- list(vec = my_vector, mat = my_matrix, df = my_df)
# Print out my_list
my_list## $vec
## [1] 1 2 3 4 5 6 7 8 9 10
##
## $mat
## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
##
## $df
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4Great! Not only do you know how to construct lists now, you can also name them; a skill that will prove most useful in practice. Continue to the next exercise.
Creating a named list (2)
Being a huge movie fan (remember your job at LucasFilms), you decide to start storing information on good movies with the help of lists.
Start by creating a list for the movie “The Shining”. We have already created the variables mov, act and rev in your R workspace. Feel free to check them out in the console.
mov <- "The Shining"
act <- c("Jack Nicholson", "Shelley Duvall", "Danny Lloyd", "Scatman Crothers", "Barry Nelson")
comments <- c("Best Horror Film I Have Ever Seen", "A truly brilliant and scary film from Stanley Kubrick", "A masterpiece of psychological horror")
rev <- data.frame(scores = c(4.5, 4.0, 5.0), sources = c("IMDb1", "IMDb2", "IMDb3"), comments = comments)
# The variables mov, act and rev are available
# Finish the code to build shining_list
shining_list <- list(moviename = mov, actors = act, reviews = rev)Wonderful! You now know how to construct and name lists. As in the previous chapters, let’s look at how to select elements for lists. Head over to the next exercise
Selecting elements from a list
Your list will often be built out of numerous elements and components. Therefore, getting a single element, multiple elements, or a component out of it is not always straightforward.
One way to select a component is using the numbered position of that component. For example, to “grab” the first component of shining_list you type
shining_list[[1]]A quick way to check this out is typing it in the console. Important to remember: to select elements from vectors, you use single square brackets: [ ]. Don’t mix them up!
You can also refer to the names of the components, with [[ ]] or with the $ sign. Both will select the data frame representing the reviews:
shining_list[["reviews"]]
shining_list$reviewsBesides selecting components, you often need to select specific elements out of these components. For example, with shining_list[[2]][1] you select from the second component, actors (shining_list[[2]]), the first element ([1]). When you type this in the console, you will see the answer is Jack Nicholson.
# shining_list is already pre-loaded in the workspace
# Print out the vector representing the actors
shining_list$actors## [1] "Jack Nicholson" "Shelley Duvall" "Danny Lloyd" "Scatman Crothers"
## [5] "Barry Nelson"# Print the second element of the vector representing the actors
shining_list$actors[2]## [1] "Shelley Duvall"Creating a new list for another movie
You found reviews of another, more recent, Jack Nicholson movie: The Departed!
| Scores | Comments |
|---|---|
| 4.6 | I would watch it again |
| 5 | Amazing! |
| 4.8 | I liked it |
| 5 | One of the best movies |
| 4.2 | Fascinating plot |
It would be useful to collect together all the pieces of information about the movie, like the title, actors, and reviews into a single variable. Since these pieces of data are different shapes, it is natural to combine them in a list variable.
movie_title, containing the title of the movie, and movie_actors, containing the names of some of the actors in the movie, are available in your workspace.
# Use the table from the exercise to define the comments and scores vectors
scores <- c(4.6, 5, 4.8, 5, 4.2)
comments <- c("I would watch it again", "Amazing!", "I liked it", "One of the best movies", "Fascinating plot")
movie_title <- "The Departed"
movie_actors <- c("Leonardo DiCaprio", "Matt Damon", "Jack Nicholson", "Mark Wahlberg", "Vera Farmiga", "Martin Sheen")
# Save the average of the scores vector as avg_review
avg_review <- mean(scores)
# Combine scores and comments into the reviews_df data frame
reviews_df <- data.frame(scores, comments)
# Create and print out a list, called departed_list
departed_list <- list(movie_title, movie_actors, reviews_df, avg_review)
departed_list## [[1]]
## [1] "The Departed"
##
## [[2]]
## [1] "Leonardo DiCaprio" "Matt Damon" "Jack Nicholson"
## [4] "Mark Wahlberg" "Vera Farmiga" "Martin Sheen"
##
## [[3]]
## scores comments
## 1 4.6 I would watch it again
## 2 5.0 Amazing!
## 3 4.8 I liked it
## 4 5.0 One of the best movies
## 5 4.2 Fascinating plot
##
## [[4]]
## [1] 4.72Good work! You successfully created another list of movie information, and combined different components into a single list. Congratulations on finishing the course!