warmup 1

A simple warmup question.

You can find help on functions using the help function. For example, to find help on the max function, you can type help(max) in the console window.

1. Insert a code block
2. What is the description of the min funtion? Paste the result as a comment in the block you created. Comments in R are preceded by #. Place # before the description to comment it out.

Insert code block below here

The help function can be used to look up the documentation for a function, or to look up the documentation to a package. We can learn how to use the dplyr package by reading its documentation like this:

Calculating total winnings

Calculating total winnings (1)

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 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

# Assign to total_daily how much you won/lost on each day
total_daily <- poker_vector + roulette_vector

Calculating total winnings (2)

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 <- sum(total_poker,total_roulette)

# Print out total_week
print(total_week)

## [1] -84

Comparing total winnings (3)

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.

Calculate total_poker and total_roulette as in the previous exercise. Use the sum() function twice.
Check if your total gains in poker are higher than for roulette by using a comparison. Simply print out the result of this comparison.
What do you conclude, should you focus on roulette or on poker?

# 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

# Calculate total gains for poker and roulette
total_poker <- sum(poker_vector)
total_roulette <- sum(roulette_vector)

# Check if you realized higher total gains in poker than in roulette 
total_poker > total_roulette

## [1] TRUE

Vector selection: the good times

Vector selection: the good times (1)

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 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

# Define a new variable based on a selection
poker_wednesday <- poker_vector[3]

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)]

## Monday Friday 
##    140    240

# 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

# Define a new variable based on a selection
poker_midweek <- poker_vector[c(2,3,4)]
poker_midweek

##   Tuesday Wednesday  Thursday 
##       -50        20      -120

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 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

# Define a new variable based on a selection
roulette_selection_vector <- roulette_vector[2:5]

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] 36.66667

Selection by comparison

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 other

As 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 TRUE

This command tests for every element of the vector if the condition stated by the comparison operator is TRUE or FALSE
* Check which elements in poker_vector are positive (i.e. > 0) and assign this to selection_vector.
* Print out selection_vector so you can inspect it. The printout tells you whether you won (TRUE) or lost (FALSE) any money for each day.

# 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

# Which days did you make money on poker?
selection_vector <- poker_vector > 0
  
# Print out selection_vector
selection_vector

##    Monday   Tuesday Wednesday  Thursday    Friday 
##      TRUE     FALSE      TRUE     FALSE      TRUE

Selection 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 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

# Which days did you make money on poker?
selection_vector <- poker_vector > 0

# Select from poker_vector these days
poker_winning_days <- poker_vector[selection_vector]

Advanced selection

Just like you did for poker, you also want to know those days where you realized a positive return for roulette.

# 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

# Which days did you make money on roulette?
selection_vector <- roulette_vector > 0

# Select from roulette_vector these days
roulette_winning_days <- days_vector[selection_vector] 
roulette_winning_days

## [1] "Wednesday" "Friday"

Matrix

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:

• The first argument is the collection of elements that R will arrange into the rows and columns of the matrix. Here, we use 1:9 which is a shortcut for c(1, 2, 3, 4, 5, 6, 7, 8, 9).
  The argument 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.
  
• The third argument 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    9

Analyze 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, byrow = TRUE, nrow = 3)
star_wars_matrix

##         [,1]  [,2]
## [1,] 460.998 314.4
## [2,] 290.475 247.9
## [3,] 309.306 165.8

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_vector

We 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  165.8

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)
star_wars_matrix <- matrix(box_office, nrow = 3, byrow = TRUE,
                           dimnames = list(c("A New Hope", "The Empire Strikes Back", "Return of the Jedi"), 
                                           c("US", "non-US")))

# Calculate worldwide box office figures
worldwide_vector <- rowSums(star_wars_matrix)
worldwide_vector

##              A New Hope The Empire Strikes Back      Return of the Jedi 
##                 775.398                 538.375                 475.106

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)
star_wars_matrix <- matrix(box_office, nrow = 3, byrow = TRUE,
                           dimnames = list(c("A New Hope", "The Empire Strikes Back", "Return of the Jedi"), 
                                           c("US", "non-US")))

# 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)
all_wars_matrix

##                              US non-US worldwide_vector
## A New Hope              460.998  314.4          775.398
## The Empire Strikes Back 290.475  247.9          538.375
## Return of the Jedi      309.306  165.8          475.106

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.

Type the name of these matrices in the console and hit Enter 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.

box_office <- c(474.5, 552.5, 310.7, 338.7, 380.3, 468.5)
star_wars_matrix2 <- matrix(box_office, nrow = 3, byrow = TRUE,
                           dimnames = list(c("The Phantom Menace", "Attack of the Clones", "Revenge of the Sith"), 
                                           c("US", "non-US")))
# 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  165.8

star_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)
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

The total box office revenue for the entire saga

Just like cbind() has rbind(), colSums() has rowSums(). 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 2087.800

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]
non_us_all

##              A New Hope The Empire Strikes Back      Return of the Jedi 
##                   314.4                   247.9                   165.8 
##      The Phantom Menace    Attack of the Clones     Revenge of the Sith 
##                   552.5                   338.7                   468.5

# Average non-US revenue
mean(non_us_all)

## [1] 347.9667

# Select the non-US revenue for first two movies
non_us_some <- non_us_all[2]
non_us_some

## The Empire Strikes Back 
##                   247.9

# Average non-US revenue for first two movies
mean(non_us_some)

## [1] 247.9

A little arithmetic with matrices (1)

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  93.70

Great! What do these results tell you? A staggering 92 million people went to see A New Hope in US theaters!

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 Price Star Wars (in dollars)
ticket_price <- c(5.0, 6.0, 6.0, 6.0, 7.0, 7.0, 4.0, 4.0, 4.5, 4.5, 4.9, 4.9)

# Matrix Ticket Price by Star Wars movies
ticket_prices_matrix <- matrix(ticket_price, nrow = 6, byrow = TRUE,
                           dimnames = list(c("A New Hope", "The Empire Strikes Back", "Return of the Jedi", "The Phantom Menace", "Attack of the Clones", "Revenge of the Sith"), 
                                           c("US", "non-US")))

# Print Ticket Price Star Wars matrix                                 
ticket_prices_matrix 

##                          US non-US
## A New Hope              5.0    6.0
## 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

# all_wars_matrix and ticket_prices_matrix are 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

ticket_prices_matrix

##                          US non-US
## A New Hope              5.0    6.0
## 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] 75.01339

Factor

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 for categorical variables"

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 Male

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 < High

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 Male

Summarizing 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      3

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] NA

How 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 (1)

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")

# Convert speed_vector to ordered factor vector
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 < fast

summary(factor_speed_vector)

##   slow medium   fast 
##      2      2      1

It is now indicated that the Levels indeed have an order associated, with the < sign.

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 than data analyst 5?
da2 > da5

## [1] FALSE

Bellissimo! What do 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!

DataFrames

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:

• ‘Are you married?’ or ‘yes/no’ questions (logical)
  
• ‘How old are you?’ (numeric)
  
• ‘What is your opinion on this product?’ or other ‘open-ended’ questions (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.

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    1

Quick, 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.

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:

The total number of observations (e.g. 32 car types)
The total number of variables (e.g. 11 car features)
A full list of the variables names (e.g. mpg, cyl … )
The data type of each variable (e.g. num)
The first observations

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:

The type of planet (Terrestrial or Gas Giant).
The planet’s diameter relative to the diameter of the Earth.
The planet’s rotation across the sun relative to that of the Earth.
If the planet has rings or not (TRUE or FALSE).

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 on the right. 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)
planets_df

##      name               type diameter rotation rings
## 1 Mercury Terrestrial planet    0.382    58.64 FALSE
## 2   Venus Terrestrial planet    0.949  -243.02 FALSE
## 3   Earth Terrestrial planet    1.000     1.00 FALSE
## 4    Mars Terrestrial planet    0.532     1.03 FALSE
## 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  TRUE

# Check the structure of planets_df
str(planets_df)

## 'data.frame':    8 obs. of  5 variables:
##  $ name    : Factor w/ 8 levels "Earth","Jupiter",..: 4 8 1 3 2 6 7 5
##  $ type    : Factor w/ 2 levels "Gas giant","Terrestrial planet": 2 2 2 2 1 1 1 1
##  $ 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 ...

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!

# 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 FALSE

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"]

# Select first 5 values of diameter column
planets_df[1:5,"diameter"]

## [1]  0.382  0.949  1.000  0.532 11.209

Only 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

# 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  TRUE

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 TRUE

This 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.

# 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  TRUE

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)

# 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 FALSE

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 3

10, 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

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.

# Use order() to create positions
positions <- order(planets_df$diameter)
positions

## [1] 1 4 2 3 8 7 6 5

# 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  TRUE

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

Lists, why would you need them?

Congratulations! At this point in the course you are already familiar with:

• Vectors (one dimensional array): can hold numeric, character or logical values. The elements in a vector all have the same data type.
  
• Matrices (two dimensional array): can hold numeric, character or logical values. The elements in a matrix all have the same data type.
  
• Data frames (two-dimensional objects): can hold numeric, character or logical values. Within a column all elements have the same data type, but different columns can be of different data type.

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!

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)
my_list

## [[1]]
##  [1]  1  2  3  4  5  6  7  8  9 10
## 
## [[2]]
##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9
## 
## [[3]]
##                    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

Creating a named list

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    4

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.

# The variables mov, act and rev are available mov act rev

# Finish the code to build shining_list shining_list <- list(moviename = mov, actors=act, reviews=rev)

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$reviews

Besides 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
shining_list

# Print out the vector representing the actors
shining_list[["actors"]]

# Print the second element of the vector representing the actors
shining_list$actors[[2]]

Adding more movie information to the list

Being proud of your first list, you shared it with the members of your movie hobby club. However, one of the senior members, a guy named M. McDowell, noted that you forgot to add the release year. Given your ambitions to become next year’s president of the club, you decide to add this information to the list.

To conveniently add elements to lists you can use the c() function, that you also used to build vectors:

ext_list <- c(my_list , my_val)

This will simply extend the original list, my_list, with the component my_val. This component gets appended to the end of the list. If you want to give the new list item a name, you just add the name as you did before:

ext_list <- c(my_list, my_name = my_val)

# shining_list, the list containing movie name, actors and reviews, is pre-loaded in the workspace
shining_list

# We forgot something; add the year to shining_list
shining_list_full <- c(shining_list, year=1980)

# Have a look at shining_list_full
shining_list_full