Vectors and lists in R

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In programming, a data structure is a format for organizing and storing data. Data structures are important to understand because you will work with them frequently when you use R for data analysis. The most common data structures in the R programming language include:

• Vectors
• Data frames
• Matrices
• Arrays

Think of a data structure like a house that contains your data.

This reading will focus on vectors. Later on, you’ll learn more about data frames, matrices, and arrays.

There are two types of vectors: atomic vectors and lists. Coming up, you’ll learn about the basic properties of atomic vectors and lists, and how to use R code to create them.

Atomic vectors

First, we will go through the different types of atomic vectors. Then, you will learn how to use R code to create, identify, and name the vectors.

Earlier, you learned that a vector is a group of data elements of the same type, stored in a sequence in R. You cannot have a vector that contains both logicals and numerics.

There are six primary types of atomic vectors: logical, integer, double, character (which contains strings), complex, and raw. The last two–complex and raw–aren’t as common in data analysis, so we will focus on the first four. Together, integer and double vectors are known as numeric vectors because they both contain numbers. This table summarizes the four primary types:

Type	Description	Example
Logical	True/False	TRUE
Integer	Positive and negative whole values	3
Double	Decimal values	101.175
Character	String/character values	"Coding"

This diagram illustrates the hierarchy of relationships among these four main types of vectors:

Creating vectors

One way to create a vector is by using the c() function (called the “combine” function). The c() function in R combines multiple values into a vector. In R, this function is just the letter “c” followed by the values you want in your vector inside the parentheses, separated by a comma: c(x, y, z, …).

For example, you can use the c() function to store numeric data in a vector.

c(2.5, 48.5, 101.5)

To create a vector of integers using the c() function, you must place the letter "L" directly after each number.

c(1L, 5L, 15L)

You can also create a vector containing characters or logicals.

c(“Sara” , “Lisa” , “Anna”)

c(TRUE, FALSE, TRUE)

Determining the properties of vectors

Every vector you create will have two key properties: type and length.

You can determine what type of vector you are working with by using the typeof() function. Place the code for the vector inside the parentheses of the function. When you run the function, R will tell you the type. For example:

typeof(c(“a” , “b”))

#> [1] "character"

Notice that the output of the typeof function in this example is “character”. Similarly, if you use the typeof function on a vector with integer values, then the output will include “integer” instead:

typeof(c(1L , 3L))

#> [1] "integer"

You can determine the length of an existing vector–meaning the number of elements it contains–by using the length() function. In this example, we use an assignment operator to assign the vector to the variable x. Then, we apply the length() function to the variable. When we run the function, R tells us the length is 3.

x <- c(33.5, 57.75, 120.05)

length(x)

#> [1] 3

You can also check if a vector is a specific type by using an is function: is.logical(), is.double(), is.integer(), is.character(). In this example, R returns a value of TRUE because the vector contains integers.

x <- c(2L, 5L, 11L)

is.integer(x)

#> [1] TRUE

In this example, R returns a value of FALSE because the vector does not contain characters, rather it contains logicals.

y <- c(TRUE, TRUE, FALSE)

is.character(y)

#> [1] FALSE

Naming vectors

All types of vectors can be named. Names are useful for writing readable code and describing objects in R. You can name the elements of a vector with the names() function. As an example, let’s assign the variable x to a new vector with three elements.

x <- c(1, 3, 5)

You can use the names() function to assign a different name to each element of the vector.

names(x) <- c("a", "b", "c")

Now, when you run the code, R shows that the first element of the vector is named a, the second b, and the third c.

x

#> a b c

#> 1 3 5

Remember that an atomic vector can only contain elements of the same type. If you want to store elements of different types in the same data structure, you can use a list.

Creating lists

Lists are different from atomic vectors because their elements can be of any type—like dates, data frames, vectors, matrices, and more. Lists can even contain other lists.

You can create a list with the list() function. Similar to the c() function, the list() function is just list followed by the values you want in your list inside parentheses: list(x, y, z, …). In this example, we create a list that contains four different kinds of elements: character ("a"), integer (1L), double (1.5), and logical (TRUE).

list("a", 1L, 1.5, TRUE)

Like we already mentioned, lists can contain other lists. If you want, you can even store a list inside a list inside a list—and so on.

list(list(list(1 , 3, 5)))

Determining the structure of lists

If you want to find out what types of elements a list contains, you can use the str() function. To do so, place the code for the list inside the parentheses of the function. When you run the function, R will display the data structure of the list by describing its elements and their types.

Let’s apply the str() function to our first example of a list.

str(list("a", 1L, 1.5, TRUE))

We run the function, then R tells us that the list contains four elements, and that the elements consist of four different types: character (chr), integer (int), number (num), and logical (logi).

#> List of 4

#> $ : chr "a"

#> $ : int 1

#> $ : num 1.5

#> $ : logi TRUE

Let’s use the str() function to discover the structure of our second example. First, let’s assign the list to the variable z to make it easier to input in the str() function.

z <- list(list(list(1 , 3, 5)))

Let’s run the function.

str(z)

#> List of 1

#> $ :List of 1

#> ..$ :List of 3

#> .. ..$ : num 1

#> .. ..$ : num 3

#> .. ..$ : num 5

The indentation of the $ symbols reflect the nested structure of this list. Here, there are three levels (so there is a list within a list within a list).

Naming lists

Lists, like vectors, can be named. You can name the elements of a list when you first create it with the list() function:

list('Chicago' = 1, 'New York' = 2, 'Los Angeles' = 3)

$'Chicago'

[1] 1

$'New York'

[1] 2

$'Los Angeles'

[1] 3

Additional resource

To learn more about vectors and lists, check out R for Data Science, Chapter 20: Vectors . R for Data Science is a classic resource for learning how to use R for data science and data analysis. It covers everything from cleaning to visualizing to communicating your data. If you want to get more details about the topic of vectors and lists, this chapter is a great place to start.

Dates and times in R

In this reading, you will learn how to work with dates and times in R using the lubridate package. Coming up, you will use tools in the lubridate package to convert different types of data in R into date and date-time formats.

Loading tidyverse and lubridate packages

Before you get started working with dates and times, you should load both tidyverse and lubridate. Lubridate is part of tidyverse.

First, open RStudio.

If you haven't already installed tidyverse, you can use the install.packages() function to do so:

• install.packages("tidyverse")

Next, load the tidyverse and lubridate packages using the library() function. First, load the core tidyverse to make it available in your current R session:

library(tidyverse)

Then, load the lubridate package:

library(lubridate)

Now you’re ready to be introduced to the tools in the lubridate package.

Working with dates and times

This section covers the data types for dates and times in R and how to convert strings to date-time formats.

Types

In R, there are three types of data that refer to an instant in time:

• A date ("2016-08-16")
• A time within a day (“20:11:59 UTC")
• And a date-time. This is a date plus a time ("2018-03-31 18:15:48 UTC")

The time is given in UTC, which stands for Universal Time Coordinated, more commonly called Universal Coordinated Time. This is the primary standard by which the world regulates clocks and time.

For example, to get the current date you can run the today() function. The date appears as year, month, and day.

today()

#> [1] "2021-01-20"

To get the current date-time you can run the now() function. Note that the time appears to the nearest second.

now()

#> [1] "2021-01-20 16:25:05 UTC"

When working with R, there are three ways you are likely to create date-time formats:

• From a string
• From an individual date
• From an existing date/time object

R creates dates in the standard yyyy-mm-dd format by default.

Let's go over each.

Converting from strings

Date/time data often comes as strings. You can convert strings into dates and date-times using the tools provided by lubridate. These tools automatically work out the date/time format. First, identify the order in which the year, month, and day appear in your dates. Then, arrange the letters y, m, and d in the same order. That gives you the name of the lubridate function that will parse your date. For example, for the date 2021-01-20, you use the order ymd:

ymd("2021-01-20")

When you run the function, R returns the date in yyyy-mm-dd format.

#> [1] "2021-01-20"

It works the same way for any order. For example, month, day, and year. R still returns the date in yyyy-mm-dd format.

mdy("January 20th, 2021")

#> [1] "2021-01-20"

Or, day, month, and year. R still returns the date in yyyy-mm-dd format.

dmy("20-Jan-2021")

#> [1] "2021-01-20"

These functions also take unquoted numbers and convert them into the yyyy-mm-dd format.

ymd(20210120)

#> [1] "2021-01-20"

**Creating date-time components The ymd() function and its variations create dates. To create a date-time from a date, add an underscore and one or more of the letters h, m, and s (hours, minutes, seconds) to the name of the function:

ymd_hms("2021-01-20 20:11:59")

#> [1] "2021-01-20 20:11:59 UTC"

mdy_hm("01/20/2021 08:01")

#> [1] "2021-01-20 08:01:00 UTC"

Optional: Switching between existing date-time objects

Finally, you might want to switch between a date-time and a date.

You can use the function as_date() to convert a date-time to a date. For example, put the current date-time—now()—in the parentheses of the function.

as_date(now())

#> [1] "2021-01-20"

Additional resources

To learn more about working with dates and times in R, check out the following resources:

• lubridate.tidyverse : This is the “lubridate” entry from the official tidyverse documentation, which offers a comprehensive reference guide to the various tidyverse packages. Check out this link for an overview of key concepts and functions.
• Dates and times with lubridate: Cheat Sheet : This “cheat sheet” gives you a detailed map of all the different things you can do with the lubridate package. You don’t need to know all of this information, but the cheat sheet is a useful reference for any questions you might have about working with dates and times in R.

Other common data structures

In this reading, you will continue on the topic of data structures with an introduction to data frames and matrices. You will learn about the basic properties of each structure, and simple ways to make use of them using R code. You will also briefly explore files, which are often used to access and store data and related information.

Data structures

Recall that a data structure is like a house that contains your data.

Data frames

Data frames are the most common way of storing and analyzing data in R, so it’s important to understand what they are and how to create them. A data frame is a collection of columns–similar to a spreadsheet or SQL table. Each column has a name at the top that represents a variable, and includes one observation per row. Data frames help summarize data and organize it into a format that is easy to read and use.

For example, the data frame below shows the “diamonds” dataset, which is one of the preloaded datasets in R. Each column contains a single variable that is related to diamonds: carat, cut, color, clarity, depth, and so on. Each row represents a single observation.

There are a few key things to keep in mind when you are working with data frames:

• First, columns should be named.
• Second, data frames can include many different types of data, like numeric, logical, or character.
• Finally, elements in the same column should be of the same type.

You will learn more about data frames later on in the program, but this is a great starting point.

If you need to manually create a data frame in R, you can use the data.frame() function. The data.frame() function takes vectors as input. In the parentheses, enter the name of the column, followed by an equals sign, and then the vector you want to input for that column. In this example, the x column is a vector with elements 1, 2, 3, and the y column is a vector with elements 1.5, 5.5, 7.5.

data.frame(x = c(1, 2, 3) , y = c(1.5, 5.5, 7.5))

If you run the function, R displays the data frame in ordered rows and columns.

   x y

1  1 1.5

2  2 5.5

3  3 7.5

In most cases, you won’t need to manually create a data frame yourself, as you will typically import data from another source, such as a .csv file, a relational database, or a software program.

Files

Let’s go over how to create, copy, and delete files in R. For more information on working with files in R, check out R documentation: files . R documentation is a tool that helps you easily find and browse the documentation of almost all R packages on CRAN. It’s a useful reference guide for functions in R code. Let’s go through a few of the most useful functions for working with files.

Use the dir.create function to create a new folder, or directory, to hold your files. Place the name of the folder in the parentheses of the function.

dir.create ("destination_folder")

Use the file.create() function to create a blank file. Place the name and the type of the file in the parentheses of the function. Your file types will usually be something like .txt, .docx, or .csv.

file.create (“new_text_file.txt”)

file.create (“new_word_file.docx”)

file.create (“new_csv_file.csv”)

If the file is successfully created when you run the function, R will return a value of TRUE (if not, R will return FALSE).

file.create (“new_csv_file.csv”)

[1] TRUE

Copying a file can be done using the file.copy() function. In the parentheses, add the name of the file to be copied. Then, type a comma, and add the name of the destination folder that you want to copy the file to.

file.copy (“new_text_file.txt” , “destination_folder”)

If you check the Files pane in RStudio, a copy of the file appears in the relevant folder:

You can delete R files using the unlink() function. Enter the file’s name in the parentheses of the function.

unlink (“some_.file.csv”)

Additional resource

If you want to learn more about working with data frames, matrices, and arrays in R, check out the Data Wrangling section of Stat Education's Introduction to R course. The section includes modules on data frames, matrices, and arrays (and more), and each module contains helpful examples of key coding concepts.

Optional: Matrices

A matrix is a two-dimensional collection of data elements. This means it has both rows and columns. By contrast, a vector is a one-dimensional sequence of data elements. But like vectors, matrices can only contain a single data type. For example, you can’t have both logicals and numerics in a matrix.

To create a matrix in R, you can use the matrix() function. The matrix() function has two main arguments that you enter in the parentheses. First, add a vector. The vector contains the values you want to place in the matrix. Next, add at least one matrix dimension. You can choose to specify the number of rows or the number of columns by using the code nrow = or ncol =.

For example, imagine you want to create a 2x3 (two rows by three columns) matrix containing the values 3-8. First, enter a vector containing that series of numbers: c(3:8). Then, enter a comma. Finally, enter nrow = 2 to specify the number of rows.

matrix(c(3:8), nrow = 2)

If you run the function, R displays a matrix with three columns and two rows (typically referred to as a “2x3”) that contain the numeric values 3, 4, 5, 6, 7, 8. R places the first value (3) of the vector in the uppermost row, and the leftmost column of the matrix, and continues the sequence from left to right.

[,1] [,2] [,3]

[1,] 3 5 7

[2,] 4 6 8

You can also choose to specify the number of columns (ncol = ) instead of the number of rows (nrow = ).

matrix(c(3:8), ncol = 2)

When you run the function, R infers the number of rows automatically.

[,1] [,2]

[1,] 3 6

[2,] 4 7

[3,] 5 8

Test your knowledge on programming concepts

Question 1

Why do analysts use comments In R programming? Select all that apply.

• To explain their code
• To make an R Script more readable
• To act as functions
• To provide names for variables

Explain: In R programming, comments are used to explain your code and to make an R Script more readable.

Question 2

What should you use to assign a value to a variable in R?

A. A vector

B. An argument

C. A comment

D. An operator

The correct answer is D. An operator. Explain: You should use an operator to assign a value to a variable in R. You should use operators such as <- after a variable to assign a value to it.

Question 3

Which of the following examples is the proper syntax for a function in R?

A. #first

B. <- 20

C. data_1

D. print()

The correct answer is D. print(). Explain: An example of the syntax for a function in R is print(). If you add an argument in the parentheses for the print() function, the argument will appear in the console pane of RStudio.

Question 4

Which of the following examples can you use in R for date/time data? Select all that apply.

• 06:11:13 UTC
• 2018-12-21 16:35:28 UTC
• 2019-04-16
• seven-24-2018

Explain: The examples of types of date/time data that you can use in R are 06:11:13 UTC, 2019-04-16, and 2018-12-21 16:35:28 UTC. R recognizes the syntax of each of these formats as a date/time data type.