Introduction to R

Last updated on 2024-12-03 | Edit this page

Estimated time: 40 minutes

Overview

Questions

What data types are available in R?
What is an object?
How can values be initially assigned to variables of different data types?
What arithmetic and logical operators can be used?
How can subsets be extracted from vectors?
How does R treat missing values?
How can we deal with missing values in R?

Objectives

Define the following terms as they relate to R: object, assign, call, function, arguments, options.
Assign values to objects in R.
Learn how to name objects.
Use comments to inform script.
Solve simple arithmetic operations in R.
Call functions and use arguments to change their default options.
Inspect the content of vectors and manipulate their content.
Subset and extract values from vectors.
Analyze vectors with missing data.

Creating objects in R

You can get output from R simply by typing math in the console:

R

3 + 5

OUTPUT

[1] 8

R

12 / 7

OUTPUT

[1] 1.714286

However, to do useful and interesting things, we need to assign values to objects. To create an object, we need to give it a name followed by the assignment operator <-, and the value we want to give it:

R

x <- 3

<- is the assignment operator. It assigns values on the right to objects on the left. So, after executing x <- 3, the value of x is 3. The arrow can be read as 3 goes into x.

In RStudio, typing Alt + - (push Alt at the same time as the - key) will write <- in a single keystroke in a PC, while typing Option + - (push Option at the same time as the - key) does the same in a Mac.

Historical

For historical reasons, you can also use = for assignments, but not in every context. Because of the slight differences in syntax, it is good practice to always use <- for assignments. More generally we prefer the <- syntax over = because it makes it clear what direction the assignment is operating (left assignment), and it increases the read-ability of the code.

Naming objects

Objects can be given any name such as x, current_temperature, or subject_id. You want your object names to be explicit and not too long.

They cannot start with a number (2x is not valid, but x2 is), and R is case sensitive (e.g., age is different from Age). There are some names that cannot be used because they are the names of fundamental objects in R (e.g., if, else, for, see here for a complete list). In general, even if it’s allowed, it’s best to not use them (e.g., c, T, mean, data, df, weights). If in doubt, check the help to see if the name is already in use.

It is best to avoid dots (.) within an object name as in my.dataset. There are many objects in R with dots in their names for historical reasons, but because dots have a special meaning in R (for methods) and other programming languages, it’s best to avoid them.

It is also recommended to use nouns for object names, and verbs for function names. It’s important to be consistent in the styling of your code (where you put spaces, how you name objects, etc.). Using a consistent coding style makes your code clearer to read for your future self and your collaborators.

In R, three popular style guides are Google’s, Jean Fan’s and the tidyverse’s. The tidyverse’s is very comprehensive and may seem overwhelming at first. You can install the lintr package to automatically check for issues in the styling of your code.

Objects vs. variables

What are known as objects in R are known as variables in many other programming languages. Depending on the context, object and variable can have drastically different meanings. However, in this lesson, the two words are used synonymously. For more information see: https://cran.r-project.org/doc/manuals/r-release/R-lang.html#Objects

Printing objects

When assigning a value to an object, R does not print anything. You can force R to print the value by using parentheses or by typing the object name:

R

area_hectares <- 1.0    # doesn't print anything
area_hectares         # but typing the name of the object print

OUTPUT

[1] 1

R

(area_hectares <- 1.0)  # putting parenthesis around the call also print

OUTPUT

[1] 1

Doing math on objects

Now that R has area_hectares in memory, we can do arithmetic with it. For instance, we may want to convert this area into acres (area in acres is 2.47 times the area in hectares):

R

2.47 * area_hectares

OUTPUT

[1] 2.47

We can also change an object’s value by assigning it a new one:

R

area_hectares <- 2.5
2.47 * area_hectares

OUTPUT

[1] 6.175

Assigning a value to one object does not automatically change the values of other objects. For example, let’s store the plot’s area in acres in a new object, area_acres:

R

area_acres <- 2.47 * area_hectares

and then change area_hectares to 50.

R

area_hectares <- 50

Exercise

What do you think is the current content of the object area_acres? 123.5 or 6.175?

Show me the solution

The value of area_acres is still 6.175 because you have not re-run the line area_acres <- 2.47 * area_hectares since changing the value of area_hectares.

Exercise

Create two variables r_length and r_width and assign them values. It should be noted that, because length is a built-in R function, R Studio might add “()” after you type length and if you leave the parentheses you will get unexpected results. This is why you might see other programmers abbreviate common words. Create a third variable r_area and give it a value based on the current values of r_length and r_width. Show that changing the values of either r_length and r_width does not affect the value of r_area.

Show me the solution

R

r_length <- 2.5
r_width <- 3.2
r_area <- r_length * r_width
r_area

OUTPUT

[1] 8

R

# change the values of r_length and r_width
r_length <- 7.0
r_width <- 6.5
# the value of r_area isn't changed
r_area

OUTPUT

[1] 8

Comments

All programming languages allow the programmer to include comments in their code. To do this in R we use the # character. Anything to the right of the # sign and up to the end of the line is treated as a comment and is ignored by R. You can start lines with comments or include them after any code on the line.

R

# land area in hectares
area_hectares <- 1.0      
area_acres <- area_hectares * 2.47  # convert to acres
area_acres        # print land area in acres.

OUTPUT

[1] 2.47

RStudio makes it easy to comment or uncomment a paragraph: after selecting the lines you want to comment, press at the same time on your keyboard Ctrl + Shift + C. If you only want to comment out one line, you can put the cursor at any location of that line (i.e. no need to select the whole line), then press Ctrl + Shift + C.

Functions and their arguments

Functions are “canned scripts” that automate more complicated sets of commands including operations assignments, etc. Many functions are predefined, or can be made available by importing R packages (more on that later). A function usually gets one or more inputs called arguments. Functions often (but not always) return a value. A typical example would be the function sqrt(). The input (the argument) must be a number, and the return value (in fact, the output) is the square root of that number. Executing a function (‘running it’) is called calling the function. An example of a function call is:

R

b <- sqrt(area_acres)

Here, the value of a is given to the sqrt() function, the sqrt() function calculates the square root, and returns the value which is then assigned to the object b. This function is very simple, because it takes just one argument.

The return ‘value’ of a function need not be numerical (like that of sqrt()), and it also does not need to be a single item: it can be a set of things, or even a dataset. We’ll see that when we read data files into R.

Arguments can be anything, not only numbers or filenames, but also other objects. Exactly what each argument means differs per function, and must be looked up in the documentation (see below). Some functions take arguments which may either be specified by the user, or, if left out, take on a default value: these are called options. Options are typically used to alter the way the function operates, such as whether it ignores ‘bad values’, or what symbol to use in a plot. However, if you want something specific, you can specify a value of your choice which will be used instead of the default.

Let’s try a function that can take multiple arguments: round().

R

round(3.14159)

OUTPUT

[1] 3

Here, we’ve called round() with just one argument, 3.14159, and it has returned the value 3. That’s because the default is to round to the nearest whole number. If we want more digits we can see how to do that by getting information about the round function. We can use args(round) or look at the help for this function using ?round.

R

args(round)

OUTPUT

function (x, digits = 0, ...)
NULL

R

?round

We see that if we want a different number of digits, we can type digits=2 or however many we want.

R

round(3.14159, digits = 2)

OUTPUT

[1] 3.14

If you provide the arguments in the exact same order as they are defined you don’t have to name them:

R

round(3.14159, 2)

OUTPUT

[1] 3.14

And if you do name the arguments, you can switch their order:

R

round(digits = 2, x = 3.14159)

OUTPUT

[1] 3.14

It’s good practice to put the non-optional arguments (like the number you’re rounding) first in your function call, and to specify the names of all optional arguments. If you don’t, someone reading your code might have to look up the definition of a function with unfamiliar arguments to understand what you’re doing.

Exercise

Type in ?round at the console and then look at the output in the Help pane. What other functions exist that are similar to round? How do you use the digits parameter in the round function?

Vectors and data types

A vector is the most common and basic data type in R, and is pretty much the workhorse of R. A vector is composed by a series of values. We can assign a series of values to a vector using the c() function. For example we can create a vector containing the score of movies on imdb and assign it to an object imdb_score:

R

imdb_score <- c(62, 21, 77, 80)
imdb_score

OUTPUT

[1] 62 21 77 80

The vector imdb_score contains numbers, but a vector can also contain characters. For example, we can have a vector of movie titles corresponding to the scores (title):

R

title <- c("FTA", "Dostana", "Deliverance", "Life of Brian")
title

OUTPUT

[1] "FTA"           "Dostana"       "Deliverance"   "Life of Brian"

The quotes around “FTA”, etc. are essential. Without the quotes R will assume there are objects called FTA etc. As these objects don’t exist in R’s memory, there will be an error message.

An important feature of a vector, is that all of the elements are the same type of data.

Vectors and datatypes

An atomic vector is the simplest R data type and is a linear vector of a single type. Above, we saw 2 of the 6 main atomic vector types that R uses: "character" and "numeric" (or "double"). These are the basic building blocks that all R objects are built from. The other 4 atomic vector types are:

"logical" for TRUE and FALSE (the boolean data type)
"integer" for integer numbers (e.g., 2L, the L indicates to R that it’s an integer)
"complex" to represent complex numbers with real and imaginary parts (e.g., 1 + 4i) and that’s all we’re going to say about them
"raw" for bitstreams that we won’t discuss further

Vectors are one of the many data structures that R uses. Other important ones are lists (list), matrices (matrix), data frames (data.frame), factors (factor) and arrays (array).

The function class() indicates the class (the type of element) of an object:

R

class(imdb_score)

OUTPUT

[1] "numeric"

R

class(title)

OUTPUT

[1] "character"

The function str() provides an overview of the structure of an object and its elements. It is a useful function when working with large and complex objects:

R

str(imdb_score)

OUTPUT

 num [1:4] 62 21 77 80

R

str(title)

OUTPUT

 chr [1:4] "FTA" "Dostana" "Deliverance" "Life of Brian"

As you can see there are many functions that allow you to inspect the content of a vector. Another example is length() that tells you how many elements are in a particular vector:

R

length(imdb_score)

OUTPUT

[1] 4

You can use the c() function to add other elements to your vector:

R

production_country <- c("IN", "US")
production_country

OUTPUT

[1] "IN" "US"

R

production_country <- c(production_country, "GB") # add to the end of the vector
production_country <- c("US", production_country) # add to the beginning of the vector
production_country

OUTPUT

[1] "US" "IN" "US" "GB"

First the vector production_country is created with two values. Then the value “GB” is added to the end of the vector, and the result is saved back into production_country. After that the value “US” is added to the front of the vector, and again saved back into production_country

We can do this over and over again to grow a vector, or assemble a dataset. As we program, this may be useful to add results that we are collecting or calculating.

Exercise

We’ve seen that atomic vectors can be of type character, numeric (or double), integer, and logical. But what happens if we try to mix these types in a single vector?

Show me the solution

R implicitly converts them to all be the same type.

Exercise (continued)

What will happen in each of these examples? (hint: use class() to check the data type of your objects):

Show me the solution

R

num_char <- c(1, 2, 3, "a")
num_logical <- c(1, 2, 3, TRUE)
char_logical <- c("a", "b", "c", TRUE)
tricky <- c(1, 2, 3, "4")

Exercise (continued)

Why do you think it happens?

Show me the solution

Vectors can be of only one data type. R tries to convert (coerce) the content of this vector to find a “common denominator” that doesn’t lose any information.

Exercise (continued)

How many values in combined_logical are "TRUE" (as a character) in the following example:

R

num_logical <- c(1, 2, 3, TRUE)
char_logical <- c("a", "b", "c", TRUE)
combined_logical <- c(num_logical, char_logical)

Show me the solution

Only one. There is no memory of past data types, and the coercion happens the first time the vector is evaluated. Therefore, the TRUE in num_logical gets converted into a 1 before it gets converted into "1" in combined_logical.

Exercise (continued)

You’ve probably noticed that objects of different types get converted into a single, shared type within a vector. In R, we call converting objects from one class into another class coercion. These conversions happen according to a hierarchy, whereby some types get preferentially coerced into other types. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

Subsetting vectors

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

R

title[2]

OUTPUT

[1] "Dostana"

R

title[c(3, 2)]

OUTPUT

[1] "Deliverance" "Dostana"

First R return the second element from the title vector, and after that R returns the third and second element from the title vector.

R indices start at 1. Programming languages like Fortran, MATLAB, Julia, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

We can also repeat the indices to create an object with more elements than the original one:

R

more_title <- title[c(1, 2, 3, 2, 1, 3)]
more_title

OUTPUT

[1] "FTA"         "Dostana"     "Deliverance" "Dostana"     "FTA"
[6] "Deliverance"

Here we create an new vector more_title containing the elements from title in the order given in the c function

Conditional subsetting

Sometimes we only want a subset of a vector, but we do not know the exact index number. A common way of getting a subset without knowing the index numbers, is by using a logical vector. TRUE will select the element with the same index, while FALSE will not:

R

imdb_score[c(TRUE, FALSE, TRUE, TRUE)]

OUTPUT

[1] 62 77 80

Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests. For instance, if you wanted to select only the imdb_scores above 70:

R

imdb_score > 70    # will return logicals with TRUE for the indices that meet the condition

OUTPUT

[1] FALSE FALSE  TRUE  TRUE

R

## so we can use this to select only the values above 70
imdb_score[imdb_score > 70]

OUTPUT

[1] 77 80

You can combine multiple tests using & (both conditions are true (AND)) or | (at least one of the conditions is true, (OR)):

R

imdb_score[imdb_score < 62 | imdb_score > 77]

OUTPUT

[1] 21 80

R

imdb_score[imdb_score >= 62 & imdb_score <= 77]

OUTPUT

[1] 62 77

Callout

Here, < stands for “less than”, > for “greater than”, >= for “greater than or equal to”, and == for “equal to”. The double equal sign == is a test for numerical equality between the left and right hand sides, and should not be confused with the single = sign, which performs variable assignment (similar to <-).

A common task is to search for certain strings in a vector. One could use the “or” operator | to test for equality to multiple values, but this can quickly become tedious.

R

title[title == "FTA" | title == "Dostana"] # returns both "FTA and Dostana"

OUTPUT

[1] "FTA"     "Dostana"

The function %in% allows you to test if any of the elements of a search vector (on the right hand side) are found in the target vector (on the left hand side):

R

title %in% c("FTA", "Dostana")

OUTPUT

[1]  TRUE  TRUE FALSE FALSE

Note that the output is the same length as the search vector on the left hand side, because %in% checks whether each element of the search vector is found somewhere in the target vector. Thus, you can use %in% to select the elements in the search vector that appear in your target vector:

R

title[title %in% c("FTA", "Dostana")]

OUTPUT

[1] "FTA"     "Dostana"

You can also use a vector with TRUE and FALSE based on one vector to find informations in another vectore. For instance if we want the imdb scores for the movies “FTA” and “Dostana” we can do it like this.

R

imdb_score[title %in% c("FTA", "Dostana")]

OUTPUT

[1] 62 21

Missing data

As R was designed to analyze datasets, it includes the concept of missing data (which is uncommon in other programming languages). Missing data are represented in vectors as NA.

When doing operations on numbers, most functions will return NA if the data you are working with include missing values. This feature makes it harder to overlook the cases where you are dealing with missing data. You can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.

R

#Makes an new vector with NA values
imdb_score_na <- c(imdb_score, NA, 54, NA)
#tries to calculate mean - without ignoring NA values
mean(imdb_score_na)

OUTPUT

[1] NA

R

#tries to find the maximum value - without ignoring NA values
max(imdb_score_na)

OUTPUT

[1] NA

R

#tries to calculate mean - telling R to ignore NA values
mean(imdb_score_na, na.rm = TRUE)

OUTPUT

[1] 58.8

R

#tries to find the maximum value - telling R to ignore NA values
max(imdb_score_na, na.rm = TRUE)

OUTPUT

[1] 80

If your data include missing values, you may want to become familiar with the functions is.na(), na.omit(), and complete.cases(). See below for examples.

R

## Extract those elements which are not missing values.
imdb_score_na[!is.na(imdb_score_na)]

OUTPUT

[1] 62 21 77 80 54

R

## Count the number of missing values.
sum(is.na(imdb_score_na))

OUTPUT

[1] 2

R

## Returns the object with incomplete cases removed. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
na.omit(imdb_score_na)

OUTPUT

[1] 62 21 77 80 54
attr(,"na.action")
[1] 5 7
attr(,"class")
[1] "omit"

R

## Extract those elements which are complete cases. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
imdb_score_na[complete.cases(imdb_score_na)]

OUTPUT

[1] 62 21 77 80 54

Recall that you can use the typeof() function to find the type of your atomic vector.

Exercise

Using this vector of rooms, create a new vector with the NAs removed.

R

rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)

Use the function median() to calculate the median of the rooms vector.
Use R to figure out how many households in the set use more than 2 rooms for sleeping.

Show me the solution

R

rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)
rooms_no_na <- rooms[!is.na(rooms)]
# or
rooms_no_na <- na.omit(rooms)
# 2.
median(rooms, na.rm = TRUE)

OUTPUT

[1] 1

R

# 3.
rooms_above_2 <- rooms_no_na[rooms_no_na > 2]
length(rooms_above_2)

OUTPUT

[1] 4

Data frames

Normaly we do not work with data in seperate vectors. Instead it would be in a spreadsheet like format. In R this is called data frame

A dataframe is made up by columns of vectors, so we can combine the three vectors to a dataframe.

R

df <- data.frame(title, imdb_score, production_country)
df

OUTPUT

          title imdb_score production_country
1           FTA         62                 US
2       Dostana         21                 IN
3   Deliverance         77                 US
4 Life of Brian         80                 GB

So it is possible to make an data frame directly in R, but most of the time data are collected outside R, and afterwards imported to R.

Now that we have learned about writing code, and the basics of R’s data structures, we are ready to start working a real dataset, and learn more about data frames.

Key Points

Access individual values by location using [].
Access arbitrary sets of data using [c(...)].
Use logical operations and logical vectors to access subsets of data.