Introduction to R

Last updated on 2026-04-28 | Edit this page

Estimated time: 80 minutes

Instructor Note

The main goal is to introduce users to the various objects in R, from atomic types to creating your own objects.
While this episode is foundational, be careful not to get caught in the weeds as the variety of types and operations can be overwhelming for new users, especially before they understand how this fits into their own “workflow.”

Overview

Questions

What data types are available in R?
What is an object?
How can objects of different data types be assigned to names?
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?
How can we work with dates and times in R?

Objectives

Define the following terms as they relate to R: object, assign, call, function, arguments, options.
Assign values to names 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 values from vectors.
Analyze vectors with missing data.
Work with dates and times in R using proper data types.

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

Everything that exists in R is an objects: from simple numerical values, to strings, to more complex objects like vectors, matrices, and lists. Even expressions and functions are objects in R.

However, to do useful and interesting things, we need to name objects. To do so, we need to give a name followed by the assignment operator <-, and the object we want to be named:

R

num_precincts <- 5

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

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.

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). 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 R’s reserved words 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’s also 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. The recommended writing style is called snake_case, which implies using only lowercase letters and numbers and separating each word with underscores (e.g., animals_weight, average_income). 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 yourcollaborators. 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.

Callout

Objects vs. Variables

The naming of objects in R is somehow related to variables in many other programming languages. In many programming languages, a variable has three aspects: a name, a memory location, and the current value stored in this location. R abstracts from modifiable memory locations. In R we only have objects which can be named. Depending on the context, name (of an 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

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

R

num_precincts <- 5    # doesn't print anything
(num_precincts <- 5)  # putting parenthesis around the call prints the value of `area_hectares`

OUTPUT

[1] 5

R

num_precincts         # and so does typing the name of the object

OUTPUT

[1] 5

Now that R has num_precincts in memory, we can do arithmetic with it. For instance, we may want to calculate the number of registered voters (assuming there are 1500 voters per precinct):

R

1500 * num_precincts

OUTPUT

[1] 7500

We can also change an the value assigned to an name by assigning it a new one:

R

num_precincts <- 10
1500 * num_precincts

OUTPUT

[1] 15000

This means that assigning a value to one name does not change the values of other names. For example, let’s name the number of voters num_voters:

R

num_voters <- 1500 * num_precincts

Next, let’s change (reassign) num_precincts to 50:

R

num_precincts <- 50

Challenge

Exercise

What do you think is the current value of num_voters? 15000 or 75000?

Show me the solution

The value of num_voters is still 15000. This is because you have not re-run the line num_voters <- 1500 * num_precincts since changing the value of num_precincts.

Comments

All programming languages allow the programmer to include comments in their code. Including comments to your code has many advantages: it helps you explain your reasoning and it forces you to be tidy. A commented code is also a great tool not only to your collaborators, but to your future self. Comments are the key to a reproducible analysis.

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

num_precincts <- 10      #number of precincts
num_voters <- 1500 * num_precincts  #calculate the total number of voters
num_voters        #print the total number of voters

OUTPUT

[1] 15000

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.

Challenge

Exercise

Create two variables ballot_cost and ballots_needed and assign them values.
Create a third variable total_cost and give it a value based on the current values of ballot_cost and ballots_needed.
Show that changing the values of either ballot_cost and ballots_needed does not affect the value of total_cost.

Show me the solution

R

#set the values of ballot_cost and ballots_needed
ballot_cost <- 0.0125
ballots_needed <- 2250

#give total_cost a value
total_cost <- ballot_cost * ballots_needed

#print current value of total_cost
total_cost

OUTPUT

[1] 28.125

R

#change the values of ballot_cost and ballots_needed
ballot_cost <- 0.068
ballots_needed <- 3000

#display the value of total_cost isn't changed
total_cost

OUTPUT

[1] 28.125

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(a)

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 name 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 data set. We’ll see that when we read data files into R.

Arguments can be anything, not only numbers or file names, 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.

Using the total_cost we calculated above, let’s try a function that can take multiple arguments: round().

R

round(total_cost)

OUTPUT

[1] 28

Here, we’ve called round() with just one argument, total_cost, and it has returned the value 28. 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(total_cost, digits = 2)

OUTPUT

[1] 28.12

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

R

round(total_cost, 2)

OUTPUT

[1] 28.12

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

R

round(digits = 2, x = total_cost)

OUTPUT

[1] 28.12

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.

Challenge

Exercise

As you may have noticed, in both cases of rounding, the total_cost rounded down. However, when calculating the total cost of something, you should always round UP to the nearest dollar or cent.

For this exercise, type in ?round at the console and then look at the output in the Help panel. What other function similar to round should be used instead? Apply this function to round up to the nearest dollar.

Bonus: apply this function to round to the nearest cent.

Show me the solution

The ceiling function rounds up to the nearest integer!

R

ceiling(total_cost)

OUTPUT

[1] 29

To use the function to round to the nearest cent, you can do the following:

R

ceiling(total_cost * 100) / 100

OUTPUT

[1] 28.13

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, which can be either numbers, characters, or other data types. We can assign a series of values to a vector using the c() function. For example, we can create a vector of job type strings, and we can create another vector storing numbers of votes at different precincts

R

votes_per_precinct <- c(1000, 4300, 2340, 7190)
votes_per_precinct

OUTPUT

[1] 1000 4300 2340 7190

R

job_types <- c("check-in", "check-out", "supervisor")
job_types

OUTPUT

[1] "check-in"   "check-out"  "supervisor"

The quotes around “check-in”, “check-out”, and “supervisor”are essential here. Without the quotes, R will assume there are objects called check-in, check-out, and supervisor. Since these names don’t exist in R’s memory, there will be an error message.

Additionally, you may notice there are no commas in-between the thousands. In R, you cannot add commas in numbers, as R will assume they are separate items in the list.

There are many functions that allow you to inspect the content of a vector. length() tells you how many elements are in a particular vector:

R

length(votes_per_precinct)

OUTPUT

[1] 4

An important feature of a vector is that all of the elements are the same type of data. The function typeof() indicates the type of an object:

R

typeof(votes_per_precinct)

OUTPUT

[1] "double"

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(votes_per_precinct)

OUTPUT

 num [1:4] 1000 4300 2340 7190

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

R

devices_per_precinct <- c(5, 2)
devices_per_precinct <- c(devices_per_precinct, 9) # add to the end of the vector
devices_per_precinct <- c(6, devices_per_precinct) # add to the beginning of the vector
devices_per_precinct

OUTPUT

[1] 6 5 2 9

In the first line, we take the original vector devices_per_precinct, add the value 9 to the end of it, and save the result back into devices_per_precinct. Then we add the value 6 to the beginning, again saving the result back into devices_per_precinct.

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

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 bit-streams (we won’t be discussing this further)

Date Types

Dates are a common data type that require special attention. In R, dates can be represented in two ways:

As character strings (e.g., “2018-11-06 07:02:36”, “11/06/2018 07:02:36”)
As Date or POSIXct objects which are special data types for dates and times

When dates are stored as strings, they’re treated like any other text:

R

checkin_times_as_strings <- c("2018-11-06 07:02:36", "2018-11-06 07:04:09", "2018-11-06 07:05:45")
typeof(checkin_times_as_strings)

OUTPUT

[1] "character"

However, storing dates as proper Date or POSIXct objects offers several advantages: - You can perform arithmetic with dates (calculate time differences) - You can extract components like month, year, or day - You can easily format dates for display - You can sort dates chronologically

To convert strings to Date or POSIXct objects, use the as.POSIXct() function:

R

#convert strings to POSIXct objects
checkin_times <- as.POSIXct(checkin_times_as_strings, format = "%Y-%m-%d %H:%M:%S")
typeof(checkin_times)

OUTPUT

[1] "double"

R

class(checkin_times)

OUTPUT

[1] "POSIXct" "POSIXt"

The following “leap year” scenario highlights the importance of using proper date types. Consider the following example:

R

#BAD: using strings for date arithmetic
date_start <- "2020-02-28"
date_end <- "2020-03-01"

#attempt to calculate the difference by converting strings to numeric days
#here we use substr to extract the day portion in string format.
#it draws the characters at position 9 to 10 and converts them to numeric
difference_wrong <- as.numeric(substr(date_end, 9, 10)) - as.numeric(substr(date_start, 9, 10))
difference_wrong #incorrect!

OUTPUT

[1] -27

In this example, we extract the day portion of the dates as strings and subtract them. While this works for simple cases, it fails to account for: - The transition between months (e.g., February to March). - Leap years (e.g., February 29 in 2020).

Now, compare this with proper date types:

R

#GOOD: using Date for leap year handling
date_start_correct <- as.Date(date_start)
date_end_correct <- as.Date(date_end)

difference_correct <- as.numeric(date_end_correct - date_start_correct)
difference_correct #correctly computes 2 days, accounting for February 29 in the leap year

OUTPUT

[1] 2

Now, the number of days has been calculated properly!

It’s important to note that Date objects and POSIXct objects are not made equal and, while we used the two types interchangeably above, you should ensure you choose the one that fits your data needs. The key differences between Date objects and POSIXct objects can be seen below: - Date: - Represents dates without time. - Useful for operations where time is irrelevant (e.g., calculating the number of days between two dates). - Stored as the number of days since January 1, 1970. - `POSIXct: - Represents both date and time. - Useful for operations involving time (e.g., calculating the number of seconds or hours between two timestamps). - Stored as the number of seconds since January 1, 1970.

Using proper date types ensures that leap years and other calendar-specific rules are handled correctly, making computations accurate and reliable.

Coercion

An important characteristic of vectors is that they can only contain elements of the same data type. If you attempt to combine different types in a vector, R will automatically convert them to a single, common type - a process called “coercion”. This follows a hierarchy: character > numeric (double) > integer > logical.

R

# Coercion examples
num_logical <- c(1, TRUE) # TRUE converted to 1
typeof(num_logical)

OUTPUT

[1] "double"

R

num_character <- c(1, "a") # 1 converted to "1"
typeof(num_character)

OUTPUT

[1] "character"

R

logical_character <- c(TRUE, "a") # TRUE converted to "TRUE"
typeof(logical_character)

OUTPUT

[1] "character"

R

tricky <- c(1, "2", TRUE) # Everything becomes character
typeof(tricky)

OUTPUT

[1] "character"

R will always try to find a common data type that doesn’t lose information. Typically, this means converting toward the more flexible type (with character being the most flexible).

Note: Date/POSIXct will always be treated as “numeric” (days/seconds since January 1st, 1970) when being coerced within a vector!

Challenge

Exercise

Predict the resulting data type for this vector: c(1.1, 2L, TRUE, "a")
Create a vector that contains:
- The number 5
- The logical value FALSE
- The string “data”
What is the resulting data type? Why?

Show me the solution

The vector c(1.1, 2L, TRUE, "a") will have type “character” because character is the most flexible data type.
The vector would be:

R

mixed <- c(5, FALSE, "data")
typeof(mixed)

OUTPUT

[1] "character"

It has type “character” because R coerces all elements to the most flexible data type that includes all values.

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

Subsetting vectors

Subsetting (sometimes referred to as extracting or indexing) involves accessing one or more values based on their numeric placement or “index” within a vector. If we want to subset one or several values from a vector, we must provide one index or several indices in square brackets. For instance:

R

job_types <- c("check-in", "check-out", "supervisor")
job_types[2]

OUTPUT

[1] "check-out"

R

job_types[c(3, 2)]

OUTPUT

[1] "supervisor" "check-out"

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

R

more_jobs <- job_types[c(1, 2, 3, 2, 1, 3)]
more_jobs

OUTPUT

[1] "check-in"   "check-out"  "supervisor" "check-out"  "check-in"
[6] "supervisor"

Conditional subsetting

Another common way of subsetting is by using a logical vector. TRUE will select the element with the same index, while FALSE will not:

R

votes_per_precinct <- c(1000, 4300, 2340, 7190)
votes_per_precinct[c(TRUE, FALSE, TRUE, TRUE)]

OUTPUT

[1] 1000 2340 7190

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 values greater than 2500:

R

votes_per_precinct > 2500    # will return logicals with TRUE for the indices that meet the condition

OUTPUT

[1] FALSE  TRUE FALSE  TRUE

R

## so we can use this to select only the values greater than 2866
votes_per_precinct[votes_per_precinct > 2500]

OUTPUT

[1] 4300 7190

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

R

votes_per_precinct[votes_per_precinct < 2000 | votes_per_precinct > 4000]

OUTPUT

[1] 1000 4300 7190

R

votes_per_precinct[votes_per_precinct >= 2000 & votes_per_precinct <= 4000]

OUTPUT

[1] 2340

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

job_types <- c("check-in", "check-out", "supervisor")
job_types[job_types == "check-in" | job_types == "check-out"] # returns both check-in and check-out

OUTPUT

[1] "check-in"  "check-out"

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

R

job_types %in% c("check-in", "check-out")

OUTPUT

[1]  TRUE  TRUE 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

job_types[job_types %in% c("check-in", "check-out")]

OUTPUT

[1] "check-in"  "check-out"

Missing Data

As R was designed to analyze data sets, 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

#create vector
checkin_lengths <- c(64, 74, NA, 287)

#calc with NA
mean(checkin_lengths)

OUTPUT

[1] NA

R

max(checkin_lengths)

OUTPUT

[1] NA

R

#calc without NA
mean(checkin_lengths, na.rm = TRUE)

OUTPUT

[1] 141.6667

R

max(checkin_lengths, na.rm = TRUE)

OUTPUT

[1] 287

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.
## The ! character is also called the NOT operator
checkin_lengths[!is.na(checkin_lengths)]

OUTPUT

[1]  64  74 287

R

## Count the number of missing values.
## The output of is.na() is a logical vector (TRUE/FALSE equivalent to 1/0) so the sum() function here is effectively counting
sum(is.na(checkin_lengths))

OUTPUT

[1] 1

R

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

OUTPUT

[1]  64  74 287
attr(,"na.action")
[1] 3
attr(,"class")
[1] "omit"

R

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

OUTPUT

[1]  64  74 287

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

Challenge

Exercise

Using this vector of check-in lengths, create a new vector with the NAs removed.

R

checkin_lengths <- c(54, 21, 74, 65, NA, 72, 21, 16, 46, 58, 43, 61, 39, 19, NA, 24)

Use the function median() to calculate the median of the checkin_lengths vector.
Use R to figure out how many check-ins took longer than 55 seconds.

Show me the solution

R

#1.
checkin_lengths <- c(54, 21, 74, 65, NA, 72, 21, 16, 46, 58, 43, 61, 39, 19, NA, 24)
checkin_lengths_no_na <- checkin_lengths[!is.na(checkin_lengths)]
# or
checkin_lengths_no_na <- na.omit(checkin_lengths)

# 2.
median(checkin_lengths, na.rm = TRUE)

OUTPUT

[1] 44.5

R

# 3.
checkin_lengths_above_55 <- checkin_lengths_no_na[checkin_lengths_no_na > 55]
length(checkin_lengths_above_55)

OUTPUT

[1] 5

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.
Use proper date types (Date and POSIXct) instead of strings for date arithmetic.