Introduction to R
Last updated on 2023-09-06 | Edit this page
- First commands in R
- 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.
You can get output from R simply by typing math in the console:
3 + 5
12 / 7
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:
weight_kg <- 55
<- is the assignment operator. It assigns values on
the right to objects on the left. So, after executing
x <- 3, the value of
The arrow can be read as 3 goes into
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.
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
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.,
weight_kg is different from
Weight_kg). There are some names that cannot be used
because they are the names of fundamental functions in R (e.g.,
for, see here
for a complete list). In general, even if it’s allowed, it’s best to not
use other function names (e.g.,
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 functions 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, some popular style
guides are Google’s, the
tidyverse’s style and the Bioconductor
style guide. The tidyverse’s is very comprehensive and may seem
overwhelming at first. You can install the
package to automatically check for issues in the styling of your
Objects vs. variables: What are known as
Rare known as
variablesin many other programming languages. Depending on the context,
variablecan have drastically different meanings. However, in this lesson, the two words are used synonymously. For more information see here.
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:
weight_kg <- 55 # doesn't print anything (weight_kg <- 55) # but putting parenthesis around the call prints the value of `weight_kg`
weight_kg # and so does typing the name of the object
Now that R has
weight_kg in memory, we can do arithmetic
with it. For instance, we may want to convert this weight into pounds
(weight in pounds is 2.2 times the weight in kg):
2.2 * weight_kg
We can also change an object’s value by assigning it a new one:
weight_kg <- 57.5 2.2 * weight_kg
This means that assigning a value to one object does not change the
values of other objects For example, let’s store the animal’s weight in
pounds in a new object,
weight_lb <- 2.2 * weight_kg
and then change
weight_kg to 100.
weight_kg <- 100
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:
b <- sqrt(a)
Here, the value of
a is given to 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
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:
Here, we’ve called
round() with just one argument,
3.14159, and it has returned the value
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
round function. We can use
args(round) or look at the help for this function using
function (x, digits = 0) NULL
We see that if we want a different number of digits, we can type
digits=2 or however many we want.
round(3.14159, digits = 2)
If you provide the arguments in the exact same order as they are defined you don’t have to name them:
And if you do name the arguments, you can switch their order:
round(digits = 2, x = 3.14159)
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. By specifying the name of the arguments you are also safeguarding against possible future changes in the function interface, which may potentially add new arguments in between the existing ones.
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,
such as numbers or characters. We can assign a series of values to a
vector using the
c() function. For example we can create a
vector of animal weights and assign it to a new object
weight_g <- c(50, 60, 65, 82) weight_g
 50 60 65 82
A vector can also contain characters:
molecules <- c("dna", "rna", "protein") molecules
 "dna" "rna" "protein"
The quotes around “dna”, “rna”, etc. are essential here. Without the
quotes R will assume there are objects called
protein. As these objects don’t exist
in R’s memory, there will be an error message.
There are many functions that allow you to inspect the content of a
length() tells you how many elements are in a
An important feature of a vector, is that all of the elements are the
same type of data. The function
class() indicates the class
(the type of element) of an object:
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:
num [1:4] 50 60 65 82
chr [1:3] "dna" "rna" "protein"
You can use the
c() function to add other elements to
weight_g <- c(weight_g, 90) # add to the end of the vector weight_g <- c(30, weight_g) # add to the beginning of the vector weight_g
 30 50 60 65 82 90
In the first line, we take the original vector
add the value
90 to the end of it, and save the result back
weight_g. Then we add the value
30 to the
beginning, again saving the result back into
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.
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:
"double"). These are the basic building blocks that all R
objects are built from. The other 4 atomic vector types
FALSE(the boolean data type)
"integer"for integer numbers (e.g.,
Lindicates 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
You can check the type of your vector using the
function and inputting your vector as the argument.
Vectors are one of the many data structures that R
uses. Other important ones are lists (
matrix), data frames (
factor) and arrays (
R implicitly converts them to all be the same type
 "1" "2" "3" "a"
 1 2 3 1 0
 "a" "b" "c" "TRUE"
 "1" "2" "3" "4"
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.
Only one. There is no memory of past data types, and the coercion
happens the first time the vector is evaluated. Therefore, the
num_logical gets converted into a
1 before it gets converted into
 "1" "2" "3" "1" "a" "b" "c" "TRUE"
logical → numeric → character ← logical
If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:
molecules <- c("dna", "rna", "peptide", "protein") molecules
 "peptide" "rna"
We can also repeat the indices to create an object with more elements than the original one:
more_molecules <- molecules[c(1, 2, 3, 2, 1, 4)] more_molecules
 "dna" "rna" "peptide" "rna" "dna" "protein"
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.
Finally, it is also possible to get all the elements of a vector except some specified elements using negative indices:
molecules ## all molecules
 "dna" "rna" "peptide" "protein"
molecules[-1] ## all but the first one
 "rna" "peptide" "protein"
molecules[-c(1, 3)] ## all but 1st/3rd ones
 "rna" "protein"
molecules[c(-1, -3)] ## all but 1st/3rd ones
 "rna" "protein"
Another common way of subsetting is by using a logical vector.
TRUE will select the element with the same index, while
FALSE will not:
weight_g <- c(21, 34, 39, 54, 55) weight_g[c(TRUE, FALSE, TRUE, TRUE, FALSE)]
 21 39 54
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 above 50:
## will return logicals with TRUE for the indices that meet ## the condition weight_g > 50
 FALSE FALSE FALSE TRUE TRUE
## so we can use this to select only the values above 50 weight_g[weight_g > 50]
 54 55
You can combine multiple tests using
conditions are true, AND) or
| (at least one of the
conditions is true, OR):
weight_g[weight_g < 30 | weight_g > 50]
 21 54 55
weight_g[weight_g >= 30 & weight_g == 21]
< stands for “less than”,
>= for “greater than or equal to”, and
== for “equal to”. The double equal sign
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. The function
%in% allows you to test if any of the elements of a search
vector are found:
molecules <- c("dna", "rna", "protein", "peptide") molecules[molecules == "rna" | molecules == "dna"] # returns both rna and dna
 "dna" "rna"
molecules %in% c("rna", "dna", "metabolite", "peptide", "glycerol")
 TRUE TRUE FALSE TRUE
molecules[molecules %in% c("rna", "dna", "metabolite", "peptide", "glycerol")]
 "dna" "rna" "peptide"
"four" > "five"
< on strings, R
compares their alphabetical order. Here
"four" comes after
"five", and therefore is greater than it.
It is possible to name each element of a vector. The code chunk below shows an initial vector without any names, how names are set, and retrieved.
x <- c(1, 5, 3, 5, 10) names(x) ## no names
names(x) <- c("A", "B", "C", "D", "E") names(x) ## now we have names
 "A" "B" "C" "D" "E"
When a vector has names, it is possible to access elements by their name, in addition to their index.
A C 1 3
A C 1 3
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
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.
heights <- c(2, 4, 4, NA, 6) mean(heights)
mean(heights, na.rm = TRUE)
max(heights, na.rm = TRUE)
If your data include missing values, you may want to become familiar
with the functions
complete.cases(). See below for examples.
## Extract those elements which are not missing values. heights[!is.na(heights)]
 2 4 4 6
## Returns the object with incomplete cases removed. ## The returned object is an atomic vector of type `"numeric"` ## (or `"double"`). na.omit(heights)
 2 4 4 6 attr(,"na.action")  4 attr(,"class")  "omit"
## Extract those elements which are complete cases. ## The returned object is an atomic vector of type `"numeric"` ## (or `"double"`). heights[complete.cases(heights)]
 2 4 4 6
- Using this vector of heights in inches, create a new vector with the NAs removed.
heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)
- Use the function
median()to calculate the median of the
- Use R to figure out how many people in the set are taller than 67 inches.
heights_no_na <- heights[!is.na(heights)] ## or heights_no_na <- na.omit(heights)
median(heights, na.rm = TRUE)
heights_above_67 <- heights_no_na[heights_no_na > 67] length(heights_above_67)
There exists some functions to generate vectors of different type. To
generate a vector of numerics, one can use the
constructor, providing the length of the output vector as parameter. The
values will be initialised with 0.
 0 0 0
 0 0 0 0 0 0 0 0 0 0
Note that if we ask for a vector of numerics of length 0, we obtain exactly that:
There are similar constructors for characters and logicals, named
character(2) ## the empty character
 "" ""
logical(2) ## FALSE
 FALSE FALSE
rep function allow to repeat a value a certain
number of times. If we want to initiate a vector of numerics of length 5
with the value -1, for example, we could do the following:
 -1 -1 -1 -1 -1
Similarly, to generate a vector populated with missing values, which is often a good way to start, without setting assumptions on the data to be collected:
 NA NA NA NA NA
rep can take vectors of any length as input (above, we
used vectors of length 1) and any type. For example, if we want to
repeat the values 1, 2 and 3 five times, we would do the following:
rep(c(1, 2, 3), 5)
 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
rep(c(1, 2, 3), each = 5)
 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3
sort(rep(c(1, 2, 3), 5))
 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3
Another very useful function is
seq, to generate a
sequence of numbers. For example, to generate a sequence of integers
from 1 to 20 by steps of 2, one would use:
seq(from = 1, to = 20, by = 2)
 1 3 5 7 9 11 13 15 17 19
The default value of
by is 1 and, given that the
generation of a sequence of one value to another with steps of 1 is
frequently used, there’s a shortcut:
seq(1, 5, 1)
 1 2 3 4 5
seq(1, 5) ## default by
 1 2 3 4 5
 1 2 3 4 5
To generate a sequence of numbers from 1 to 20 of final length of 3, one would use:
seq(from = 1, to = 20, length.out = 3)
 1.0 10.5 20.0
A last group of useful functions are those that generate random data.
The first one,
sample, generates a random permutation of
another vector. For example, to draw a random order to 10 students oral
exam, I first assign each student a number from 1 to ten (for instance
based on the alphabetic order of their name) and then:
 9 4 7 1 2 5 3 10 6 8
Without further arguments,
sample will return a
permutation of all elements of the vector. If I want a random sample of
a certain size, I would set this value as the second argument. Below, I
sample 5 random letters from the alphabet contained in the pre-defined
 "s" "a" "u" "x" "j"
If I wanted an output larger than the input vector, or being able to
draw some elements multiple times, I would need to set the
replace argument to
sample(1:5, 10, replace = TRUE)
 2 1 5 5 1 1 5 5 2 2
When trying the functions above out, you will have realised that the
samples are indeed random and that one doesn’t get the same permutation
twice. To be able to reproduce these random draws, one can set the
random number generation seed manually with
before drawing the random sample.
Test this feature with your neighbour. First draw two random
1:10 independently and observe that you get
Now set the seed with, for example,
repeat the random draw. Observe that you now get the same random
Repeat by setting a different seed.
 9 1 4 3 6 2 5 8 10 7
 4 9 7 6 1 10 8 3 2 5
Same permutations with seed 123
 3 10 2 8 6 9 1 7 5 4
 3 10 2 8 6 9 1 7 5 4
A different seed
 9 4 7 1 2 5 3 10 6 8
 9 4 7 1 2 5 3 10 6 8
The last function we are going to see is
draws a random sample from a normal distribution. Two normal
distributions of means 0 and 100 and standard deviations 1 and 5, noted
N(0, 1) and N(100, 5), are shown below.
The three arguments,
sd, define the size of the sample, and the parameters of
the normal distribution, i.e the mean and its standard deviation. The
defaults of the latter are 0 and 1.
 0.69641761 0.05351568 -1.31028350 -2.12306606 -0.20807859
rnorm(5, 2, 2)
 1.3744268 -0.1164714 2.8344472 1.3690969 3.6510983
rnorm(5, 100, 5)
 106.45636 96.87448 95.62427 100.71678 107.12595
Now that we have learned how to write scripts, and the basics of R’s data structures, we are ready to start working with larger data, and learn about data frames.