Introduction to R
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Creating objects in R
You can get output from R simply by typing math in the console:
R
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:
R
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 x
is 3
.
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 almost any name such as x
,
current_temperature
, or subject_id
. Here are
some further guidelines on naming objects:
 You want your object names to be explicit and not too long.
 They cannot start with a number (
2x
is not valid, butx2
is).  R is case sensitive, so for example,
weight_kg
is different fromWeight_kg
.  There are some names that cannot be used because they are the names
of fundamental functions 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 other function names (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 best to avoid dots (
.
) within names. Many function names in R itself have them and dots also have a special meaning (methods) in R and other programming languages. To avoid confusion, don’t include dots in names.  It is recommended to use nouns for object names and verbs for function names.
 Be consistent in the styling of your code, such as where you put
spaces, how you name objects, etc. Styles can include “lower_snake”,
“UPPER_SNAKE”, “lowerCamelCase”, “UpperCamelCase”, 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 come from
Google, Jean Fan and the tidyverse. The tidyverse style
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.rproject.org/doc/manuals/rrelease/Rlang.html#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
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):
R
2.2 * weight_kg
We can also change an object’s value by assigning it a new one:
R
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
:
R
weight_lb < 2.2 * weight_kg
and then change weight_kg
to 100.
R
weight_kg < 100
What do you think is the current content of the object
weight_lb
? 126.5 or 220?
Saving your code
Up to now, your code has been in the console. This is useful for
quick queries but not so helpful if you want to revisit your work for
any reason. A script can be opened by pressing Ctrl +
Shift + N. It is wise to save your script file
immediately. To do this press Ctrl + S. This will
open a dialogue box where you can decide where to save your script file,
and what to name it. The .R
file extension is added
automatically and ensures your file will open with RStudio.
Don’t forget to save your work periodically by pressing Ctrl + S.
Comments
The comment character in R is #
. Anything to the right
of a #
in a script will be ignored by R. It is useful to
leave notes and explanations in your scripts. For convenience, RStudio
provides a keyboard shortcut 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 takes 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
weight_kg < sqrt(10)
Here, the value of 10 is given to the sqrt()
function,
the sqrt()
function calculates the square root, and returns
the value which is then assigned to the object weight_kg
.
This function takes one argument, other functions might take
several.
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)
to find what arguments it takes, 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 nonoptional arguments (like the number you’re rounding) first in your function call, and to then 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.
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 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
:
R
weight_g < c(50, 60, 65, 82)
weight_g
A vector can also contain characters:
R
animals < c("mouse", "rat", "dog")
animals
The quotes around “mouse”, “rat”, etc. are essential here. Without
the quotes R will assume objects have been created called
mouse
, rat
and dog
. 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
vector. length()
tells you how many elements are in a
particular vector:
R
length(weight_g)
length(animals)
An important feature of a vector, is that all of the elements are the
same type of data. The function class()
indicates what kind
of object you are working with:
R
class(weight_g)
class(animals)
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(weight_g)
str(animals)
You can use the c()
function to add other elements to
your vector:
R
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
In the first line, we take the original vector weight_g
,
add the value 90
to the end of it, and save the result back
into weight_g
. Then we add the value 30
to the
beginning, again saving the result back into weight_g
.
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:
"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"
forTRUE
andFALSE
(the boolean data type) 
"integer"
for integer numbers (e.g.,2L
, theL
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
You can check the type of your vector using the typeof()
function and inputting your vector as the argument.
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
).
R implicitly converts them to all be the same type
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
TRUE
in num_logical
gets converted into a
1
before it gets converted into "1"
in
combined_logical
.
Challenge (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?
logical → numeric → character ← logical
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
animals < c("mouse", "rat", "dog", "cat")
animals[2]
OUTPUT
#> [1] "rat"
R
animals[c(3, 2)]
OUTPUT
#> [1] "dog" "rat"
We can also repeat the indices to create an object with more elements than the original one:
R
more_animals < animals[c(1, 2, 3, 2, 1, 4)]
more_animals
OUTPUT
#> [1] "mouse" "rat" "dog" "rat" "mouse" "cat"
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.
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
weight_g < c(21, 34, 39, 54, 55)
weight_g[c(TRUE, FALSE, FALSE, TRUE, TRUE)]
OUTPUT
#> [1] 21 54 55
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:
R
weight_g > 50 # will return logicals with TRUE for the indices that meet the condition
OUTPUT
#> [1] FALSE FALSE FALSE TRUE TRUE
R
## so we can use this to select only the values above 50
weight_g[weight_g > 50]
OUTPUT
#> [1] 54 55
You can combine multiple tests using &
(both
conditions are true, AND) or 
(at least one of the
conditions is true, OR):
R
weight_g[weight_g > 30 & weight_g < 50]
OUTPUT
#> [1] 34 39
R
weight_g[weight_g <= 30  weight_g == 55]
OUTPUT
#> [1] 21 55
R
weight_g[weight_g >= 30 & weight_g == 21]
OUTPUT
#> numeric(0)
Here, >
for “greater than”, <
stands
for “less than”, <=
for “less 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. The function
%in%
allows you to test if any of the elements of a search
vector are found:
R
animals < c("mouse", "rat", "dog", "cat", "cat")
# return both rat and cat
animals[animals == "cat"  animals == "rat"]
OUTPUT
#> [1] "rat" "cat" "cat"
R
# return a logical vector that is TRUE for the elements within animals
# that are found in the character vector and FALSE for those that are not
animals %in% c("rat", "cat", "dog", "duck", "goat", "bird", "fish")
OUTPUT
#> [1] FALSE TRUE TRUE TRUE TRUE
R
# use the logical vector created by %in% to return elements from animals
# that are found in the character vector
animals[animals %in% c("rat", "cat", "dog", "duck", "goat", "bird", "fish")]
OUTPUT
#> [1] "rat" "dog" "cat" "cat"
When using “>” or “<” on strings, R compares their alphabetical order. Here “four” comes after “five”, and therefore is “greater than” it.
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 as if the missing values were removed
(rm
stands for ReMoved) first.
R
heights < c(2, 4, 4, NA, 6)
mean(heights)
max(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 is.na()
, na.omit()
, and
complete.cases()
. See below for examples.
R
## Extract those elements which are not missing values.
heights[!is.na(heights)]
## Returns the object with incomplete cases removed.
#The returned object is an atomic vector of type `"numeric"` (or #`"double"`).
na.omit(heights)
## Extract those elements which are complete cases.
#The returned object is an atomic vector of type `"numeric"` (or #`"double"`).
heights[complete.cases(heights)]
Recall that you can use the typeof()
function to find
the type of your atomic vector.
Challenge
 Using this vector of heights in inches, create a new vector,
heights_no_na
, with the NAs removed.
R
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 theheights
vector.Use R to figure out how many people in the set are taller than 67 inches.
R
heights < c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)
# 1.
heights_no_na < heights[!is.na(heights)]
# or
heights_no_na < na.omit(heights)
# or
heights_no_na < heights[complete.cases(heights)]
# 2.
median(heights, na.rm = TRUE)
# 3.
heights_above_67 < heights_no_na[heights_no_na > 67]
length(heights_above_67)
Now that we have learned how to write scripts, and the basics of R’s data structures, we are ready to start working with the Portal dataset we have been using in the other lessons, and learn about data frames.