Introduction to R
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Estimated time: 80 minutes
 The main goal is to introduce users to the various objects in R, from atomic types to creating your own objects.
 While this epsiode 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 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 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
area_hectares < 1.0
<
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
.
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 readability 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 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’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 lowercaseletters 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 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.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
area_hectares < 1.0 # doesn't print anything
(area_hectares < 1.0) # putting parenthesis around the call prints the value of `area_hectares`
OUTPUT
[1] 1
R
area_hectares # and so does typing the name of the object
OUTPUT
[1] 1
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
This means that assigning a value to one object does not 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
The value of area_acres
is still 6.175 because you have
not rerun the line area_acres < 2.47 * area_hectares
since changing the value of area_hectares
.
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
area_hectares < 1.0 # land area in hectares
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.
Exercise
Create two variables r_length
and r_width
and assign them values. It should be noted that, because
length
is a builtin 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
.
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
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 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 nonoptional 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.
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 the number of household members for the
households we’ve interviewed and assign it to a new object
hh_members
:
R
hh_members < c(3, 7, 10, 6)
hh_members
OUTPUT
[1] 3 7 10 6
A vector can also contain characters. For example, we can have a
vector of the building material used to construct our interview
respondents’ walls (respondent_wall_type
):
R
respondent_wall_type < c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type
OUTPUT
[1] "muddaub" "burntbricks" "sunbricks"
The quotes around “muddaub”, etc. are essential here. Without the
quotes R will assume there are objects called muddaub
,
burntbricks
and sunbricks
. 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(hh_members)
OUTPUT
[1] 4
R
length(respondent_wall_type)
OUTPUT
[1] 3
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(hh_members)
OUTPUT
[1] "double"
R
typeof(respondent_wall_type)
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(hh_members)
OUTPUT
num [1:4] 3 7 10 6
R
str(respondent_wall_type)
OUTPUT
chr [1:3] "muddaub" "burntbricks" "sunbricks"
You can use the c()
function to add other elements to
your vector:
R
possessions < c("bicycle", "radio", "television")
possessions < c(possessions, "mobile_phone") # add to the end of the vector
possessions < c("car", possessions) # add to the beginning of the vector
possessions
OUTPUT
[1] "car" "bicycle" "radio" "television" "mobile_phone"
In the first line, we take the original vector
possessions
, add the value "mobile_phone"
to
the end of it, and save the result back into possessions
.
Then we add the value "car"
to the beginning, again saving
the result back into possessions
.
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
.
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
Subsetting (sometimes referred to as extracting or indexing) involves accessing out 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
respondent_wall_type < c("muddaub", "burntbricks", "sunbricks")
respondent_wall_type[2]
OUTPUT
[1] "burntbricks"
R
respondent_wall_type[c(3, 2)]
OUTPUT
[1] "sunbricks" "burntbricks"
We can also repeat the indices to create an object with more elements than the original one:
R
more_respondent_wall_type < respondent_wall_type[c(1, 2, 3, 2, 1, 3)]
more_respondent_wall_type
OUTPUT
[1] "muddaub" "burntbricks" "sunbricks" "burntbricks" "muddaub"
[6] "sunbricks"
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
hh_members < c(3, 7, 10, 6)
hh_members[c(TRUE, FALSE, TRUE, TRUE)]
OUTPUT
[1] 3 10 6
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 5:
R
hh_members > 5 # will return logicals with TRUE for the indices that meet the condition
OUTPUT
[1] FALSE TRUE TRUE TRUE
R
## so we can use this to select only the values above 5
hh_members[hh_members > 5]
OUTPUT
[1] 7 10 6
You can combine multiple tests using &
(both
conditions are true, AND) or 
(at least one of the
conditions is true, OR):
R
hh_members[hh_members < 4  hh_members > 7]
OUTPUT
[1] 3 10
R
hh_members[hh_members >= 4 & hh_members <= 7]
OUTPUT
[1] 7 6
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
possessions < c("car", "bicycle", "radio", "television", "mobile_phone")
possessions[possessions == "car"  possessions == "bicycle"] # returns both car and bicycle
OUTPUT
[1] "car" "bicycle"
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
possessions %in% c("car", "bicycle")
OUTPUT
[1] TRUE TRUE FALSE 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
possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")
OUTPUT
[1] TRUE TRUE FALSE FALSE FALSE
R
possessions[possessions %in% c("car", "bicycle", "motorcycle", "truck", "boat", "bus")]
OUTPUT
[1] "car" "bicycle"
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
rooms < c(2, 1, 1, NA, 7)
mean(rooms)
OUTPUT
[1] NA
R
max(rooms)
OUTPUT
[1] NA
R
mean(rooms, na.rm = TRUE)
OUTPUT
[1] 2.75
R
max(rooms, na.rm = TRUE)
OUTPUT
[1] 7
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
rooms[!is.na(rooms)]
OUTPUT
[1] 2 1 1 7
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(rooms))
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(rooms)
OUTPUT
[1] 2 1 1 7
attr(,"na.action")
[1] 4
attr(,"class")
[1] "omit"
R
## Extract those elements which are complete cases. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
rooms[complete.cases(rooms)]
OUTPUT
[1] 2 1 1 7
Recall that you can use the typeof()
function to find
the type of your atomic vector.
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
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 SAFI dataset we have been using in the other lessons, and learn about data frames.