Introduction to R
Overview
Teaching: min
Exercises: minQuestions
How can I use R to store values in variables?
How do I find and use functions in R?
How can I remove missing data?
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:
3 + 5
[1] 8
12 / 7
[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:
Sepal.Length <- 5.1
<- 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.
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, Species, or
sample_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., Sepal.Length is different from Sepal.length). 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, length). 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, 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
objectsinRare known asvariablesin many other programming languages. Depending on the context,objectandvariablecan 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 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:
Sepal.Length <- 5.1 # doesn't print anything
(Sepal.Length <- 5.1) # but putting parentheses around the call prints the value of `Sepal.Length`
[1] 5.1
Sepal.Length # and so does typing the name of the object
[1] 5.1
Now that R has Sepal.Length in memory, we can do arithmetic with it. For
instance, we may want to convert this measurement into inches ( 1 inch = 2.54 cm):
Sepal.Length/2.54
[1] 2.007874
We can also change an object’s value by assigning it a new one:
Sepal.Length <- 4.9
Sepal.Length/2.54
[1] 1.929134
This means that assigning a value to one object does not change the values of
other objects For example, let’s store the observation in inches in a new
object, Sepal.Length.inches:
Sepal.Length.inches <- Sepal.Length/2.54
and then change Sepal.Length to 2.54.
Sepal.Length <- 2.54
What do you think is the current content of the object Sepal.Length.inches? 1 or 1.93 ?
Challenge 1
What are the values after each statement in the following?
Petal.Length <- 1.5 # Petal.Length? Petal.Width <- 0.2 # Petal.Width? Petal.Length <- Petal.Length - 0.5 # Petal.Length? Petal.Width <- Petal.Width * 5 # Petal.Width? Petal.Ratio <- Petal.Length/Petal.Width # Petal.Ratio?Solution to Challenge 1
Petal.Length <- 1.5 # 1.5
Petal.Width <- 0.2 # 0.2
Petal.Length <- Petal.Length - 0.5 # 1
Petal.Width <- Petal.Width * 5 # 1
Petal.Ratio <- Petal.Length/Petal.Width # 1
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.
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 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:
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().
round(3.14159)
[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.
args(round)
function (x, digits = 0)
NULL
?round
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)
[1] 3.14
If you provide the arguments in the exact same order as they are defined you don’t have to name them:
round(3.14159, 2)
[1] 3.14
And if you do name the arguments, you can switch their order:
round(digits = 2, x = 3.14159)
[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 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 measurements and assign
it to a new object widths:
widths <- c(0.12, 0.23, 0.41, 0.33)
widths
[1] 0.12 0.23 0.41 0.33
A vector can also contain characters:
Species <- c("setosa", "versicolor", "virginica")
Species
[1] "setosa" "versicolor" "virginica"
The quotes around “setosa”, “versicolor”, and “virginica” are essential here. Without the quotes R
will assume objects have been created called setosa, versicolor, and virginica.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:
length(widths)
[1] 4
length(Species)
[1] 3
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:
class(widths)
[1] "numeric"
class(Species)
[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:
str(widths)
num [1:4] 0.12 0.23 0.41 0.33
str(Species)
chr [1:3] "setosa" "versicolor" "virginica"
You can use the c() function to add other elements to your vector:
widths <- c(widths, 0.22) # add to the end of the vector
widths <- c(0.3, widths) # add to the beginning of the vector
widths
[1] 0.30 0.12 0.23 0.41 0.33 0.22
In the first line, we take the original vector widths,
add the value 0.22 to the end of it, and save the result back into
widths. Then we add the value 0.3 to the beginning, again saving the result
back into widths.
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"forTRUEandFALSE(the boolean data type)"integer"for integer numbers (e.g.,2L, theLindicates 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).
Challenge 2
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?
What will happen in each of these examples? (hint: use
class()to check the data type of your objects):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")Why do you think it happens?
How many values in
combined_logicalare"TRUE"(as a character) in the following example:num_logical <- c(1, 2, 3, TRUE) char_logical <- c("a", "b", "c", TRUE) combined_logical <- c(num_logical, char_logical)Solution to Challenge 2
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.
num_char is character num_logical is numeric char_logical is character tricky is character
Only one. There is no memory of past data types, and the coercion happens the first time the vector is evaluated. Therefore, the
TRUEinnum_logicalgets converted into a1before it gets converted into"1"incombined_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:
Species <- c("setosa", "versicolor", "virginica", "sibirica")
Species[2]
[1] "versicolor"
Species[c(3, 2)]
[1] "virginica" "versicolor"
We can also repeat the indices to create an object with more elements than the original one:
more_species <- Species[c(1, 2, 3, 2, 1, 4)]
more_species
[1] "setosa" "versicolor" "virginica" "versicolor" "setosa"
[6] "sibirica"
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:
widths <- c(0.21, 0.34, 0.39, 0.54, 0.55)
widths[c(TRUE, FALSE, TRUE, TRUE, FALSE)]
[1] 0.21 0.39 0.54
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 0.5:
widths > 0.5 # will return logicals with TRUE for the indices that meet the condition
[1] FALSE FALSE FALSE TRUE TRUE
## so we can use this to select only the values above 0.5
widths[widths > 0.5]
[1] 0.54 0.55
You can combine multiple tests using & (both conditions are true, AND) or |
(at least one of the conditions is true, OR):
widths[widths < 0.3 | widths > 0.5]
[1] 0.21 0.54 0.55
widths[widths >= 0.3 & widths <= 0.51]
[1] 0.34 0.39
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. The function %in% allows you to test if any of the elements of
a search vector are found:
Species <- c("setosa", "versicolor", "virginica", "sibirica")
Species[Species == "sibirica" | Species == "versicolor"] # returns both sibirica and versicolor
[1] "versicolor" "sibirica"
Species %in% c("versicolor", "virginica", "sibirica","cristata","spuria")
[1] FALSE TRUE TRUE TRUE
Challenge 3
- Can you figure out why
"four" > "five"returnsTRUE?Solution to Challenge 3
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 while ignoring
the missing values.
heights <- c(2, 4, 4, NA, 6)
mean(heights)
[1] NA
max(heights)
[1] NA
mean(heights, na.rm = TRUE)
[1] 4
max(heights, na.rm = TRUE)
[1] 6
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.
## Extract those elements which are not missing values.
heights[!is.na(heights)]
[1] 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)
[1] 2 4 4 6
attr(,"na.action")
[1] 4
attr(,"class")
[1] "omit"
## Extract those elements which are complete cases. The returned object is an atomic vector of type `"numeric"` (or `"double"`).
heights[complete.cases(heights)]
[1] 2 4 4 6
Recall that you can use the typeof() function to find the type of your atomic vector.
Challenge 4
- Using this vector of heights in inches, create a new vector,
heights_no_na, 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 theheightsvector.Use R to figure out how many samples in the set are taller than 67 inches.
Solution to Challege 4
- Remove missing values:
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)[1] 643.
heights_above_67 <- heights_no_na[heights_no_na > 67] length(heights_above_67)[1] 6
Now that we have learned how to write scripts and the basics of R’s data structures, we are ready to learn about data frames and start working with two datasets: the classic “iris” dataset from a 1936 paper and one from a recent plant physiology publication.
Key Points
Arithmetic operators can do math on numbers.
The assignment operator assigns values to objects.
Objects have types like numeric, character, or logical.
R has built in functions that do basic functions.
The subset operator lets you select data by position or value.
NAs represent missing values.