This lesson is in the early stages of development (Alpha version)

# Introduction to R

## Overview

Teaching: 50 min
Exercises: 30 min
Questions
• What is an object?

• What is a function and how can we pass arguments to functions?

• How can values be initially assigned to variables of different data types?

• How can a vector be created What are the available data types?

• How can subsets be extracted from vectors?

• How does R treat missing values?

• How can we deal with missing values in R?

Objectives
• 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.

• Define the following terms as they relate to R: object, vector, assign, call, function.

## Creating objects in R

You can get output from R simply by typing math in the console:

3 + 5
[1] 8
7 * 2   # multiply 7 by 2
[1] 14
sqrt(36) # take the square root of 36
[1] 6

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:

time_minutes <- 5        # assign the number 5 to the object time_minutes

<- is the assignment operator. It assigns values on the right to objects on the left. Here we are creating a symbol called time_minutes and assigning it the numeric value 5. Some R users would say “time_minutes gets 5.” time_minutes is now a numeric vector with one element. Or you could say time_minutes is a numeric vector, and the first element is the number 5.

When you assign something to a symbol, nothing happens in the console, but in the Environment pane in the upper right, you will notice a new object, time_minutes.

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, checkouts, or isbn. You want your object names to be explicit and not too long. Here are some tips for assigning values:

• Do not use names of functions that already exist in R: 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.
• R is case sensitive: age is different from Age and y is different from Y.
• No blank spaces or symbols other than underscores: R users get around this in a couple of ways, either through capitalization (e.g. myData) or underscores (e.g. my_data). 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 dots have a special meaning in R (for methods) and other programming languages.
• Do not begin with numbers or symbols: 2x is not valid, but x2 is.
• Be descriptive, but make your variable names short: It’s good practice to be descriptive with your variable names. If you’re loading in a lot of data, choosing myData or x as a name may not be as helpful as, say, ebookUsage. Finally, keep your variable names short, since you will likely be typing them in frequently.

## 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.r-project.org/doc/manuals/r-release/R-lang.html#Objects

## Evaluating Expressions

If you now type y into the console, and press Enter on your keyboard, R will evaluate the expression. In this case, R will print the elements that are assigned to y (the number 5). We can do this easily since y only has one element, but if you do this with a large dataset loaded into R, it will overload your console because it will print the entire thing. The [1] indicates that the number 5 is the first element of this vector.

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

time_minutes <- 5    # doesn't print anything
(time_minutes <- 5)  # putting parenthesis around the call prints the value of y
[1] 5
time_minutes         # so does typing the name of the object
[1] 5
print(time_minutes)  # so does using the print() function.
[1] 5

Now that R has time_minutes in memory, we can do arithmetic with it. For instance, we may want to convert it into seconds (60 seconds in 1 minute):

60 * time_minutes
[1] 300

We can also change an object’s value by assigning it a new one:

time_minutes <- 10
60 * time_minutes
[1] 600

This overwrites the previous value without prompting you, so be careful! Also, assigning a value to one object does not change the values of other objects For example, let’s store the time in seconds in a new object, time_seconds:

time_seconds <- 60 * time_minutes

Then change time_minutes to 30:

time_minutes <- 30

## Exercise

What do you think is the current content of the object time_seconds? 600 or 1800?

## Solution

The value of time_seconds is still 600 because you have not re-run the line time_seconds <- 60 * time_minutes since changing the value of time_minutes.

## Exercise

Create two variables length and width and assign them values. It should be noted that R Studio might add “()” after width 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 area and give it a value based on the current values of length and width. Show that changing the values of either length and width does not affect the value of area.

## Solution

length <- 2.5
width <- 3.2
area <- length * width
area
[1] 8
# change the values of length and width
length <- 7.0
width <- 6.5
# the value of area isn't changed
area
[1] 8

All programming languages allow the programmer to include comments in their code. 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 will not be evaluated by R. You can start lines with comments or include them after any code on the line.

Comments are essential to helping you remember what your code does, and explaining it to others. Commenting code, along with documenting how data is collected and explaining what each variable represents, is essential to reproducible research. See the Software Carpentry lesson on R for Reproducible Scientific Analysis.

time_minutes <- 5    # time in minutes
time_seconds <- 60 * time_minutes	# convert to seconds
time_seconds				# print time in seconds
[1] 300

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.

## Removing objects from the environment

To remove an object from your R environment, use the rm() function. Remove multiple objects with rm(list = c("add", "objects", "here)), adding the objects in c() using quotation marks. To remove all objects, use rm(list = ls()) or click the broom icon in the Environment Pane, next to “Import Dataset.”

x <- 5
y <- 10
z <- 15
rm(x)  # remove x
rm(list =c("y", "z"))  # remove y and z
rm(list = ls())  # remove all objects

## Functions and their arguments

R is a “functional programming language,” meaning it contains a number of functions you use to do something with your data. Functions are “canned scripts” that automate more complicated sets of commands. Many functions are predefined, or can be made available by importing R packages as we saw in the “Before We Start” lesson.

Call a function on a variable by entering the function into the console, followed by parentheses and the variables. A function usually gets one or more inputs called arguments. For example, if you want to take the sum of 3 and 4, you can type in sum(3, 4). In this case, the arguments must be a number, and the return value (the output) is the sum of those numbers. An example of a function call is:

sum(3, 4)

The function is.function() will check if an argument is a function in R. If it is a function, it will print TRUE to the console.

Functions can be nested within each other. For example, sqrt() takes the square root of the number provided in the function call. Therefore you can run sum(sqrt(9), 4) to take the sum of the square root of 9 and add it to 4.

Typing a question mark before a function will pull the help page up in the Navigation Pane in the lower right. Type ?sum to view the help page for the sum function. You can also call help(sum). This will provide the description of the function, how it is to be used, and the arguments.

In the case of sum(), the ellipses . . . represent an unlimited number of numeric elements.

is.function(sum)        # check to see if sum() is a function
sum(3, 4, 5, 6, 7)      # sum takes an unlimited number (. . .) of numeric elements

### Arguments

Some functions take arguments which may either be specified by the user, or, if left out, take on a default value. However, if you want something specific, you can specify a value of your choice which will be used instead of the default. This is called passing an argument to the function.

For example, sum() takes the argument option na.rm. If you check the help page for sum (call ?sum), you can see that na.rm requires a logical (TRUE/FALSE) value specifying whether NA values (missing data) should be removed when the argument is evaluated.

By default, na.rm is set to FALSE, so evaluating a sum with missing values will return NA:

sum(3, 4, NA)                #
[1] NA

Even though we do not see the argument here, it is operating in the background, as the NA value remains. 3 + 4 + NA is NA.

But setting the argument na.rm to TRUE will remove the NA:

sum(3, 4, NA, na.rm = TRUE)
[1] 7

It is very important to understand the different arguments that functions take, the values that can be added to those functions, and the default arguments. Arguments can be anything, not only TRUE or FALSE, but also other objects. Exactly what each argument means differs per function, and must be looked up in the documentation.

It’s good practice to put the non-optional arguments 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.

## Exercise

Type in ?round at the console and then look at the output in the Help pane. What other functions exist that are similar to round? How do you use the digits parameter in the round function?

## 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 a sequence of elements of the same type. Vectors can only contain “homogenous” data–in other words, all data must be of the same type. The type of a vector determines what kind of analysis you can do on it. For example, you can perform mathematical operations on numeric objects, but not on character objects.

We can assign a series of values to a vector using the c() function. c() stands for combine. If you read the help files for c() by calling help(c), you can see that it takes an unlimited . . . number of arguments.

For example we can create a vector of checkouts for a collection of books and assign it to a new object checkouts:

checkouts <- c(25, 15, 18)
checkouts
[1] 25 15 18

A vector can also contain characters. For example, we can have a vector of the book titles (title) and authors (author):

title <- c("Macbeth","Dracula","1984")

The quotes around “Macbeth”, etc. are essential here. Without the quotes R will assume there are objects called Macbeth and Dracula in the environment. As these objects don’t yet 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(checkouts)  # print the number of values in the checkouts vector
[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(checkouts)
[1] "numeric"
class(title)
[1] "character"

Type ?str into the console to read the description of the str function. You can call str() on an R object to compactly display information about it, including the data type, the number of elements, and a printout of the first few elements.

str(checkouts)
num [1:3] 25 15 18
str(title)
chr [1:3] "Macbeth" "Dracula" "1984"

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

author <- "Stoker"
author <- c(author, "Orwell") # add to the end of the vector
author <- c("Shakespeare", author)
author
[1] "Shakespeare" "Stoker"      "Orwell"

In the first line, we create a character vector author with a single value "Stoker". In the second line, we add the value "Orwell" to it, and save the result back into author. Then we add the value "Shakespeare" to the beginning, again saving the result back into author.

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.

Use this to open a tab in the Script Pane (upper left) to view your data. This is helpful if you have a very long vector you need to browse.

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 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).

## Exercise

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?

## Solution

R implicitly converts them to all be the same type.

What will happen in each of these examples? (hint: use typeof() 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?

## Solution

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.

How many values in combined_logical are "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

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.

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. This hierarchy is: logical < integer < numeric < complex < character < list.

You can also coerce a vector to be a specific data type with as.character(), as.logical(), as.numeric, etc. For example, to coerce a number to a character:

x <- as.character(200)

We can test this in a few ways: if we print x to the console, we see quotation marks around it, letting us know it is a character:

x
[1] "200"

We can also call class()

class(x)
[1] "character"

And if we try to add a number to x, we will get an error message non-numeric argument to binary operator–in other words, x is non-numeric and cannot be added to a number.

x + 5

## Subsetting vectors

If we want to subset (or extract) one or several values from a vector, we must provide one or several indices in square brackets. For this example, we will use the state data, which is built into R and includes data related to the 50 states of the U.S.A. Type ?state to see the included datasets. state.name is a built in vector in R of all U.S. states:

state.name
[9] "Florida"        "Georgia"        "Hawaii"         "Idaho"
[13] "Illinois"       "Indiana"        "Iowa"           "Kansas"
[17] "Kentucky"       "Louisiana"      "Maine"          "Maryland"
[21] "Massachusetts"  "Michigan"       "Minnesota"      "Mississippi"
[29] "New Hampshire"  "New Jersey"     "New Mexico"     "New York"
[33] "North Carolina" "North Dakota"   "Ohio"           "Oklahoma"
[37] "Oregon"         "Pennsylvania"   "Rhode Island"   "South Carolina"
[41] "South Dakota"   "Tennessee"      "Texas"          "Utah"
[45] "Vermont"        "Virginia"       "Washington"     "West Virginia"
[49] "Wisconsin"      "Wyoming"
state.name[1]
[1] "Alabama"

You can use the : colon to create a vector of consecutive numbers.

state.name[1:5]
[1] "Alabama"    "Alaska"     "Arizona"    "Arkansas"   "California"

If the numbers are not consecutive, you must use the c() function:

state.name[c(1, 10, 20)]
[1] "Alabama"  "Georgia"  "Maryland"

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

state.name[c(1, 2, 3, 2, 1, 3)]

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:

five_states <- state.name[1:5]
five_states[c(TRUE, FALSE, TRUE, FALSE, TRUE)]
[1] "Alabama"    "Arizona"    "California"

Typically, these logical vectors are not typed by hand, but are the output of other functions or logical tests. state.area is a vector of state areas in square miles. We can use the < operator to return a logical vector with TRUE for the indices that meet the condition:

state.area < 10000
[1] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
[12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[23] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[34] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
[45]  TRUE FALSE FALSE FALSE FALSE FALSE
state.area[state.area < 10000]
[1] 5009 2057 6450 8257 9304 7836 1214 9609

The first expression gives us a logical vector of length 50, where TRUE represents those states with areas less than 10,000 square miles. The second expression subsets state.name to include only those names where the value is TRUE.

You can also specify character values. state.region gives the region that each state belongs to:

state.region == "Northeast"
[1] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
[12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE
[23] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE
[34] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
[45]  TRUE FALSE FALSE FALSE FALSE FALSE
state.name[state.region == "Northeast"]
[1] "Connecticut"   "Maine"         "Massachusetts" "New Hampshire"
[5] "New Jersey"    "New York"      "Pennsylvania"  "Rhode Island"
[9] "Vermont"

Again, a TRUE/FALSE index of all 50 states where the region is the Northeast, followed by a subset of state.name to return only those TRUE values.

Sometimes you need to do multiple logical tests (think Boolean logic). You can combine multiple tests using | (at least one of the conditions is true, OR) or & (both conditions are true, AND). Use help(Logic) to read the help file.

state.name[state.area < 10000 | state.region == "Northeast"]
[1] "Connecticut"   "Delaware"      "Hawaii"        "Maine"
[5] "Massachusetts" "New Hampshire" "New Jersey"    "New York"
[9] "Pennsylvania"  "Rhode Island"  "Vermont"
state.name[state.area < 10000 & state.region == "Northeast"]
[1] "Connecticut"   "Massachusetts" "New Hampshire" "New Jersey"
[5] "Rhode Island"  "Vermont"

The first result includes both states with fewer than 10,000 sq. mi. and all states in the Northeast. New York, Pennsylvania, Delaware and Maine have areas with greater than 10,000 square miles, but are in the Northeastern U.S. Hawaii is not in the Northeast, but it has fewer than 10,000 square miles. The second result includes only states that are in the Northeast and have fewer than 10,000 sq. mi.

R contains a number of operators you can use to compare values. Use help(Comparison) to read the R help file. Note that two equal signs (==) are used for evaluating equality (because one equals sign (=) is used for assigning variables).

Insert table

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:

west_coast <- c("California", "Oregon", "Washington")
state.name[state.name == "California" | state.name == "Oregon" | state.name == "Washington"]
[1] "California" "Oregon"     "Washington"
state.name %in% west_coast
[1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
[12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[34] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[45] FALSE FALSE  TRUE FALSE FALSE FALSE
state.name[state.name %in% west_coast]
[1] "California" "Oregon"     "Washington"

## 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. R functions have special actions when they encounter 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. As we saw above, you can add the argument na.rm=TRUE to calculate the result while ignoring the missing values.

rooms <- c(2, 1, 1, NA, 4)
mean(rooms)
[1] NA
max(rooms)
[1] NA
mean(rooms, na.rm = TRUE)
[1] 2
max(rooms, na.rm = TRUE)
[1] 4

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.

## Use any() to check if any values are missing
any(is.na(rooms))
[1] TRUE
## Use table() to tell you how many are missing vs. not missing
table(is.na(rooms))

FALSE  TRUE
4     1
## Identify those elements that are not missing values.
complete.cases(rooms)
[1]  TRUE  TRUE  TRUE FALSE  TRUE
## Identify those elements that are missing values.
is.na(rooms)
[1] FALSE FALSE FALSE  TRUE FALSE
## Extract those elements that are not missing values.
rooms[complete.cases(rooms)]
[1] 2 1 1 4

You can also use !is.na(rooms), which is exactly the same as complete.cases(rooms). The exclamation mark indicates logical negation.

!c(TRUE, FALSE)
[1] FALSE  TRUE

How you deal with missing data in your analysis is a decision you will have to make–do you remove it entirely? Do you replace it with zeros? That will depend on your own methodological questions.

## Exercise

1. Using this vector of rooms, create a new vector with the NAs removed.

rooms <- c(1, 2, 1, 1, NA, 3, 1, 3, 2, 1, 1, 8, 3, 1, NA, 1)

2. Use the function median() to calculate the median of the rooms vector.

3. Use R to figure out how many households in the set use more than 2 rooms for sleeping.

## Solution

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)
[1] 1
# 3.
rooms_above_2 <- rooms_no_na[rooms_no_na > 2]
length(rooms_above_2)
[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 library catalog dataset and learn about data frames.

## Key Points

• Use the assignment operator <- to assign values to objects. You can now manipulate that object in R

• R contains a number of functions you use to do something with your data. Functions automate more complicated sets of commands. Many functions are predefined, or can be made available by importing R packages

• A vector is a sequence of elements of the same type. All data in a vector must be of the same type–character, numeric (or double), integer, and logical. Create vectors with c(). Use [ ] to subset values from vectors.