Learning Objectives

• Define the following terms as they relate to R: script, function, working directory, assign, object, variable.
• Call functions with zero or more named or unnamed arguments.
• Solve mathematical operations in R.
• Assign values to objects and variables in R.
• Describe what vectors are and how they are manipulated in R.
• Inspect the content of vectors in R and manipulate their content.
• Extract values from vectors in R.
• Employ logic (i.e. TRUE, FALSE) to subset data in a vector.
• Analyze vectors with missing data.

Creating objects

You can get output from R simply by typing in 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 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 use always <- for assignments.

In RStudio, typing Alt + - (push Alt at the same time as the - key) will write <- in a single keystroke.

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., 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., 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 also best to avoid dots (.) within a variable 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 variable 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 variables, etc.). Using a consistent coding style makes your code clearer to read for your future self and your collaborators. In R, two popular style guides are Hadley Wickham’s and Google’s (this one is also comprehensive).

When assigning a value to an object, R does not print anything. You can force to print the value by using parentheses or by typing the 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 in pounds (weight in pounds is 2.2 times the weight in kg):

2.2 * weight_kg

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

weight_kg <- 57.5
2.2 * weight_kg

This means that assigning a value to one variable does not change the values of other variables. For example, let’s store the animal’s weight in pounds in a new variable, weight_lb:

weight_lb <- 2.2 * weight_kg

and then change weight_kg to 100.

weight_kg <- 100

What do you think is the current content of the object weight_lb? 126.5 or 200?

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 Crtl + Shift + C.

Challenge

What are the values after each statement in the following?

mass <- 47.5            # mass?
age  <- 122             # age?
mass <- mass * 2.0      # mass?
age  <- age - 20        # age?
mass_index <- mass/age  # mass_index?

Functions and their arguments

Functions are “canned scripts” that automate something complicated or convenient or both. 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, the sqrt() function calculates the square root, and returns the value which is then assigned to variable 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 data set. 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) 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 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.

Call out

What is called objects in R is 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

Vectors and data types

A vector is the most common and basic data structure in R, and is pretty much the workhorse of R. It’s a group of values, mainly either numbers or characters. You can assign this list of values to a variable, just like you would for one item. For example we can create a vector of animal weights:

weight_g <- c(50, 60, 65, 82)
weight_g

A vector can also contain characters:

animals <- c("mouse", "rat", "dog")
animals

The quotes around “mouse”, “rat”, etc. are essential here. Without the quotes R will assume there is a variable called mouse, rat, etc.

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(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 the class (the type of element) of an object:

class(weight_g)
class(animals)

The function str() provides an overview of the object and the elements it contains. It is a really useful function when working with large and complex objects:

str(weight_g)
str(animals)

You can add elements to your vector by using the c() function:

weight_g <- c(weight_g, 90) # adding at the end of the vector
weight_g <- c(30, weight_g) # adding at the beginning of the vector
weight_g

What happens here is that we take the original vector weight_g, and we are adding another item first to the end of the other ones, and then another item at the beginning. 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.

We just saw 2 of the 6 atomic vector types that R uses: "character" and "numeric". These are the basic building blocks that all R objects are built from. The other 4 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" that we won’t discuss further

Vectors are one of the many data structures that R uses. Other important ones are lists (list), matrices (matrix), data frames (data.frame) and factors (factor).

Challenge

• We’ve seen that atomic vectors can be of type character, numeric, 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?

• 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

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets. For instance:

animals <- c("mouse", "rat", "dog", "cat")
animals[2]
#> [1] "rat"
animals[c(3, 2)]
#> [1] "dog" "rat"

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

more_animals <- animals[c(1, 2, 3, 2, 1, 4)]
more_animals
#> [1] "mouse" "rat"   "dog"   "rat"   "mouse" "cat"

R indexes start at 1. Programming languages like Fortran, MATLAB, 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:

weight_g <- c(21, 34, 39, 54, 55)
weight_g[c(TRUE, FALSE, TRUE, TRUE, FALSE)]
#> [1] 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:

weight_g > 50    # 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 50
weight_g[weight_g > 50]
#> [1] 54 55

You can combine multiple tests using & (both conditions are true, AND) or | (at least one of the conditions is true, OR):

weight_g[weight_g < 30 | weight_g > 50]
#> [1] 21 54 55
weight_g[weight_g >= 30 & weight_g == 21]
#> numeric(0)

Here, < stands for “smaller than”, > or “bigger than”, >= for “bigger or equal to”, and == for “equal to”. The double equal sign == should not be confused with the single = sign, which assigns to a variable (roughly speaking the same as “<-”).

A common task is to search for certain strings in a vector. One could use the “or” operator | but this can become quickly tedious. The function %in% allows you to test if a value is found in a vector:

animals <- c("mouse", "rat", "dog", "cat")
animals[animals == "cat" | animals == "rat"] # returns both rat and cat
#> [1] "rat" "cat"
animals %in% c("rat", "cat", "dog", "duck")
#> [1] FALSE  TRUE  TRUE  TRUE
animals[animals %in% c("rat", "cat", "dog", "duck")]
#> [1] "rat" "dog" "cat"

Challenge (optional)

• Can you figure out why "four" > "five" returns TRUE?

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)
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.

## Extract those elements which are not missing values.
heights[!is.na(heights)]

## Returns the object with incomplete cases removed. The returned object is atomic.
na.omit(heights)

## Extract those elements which are complete cases.
heights[complete.cases(heights)]

Challenge

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

lengths <- c(10,24,NA,18,NA,20)
2. Use the function median() to calculate the median of the lengths vector.

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.

Data Carpentry, 2017.