Creating objects in R

Overview

Teaching: 30 min
Exercises: 10 min

Questions

• How do I create objects in R?

Objectives

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

• Create objects and assign values to them in R.

• Learn how to name objects.

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:

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 also looks like a mouth (with tongue), which makes it easy to pronounce as x eats 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, but x2 is).
• R is case sensitive, so for example, 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 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.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:

weight_kg <- 55    # doesn't print anything
(weight_kg <- 55)  # but putting parenthesis around the call prints the value of `weight_kg`

[1] 55

weight_kg          # and so does typing the name of the object

[1] 55

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):

2.2 * weight_kg

[1] 121

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

weight_kg <- 57.5
2.2 * weight_kg

[1] 126.5

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:

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 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. 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

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?

Solution

1. mass equals 47.5
2. age equals 122
3. mass equals 95.0
4. age equals 102 mass_index equals 0.9313725

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:

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 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.

Key Points