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Introduction to R

Data Carpentry contributors

Learning Objectives

  • Familiarize participants with R syntax
  • Understand the concepts of objects and assignment
  • Understand the concepts of vector and data types
  • Get exposed to a few functions

The R syntax

Start by showing an example of a script

  • Point to the different parts:
  • a function
  • the assignment operator <-
  • the = for arguments
  • the comments # and how they are used to document function and its content
  • the $ operator
  • Point to indentation and consistency in spacing to improve clarity

Example of a simple R script

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 objects, we need to give it a name followed by the assignment operator <- and the value we want to give it:

weight_kg <- 55

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 represent 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). 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 variable, etc.).

In R, two popular style guides are Hadley Wickham’s and Google’s.

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)
>weight_kg

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 variable.

>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?

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?

Exercise: how much does the genome weigh?

We know that 978Mb = 1picogram We also know that a bacteria genome is 4.6 Mb and human genome is 3000Mb. Can you do the math?

Let's create a variable that is called genome_length_mb and assign the value of the bacteria genome length. After that divide the genome length in Mb by 978.

>(genome_length_mb <- 4.6)
>genome_length_mb
>genome_weight_pg <- genome_length_mb / 978.0

It turns out an E. coli genome doesn't weigh very much.

Now let's change the variable's value by assigning it the human one. Note that we are using the same variables names.

>genome_length_mb <- 3000.0

At this point genome_weight_pg is still unchanged, and we need to assign a new value. This means that assigning a value to one variable does not change the values of other variables.

>genome_weight_pg
>
>genome_weight_pg <- genome_length_mb / 978.0

Allegra

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:

>weights <- c(50, 60, 65, 82)
>weights

A vector can also contain characters:

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

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(weights)
>length(animals)

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

class(weights)
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(weights)
str(animals)

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

weights <- c(weights, 90) # adding at the end
weights <- c(30, weights) # adding at the beginning
weights

What happens here is that we take the original vector weights, 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 build a vector or a dataset. As we program, this may be useful to autoupdate results that we are collecting or calculating.

We just saw 2 of the 6 data types that R uses: "character" and "numeric". 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).

We will soon use our “surveys” dataset to explore the data.frame data structure.

Exercise: operations with vectors

If x is a generic vector below a list of simple operations you can do on x:

  • unique() returns a vector without repeated elements

  • duplicated() returns TRUE or FALSE according to the element is repeated or not

  • unique(x[duplicated(x)]) returns all elements that are repeated in vector x and is an example of how to combine two commands

  • !duplicated(x) opposite of the vector

  • x[!duplicated(x)] returns all elements without repetitions

  • summary(x) gives a generic summary of some statistics of x

  • which(x > 2) returns the indexes of x that satisfies a given condition (x>2, in this case)

Use the two vectors to make some simple operations:

x <- c(2,23,44,34,54,27, 22, 2016, 44, 22, 56, 3, 8, 4 )

y<- c("Here", "we", "are,", "Cronenberg", "Morty", "a", "reality", "where", "everyone", "in", "the", "world", "got", "genetically", "Cronenberged", "We", "will", "fit", "right", "in", "Cronenberg", "Morty","it", "will", "be", "like", "we", "never", "even", "left", "Cronenberg", "world")