Bootcamp Part 1: R Basics

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 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.

Naming variables

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 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, some popular style guides are Google’s, the tidyverse’s style and the Bioconductor style guide. 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 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 here.

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
Challenge:

What do you think is the current content of the object weight_lb? 126.5 or 220?

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.

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 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 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 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) 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. By specifying the name of the arguments you are also safeguarding against possible future changes in the function interface, which may potentially add new arguments in between the existing ones.

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, such as 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 animal weights and assign it to a new object weight_g:

weight_g <- c(50, 60, 65, 82)
weight_g
[1] 50 60 65 82

A vector can also contain characters:

states <- c("CT", "MA", "NY")
states
[1] "CT" "MA" "NY"

The quotes around “CT”, “MA”, etc. are essential here. Without the quotes R will assume there are objects called CT, MA and NY. 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(weight_g)
[1] 4
length(states)
[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(weight_g)
[1] "numeric"
class(states)
[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(weight_g)
 num [1:4] 50 60 65 82
str(states)
 chr [1:3] "CT" "MA" "NY"

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

weight_g <- c(weight_g, 90) # add to the end of the vector
weight_g <- c(30, weight_g) # add to the beginning of the vector
weight_g
[1] 30 50 60 65 82 90

In the first line, we take the original vector weight_g, add the value 90 to the end of it, and save the result back into weight_g. Then we add the value 30 to the beginning, again saving the result back into weight_g.

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" 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).

Challenge:

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?

R implicitly converts them to all be the same type

Challenge:

What will happen in each of these examples? (hint: use class() to check the data type of your objects and type in their names to see what happens):

num_char <- c(1, 2, 3, "a")
num_logical <- c(1, 2, 3, TRUE, FALSE)
char_logical <- c("a", "b", "c", TRUE)
tricky <- c(1, 2, 3, "4")
class(num_char)
[1] "character"
num_char
[1] "1" "2" "3" "a"
class(num_logical)
[1] "numeric"
num_logical
[1] 1 2 3 1 0
class(char_logical)
[1] "character"
char_logical
[1] "a"    "b"    "c"    "TRUE"
class(tricky)
[1] "character"
tricky
[1] "1" "2" "3" "4"
Challenge:

Why do you think it happens?

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.

Challenge:

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)

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.

combined_logical
[1] "1"    "2"    "3"    "1"    "a"    "b"    "c"    "TRUE"
Challenge:

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?

logical → numeric → character ← 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:

states <- c("CT", "MA", "NY", "PA")
states[2]
[1] "MA"
states[c(3, 2)]
[1] "NY" "MA"

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

more_states <- states[c(1, 2, 3, 2, 1, 4)]
more_states
[1] "CT" "MA" "NY" "MA" "CT" "PA"

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.

Finally, it is also possible to get all the elements of a vector except some specified elements using negative indices:

states ## all states
[1] "CT" "MA" "NY" "PA"
states[-1] ## all but the first one
[1] "MA" "NY" "PA"
states[-c(1, 3)] ## all but 1st/3rd ones
[1] "MA" "PA"
states[c(-1, -3)] ## all but 1st/3rd ones
[1] "MA" "PA"

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:

## will return logicals with TRUE for the indices that meet
## the condition
weight_g > 50
[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 “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:

states <- c("CT", "MA", "NY", "PA")
states[states == "MA" | states == "NY"] # returns both MA and NY
[1] "MA" "NY"
states %in% c("MA", "NY", "MA", "PA", "WI")
[1] FALSE  TRUE  TRUE  TRUE
states[states %in% c("MA", "NY", "MA", "PA", "WI")]
[1] "MA" "NY" "PA"
Challenge:

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

"four" > "five"
[1] TRUE

When using > or < on strings, R compares their alphabetical order. Here "four" comes after "five", and therefore is greater than it.

Names

It is possible to name each element of a vector. The code chunk below shows an initial vector without any names, how names are set, and retrieved.

x <- c(1, 5, 3, 5, 10)
names(x) ## no names
NULL
names(x) <- c("A", "B", "C", "D", "E")
names(x) ## now we have names
[1] "A" "B" "C" "D" "E"

When a vector has names, it is possible to access elements by their name, in addition to their index.

x[c(1, 3)]
A C 
1 3 
x[c("A", "C")]
A C 
1 3

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
Challenge:
  1. Using this vector of heights in inches, create a new vector 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)
  1. Use the function median() to calculate the median of the heights vector.
  2. Use R to figure out how many people in the set are taller than 67 inches.
heights_no_na <- heights[!is.na(heights)]
## or
heights_no_na <- na.omit(heights)
median(heights, na.rm = TRUE)
[1] 64
heights_above_67 <- heights_no_na[heights_no_na > 67]
length(heights_above_67)
[1] 6

Generating vectors

There exists some functions to generate vectors of different type. To generate a vector of numerics, one can use the numeric() constructor, providing the length of the output vector as parameter. The values will be initialised with 0.

numeric(3)
[1] 0 0 0
numeric(10)
 [1] 0 0 0 0 0 0 0 0 0 0

Note that if we ask for a vector of numerics of length 0, we obtain exactly that:

numeric(0)
numeric(0)

There are similar constructors for characters and logicals, named character() and logical() respectively.

Challenge:

What are the defaults for character and logical vectors?

character(2) ## the empty character
[1] "" ""
logical(2)   ## FALSE
[1] FALSE FALSE

Categorical Data

Factors

Since factors are special vectors, the same rules for selecting values using indices apply.

expression <- factor(c("high","low","low","medium","high","medium","medium","low","low","low"))

In this vector we can imagine gene expression data has been stored as 3 categories or levels: low, medium, and high.

Let’s extract the values of the factor with high expression:

expression[expression == "high"]    ## This will only return those elements in the factor equal to "high"
[1] high high
Levels: high low medium

Under the hood, factors are stored as integer values in R. To view the integer assignments under the hood you can use str():

str(expression)
 Factor w/ 3 levels "high","low","medium": 1 2 2 3 1 3 3 2 2 2

The categories are referred to as “factor levels”. As we learned earlier, the levels in the expression factor were assigned integers alphabetically, with high=1, low=2, medium=3. However, it makes more sense for us if low=1, medium=2 and high=3. We can change the order of the categories by releveling the factor.

To relevel the categories, you can add the levels argument to the factor() function, and give it a vector with the categories listed in the required order:

expression <- factor(expression, levels=c("low", "medium", "high"))     # you can re-factor a factor

Now we have a releveled factor with low as the lowest or first category, medium as the second and high as the third. This is reflected in the way they are listed in the output of str(), as well as in the numbering of which category is where in the factor.

str(expression)
 Factor w/ 3 levels "low","medium",..: 3 1 1 2 3 2 2 1 1 1

Note: Releveling often becomes necessary when you need a specific category in a factor to be the “base” category, i.e. category that is equal to 1. One example would be if you need the “control” to be the “base” in a given RNA-seq experiment.

Data Frame

A data.frame is the de facto data structure for most tabular data and what we use for statistics and plotting. A data.frame is similar to a matrix in that it’s a collection of vectors of the same length and each vector represents a column. However, in a dataframe each vector can be of a different data type (e.g., characters, integers, factors).

We can create a dataframe by bringing vectors together to form the columns. We do this using the data.frame() function, and giving the function the different vectors we would like to bind together. This function will only work for vectors of the same length.

# Create a data frame and store it as a variable called 'df'
ages <- c(12, 14, 14, 16, 12, 15)
is_diabetic <- c(TRUE, FALSE, FALSE, FALSE, TRUE, FALSE)
sex <- factor(c("Male", "Female", "Female", "Male", "Male", "Female"))
df <- data.frame(ages, is_diabetic, sex)

We can see that a new variable called df has been created in our Environment within a new section called Data. In the Environment, it specifies that df has 6 observations of 3 variables. What does that mean? In R, rows always come first, so it means that df has 6 rows and 3 columns. We can get additional information if we click on the blue circle with the white triangle in the middle next to df. It will display information about each of the columns in the data frame, giving information about what the data type is of each of the columns and the first few values of those columns.

Another handy feature in RStudio is that if we hover the cursor over the variable name in the Environment, df, it will turn into a pointing finger. If you click on df, it will open the data frame as it’s own tab next to the script editor. We can explore the table interactively within this window. To close, just click on the X on the tab.

As with any variable, we can print the values stored inside to the console if we type the variable’s name and run.

df
  ages is_diabetic    sex
1   12        TRUE   Male
2   14       FALSE Female
3   14       FALSE Female
4   16       FALSE   Male
5   12        TRUE   Male
6   15       FALSE Female

Lists

Lists are a data structure in R that can be perhaps a bit daunting at first, but soon become amazingly useful. A list is a data structure that can hold any number of any types of other data structures.

If you have variables of different data structures you wish to combine, you can put all of those into one list object by using the list() function and placing all the items you wish to combine within parentheses:

list1 <- list(ages, df, age)

We see list1 appear within the Data section of our environment as a list of 3 components or variables. If we click on the blue circle with a triangle in the middle, it’s not quite as interpretable as it was for data frames.

Essentially, each component is preceded by a colon. The first colon give the expression vector, the second colon precedes the df data frame, with the dollar signs indicating the different columns, the last colon gives the single value, age.

Let’s type list1 and print to the console by running it.

list1
[[1]]
[1] 12 14 14 16 12 15

[[2]]
  ages is_diabetic    sex
1   12        TRUE   Male
2   14       FALSE Female
3   14       FALSE Female
4   16       FALSE   Male
5   12        TRUE   Male
6   15       FALSE Female

[[3]]
[1] 102

There are three components corresponding to the three different variables we passed in, and what you see is that structure of each is retained. Each component of a list is referenced based on the number position.

The materials in this lesson have been adapted from work created by the (HBC)](http://bioinformatics.sph.harvard.edu/) and Data Carpentry (http://datacarpentry.org/). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.