Introduction to R

Overview

Teaching: min
Exercises: min

Questions

Objectives

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

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.

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:

patient_age <- 73

<- 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.

Objects can be given any name such as x, patient_education, 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., patient_age is different from Patient_age). 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, three popular style guides are Google’s, Jean Fan’s and the tidyverse’s. 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: 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:

patient_age <- 73    # doesn't print anything
(patient_age <- 73)  # but putting parenthesis around the call prints the value of `patient_age`

## [1] 73

patient_age          # and so does typing the name of the object

## [1] 73

Now that R has patient_age in memory, we can do arithmetic with it. For instance, we may want to convert this age into days:

365 * patient_age

## [1] 26645

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

patient_age <- 94
365 * patient_age

## [1] 34310

This means that assigning a value to one object does not change the values of other objects For example, let’s store the patient’s age in days in a new object, patient_age_days:

patient_age_days <- 365 * patient_age

and then change patient_age to 65.

patient_age <- 65

What do you think is the current value of the object patient_age_days? 23,725, 26,645, or 34,310?

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 for weight, height and bmi after each statement?

# Enter height and weight
weight <- 165                   # weight?
height  <- 66                   # height?

# Convert to metric
weight <- weight/2.2             # weight? 
height  <- height/39.37          # height? 

# Calculate BMI
bmi <- weight/(height*height)   # bmi? 

Solution

# Enter height and weight
weight <- 165                   # weight = 75
height  <- 66                   # heigh = 66

# Convert to metric
weight <- weight/2.2            # weight = 75
height  <- height/39.37         # height = 1.68

# Calculate BMI
bmi <- weight/(height*height)   # bmi = 26.7

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:

a <- 3
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) 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.

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, which can be either 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 patient ages and assign it to a new object patient_age:

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

## [1] 50 60 65 82

A vector can also contain characters:

marital_status <- c("married", "divorced", "single")
marital_status

## [1] "married"  "divorced" "single"

The quotes around “married”, “single”, etc. are essential here. Without the quotes R will assume objects have been created called married, divorced and single. 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(patient_age)

## [1] 4

length(marital_status)

## [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(patient_age)

## [1] "numeric"

class(marital_status)

## [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(patient_age)

##  num [1:4] 50 60 65 82

str(marital_status)

##  chr [1:3] "married" "divorced" "single"

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

patient_age <- c(patient_age, 90) # add to the end of the vector
patient_age <- c(85, patient_age) # add to the beginning of the vector
patient_age

## [1] 85 50 60 65 82 90

In the first line, we take the original vector patient_age, add the value 90 to the end of it, and save the result back into patient_age. Then we add the value 85 to the beginning, again saving the result back into patient_age.

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?
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")
Solution

R implicitly converts them to all be the same type

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. Can you draw a diagram that represents the hierarchy of how these data types are coerced?

Solution

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:

marital_status <- c("married", "divorced", "complicated", "single")
marital_status[2]

## [1] "divorced"

marital_status[c(3, 2)]

## [1] "complicated" "divorced"

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

more_marital_status <- marital_status[c(1, 2, 3, 2, 1, 4)]
more_marital_status

## [1] "married"     "divorced"    "complicated" "divorced"    "married"    
## [6] "single"

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:

patient_age <- c(99, 66, 84, 77, 71)
patient_age <- patient_age[c(TRUE, FALSE, TRUE, TRUE, FALSE)]
patient_age

## [1] 99 84 77

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 80:

patient_age > 80    # will return logicals with TRUE for the indices that meet the condition

## [1]  TRUE  TRUE FALSE

## so we can use this to select only the values above 80
patient_age[patient_age > 80]

## [1] 99 84

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

patient_age[patient_age < 90 & patient_age > 70]

## [1] 84 77

patient_age[patient_age >= 80 | patient_age == 75]

## [1] 99 84

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:

marital_status <- c("married", "divorced", "complicated", "single")
marital_status[marital_status == "divorced" | marital_status == "single"] # returns both divorced and single

## [1] "divorced" "single"

marital_status %in% c("married", "divorced", "single")

## [1]  TRUE  TRUE FALSE  TRUE

marital_status[marital_status %in% c("married", "divorced", "single")]

## [1] "married"  "divorced" "single"

Challenge

Given the vector ages <- c(60, 72, 65, 81, 83, 70), write a statement that only returns ages that are less than 70 or above 80.

Hint: use the OR ( ) operator
Solution
  ages[(ages>80|(ages<70)]

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

Recall that you can use the typeof() function to find the type of your atomic vector.

Challenge
Create a vector called heights that holds a group of heights in inches
 heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)
Using the heights vector, create a new vector, heights_no_na, with the NAs removed.

Use the function median() to calculate the median height.

Use R to figure out how many people in the set are taller than 67 inches.
Solution
heights <- c(63, 69, 60, 65, NA, 68, 61, 70, 61, 59, 64, 69, 63, 63, NA, 72, 65, 64, 70, 63, 65)

# 1.
heights_no_na <- heights[!is.na(heights)]
# or
heights_no_na <- na.omit(heights)
# or
heights_no_na <- heights[complete.cases(heights)]

# 2.
median(heights, na.rm = TRUE)
## [1] 64
# 3.
heights_above_67 <- heights_no_na[heights_no_na > 67]
length(heights_above_67)
## [1] 6

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 ADNI dataset we have been using in the other lessons, and learn about data frames.

Key Points

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R for health scientists

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Introduction to R

Overview

Creating objects in R

Objects vs. variables

Comments

Challenge

Solution

Functions and their arguments

Vectors and data types

Challenge

Solution

Solution

Solution

Solution

Subsetting vectors

Conditional subsetting

Challenge

Solution

Missing data

Challenge

Solution

Key Points

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