R Basics

Overview

Teaching: 60 min
Exercises: 20 min

Questions

• What will these lessons not cover?

• What are the basic features of the R language?

• What are the most common objects in R?

Objectives

• Be able to create the most common R objects including vectors

• Understand that vectors have modes, which correspond to the type of data they contain

• Be able to use arithmetic operators on R objects

• Be able to retrieve (subset), name, or replace, values from a vector

• Be able to use logical operators in a subsetting operation

• Understand that lists can hold data of more than one mode and can be indexed

This lesson has been adapted from https://datacarpentry.org/genomics-r-intro/02-r-basics/index.html

“The fantastic world of R awaits you” OR “Nobody wants to learn how to use R”

Before we begin this lesson, we want you to be clear on the goal of the workshop and these lessons. We believe that every learner can achieve competency with R. You have reached competency when you find that you are able to use R to handle common analysis challenges in a reasonable amount of time (which includes time needed to look at learning materials, search for answers online, and ask colleagues for help). As you spend more time using R (there is no substitute for regular use and practice) you will find yourself gaining competency and even expertise. The more familiar you get, the more complex the analyses you will be able to carry out, with less frustration, and in less time - the fantastic world of R awaits you!

What these lessons will not teach you

Nobody wants to learn how to use R. People want to learn how to use R to analyze their own research questions! Ok, maybe some folks learn R for R’s sake, but these lessons assume that you want to start analyzing genomic data as soon as possible. Given this, there are many valuable pieces of information about R that we simply won’t have time to cover. Hopefully, we will clear the hurdle of giving you just enough knowledge to be dangerous, which can be a high bar in R! We suggest you look into the additional learning materials in the tip box below.

Here are some R skills we will not cover in these lessons

• How to create and work with R matrices and R lists
• How to create and work with loops and conditional statements, and the “apply” family of functions (which are super useful, read more here)
• How to do basic string manipulations (e.g. finding patterns in text using grep, replacing text)
• How to plot using the default R graphic tools
• How to use advanced R statistical functions

Tip: Where to learn more

The following are good resources for learning more about R. Some of them can be quite technical, but if you are a regular R user you may ultimately need this technical knowledge.

• R for Beginners: By Emmanuel Paradis and a great starting point
• The R Manuals: Maintained by the R project
• R contributed documentation: Also linked to the R project; importantly there are materials available in several languages
• R for Data Science: A wonderful collection by noted R educators and developers Garrett Grolemund and Hadley Wickham
• Practical Data Science for Stats: Not exclusively about R usage, but a nice collection of pre-prints on data science and applications for R
• Programming in R Software Carpentry lesson: There are several Software Carpentry lessons in R to choose from

Creating objects in R

Reminder

At this point you should be coding along in your notebook we created in the last episode. Writing your commands in the notebook (and commenting it) will make it easier to record what you did and why.

What might be called a variable in many languages is called an object in R.

To create an object you need:

• a name (e.g. ‘a’)
• a value (e.g. ‘1’)
• the assignment operator (‘<-‘)

In your script, “genomics_r_basics.R”, using the R assignment operator ‘<-‘, assign ‘1’ to the object ‘a’ as shown. Remember to leave a comment in the line above (using the ‘#’) to explain what you are doing:

# this line creates the object 'a' and assigns it the value '1'

a <- 1

Next, run this line of code in your script. You can run a line of code by hitting the Run button that is just above the first line of your notebook in the header of the pane or you can use the appropriate shortcut:

• execution shortcut: Shift+Enter

Beneath the cell you should see a new cell appear. There won’t be any output shown but there will now be an object called a in our environment.

To see what objects are in the environment we can use the ls() function.

# this line tells us the objects in the environment

ls()

'a'

If we cant to get back the value stored in an object we use its name

# this line outputs the value stored in the object called 'a'

a

[1] 1

Exercise: Create some objects in R

Create the following objects; give each object an appropriate name (your best guess at what name to use is fine):

1. Create an object that has the value of number of pairs of human chromosomes
2. Create an object that has a value of your favorite gene name
3. Create an object that has this URL as its value: “ftp://ftp.ensemblgenomes.org/pub/bacteria/release-39/fasta/bacteria_5_collection/escherichia_coli_b_str_rel606/”
4. Create an object that has the value of the number of chromosomes in a diploid human cell

Solution

Here as some possible answers to the challenge:

human_chr_number <- 23
gene_name <- 'pten'
ensemble_url <- 'ftp://ftp.ensemblgenomes.org/pub/bacteria/release-39/fasta/bacteria_5_collection/escherichia_coli_b_str_rel606/'
human_diploid_chr_num <-  2 * human_chr_number

Naming objects in R

Here are some important details about naming objects in R.

• Avoid spaces and special characters: Object names cannot contain spaces or the minus sign (-). You can use ‘_’ to make names more readable. You should avoid using special characters in your object name (e.g. ! @ # . , etc.). Also, object names cannot begin with a number.
• Use short, easy-to-understand names: You should avoid naming your objects using single letters (e.g. ‘n’, ‘p’, etc.). This is mostly to encourage you to use names that would make sense to anyone reading your code (a colleague, or even yourself a year from now). Also, avoiding excessively long names will make your code more readable.
• Avoid commonly used names: There are several names that may already have a definition in the R language (e.g. ‘mean’, ‘min’, ‘max’). One clue that a name already has meaning is that if you start typing a name in RStudio and it gets a colored highlight or RStudio gives you a suggested autocompletion you have chosen a name that has a reserved meaning.
• Use the recommended assignment operator: In R, we use ‘<- ‘ as the preferred assignment operator. ‘=’ works too, but is most commonly used in passing arguments to functions (more on functions later).

There are a few more suggestions about naming and style you may want to learn more about as you write more R code. There are several “style guides” that have advice, and one to start with is the tidyverse R style guide.

Tip: Pay attention to warnings in the output

If you enter a line of code in your notebook that contains an error, Jupyter may give you an error message and underline this mistake. Sometimes these messages are easy to understand, but often the messages may need some figuring out. Paying attention to these warnings will help you avoid mistakes. In the example below, our object name has a space, which is not allowed in R. The error message does not say this directly, but R is “not sure” about how to assign the name to “human_ chr_number” when the object name we want is “human_chr_number”.

Reassigning object names or deleting objects

Once an object has a value, you can change that value by overwriting it. R will not give you a warning or error if you overwriting an object, which may or may not be a good thing depending on how you look at it.

# gene_name has the value 'pten' or whatever value you used in the challenge.
# We will now assign the new value 'tp53'
gene_name <- 'tp53'

You can also remove an object from R’s memory entirely. The rm() function will delete the object.

# delete the object 'gene_name'
rm(gene_name)

If you run a line of code that has only an object name, R will normally display the contents of that object. In this case, we are told the object no longer exists.

Error: object 'gene_name' not found

Understanding object data types (modes)

In R, every object has two properties:

• Length: How many distinct values are held in that object
• Mode: What is the classification (type) of that object.

We will get to the “length” property later in the lesson. The “mode” property corresponds to the type of data an object represents. The most common modes you will encounter in R are:

There are a few other modes (i.e. “complex”, “raw” etc.) but these are the three we will work with in this lesson.

Data types are familiar in many programming languages, but also in natural language where we refer to them as the parts of speech, e.g. nouns, verbs, adverbs, etc. Once you know if a word - perhaps an unfamiliar one - is a noun, you can probably guess you can count it and make it plural if there is more than one (e.g. 1 Tuatara, or 2 Tuataras). If something is a adjective, you can usually change it into an adverb by adding “-ly” (e.g. jejune vs. jejunely). Depending on the context, you may need to decide if a word is in one category or another (e.g “cut” may be a noun when it’s on your finger, or a verb when you are preparing vegetables). These concepts have important analogies when working with R objects.

Exercise: Create objects and check their modes

Create the following objects in R, then use the mode() function to verify their modes. Try to guess what the mode will be before you look at the solution

1. chromosome_name <- 'chr02'
2. od_600_value <- 0.47
3. chr_position <- '1001701'
4. spock <- TRUE
5. pilot <- Earhart

Solution

Error in eval(expr, envir, enclos): object 'Earhart' not found

mode(chromosome_name)

[1] "character"

mode(od_600_value)

[1] "numeric"

mode(chr_position)

[1] "character"

mode(spock)

[1] "logical"

mode(pilot)

Error in mode(pilot): object 'pilot' not found

Notice from the solution that even if a series of numbers is given as a value R will consider them to be in the “character” mode if they are enclosed as single or double quotes. Also, notice that you cannot take a string of alphanumeric characters (e.g. Earhart) and assign as a value for an object. In this case, R looks for an object named Earhart but since there is no object, no assignment can be made. If Earhart did exist, then the mode of pilot would be whatever the mode of Earhart was originally. If we want to create an object called pilot that was the name “Earhart”, we need to enclose Earhart in quotation marks.

pilot <- "Earhart"
mode(pilot)

[1] "character"

Mathematical and functional operations on objects

Once an object exists (which by definition also means it has a mode), R can appropriately manipulate that object. For example, objects of the numeric modes can be added, multiplied, divided, etc. R provides several mathematical (arithmetic) operators including:

These can be used with literal numbers:

(1 + (5 ** 0.5))/2

[1] 1.618034

and importantly, can be used on any object that evaluates to (i.e. interpreted by R) a numeric object:

# multiply the object 'human_chr_number' by 2

human_chr_number * 2

[1] 46

Exercise: Compute the golden ratio

One approximation of the golden ratio (φ) can be found by taking the sum of 1 and the square root of 5, and dividing by 2 as in the example above. Compute the golden ratio to 3 digits of precision using the sqrt() and round() functions. Hint: remember the round() function can take 2 arguments.

Solution

round((1 + sqrt(5))/2, digits = 3)

[1] 1.618

Notice that you can place one function inside of another.

Vectors

Vectors are probably the most used commonly used object type in R. A vector is a collection of values that are all of the same type (numbers, characters, etc.). One of the most common ways to create a vector is to use the c() function - the “concatenate” or “combine” function. Inside the function you may enter one or more values; for multiple values, separate each value with a comma:

# Create the SNP gene name vector

snp_genes <- c("OXTR", "ACTN3", "AR", "OPRM1")

Vectors always have a mode and a length. You can check these with the mode() and length() functions respectively. Another useful function that gives both of these pieces of information is the str() (structure) function.

# Check the mode, length, and structure of 'snp_genes'
mode(snp_genes)

[1] "character"

length(snp_genes)

[1] 4

str(snp_genes)

 chr [1:4] "OXTR" "ACTN3" "AR" "OPRM1"

Vectors are quite important in R. Another data type that we will work with later in this lesson, data frames, are collections of vectors. What we learn here about vectors will pay off even more when we start working with data frames.

Creating and subsetting vectors

Let’s create a few more vectors to play around with:

# Some interesting human SNPs
# while accuracy is important, typos in the data won't hurt you here

snps <- c('rs53576', 'rs1815739', 'rs6152', 'rs1799971')
snp_chromosomes <- c('3', '11', 'X', '6')
snp_positions <- c(8762685, 66560624, 67545785, 154039662)

Once we have vectors, one thing we may want to do is specifically retrieve one or more values from our vector. To do so, we use bracket notation. We type the name of the vector followed by square brackets. In those square brackets we place the index (e.g. a number) in that bracket as follows:

# get the 3rd value in the snp_genes vector
snp_genes[3]

[1] "AR"

In R, every item your vector is indexed, starting from the first item (1) through to the final number of items in your vector. You can also retrieve a range of numbers:

# get the 1st through 3rd value in the snp_genes vector

snp_genes[1:3]

[1] "OXTR"  "ACTN3" "AR"   

If you want to retrieve several (but not necessarily sequential) items from a vector, you pass a vector of indices; a vector that has the numbered positions you wish to retrieve.

# get the 1st, 3rd, and 4th value in the snp_genes vector

snp_genes[c(1, 3, 4)]

[1] "OXTR"  "AR"    "OPRM1"

There are additional (and perhaps less commonly used) ways of subsetting a vector (see these examples). Also, several of these subsetting expressions can be combined:

# get the 1st through the 3rd value, and 4th value in the snp_genes vector
# yes, this is a little silly in a vector of only 4 values.
snp_genes[c(1:3,4)]

[1] "OXTR"  "ACTN3" "AR"    "OPRM1"

Adding to, removing, or replacing values in existing vectors

Once you have an existing vector, you may want to add a new item to it. To do so, you can use the c() function again to add your new value:

# add the gene 'CYP1A1' and 'APOA5' to our list of snp genes
# this overwrites our existing vector
snp_genes <- c(snp_genes, "CYP1A1", "APOA5")

We can verify that “snp_genes” contains the new gene entry

snp_genes

[1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1" "APOA5" 

Using a negative index will return a version of a vector with that index’s value removed:

snp_genes[-6]

[1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1"

We can remove that value from our vector by overwriting it with this expression:

snp_genes <- snp_genes[-6]
snp_genes

[1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1"

We can also explicitly rename or add a value to our index using double bracket notation:

snp_genes[7]<- "APOA5"
snp_genes

[1] "OXTR"   "ACTN3"  "AR"     "OPRM1"  "CYP1A1" NA       "APOA5" 

Notice in the operation above that R inserts an NA value to extend our vector so that the gene “APOA5” is an index 7. This may be a good or not-so-good thing depending on how you use this.

Exercise: Examining and subsetting vectors

Answer the following questions to test your knowledge of vectors

Which of the following are true of vectors in R? A) All vectors have a mode or a length
B) All vectors have a mode and a length
C) Vectors may have different lengths
D) Items within a vector may be of different modes
E) You can use the c() to add one or more items to an existing vector
F) You can use the c() to add a vector to an exiting vector

Solution

A) False - Vectors have both of these properties
B) True
C) True
D) False - Vectors have only one mode (e.g. numeric, character); all items in
a vector must be of this mode. E) True
F) True

Logical Subsetting

There is one last set of cool subsetting capabilities we want to introduce. It is possible within R to retrieve items in a vector based on a logical evaluation or numerical comparison. For example, let’s say we wanted get all of the SNPs in our vector of SNP positions that were greater than 100,000,000. We could index using the ‘>’ (greater than) logical operator:

snp_positions[snp_positions > 100000000]

[1] 154039662

In the square brackets you place the name of the vector followed by the comparison operator and (in this case) a numeric value. Some of the most common logical operators you will use in R are:

The magic of programming

The reason why the expression snp_positions[snp_positions > 100000000] works can be better understood if you examine what the expression “snp_positions > 100000000” evaluates to:

snp_positions > 100000000

[1] FALSE FALSE FALSE  TRUE

The output above is a logical vector, the 4th element of which is TRUE. When you pass a logical vector as an index, R will return the true values:

snp_positions[c(FALSE, FALSE, FALSE, TRUE)]

[1] 154039662

If you have never coded before, this type of situation starts to expose the “magic” of programming. We mentioned before that in the bracket notation you take your named vector followed by brackets which contain an index: named_vector[index]. The “magic” is that the index needs to evaluate to a number. So, even if it does not appear to be an integer (e.g. 1, 2, 3), as long as R can evaluate it, we will get a result. That our expression snp_positions[snp_positions > 100000000] evaluates to a number can be seen in the following situation. If you wanted to know which index (1, 2, 3, or 4) in our vector of SNP positions was the one that was greater than 100,000,000?

We can use the which() function to return the indices of any item that evaluates as TRUE in our comparison:

which(snp_positions > 100000000)

[1] 4

Why this is important

Often in programming we will not know what inputs and values will be used when our code is executed. Rather than put in a pre-determined value (e.g 100000000) we can use an object that can take on whatever value we need. So for example:

snp_marker_cutoff <- 100000000
snp_positions[snp_positions > snp_marker_cutoff]

[1] 154039662

Ultimately, it’s putting together flexible, reusable code like this that gets at the “magic” of programming!

A few final vector tricks

Finally, there are a few other common retrieve or replace operations you may want to know about. First, you can check to see if any of the values of your vector are missing (i.e. are NA). Missing data will get a more detailed treatment later, but the is.NA() function will return a logical vector, with TRUE for any NA value:

# current value of 'snp_genes': 
# chr [1:7] "OXTR" "ACTN3" "AR" "OPRM1" "CYP1A1" NA "APOA5"

is.na(snp_genes)

[1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE

Sometimes, you may wish to find out if a specific value (or several values) is present a vector. You can do this using the comparison operator %in%, which will return TRUE for any value in your collection that is in the vector you are searching:

# current value of 'snp_genes':
# chr [1:7] "OXTR" "ACTN3" "AR" "OPRM1" "CYP1A1" NA "APOA5"

# test to see if "ACTN3" or "APO5A" is in the snp_genes vector
# if you are looking for more than one value, you must pass this as a vector

c("ACTN3","APOA5") %in% snp_genes

[1] TRUE TRUE

Review Exercise 1

What data types/modes are the following vectors? a. snps
b. snp_chromosomes
c. snp_positions

Solution

typeof(snps)

[1] "character"

typeof(snp_chromosomes)

[1] "character"

typeof(snp_positions)

[1] "double"

Review Exercise 2

Add the following values to the specified vectors: a. To the snps vector add: ‘rs662799’
b. To the snp_chromosomes vector add: 11
c. To the snp_positions vector add: 116792991

Solution

snps <- c(snps, 'rs662799')
snps

[1] "rs53576"   "rs1815739" "rs6152"    "rs1799971" "rs662799" 

snp_chromosomes <- c(snp_chromosomes, "11") # did you use quotes?
snp_chromosomes

[1] "3"  "11" "X"  "6"  "11"

snp_positions <- c(snp_positions, 116792991)
snp_positions

[1]   8762685  66560624  67545785 154039662 116792991

Review Exercise 3

Make the following change to the snp_genes vector:

Hint: Your vector should look like this in ‘Environment’: chr [1:7] "OXTR" "ACTN3" "AR" "OPRM1" "CYP1A1" NA "APOA5". If not recreate the vector by running this expression: snp_genes <- c("OXTR", "ACTN3", "AR", "OPRM1", "CYP1A1", NA, "APOA5")

a. Create a new version of snp_genes that does not contain CYP1A1 and then
b. Add 2 NA values to the end of snp_genes

Solution

snp_genes <- snp_genes[-5]
snp_genes <- c(snp_genes, NA, NA)
snp_genes

[1] "OXTR"  "ACTN3" "AR"    "OPRM1" NA      "APOA5" NA      NA     

Review Exercise 4

Using indexing, create a new vector named combined that contains:

• The the 1st value in snp_genes
• The 1st value in snps
• The 1st value in snp_chromosomes
• The 1st value in snp_positions

Solution

combined <- c(snp_genes[1], snps[1], snp_chromosomes[1], snp_positions[1])
combined

[1] "OXTR"    "rs53576" "3"       "8762685"

Review Exercise 5

What type of data is combined?

Solution

typeof(combined)

[1] "character"

Bonus material: Lists

Lists are quite useful in R, but we won’t be using them in the genomics lessons. That said, you may come across lists in the way that some bioinformatics programs may store and/or return data to you. One of the key attributes of a list is that, unlike a vector, a list may contain data of more than one mode. Learn more about creating and using lists using this nice tutorial. In this one example, we will create a named list and show you how to retrieve items from the list.

# Create a named list using the 'list' function and our SNP examples
# Note, for easy reading we have placed each item in the list on a separate line
# Nothing special about this, you can do this for any multiline commands
# To run this command, make sure the entire command (all 4 lines) are highlighted
# before running
# Note also, as we are doing all this inside the list() function use of the
# '=' sign is good style
snp_data <- list(genes = snp_genes,
                 refference_snp = snps,
                 chromosome = snp_chromosomes,
                 position = snp_positions)
# Examine the structure of the list
str(snp_data)

List of 4
 $ genes         : chr [1:8] "OXTR" "ACTN3" "AR" "OPRM1" ...
 $ refference_snp: chr [1:5] "rs53576" "rs1815739" "rs6152" "rs1799971" ...
 $ chromosome    : chr [1:5] "3" "11" "X" "6" ...
 $ position      : num [1:5] 8.76e+06 6.66e+07 6.75e+07 1.54e+08 1.17e+08

To get all the values for the position object in the list, we use the $ notation:

# return all the values of position object

snp_data$position

[1]   8762685  66560624  67545785 154039662 116792991

To get the first value in the position object, use the [] notation to index:

# return first value of the position object

snp_data$position[1]

[1] 8762685

Key Points

• Effectively using R is a journey of months or years. Still you don’t have to be an expert to use R and you can start using and analyzing your data with with about a day’s worth of training

• It is important to understand how data are organized by R in a given object type and how the mode of that type (e.g. numeric, character, logical, etc.) will determine how R will operate on that data.

• Working with vectors effectively prepares you for understanding how data are organized in R.