Intro to R and RStudio for Genomics: R Basics (2024)

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

Before we begin this lesson, we want you to be clear on the goal ofthe workshop and these lessons. We believe that every learner canachieve competency with R. You have reached competencywhen you find that you are able to use R to handle commonanalysis challenges in a reasonable amount of time (whichincludes time needed to look at learning materials, search for answersonline, and ask colleagues for help). As you spend more time using R(there is no substitute for regular use and practice) you will findyourself gaining competency and even expertise. The more familiar youget, the more complex the analyses you will be able to carry out, withless frustration, and in less time - the fantastic world of R awaitsyou!

What these lessons will not teach you

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

Here are some R skills we will not cover in theselessons

• How to create and work with R matrices
• How to create and work with loops and conditional statements, andthe “apply” family of functions (which are super useful, read thisblog post to learn more about these functions)
• How to do basic string manipulations (e.g.finding patterns in textusing grep, replacing text)
• How to plot using the default R graphic tools (we willcover plot creation, but will do so using the popular plotting packageggplot2)
• How to use advanced R statistical functions

Creating objects in R

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

To create an object you need:

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

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

# this line creates the object 'first_value' and assigns it the value '1'first_value <- 1

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

• Windows execution shortcut: Ctrl+Enter
• Mac execution shortcut: Cmd(⌘)+Enter

To run multiple lines of code, you can highlight all the line youwish to run and then hit Run or use the shortcut key combolisted above.

In the RStudio ‘Console’ you should see:

first_value <- 1>

The ‘Console’ will display lines of code run from a script and anyoutputs or status/warning/error messages (usually in red).

In the ‘Environment’ window you will also get a table:

The ‘Environment’ window allows you to keep track of the objects youhave created in R.

human_chr_number <- 23gene_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 namescannot contain spaces or the minus sign (-). You can use‘_’ to make names more readable. You should avoid using specialcharacters in your object name (e.g.! @ # . , etc.). Also, object namescannot begin with a number.
• Use short, easy-to-understand names: You shouldavoid naming your objects using single letters (e.g.‘n’, ‘p’, etc.).This is mostly to encourage you to use names that would make sense toanyone reading your code (a colleague, or even yourself a year fromnow). Also, avoiding excessively long names will make your code morereadable.
• Avoid commonly used names: There are several namesthat 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 youstart typing a name in RStudio and it gets a colored highlight orRStudio gives you a suggested autocompletion you have chosen a name thathas a reserved meaning.
• Use the recommended assignment operator: In R, weuse ‘<-’ as the preferred assignment operator. ‘=’ works too, but ismost commonly used in passing arguments to functions (more on functionslater). There is a shortcut for the R assignment operator:
  • Windows execution shortcut: Alt+-
  • Mac execution shortcut: Option+-

There are a few more suggestions about naming and style you may wantto learn more about as you write more R code. There are several “styleguides” that have advice. One of the more widely used is the tidyverse R styleguide, but there is also a Google R styleguide, and Jean Fan’s Rstyle guide, among others.

Tip: Pay attention to warnings in the script console

If you enter a line of code in your script that contains an error,RStudio may give you an error message and underline this mistake.Sometimes these messages are easy to understand, but often the messagesmay need some figuring out. Paying attention to these warnings will helpyou 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 overwritingit. 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 atit.

# 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. Therm() 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 willnormally display the contents of that object. In this case, we are toldthe object no longer exists.

Error: object 'gene_name' not found

Understanding object data types (classes and modes)

In R, every object has several properties:

• Length: How many distinct values are held in thatobject
• Mode: What is the classification (type) of thatobject.
• Class: A property assigned to an object thatdetermines how a function will operate on it.

We will get to the “length” property later in the lesson. The“mode” property corresponds to the type of dataan object represents and the “class” propertydetermines how functions will work with that object.

The most common modes you will encounter in R are:

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

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

mode(chromosome_name)

[1] "character"

mode(od_600_value)

[1] "numeric"

mode(chr_position)

[1] "character"

mode(spock)

[1] "logical"

mode(pilot)

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

class(chromosome_name)

[1] "character"

class(od_600_value)

[1] "numeric"

class(chr_position)

[1] "character"

class(spock)

[1] "logical"

class(pilot)

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

Notice that in the two challenges, mode() andclass() return the same results. This time…

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

pilot <- "Earhart"mode(pilot)

[1] "character"

pilot <- "Earhart"typeof(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 thenumeric modes can be added, multiplied, divided, etc. R provides severalmathematical (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 2human_chr_number * 2

[1] 46

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

[1] 1.618

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 tocreate a vector is to use the c() function - the“concatenate” or “combine” function. Inside the function you may enterone or more values; for multiple values, separate each value with acomma:

# Create the SNP gene name vectorsnp_genes <- c("OXTR", "ACTN3", "AR", "OPRM1")

Vectors always have a mode and alength. You can check these with themode() and length() functions respectively.Another useful function that gives both of these pieces of informationis 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 workwith later in this lesson, data frames, are collections of vectors. Whatwe learn here about vectors will pay off even more when we start workingwith 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 heresnps <- 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 specificallyretrieve one or more values from our vector. To do so, we usebracket notation. We type the name of the vectorfollowed 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 vectorsnps[3]

[1] "rs6152"

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 alsoretrieve a range of numbers:

# get the 1st through 3rd value in the snp vectorsnps[1:3]

[1] "rs53576" "rs1815739" "rs6152" 

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

# get the 1st, 3rd, and 4th value in the snp vectorsnps[c(1, 3, 4)]

[1] "rs53576" "rs6152" "rs1799971"

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

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

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

Adding to, removing, or replacing values in existing vectors

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

# add the gene "CYP1A1" and "APOA5" to our list of snp genes# this overwrites our existing vectorsnp_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 thatindex’s value removed:

snp_genes[-6]

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

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

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 usingdouble bracket notation:

snp_genes[6]<- "APOA5"snp_genes

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

Logical Subsetting

There is one last set of cool subsetting capabilities we want tointroduce. It is possible within R to retrieve items in a vector basedon a logical evaluation or numerical comparison. For example, let’s saywe wanted get all of the SNPs in our vector of SNP positions that weregreater 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 bythe comparison operator and (in this case) a numeric value. Some of themost common logical operators you will use in R are:

snp_positions > 100000000

[1] FALSE FALSE FALSE TRUE

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

[1] 154039662

which(snp_positions > 100000000)

[1] 4

snp_marker_cutoff <- 100000000snp_positions[snp_positions > snp_marker_cutoff]

[1] 154039662

A few final vector tricks

Finally, there are a few other common retrieve or replace operationsyou may want to know about. First, you can check to see if any of thevalues of your vector are missing (i.e.are NA, that standsfor not avaliable). Missing data will get a more detailedtreatment later, but the is.NA() function will return alogical 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 FALSE

Sometimes, you may wish to find out if a specific value (or severalvalues) is present a vector. You can do this using the comparisonoperator %in%, which will return TRUE for any value in yourcollection 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 vectorc("ACTN3","APOA5") %in% snp_genes

[1] TRUE TRUE

mode(snps)

[1] "character"

mode(snp_chromosomes)

[1] "character"

mode(snp_positions)

[1] "numeric"

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

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

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

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

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

typeof(combined)

[1] "character"

# 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 stylesnp_data <- list(genes = snp_genes, refference_snp = snps, chromosome = snp_chromosomes, position = snp_positions)# Examine the structure of the liststr(snp_data)

List of 4 $ genes : chr [1:7] "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

# return all the values of position objectsnp_data$position

[1] 8762685 66560624 67545785 154039662 116792991

# return first value of the position objectsnp_data$position[1]

[1] 8762685