Fluency is possible for all, through time and effort

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 you will find yourself gaining competency and even expertise. There is no subsititute for regular use and practice. The more familiar you get, the more complex the analyses you will be able to carry out, with less frustration, and in less time!

What these lessons will not teach you

Few people want to learn how to use R for its own sake. People want to learn how to use R to analyze their own research questions! These lessons assume that you want to start analyzing 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 below.

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

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.

Reminder

At this point you should be coding along in the “r_basics.R” script we created in the last episode. Writing your commands and comments in the script will make it easier to record what you did and why.

We should also be regularly saving the file by clicking the single floppy disk icon or typing Ctrl + S.

Creating objects in R

What might be called a variable in many languages is called an object in R (but you can also call it a variable).

To create an object you need:

In your script, “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. In the RStudio ‘Console’ you should see:

> a <- 1
>

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

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

Values
a 1

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

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: “https://genome.ucsc.edu”
  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 <- 'https://genome.ucsc.edu'
human_diploid_chr_num <-  2 * human_chr_number


Comment on the assignment operator

Many people use = as their preferred assignment operator in R instead of <-, and this is just as well. Here is a blog post discussing the differences.

Naming objects in R

Here are some important details about naming objects in R.

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 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 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”.

rstudio script warning

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:

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:

Mode (abbreviation) Type of data
Numeric (num) Numbers such floating point/decimals (1.0, 0.5, 3.14), there are also more specific numeric types (dbl - Double, int - Integer). These differences are not relevant for most beginners and pertain to how these values are stored in memory
Character (chr) A sequence of letters/numbers in single ’’ or double ” ” quotes
Logical Boolean values - TRUE or FALSE

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 parrot, or 2 parrots). If something is an adjective, you can usually change it into an adverb by adding “-ly” (e.g. slow and slowly). 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 
chromosome_name <- 'chr02'
od_600_value <- 0.47
chr_position <- '1001701'
spock <- TRUE
pilot <- Earhart
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:

Operator Description
+ addition
- subtraction
* multiplication
/ division
^ or ** exponentiation
a%/%b integer division (division where the remainder is discarded)
a%%b modulus (returns the remainder after division)

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 a vector of country names
countries <- c("Thailand","Lesotho","Suriname","Canada")

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 'countries'
mode(countries)
[1] "character"
length(countries)
[1] 4
str(countries)
 chr [1:4] "Thailand" "Lesotho" "Suriname" "Canada"

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:

# Top level domain of country
country_tld <- c('th', 'ls', 'sr', 'ca')

# Continent of country
continents <- c('Asia', 'Africa', 'South America', 'North America')

# Population of country
country_population <- c(69950850, 2108328, 575990, 38436447) # Source: Wikipedia

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 countries vector
countries[3]
[1] "Suriname"

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

# get the 1st through 3rd value in the countries vector
countries[1:3]
[1] "Thailand" "Lesotho"  "Suriname"

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 countries vector
countries[c(1, 3, 4)]
[1] "Thailand" "Suriname" "Canada"

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 4th value, and then the 1st through the 3rd value
# in other words, you can reorder the countries
countries[c(4, 1:3)]
[1] "Canada"   "Thailand" "Lesotho"  "Suriname"

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 countries 'Bolivia' and 'Tonga' to our list of countries
# this overwrites our existing vector
countries <- c(countries, "Bolivia", "Tonga")

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

countries
[1] "Thailand" "Lesotho"  "Suriname" "Canada"   "Bolivia"  "Tonga"

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

countries[-6]
[1] "Thailand" "Lesotho"  "Suriname" "Canada"   "Bolivia"

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

countries <- countries[-6]
countries
[1] "Thailand" "Lesotho"  "Suriname" "Canada"   "Bolivia"

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

countries[7]<- "Estonia"
countries
[1] "Thailand" "Lesotho"  "Suriname" "Canada"   "Bolivia"  NA         "Estonia"

Notice in the operation above that R inserts an NA value to extend our vector so that the country “Estonia” 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

Are the following true or false of vectors in R?

  1. All vectors have a mode or a length
  2. All vectors have a mode and a length
  3. Vectors may have different lengths
  4. Items within a vector may be of different modes
  5. You can use the c() function to add one or more items to an existing vector
  6. You can use the c() function to add a vector to an exiting vector
Solution
  1. False - Vectors have both of these properties
  2. True
  3. True
  4. False - Vectors have only one mode (e.g. numeric, character); all items in a vector must be of this mode.
  5. True
  6. True


Logical Subsetting

There is one last set of 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 countries in our vector of country populations that were less than 10,000,000. We could index using the ‘<’ (less than) logical operator:

country_population[country_population < 10000000]
[1] 2108328  575990

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:

Operator Description
< less than
<= less than or equal to
> greater than
>= greater than or equal to
== exactly equal to
!= not equal to
!x not x
a | b a or b
a & b a and b

The power of programming

The reason why the expression country_population[country_population < 10000000] works can be better understood if you examine what the expression country_population < 10000000 evaluates to:

country_population < 10000000
[1] FALSE  TRUE  TRUE FALSE

The output above is a logical vector, the 2nd and 3rd elements of which are TRUE. When you pass a logical vector as an index, R will return the TRUE values:

country_population[c(FALSE, TRUE, TRUE, FALSE)]
[1] 2108328  575990

If you have never coded before, this type of situation starts to expose the power 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 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 country_population < 10000000 evaluates to a number can be seen in the following situation. If you wanted to know which indices (1, 2, 3, or 4) in our vector of country populations was less than 10,000,000, you could use the which() function.

The which() function to returns the indices of any item that evaluates as TRUE in our comparison:

which(country_population < 10000000)
[1] 2 3

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 10000000) we can use an object that can take on whatever value we need. So for example:

population_cutoff <- 10000000
country_population[country_population < population_cutoff]
[1] 2108328  575990

Ultimately, it’s putting together flexible, reusable code like this that gets at the power 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). The is.NA() function will return a logical vector, with TRUE for any NA value:

# current value of 'countries':
# chr [1:7] "Thailand" "Lesotho" "Suriname" "Canada" "Bolivia" NA "Estonia"

is.na(countries)
[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 'countries':
# chr [1:7] "Thailand" "Lesotho" "Suriname" "Canada" "Bolivia" NA "Estonia"

# test to see if "Lesotho" or "Canada" is in the countries vector
# if you are looking for more than one value, you must pass this as a vector
c("Lesotho","Canada") %in% countries
[1] TRUE TRUE

Review Exercise 1

What data types/modes are the following vectors?

  1. country_tld
  2. continents
  3. country_population
Solution 
typeof(country_tld)
[1] "character"
typeof(continents)
[1] "character"
typeof(country_population)
[1] "double"


Review Exercise 2

Add the following values to the specified vectors:

  1. To the country_tld vector add: “bo”
  2. To the continents vector add: “South America”
  3. To the country_population vector add: 11428245
Solution 
country_tld <- c(country_tld, 'bo')
country_tld
[1] "th" "ls" "sr" "ca" "bo"
continents <- c(continents, "South America") # did you use quotes?
continents
[1] "Asia"          "Africa"        "South America" "North America"
[5] "South America"
country_population <- c(country_population, 11428245)
country_population
[1] 69950850  2108328   575990 38436447 11428245


Review Exercise 3

Make the following change to the countries vector:

  1. Create a new version of countries that does not contain Bolivia and then
  2. Add 2 NA values to the end of countries

Hint: Before starting, your vector should look like this in ‘Environment’:

chr [1:7] "Thailand" "Lesotho" "Suriname" "Canada" "Bolivia" NA "Estonia".

If not recreate the vector by running this expression:

countries <- c("Thailand", "Lesotho", "Suriname", "Canada", "Bolivia", NA, "Estonia")

Solution 
countries <- countries[-5]
countries <- c(countries, NA, NA)
countries
[1] "Thailand" "Lesotho"  "Suriname" "Canada"   NA         "Estonia"  NA        
[8] NA


Review Exercise 4

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

  • The the 1st value in countries
  • The 1st value in country_tld
  • The 1st value in continents
  • The 1st value in country_population
Solution 
combined <- c(countries[1], country_tld[1], continents[1], country_population[1])
combined
[1] "Thailand" "th"       "Asia"     "69950850"


Review Exercise 5

What type of data is combined? Why?

Solution 
typeof(combined)
[1] "character"


Bonus material: Lists

Lists are quite useful in R, but we won’t be using them in the these 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 country 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
country_data <- list(countries = countries,
                 country_tld = country_tld,
                 continents = continents,
                 country_population = country_population)
# Examine the structure of the list
str(country_data)
List of 4
 $ countries         : chr [1:8] "Thailand" "Lesotho" "Suriname" "Canada" ...
 $ country_tld       : chr [1:5] "th" "ls" "sr" "ca" ...
 $ continents        : chr [1:5] "Asia" "Africa" "South America" "North America" ...
 $ country_population: num [1:5] 69950850 2108328 575990 38436447 11428245

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

# return all the values of country_population object

country_data$country_population
[1] 69950850  2108328   575990 38436447 11428245

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

# return first value of the country_population object

country_data$country_population[1]
[1] 69950850