Objectives

Introduction to R and RStudio

In these lessons we’ll use the gapminder dataset to explore the relationship between a country’s life expectancy and the total value of its finished goods and services, also known as the Gross Domestic Product (GDP). To explore this relationship we need data and a platform to analyze the data.

We could explore the data with a spreadsheet program like Excel or Google Sheets, but it would be cumbersome to record the steps used to explore and make changes to the original data. Instead, we’ll use a programming language to explore and summarize the data. In particular we’ll use R and RStudio to create tabular summaries of the data as well as plots.

We’ll use R because it is:

We’ll also use R because this is a precursor workshop to RNA-seq Demystified, which will teach you how to use R for differential expression analysis.

To get started, we’ll use RStudio, an integrated development environment (IDE). It acts as a graphical interface to R that has many helpful features, as we’ll see. Note that R and RStudio are different, but complimentary. You need R to use RStudio.

Log in to RStudio Server

To get started, open a web browser and enter the following URL.

http://bfx-workshop01.med.umich.edu

You should now be looking at a page that will allow you to login to the RStudio server:

rstudio default session

Enter your user credentials and click Sign In. The credentials are the same we used on the command line yesterday, but if you forget yours, a helper can retrieve it for you, just ask in Slack. You should now see the RStudio interface:

rstudio default session

Checkpoint

Orienting on RStudio

To orient ourselves on the interface of RStudio, look in the lower right pane, that has a list of files and folders. Click on the welcome.R script.

There should now be four panes in your RStudio window, with the welcome.R script in the upper left pane, this is the “Source” or “Scripts” pane. It displays the code that we will write to perform our analysis.

In the Scripts pane, there is a line of icons, towards the right side of the pane there is a “Run” button. First highlight all the code in the Source pane, and then click “Run”.

Checkpoint

A number of things just happened in all the panes:

  1. There is a record of the code that was run along with the results in the lower left pane. This is the “Console” panel, and here is where the output of executing code is displayed. You can also type directly in the prompt and execute code, but it is not saved in the script.
  2. There is a phenotypes_by_species object listed in the upper right pane. This is the “Environment” panel, and it shows all the existing variables in the session, as well as a brief description.
  3. There is a plot in the lower right pane where the file structure was previously listed. This is the “Files / Plots / Help” pane, and it can show the file browser that we saw at the start, display plots that are executed in the Console, or display help pages as we’ll see later.

Questions?

Configuring RStudio

All of the panes in RStudio have configuration options. For example, you can minimize/maximize a pane or resize panes by dragging the borders. The most important customization options for pane layout are in the View menu. Other options such as font sizes, colors/themes, and more are in the Tools menu under Global Options.

Let’s all enable soft-wrapping of the code files so that we don’t have to scroll left and right if our code exceeds the width of the window. To do this click Tools then Global Options then Code, and check the box for “Soft-wrap R source files”, then click OK.

Creating an RStudio project

We’ll now begin our examination of the gapminder dataset by creating an RStudio Project. An RStudio project allows you to more easily:

  1. To create a project, go to the File menu, and click New Project…. If you are prompted to save your ~/.Rhistory, select No. The following window should appear:

rstudio default session

  1. In this window, select Existing Directory. For “Project working directory”, click Browse…, select the “CF_R” folder, and click Choose. This will use the /home/workshop/user/CF_R folder as the project directory.

  2. Finally click Create Project. In the “Files” tab of your output pane (more about the RStudio layout in a moment), you should see an RStudio project file, CF_R.Rproj. All RStudio projects end with the “.Rproj” file extension.

Checkpoint

Creating a script

Let’s create an R script in this project to record the commands we’ll learn:

  • Click the File menu and select New File and then R Script.
  • Before we go any further, save your script by clicking the save/disk icon that is in the bar above the first line in the script editor, or click the File menu and select Save.
  • In the “Save File” window that opens, select New Folder. Name it “scripts”.
  • Finally, name your file “r_basics” in the “File name” field.

The new script r_basics.R is now in the scripts folder. You can see that by clicking the scripts folder in the “Files” pane. And you can go back up to the main project folder by clicking the .. to the right of the up arrow in the “Files” pane. By convention, R scripts end with the file extension .R.

Checkpoint

Commands in R

To get started, on line 1 of r_basics.R type 2+2, and then click the Run button in the Scripts pane. You should see in the Console pane:

> 2+2
[1] 4

You can highlight as many lines of code as you like and click Run to run them, or you can use execution keyboard shortcut:

Let’s go ahead and delete 2+2 and replace it with library(tidyverse) and then hit Ctrl+Enter.

library(tidyverse)
── Attaching core tidyverse packages ─────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ───────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Note: Package loading messages

When loading a package you may get lots of feedback. These aren’t necessarily errors, but just give more information on the state of the environment after loading the package. The tidyverse is actually a collection of packages. The first section of the output states which packages were lodaed and their versions. The second section notes “Conflicts” that occur because the name of a function is used multiple times. So dplyr::filter() masks stats::filter() means that the dplyr library and the stats library have functions called filter(), and that when calling filter(), the dplyr version will be the default.

Checkpoint

Loading a csv file

Let’s jump right in and load some of the gapminder data using the read_csv() function:

gapminder_1997 = read_csv('data/gapminder_1997.csv')
Rows: 142 Columns: 6
── Column specification ───────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): country, continent
dbl (4): year, pop, lifeExp, gdpPercap

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Remember, with the cursor on this line we can click Run, or we can type Ctrl+Enter. We should see some output in the Console pane as well as gapminder_1997 in the Environment pane. We’ll explore the resulting data in later lessons.

Checkpoint

Let’s breakdown this command:

The output of read_csv() in the Console pane gives us useful information such as the dimensions of the data, the delimiter of the file, and how the columns of the data were interpreted.

Basic R data types

We notice in the output of read_csv() that the country and continent columns were intepreted as character strings (chr) and the year, pop, lifeExp, and gdpPercap columns were interpreted as numbers (dbl).

Mode (abbreviation) Type of data
Numeric (num) Numbers such as 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.
Factor (fct) A categorical variable with defined “levels” or categories.
Logical Boolean values - TRUE or FALSE.

Assigning values to objects

Let’s assign values to some objects and observe each object after assignment.

name = 'Ben'
name
[1] "Ben"
age = 26
age
[1] 26
name = 'Harry Potter'
name
[1] "Harry Potter"

Notice that with each assignment, the object appears in the Environment pane. Also notice that by assigning name twice, the value becomes the last assigned value, overwriting our initial assignment. Also notice that if we evaluate the name of an object, it is printed in the Console pane.

Let’s try to assign some more objects and see how things can go wrong:

1number = 3
Error: <text>:1:2: unexpected symbol
1: 1number
     ^
Flower = 'marigold'
flower = 'rose'
favorite number = 12
Error: <text>:1:10: unexpected symbol
1: favorite number
             ^

Checkpoint

What happened here? We got “unexpected symbol” errors, which is R’s obtuse way of saying it didn’t like the name of the object.

Guidelines on naming objects

  • Object names should be explicit and brief.
  • Object names cannot start with numbers or contain spaces.
  • Object names are case-sensitive.
  • Either separate words in object names like object_name or objectName. Be consistent.
  • Some names cannot be used because they are the names of fundamental functions (e.g. if, else, for; see here for complete list).

Calling functions

Earlier we ran the code gapminder_1997 = read_csv('data/gapminder_1997.csv'). As we said before, read_csv() is a function and 'data/gapminder_1997.csv' is an argument to that function. What happens if we just do:

read_csv()
Error in read_csv(): argument "file" is missing, with no default

We get an error in the Console pane. The key part of the message is “argument ‘file’ is missing, with no default”. In other words, this function needs to be told what to read because there is no default.

Not every function needs arguments, but many do. Try the following functions:

Sys.Date()
[1] "2024-10-16"
getwd()
[1] "/Users/cgates/git/workshop-computational-foundations/source"
round(3.1415, 2)
[1] 3.14

Notice that we threw in round() which actually takes two arguments. How could we have known that?

Getting help with functions

When a function is unfamiliar, we’ll often look at the manual page for the function to understand what arguments are required, what it does, and what it outputs. By prepending a ? in front of a function name, you can access the manual page.

?round

The help page for round() tells us the function does essentially what we’d expect, and gives some other related functions. Note also that the arguments section gives us the names of the arguments and what is expected of them. There is often a Details section to describe nuances, and a Value section to describe the output. Finally, there is an Examples section which gives examples of how to run the code.

Arguments

When we called round(3.1415, 2) it seemed like the first argument is the thing we want to round, and the second argument is how many digits we want. That tracks when we look at ?round. R can evaluate arguments of a function based on their position, as we just saw. However, the preferred way to call a function is to use the names of the arguments, as in:

round(x = 3.14159, digits = 2)
[1] 3.14

Calling a function, and using named arguments, increases the readability of the code and reduces the chance of error, especially with complex functions having many arguments.

Searching for functions

Prepending a ? in front a function name to find out more about the function requires knowing the name of the function beforehand. That won’t always be the case so there are a couple ways to search for R functions.

  1. Search the internet for “R function that does X”.
  2. Use help.search(), as in help.search('Chi-squared test')

Note that in the results of help.search() we see things like, stats::chisq.test. Here the :: is R notation for package_name::function.

Checkpoint

Errors happen

We already ran some code in this lesson that produced errors. We’re going to run lots of code in this workshop and the next, and it’s likely we’ll see more errors! The key steps to correcting an error are:

  1. Read the error message to try to understand what might have gone wrong. Note, not all error messages are clear, so don’t worry if the error message doesn’t help you.
  2. Immediately check the spelling of the command; most errors are caused by typos.
  3. Check that the objects going into the function are what’s expected.

If you’re still stuck as to why an error occurred (something we all encounter), reach out for help. Make sure to post in Slack:

  1. The exact command that caused the error.
  2. The exact error message that resulted.

This way we’ll more quickly be able to diagnose the problem.

Base R and the tidyverse

If you’ve used R before, you may have learned commands that are different what we’ll learn in this workshop. We’ll focus on functions from the tidyverse, a collection of R packages designed to work well together and and offer many features that aren’t part of a fresh install of R (that is, “base R”). Generally the tidyverse helps us write code that is easy to read and maintain, as we’ll see.

The tidyverse is geared for data in the form of tables, and it is very good at manipulating, summarizing, and visualizing such data. However, data occurs in a variety of other shapes and forms. In particular, in a bioinformatics context, the Bioconductor repository of packages utilize data types that are not tables, and therefore do not always work well with tidyverse functions. We’ll see clearer examples of this in the RNA-seq Demystified workshop, and you will undoubtedly encounter many examples in the future.

Some people ask “Should I learn tidyverse or base R?” and we think that rather than either/or, it’s better to think of both/and. Knowing base R and its approach will help in some contexts, while knowing tidyverse will help in others.

Cheatsheets!

The tidyverse packages have excellent cheatsheets that describe the functionality and usage of the packages. You can find them in RStudio by going to the “Help” menu and selecting “Cheat Sheets”. The two that will be most helpful in this workshop are “Data Visualization with ggplot2” and “Data Transformation with dplyr”.



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