1. Introductions
2. Class overview
3. Getting R up and running

_{[Photo by Belinda Fewings on Unsplash]}

Poll: How are you feeling right now?

Carrie Wright (she/her)

Senior Staff Scientist and Training Lead, Fred Hutchinson Cancer Center

Associate, Department of Biostatistics, JHSPH

PhD in Biomedical Sciences

Email: cwright2@fredhutch.org Web: https://carriewright11.github.io

Ava Hoffman (she/her)

Senior Staff Scientist, Fred Hutchinson Cancer Center

Associate, Department of Biostatistics, JHSPH

PhD in Ecology

Email: ahoffma2@fredhutch.org Web: https://avahoffman.com

Elizabeth Humphries (she/her)

Staff Scientist, Fred Hutchinson Cancer Center

PhD in Molecular Epidemiology

Email: ehumphri@fredhutch.org

NOTE this is not her dog

Please introduce yourself on Slack!

Slack Workspace

Learning a programming language can be very intense and sometimes overwhelming.

We recommend fully diving in and minimizing other commitments to get the most out of this course.

Like learning a spoken language, programming takes practice.

Learning R has been career changing for all of us, and we want to share that!

We want you to succeed – We will get through this together!

• R is a language and environment for statistical computing and graphics developed in 1991

• R is the open source implementation of the S language, which was developed by Bell laboratories in the 70s.

• The aim of the S language, as expressed by John Chambers, is “to turn ideas into software, quickly and faithfully”

_{[source: http://www.r-project.org/, https://en.wikipedia.org/wiki/S_(programming_language), https://en.wikipedia.org/wiki/Bell_Labs)]}

• Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand developed R

• R is both open source and open development

_{[source: http://www.r-project.org/, https://en.wikipedia.org/wiki/R_(programming_language)]}

• Free (open source)

• High level language designed for statistical computing

• Powerful and flexible - especially for data wrangling and visualization

• Extensive add-on software (packages)

• Strong community

_{[source: https://github.com/rladies/meetup-presentations_baltimore]}

• Little centralized support, relies on online community and package developers

• Annoying to update

• Slower, and more memory intensive, than the more traditional programming languages (C, Perl, Python)

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What do you hope to get out of the class?

Why do you want to use R?

_{[Photo by Nick Fewings on Unsplash]}

https://daseh.org/

Materials will be uploaded the night before class. We are constantly trying to improve content! Please refresh/download materials before class.

• Understanding basic programming syntax
• Reading data into R
• Recoding and manipulating data
• Using add-on packages (more on what this is soon!)
• Making exploratory plots
• Performing basic statistical tests
• Writing R functions
• Building intuition

ONLINE VIRTUAL COURSE

• Lecture with slides, interactive
  
• Lab/Practical experience
  
• Two 10 min breaks each day - timing may vary
  
• July 8-18, 10:30am - 2:00pm PST on Zoom

IN-PERSON CODE-A-THON

• Mostly independent group work
  
• Frequent check-ins with instructors and other groups
  
• Some lectures about the practical aspects of coding
  
• July 29-31 (in person in Seattle)

https://forms.gle/hQ7jdHZSZoDpHAdE6

Let us know anonymously how you’re doing with the material.

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While we do have homework assignments on the course schedule, these are strictly optional!!!

We encourage you to try the assignments, as the best way to get comfortable with any programming language is through practice.

If you can, we suggest working virtually with a large monitor or two screens. This setup allows you to follow along on Zoom while also doing the hands-on coding.

_{[source - reddit.com]}

We are collecting data about user experience with our course to learn more about how to improve the data science education experience. This data may ultimately be used for a research publication and reporting to the NIH.

https://forms.gle/e2CQFDJsgyZwLV3S9

• Install the latest R version (4.3.2 (called ‘Eye Holes’) as of October 31, 2023)

• Install RStudio

More detailed instructions on the website.

RStudio is an integrated development environment (IDE) that makes it easier to work with R.

More on that soon!

This course will involve moving files around on your computer and downloading files.

If you are new to this - check out these videos.

If you have a PC: https://youtu.be/we6vwB7DsNU

If you have a Mac: https://www.youtube.com/watch?v=Ao9e0cDzMrE

You can find these on the resource page of the website.

R jargon: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf

Package - a package in R is a bundle or “package” of code (and or possibly data) that can be loaded together for easy repeated use or for sharing with others.

Packages are analogous to a software application like Microsoft Word on your computer. Your operating system allows you to use it, just like having R installed (and other required packages) allows you to use packages.

Function - a function is a piece of code that allows you to do something in R. You can write your own, use functions that come directly from installing R, or use functions from additional packages.

You can think of a function as verb in R.

A function might help you add numbers together, create a plot, or organize your data. More on that soon!

sum(1, 20234)

[1] 20235

Argument - what you pass to a function

• can be data like the number 1 or 20234

sum(1, 20234)

[1] 20235

• can be options about how you want the function to work such as digits

round(0.627, digits = 2)

[1] 0.63

round(0.627, digits = 1)

[1] 0.6

Object - an object is something that can be worked with or on in R - can be lots of different things! You can think of objects as nouns in R.

• a matrix of numbers
• a plot
• a function
• data

… many more

• Variable: something measured or counted that is a characteristic about a sample

examples: temperature, length, count, color, category

• Sample: individuals that you have data about -

examples: people, houses, viruses etc.

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

[source]

Sample = Row
Variable = Column

Data objects that looks like this is often called a data frame.

Fancier versions from the tidyverse are called tibbles (more on that soon!).

• When you download R, it has a “base” set of functions/packages (base R)
  • You can install additional packages for your uses from CRAN or GitHub
  • These additional packages are written by RStudio or R users/developers (like us)
  • There are also packages for bioinformatics available at Bioconductor

• Not all packages available on CRAN or GitHub are trustworthy
• Posit makes many useful packages
• How to trust an R package
• Many packages have accompanying academic papers published in peer-reviewed journals
• Widely used packages have better documentation (official and in forums) and are more likely free of errors

We will mostly show you how to use tidyverse packages and functions.

This is a newer set of packages designed for data science that can make your code more intuitive as compared to the original older Base R.

Tidyverse advantages:
- consistent structure - making it easier to learn how to use different packages
- particularly good for wrangling (manipulating, cleaning, joining) data
- more flexible for visualizing data

Packages for the tidyverse are managed by a team of respected data scientists at Posit.

See this article for more info.

We will practice this in labs :)

Differs depending on the source (CRAN, GitHub, etc)

Must be done once for each installation of R (e.g., version 4.2 >> 4.3).

You can install packages from CRAN using the tool menu in RStudio:

tools > Install Packages

Type in the package name to install.

We use a function called install.packages() for CRAN packages.

Here is an example where we “install” the dplyr package:

install.packages("dplyr")

The package name is enclosed in quotation marks.

After installing packages, you will need to “load” them into memory so that you can use them.

This must be done every time you start R.

We use a function called library to load packages.

Here is an example where we “load” the dplyr package:

library(dplyr)

Quotation marks are optional.

Install the remotes package.

install.packages("remotes")

Then load the package.

library(remotes)

Next, run the following.

It will install our custom package, dasehr from GitHub.

install_github("fhdsl/dasehr")

Found on our website under the Resources tab: https://daseh.org/resources.html

• videos from previous offerings of the class
• cheatsheets for each class

Error messages can be scary!

• Check out the FAQ/Help page on the website: https://daseh.org/help.html
• Ask questions in Slack! Copy+pasting your error messages is really helpful!

We will also dedicate time today to debug any installation issues

• R is a powerful data visualization and analysis software language.
• Add-on packages like the tidyverse can help make R more intuitive.
• Functions (like verbs) perform specific tasks in R and are found within packages.
• Arguments within functions specify how to perform a function.
• Objects (like nouns) are data or variables.
• We will be both installing and loading packages.
• Materials will be updated frequently as we improve it. Please use the Google Form survey so you can provide feedback throughout the class!
• Lots of resources can be found on the website. You will have access to the website after the class is over.

🏠 Class Website

🏠 Class Website

Image by Gerd Altmann from Pixabay