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

_{[Photo by Belinda Fewings on Unsplash]}

Carrie Wright

Assistant Scientist, Department of Biostatistics, JHSPH

PhD in Biomedical Sciences

Email: cwrigh60@jhu.edu

Website: https://carriewright11.github.io

Ava Hoffman

Research Associate, Department of Biostatistics, JHSPH

PhD in Ecology

Email: ava.hoffman@jhu.edu

Website: https://avahoffman.com

Candace Savonen

Research Associate, Department of Biostatistics, JHSPH

Masters in Neuroscience

Former Data Analyst for Childhood Cancer Data Lab

Email: csavone1@jhu.edu

Website: https://www.cansavvy.com/

Grant Schumock

PhD Candidate, Department of Biostatistics, JHSPH

BS in Nuclear Engineering

Email: gschumo1@jhmi.edu

Qier Meng

ScM Student, Department of Biostatistics, JHSPH

Bachelor’s Degree in Mathematics

Bachelor’s Degree in Neuroscience

Email: qmeng11@jhmi.edu

• R is a language and environment for statistical computing and graphics

• 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)]}

• In 1991 Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand began developing R

• R is named partly after the first names of the first two authors and a play on the name of S.

• R is both open source and open development

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

• High level language designed for statistical computing

• Powerful and flexible - especially for data wrangling and visualization

• Free (open source)

• Extensive add-on software (packages)

• Strong community

_{[source: https://rladies-baltimore.github.io/]}

• 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, Java, 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]}

http://jhudatascience.org/intro_to_R_class

Materials will be uploaded the night 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

• Lecture with slides (possibly “Interactive”)
• Lab/Practical experience
• Two 10 min breaks each day - timing may vary
• Jan 10-21, 2022, 8:30AM-11:50AM on Zoom
• No class on Jan 17th for Martin Luther King Jr. Day

https://courseplus.jhu.edu/core/index.cfm/go/syl:syl.public.view/coid/16733/

• Surveys throughout the class for the instructors
• Upload homework

End of class Survey - link in email.

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1. Attendance/Participation: 20% - this can be asynchronous - just some sort of interaction with the instructors/TAs (turning in assignments, emailing etc.)
2. Homework: 3 x 15%
3. Final “Project”: 35%

Homeworks and Final Project due by Wednesday, Jan 26, 2022 at 11:59pm EST.

If you turn homework in earlier this can allow us to potentially give you feedback earlier.

Note: Only people taking the course for credit must turn in the assignments. However, we will evaluate all submitted assignments in case others would like feedback on their work.

• Install the latest version from: http://cran.r-project.org/

• Install RStudio

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

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 sort of 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 particular 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.

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

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 in R - can be lots of different things!

• a matrix of numbers
• a plot
• a function

… 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!).

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 RStudio.

See this article for more info.

We have an R package called jhur that will make sure all the packages are installed.

You can just copy and paste the below code into your console - we’ll explain what it all means in the next day or two

install.packages("remotes")
remotes::install_github("muschellij2/jhur")

Note it may take ~5-10 minutes to run.

Want more?

• Tidyverse Skills for Data Science Book: https://jhudatascience.org/tidyversecourse/ (more about the tidyverse, some modeling, and machine learning)

• Tidyverse Skills for Data Science Course: https://www.coursera.org/specializations/tidyverse-data-science-r
  (same content with quizzes, can get certificate with $)

• R for Data Science: http://r4ds.had.co.nz/
  (great general information)

• Open Case Studies: https://www.opencasestudies.org/
  (resource for specific public health cases with statistical implementation and interpretation)

• Dataquest: https://www.dataquest.io/
  (general interactive resource)

Need help?

• Various “Cheat Sheets”: https://www.rstudio.com/resources/cheatsheets/

• R reference card: http://cran.r-project.org/doc/contrib/Short-refcard.pdf

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

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

• R terminology: https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf

Interested in Reproducibility?

Check out Candace’s courses:

• Introduction: https://jhudatascience.org/Reproducibility_in_Cancer_Informatics/

• Advanced: https://jhudatascience.org/Adv_Reproducibility_in_Cancer_Informatics/