Overview | Learning Objectives | Setup | Credits | Additional Resources
Abhirupa Ghosh, PhD & Kewalin Samart
The Gignoux Lab, Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus
“We are statistical and population geneticists interested in the confluence of epidemiology, AI and evolution. We use simulations, new methods, and large-scale datasets to improve personalized medicine. We are interested in improving risk prediction across the spectrum of diversity of human populations, integrating insights from functional genomics, and identifying the clinical and translational potential of modern genomics.”
This repo contains the workshop material for using R/tidyverse to analyze & visualize diverse datasets, e.g., transcriptomics, gapminder | as HTML (transcriptomics & gapminder).
- Part 1: Getting started w/
readr- Installation and Setup | Cheatsheets
- Loading packages
- Data import
- Knowing your data: basic data exploration
- Part 2: Reshaping data w/
tidyr- Gather, Spread
- Unite, Separate
- Part 3: Data wrangling w/
dplyr- Filter, Select
- Mutate
- Distinct and Arrange
- Group_by and Summarize
- More data wrangling
- Part 4: Visualizing tidy data w/
ggplot- Basics of ggplot
- Barplots and histograms
- Scatter plots
- Boxplots and violin plots
- Some data sleuthing!
- Part 5. Export and Wrap-up w/
rmarkdown- Saving your plots
- Saving your data files
- Summary of everything that you learnt in the workshop!
By the end of this workshop, you will be able to load your genomic
dataset, perform basic data tidying & wrangling, data visualization, and
save/export your results using tidyverse! Hopefully, you will also
have a newfound appreciation for reproducible research and R!
- Install the following software if you don't yet have them. If you do
have these installed, skip to #2:
- R version
3.6+(Current:4.2.0) | Download R - RStudio version
1.3+(Current:2022.02.2-485) | Download RStudio OR use RStudio Cloud
- R version
- Ensure that your version of R is
3.6+. The latest version is4.2.0. To check your R version, type in your console:version - Check your RStudio version. It should be
v1.3+Open RStudio. In the top menu bar click: RStudio > About RStudio > - Install tidyverse, here, gapminder (not needed for
transcriptomics workshop), gganimate:
install.packages(c("tidyverse", "here", "gapminder"))
devtools::install_github(‘thomasp85/gganimate’) - Access useful Cheatsheets here.
Other Resources: Software Carpentry Video Tutorial for installing R and R Studio
Video Tutorial
Install R by downloading and running this .exe file from
CRAN. Also,
please install the RStudio
IDE. Note
that if you have separate user and admin accounts, you should run the
installers as administrator (right-click on .exe file and select "Run as
administrator" instead of double-clicking). Otherwise problems may occur
later, for example when installing R packages.
Video Tutorial Install R
by downloading and running this .pkg file from
CRAN. Also, please
install the RStudio
IDE.
You can download the binary files for your distribution from
CRAN. Or you can use your
package manager (e.g. for Debian/Ubuntu run
sudo apt-get install r-base and for Fedora run sudo dnf install R).
Also, please install the RStudio
IDE.
Arjun Krishnan and Janani Ravi co-developed the content for the transcriptomics part for this workshop; R-Ladies East Lansing members (Kayla J, Nafiseh H, Veronica F, Cara F, Camille A) and Janani Ravi helped with the gapminder material.
- Krishnan Lab | JRaviLab
- R-Ladies East Lansing, incl. Nafiseh Haghtalab, Kayla Johnson, Veronica Frans, Cara Feldscher, Camille Archer
- R-Ladies Chennai | R-Ladies Bangalore | R-Ladies Tunis
This work is licensed under a BSD-3-Clause License.
- If you like it, leave your star in this project 🌟
- If you would like to suggest/contribute to this project, feel free to open a issue 💟
- Please follow our contributing guidelines (coming soon!).
- You can access all relevant material pertaining to this workshop here.
- Other related RLEL workshops & useful cheatsheets.
- Computational Biology/Bioinformatics Resources collated by Arjun and me.
- Data-to-viz.com & R Graph Gallery
- R for Data Science: Wickham & Grolemund #R4DS https://r4ds.had.co.nz
- Hands-On Programming with R: Grolemund #HOPR https://rstudio-education.github.io/hopr
- R Programming for Data Science: Peng https://leanpub.com/rprogramming
- Learning Statistics with R: Navarro https://learningstatisticswithr.com/book