The files in this repository are used in the Cushing/Whitney Meidcal Library's "Working with Data in R" workshop. This workshop demonstrates uses of the tidyverse packages including tidyr and dplyr (files 1 and 2). Files 3 and 4 provide more indepth examples of usecases for reshaping data for statistical tests and graphical applications (and these files leverage lubridate and ggpubr).
There are no slides associated with this workshop. See links to useful related resources below:
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
an effective data handling and storage facility, a suite of operators for calculations on arrays, in particular matrices, a large, coherent, integrated collection of intermediate tools for data analysis, graphical facilities for data analysis and display either on-screen or on hardcopy, and a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities. The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.
Many users think of R as a statistics system. We prefer to think of it as an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics. ~ rproject.org/about.html
- Tidyverse: https://www.tidyverse.org/
- Cheatsheets: https://rstudio.com/resources/cheatsheets/
- Statistical tools for high-throughput data analysis (STHDA): http://www.sthda.com/english/
- The R Graph Gallery: https://www.r-graph-gallery.com/
- Free LinkedIn Leaning access through Yale: https://your.yale.edu/yale-link/linkedin-learning
- More on ggplot2 for graphic: http://r-statistics.co/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html
- Even more on ggplot2: https://ggplot2.tidyverse.org/
- Programming with R, a free tutorial: https://swcarpentry.github.io/r-novice-inflammation/02-func-R/index.html