Introductory Statistics with R

This lesson introduces the R statistical programming language with a goal to promote efficient and reproducible data analyses. You will learn how to code in R while reviewing basic statistical concepts including numerical summaries (e.g. mean, median, standard deviation) and graphical displays of biomedical data. This lesson borrows from Data Science for the Biomedical Sciences (Chen & Brown, 2021) and Data Carpentry Ecology R.

Prerequisites

If you have stored files on a computer at all and recognize the word “file” and either “directory” or “folder” (two common words for the same thing), you’re ready for this lesson. You are not expected to have any prior programming experience.

Schedule

	Setup	Download files required for the lesson
00:00	1. Introduction to R and RStudio	Why should I learn R? How are R and RStudio related?
00:10	2. Knowing your way around RStudio	How do I use RStudio?
00:30	3. Interacting with R	Interact with R using the console or a script
00:35	4. Seeking help	How do I get help with R and RStudio? Where can I learn more?
00:45	5. Creating objects in R	How do I create objects in R?
01:25	6. Vectors and data types	What are data types in R?
02:00	7. Subsetting vectors	How can I select specific elements of a data vector?
02:30	8. Missing data	What does R do when data are missing?
03:00	9. Loading data into R	How do I load data into R?
04:00	10. Factors	How do I work with factors in R?
04:45	11. Using the dplyr package	How do I select, filter, and subset data in R?
05:20	12. Descriptive Calculations	How do I obtain summary statistics for data? What do the mean, median, and standard deviation of a data set summarize?
06:00	13. Data Transformation	How do I transform data?
06:40	14. Cleaning data	How do I re-organize and clean data in R?
07:20	15. Data visualization	How do I visualize data in R?
08:10	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.