Intro to R

Course Description

Students, faculty, and staff in the health sciences, community health, and epidemiology will learn to use R, a statistical software frequently used in health sciences research. This course provides an overview on data entry, data management and manipulation, and visualization using R and also covers basic statistical applications, including linear regression, and logistic regression. To demonstrate proficiency, students will be given a dataset with steps to follow and analyze based on the learning outcomes.

This class is expected to help students build capacity with R and the research process. They can begin to consider how to use R to apply their biostatistics knowledge to their graduate thesis or other research.

Learning outcomes

By the completion of this course, students will be expected to

1. Be familiar with R windows/terminal.
2. Enter/import data into R.
3. Manipulate R data and create new variables.
4. Understand R data and procedures/steps.
5. Be able to correct common errors in R code.
6. Use common procedures to analyze data.
7. Successfully perform common procedures and analyze data of a given dataset

Course material

• Download the syllabus here
• Folder for Data
  • data1.csv and data1.xlsx
  • data2.csv and data2.xlsx
  • test.csv
• Folder for Code
  • Day 1 R script: data_1_script.R
  • Day 2 R script: data_2_script.R
  • Day 2 RMarkdown: intro_to_R_day2
  • Day 3 RMarkdown: intro_to_R_day3
• Folder for Slides
  • Day 1 Slides
  • Day 2 Slides

Helpful packages and places

• CRAN Task Views https://cran.r-project.org/web/views/
• UCLA Guides https://stats.oarc.ucla.edu/other/dae/
• From Base R to Tidyverse https://rebeccabarter.com/blog/2019-08-05_base_r_to_tidyverse
• Tidyverse https://www.tidyverse.org/
• Tidymodels https://www.tidymodels.org/
• Long to wide and back again https://rebeccabarter.com/blog/2019-08-05_base_r_to_tidyverse_pt2
• Spatial data analysis https://r.geocompx.org/
• Canadian census data https://mountainmath.github.io/cancensus/index.html
• BBC Code Book https://bbc.github.io/rcookbook/
• Keiran Healey's book Data Visualization https://kieranhealy.org/publications/dataviz/
• socviz package https://kjhealy.github.io/socviz/