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This introductory on R course will be offered via Zoom once a week for three months. The course is designed for abolute beginners in R and will require basic knowledge of quantitative analysis. At the end of the course, participants will gain a good understanding of the R programming environment, be able to write codes for basic descriptive and regression analysis and make publication quality charts. Certificates will be awarded to all participants upon successful completion of a project of their choice.

Course outline:

Lecture 1. Getting familiar with the interface, understading the basic operators, dataframes, matrices, and tibbles.

Lecture 2. Settting working directories, loading example and local datasets, creating toy datasets, and importing data from Excel, SPSS, Stata and SAS.

Lecture 3. Using the basic housekeeping commands, introduction to R packages and repositories, and dealing with conflicting packages.

Lecture 4. Introduction to data wrangling with R e.g. cleaning, renaming, recoding, reshaping.

Lecture 5. Handling missing values and outliers.

Lecture 6. Subsetting and merging datasets (rind, cbind)

Lecture 7: Introduction to joins: inner join, left join, right join, full join, anti join.

Lecture 8: Review and assessment.

Lecture 9: Introduction to data visualization using ggplot e.g. line, bar, pie, histogram, scatter and boxplots.

Lecture 10: Basic summary statistics e.g. one-, two- and three-way tables of mean, meadian, proportion.

Lecture 11: ANOVA and linear regression.

Lecture 12: Binary, multinomial, and Poisson regression.

Course requirements:

Windows/Mac computer with BOTH R and R studio installed.

Links to download the applications:

https://cran.r-project.org/bin/windows/base/

https://www.rstudio.com/products/rstudio/download/