Programming in R
- The key objective of this class is to help students to master basic skills of using R for data manipulation and exploratory data analysis.
- be able to install R and Rstudio on your PC/laptop
- be able to perform simple and complicated mathematical and logical operations using R
- be able to understand basic principles of programming in R and recognize key R data types and data classes
- be able to import external data sets into R
- be able to clean, recode, transform, subset, and merge your data using base R tools and tidyverse
- be able to perform exploratory data analysis in R: frequencies, shares, means, variances, correlations, etc.
- be able to create effective data visualizations using base R and ggplot2
- be able to summarize outputs of your analysis in tabular forms
- be able to write your own simple R functions, profile and debug your code.
- be able to prepare html and pdf reports on your analyses using Rmarkdown
- Day 1 (September 6): Getting started with R
- Day 2 (September 13): Exploratory data analysis using base R – 2
- Day 3 (September 20): Exploratory data analysis using base R – 2
- Day 4 (September 27): Exploratory data analysis using tidyverse
- Day 5 (October 4): Data visualization using ggplot2 and ggpubr
- Day 6 (October 11): Miscellaneous topics
- Day 7 (October 18): Practical session
- Class activityActive involvement in discussions, correct responses to my questions and smart questions to me, presentations of your homework, etc. Please notice that in the first place I will evaluate the quality of your participation, not frequency (although one smart comment on the final day of the course will definitely not earn you an excellent grade for this component).
- Home assignmentsafter some (not all) lectures you will be asked to complete written home assignments. Most of those assignments will be like “please complete the following programming tasks, report the results, and make short written comments explaining what you have done ” or “please answer some questions covering the content of the last lecture”. Those assignments will be relatively short and simple (1-2 pages) and are expected to be submitted before the start of the next class day (i.e., 18:10 next Thursday relatively to the day of assignment).
- Final examyou will be asked to complete several programming and analytical tasks on a real-world data set (see example below). The DEADLINE for submitting your final exam paper is Tuesday, OCTOBER 25, 18:10 (please notice that the date is preliminary can be changed).
- 2022/2023 1st module0.3 * Home assignments + 0.2 * Class activity + 0.5 * Final exam
- Wickham, H., & Grolemund, G. (2016). R for Data Science : Import, Tidy, Transform, Visualize, and Model Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1440131
- Robert I. Kabacoff. (2015). R in Action : Data Analysis and Graphics with R: Vol. Second edition. Manning.
- Wickham, H. (2015). Advanced R, Second Edition. Boca Raton, FL: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=934735