Data Science with R (DataSciR)

This is the website for the Data Science with R (DataSciR) course offered in 2021. Click here if you are looking for the 2020 course website.

News

27 July 2022: I am proud to announce that the paper “Data-Driven Prediction of Athletes’ Performance based on their Social Media Presence” by Frank Dreyer, Jannik Greif, Kolja Günther, Myra Spiliopoulou and Uli Niemann has been accepted for presentation as a full paper at the 25th International Conference on Discovery Science 2022 in Montpellier, France. This paper is a follow-up to the project “The Impact of NBA Player-related Social Media Posts on Their On-court Performance - An Analysis” of Frank, Jannik and Kolja as part of the Data Science with R course. Congratulations especially to the three students on this great achievement! 😀

Administrative Information

• Course day/time: Fridays, 9:15-10:45 (via Zoom)
• Instructor: Uli Niemann
• Course type: seminar
• ECTS credits: 6
• Audience: all FIN Master degree programs
• Course language: english
• Application: see section Application & registration
• Prerequisites: see section Prerequisites
• Grading: based on several deliverables in the context of a semester-long data science project → see Project page

Application & registration

The course is limited to max. 30 students. Please apply for DataSciR by completing the following tasks until 03. April 2021:

1. Enroll to the course in the LSF.
2. Send a short motivation email (not more than 300 words) to the course instructor (firstname.lastname@ovgu.de) using the subject [DataSciR] Course application. Please send this email using your ovgu email address and please also give your matriculation number.

On 05.04.2021, you will be notified if you can attend the course.

After admission, please complete this registration form until 20.05.2021.

Course description

Ten years ago, who would have thought, that R, the “environment for statistical computing and graphics”, would become one of the most popular programming languages for data scientists?

The impressive growth of R is not a coincidence. As free & open-source alternative to expensive & proprietary software like SPSS, Matlab and Excel, R’s strengths have always been its capabilities for statistical data analysis as well as its functionalities to create powerful, aesthetically appealing graphics and charts.

While R attracted a rather exclusively academic audience in the 90’s & 00’s, the R community since has grown not only by sheer number but also in diversity, as people from different industries and backgrounds discover R’ usefulness for a wide range of applications. As of February 2020, more than 15,000 (!) packages have been published to CRAN, ca. half of them since 2015.

Especially in the last decade, the functionality and versatility of R has gained momentum. Among the most popular R packages are:

In Data Science with R (DataSciR), you will learn fundamentals of R and how to use the following packages for Data Science:

• the “tidyverse” which includes packages like dplyr and tidyr for data manipulation and ggplot2 for data visualization,
• rmarkdown and knitr for reproducible & automated reporting,
• shiny for creating interactive web applications, and
• tidymodels for inferential and predictive modeling.

You will demonstrate your proficiency in these packages on a semester-long graded data science project.

(Tentative) Schedule

Wk	Date	Topic	Slides	Videos	Exer-cises	Quiz-zes	Additional Materials
1	05.-11.04	Welcome & Introduction	Welcome, First tour of R & RStudio	Welcome & Course Intro, First tour of R & RStudio	Ex 1	–	–
2	12.-18.04	Visualizing data with ggplot2	Introduction to ggplot2, Effective visualizations	Intro to ggplot2, Visualizing numerical and categorical data, Tips for effective vizualizations	Ex 2, Code-along	Q 1	R4DS chapters: Data Visualization, Exploratory Data Analysis, Graphics for communication. ggplot2 cheat sheet. Official ggplot2 book. curated list of ggplot2 resources and extensions. Keynote talk: Alberto Cairo - How Charts Lie: Getting Smarter About Visual Information
3	19.-25.04	The Tidyverse	The Tidyverse	Tidyverse intro & dplyr, Join functions, tidyr, readr&tibble	Ex 3, Code-along	Q 2	R4DS chapters: Data transformation, Tibbles, Data import, Tidy data, Relational data, Pipes. Cheat sheets: dplyr, Data Import (readr & tidyr).
4	26.04-02.05	R Markdown	R Markdown	R Markdown	Ex 4, no code-along this week	Q 3	R4DS chapters: R Markdown, R Markdown formats, R Markdown workflow. Quick Markdown tutorial. R Markdown cheat sheet. RStudio’s R Markdown reference guide. RStudio’s R Markdown Get Started Tutorial. List of all available Knitr chunk options. Book R Markdown: The Definitive Guide
5	03.-09.05	The R language: vectors, classes, functions, iteration	Vectors, classes, functions, iteration	Vectors, Classes, Functions, Iteration	Ex 5, Code-along	Q 4	R4DS chapters: Factors, Dates and times, Functions, Iteration
6	10.-16.05	Linear models	Linear models	Correlation, Linear regression	Ex 6, Code-along	Q 5	Tidy Modeling with R chapter: Fitting models with parsnip. R4DS chapters: Model basics, Model building, Many models
	20.05	Deadline: project proposal submission & registration
7	17.-23.05	Data modeling with tidymodels	tidymodels	tidymodels	Ex 7, Code-along	Q 6	Tidymodels tutorial. Book Tidy Modeling with R.
8	24.-31.05	Creating web applications with shiny	shiny	shiny	Ex 8	Q 7	RStudio’s “Learn Shiny” tutorial. Mastering Shiny. Shiny cheat sheet. “Awesome” Shiny Extensions.
9	01.06-07.06	Misc. topics	Misc. topics	Misc. topics	–	–	–
	06.07	Deadline: project submission
	09.07	Deadline: final presentation

Student Projects

#	Team Members	Title	Code Repo	Website	Presentation Date & Time
1	Diana Guzman, Philipp Blüml	Analysis of Finance-related Reddit Communities	URL	URL	Fri, 09.07, 09:00
2	Anish Kumar Singh, Priyanka Singh, Ramanpreet Kaur, Venkata Srinath Mannam	Classifying Whether Twitter Authors Spread Hate Using Supervised Learning	URL	URL	Fri, 09.07, 09:30
3	Kiran Babu Thatha, Marcel Schulte, Obinna Patrick Nkwocha, Shweta Pandey, Thorben Hebbelmann	Multi-Perspective and Predictive Analysis of Forests: Issues, Challenges and Global Trends	URL	URL	Fri, 09.07, 10:00
4	Ammar Ateeq, Muhammad Hashim Naveed, Sidra Aziz	Effect of COVID-19 Induced Lockdown on Mental Wellbeing of Students Attending Online Classes	URL	URL	Fri, 09.07, 10:30
5	Indrani Sarkar, Indranil Maji, Michael Thane, Sharanya Hunasamaranahalli Thotadarya	Trends in Programming Language Popularity	URL	URL	Fri, 09.07, 11:00
6	Madhuri Sajith, Usama Ashfaq, Vishnu Jayanand, Sujith Nyarakkad Sudhakaran, Ranjiraj Rajendran Nair	Behavioral and Psychological Distress of COVID-19 and Infodemics	URL	URL	Mon, 12.07, 09:00
7	Jannik Greif, Kolja Günther, Frank Dreyer	The Impact of NBA Player-related Social Media Posts on Their On-court Performance - An Analysis	URL	URL	Fri, 16.07, 09:00

Prerequisites

There are no mandatory prerequisites for DataSciR. However, you are expected to have a profound knowledge of fundamental data mining techniques, such as classification, regression and clustering. Hence, it is recommended that you have heard at least one of the following lectures (or comparable):

• Data Mining I - Introduction to Data Mining
• Machine Learning
• Visual Analytics

Also, you should have a basic programming and statistics knowledge. For example, you will learn the most important vector types and classes in R, but you will not learn what a vector or a class is in general. Accordingly, you should know what the terms mean, standard deviation, probability, etc. mean.

Recommended Reading

Data Mining / Statistical Analysis:

• Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning. Springer, 2017.
• Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. Introduction to Data Mining. Pearson, 2005.
• Jiawei Han, Micheline Kamber and Jian Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2011.

R-specific:

• Hadley Wickham, and Garrett Grolemund. R for Data Science. O’Reilly, 2017.
• Max Kuhn, and Julia Silge. Tidy Modeling with R. 2021. Draft version.
• Max Kuhn. The caret package. Online documentation.
• Hadley Wickham. ggplot2 - Elegant Graphics for Data Analysis. 3rd edition. Draft version.
• Hadley Wickham. Mastering Shiny. O’Reilly, 2021. Draft version.
• Yihui Xie, J. J. Allaire, and Garrett Grolemund. R Markdown: The Definitive Guide. Chapman & Hall/CRC, 2018.
• Hadley Wickham. Advanced R. 2nd edition, Chapman & Hall/CRC, 2019.
• Max Kuhn, and Kjell Johnson. Applied Predictive Modeling. Springer, 2013.
• Max Kuhn, and Kjell Johnson. Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press, 2019.
• Bradley Boehmke. Hands-on Machine Learning with R. Chapman and Hall/CRC, 2019.

other:

• Introduction to Data Science course held by Mine Çetinkaya-Rundel at the University of Edinburgh.
• Jenny Bryan, and others. Happy Git and GitHub for the useR. 2018.
• Jeffrey Leak. Organizing Data Science Projects. Learnpub.com.
• RStudio cheat sheets
• RStudio primers
• RStudio webinars
• Quick-R (short tutorials on various topics, e.g. data import, statistics and graph generation)

Software

By the end of the first week, you should have installed the following software on your own laptop:

1. R (>=4.0.0)
2. RStudio (>=1.4)
3. on Windows: Rtools

Also, please check whether you can successfully install packages. To do so, click on the Packages tab in the bottom-right pane in RStudio. Then, click on the Install button and specify an arbitrary package, e.g. dplyr. Finally, click on Install. Alternatively, you can install a package from the console with install.packages("dplyr"). If everything is set up correctly, no error messages should be displayed when you load the installed package with library(dplyr).