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Syllabus

Coding 1: Data Management and Analysis with R

  • Instructor: Kaufmann Marc; Email: [email protected]
  • Credits: 2 CEU (4 ECTS)
  • Term: Winder 2023-2024
  • Course level: MA/MSc
  • Prerequisites: None

Course description

The course serves as an introduction to the R programming language and software environment for data exploration, data munging, data analysis, and data visualization.

Learning outcomes

  • Produce meaningful descriptive statistics and informative graphs
  • Become familiar with the R ecosystem
  • Learn how to get help, how to debug, how to do basic programming
  • Learn how to use R for the most common data tasks: loading, cleaning, transforming, summarizing, and visualizing data

Reading list

Class materials are hosted on https://github.com/MarcKaufmann/R-Coding. Look there for an up-to-date syllabus.

Assessment

  • Four data reports (100 points)
    • 15, 20, 25, and 40 points respectively (increasing over time)

There will be no final exam.

The grading thresholds are those set by CEU (see https://documents.ceu.edu/documents/p-1105-2v2304, Annex 2).

If you have a question regarding a particular assignment -- whether about the grade or otherwise -- make sure to email me.

Technical/laptop requirement

You will need your laptop with RStudio and R installed in order to participate in class. Optionally, you can install git too.

Tentative Course schedule

Note: This is subject to reording and minor changes, but the broad set of topics should remain fairly stable.

  1. R ecosystem, basic syntax; how to get help; first plot
  2. ggplot2: data visualization; introduction to functions
  3. Tidyverse: choosing data with filter(), select(), arrange()
  4. Tidyverse: transforming data with mutate(), summarise(), group_by()
  5. Tidyverse: more transformations with group_by(), mutate(), and filter()
  6. Tidyverse: reading data with read_csv() and parse_*()
  7. Tidyverse: tidying data with pivot_wider() and pivot_longer()
  8. Tidyverse: left_join() and join()
  9. Visualizing Spatial Data

Items spread across lectures:

  • Functions, loops, and friends; sampling, simulating, bootstrapping (GD)
  • working with data the non-tidyverse way; lubridate for working with dates

Additional topics (time permitting):

  • Shiny apps

Short bio of the instructor (1 para)

Marc Kaufmann teaches how to think with economic models, and how to use them to ask the right questions. He studied at Cambridge, Université Libre de Bruxelles, Berkeley, and Harvard. He studies the perceptions and beliefs people hold, how this affects their decisions and specifically their productivity.