The course Programming in R will take place in the winter semester 2023/2024 in attendance in the room CIP-Pool SG1 (SGBG U25B).
The tutorial will take place every second week on Friday from 08:30 -12:00. The first tutorial will be held on November 3.
Teaching materials will be made available via the associated GRIPS course, which will also be used to communicate all organisational matters.
PROGRAMMING WITH R
This course teaches basic skills in the use of the free software R. This provides a flexible and user-friendly programming environment and language, which is particularly suitable for statistical data analysis, the creation of scientific graphics and for simulations. The R software is now a widely used tool in many subject areas (New York Times article). In addition to teaching the R-specific syntax, the foundation is laid in particular for independent data analysis. Programming skills acquired in this course can also be helpful for learning other common programming environments, such as Python, Matlab, Gauss, Ox, Stata etc.
1. Installation, user interface
2. R basics and handling of data sets
3. Graphics and data visualisation
4. Flow control (loops etc.)
5. Statistical evaluation, regression analysis
6. Numerical optimisation
7. Efficient programming
8. Statistical simulations
9. Creating your own R packages
Kleiber, C. und Zeileis, A. (2004). Applied Econometrics with R. Springer.
Ligges, U. (2008). Programmieren mit R. Springer.
Vinod, H. (2008). Hands-on Intermediate Econometrics using R. World Scientific.
Everitt, B. (2005). An R and S-PLUS® Companion to Multivariate Analysis. Springer.
Chambers, J. M. (2008). Software for Data Analysis - Programming with R. Springer.
TARGET GROUP / PREREQUISITES
The course can be included as an elective course in both the Bachelor's and Master's degree programmes (2 ECTS) as well as in the IT training accompanying the degree programme (Studienbegleitende IT-Ausbildung). Together with Programming in EViews (Prof. Dr. Knoppik) and Introduction to Data Analysis with STATA (Prof. Dr. Lea Cassar), the course is also part of a 3-course package (Programmieren für die Volkswirtschaftslehre) that can be added to the Bachelor's specialisation module Empirical Economic Research.
Alternatives to a certificate of achievement in Flexnow are a graded certificate (without the possibility of entry in FlexNow) or an ungraded certificate as proof of attendance in the course.
Prerequisite for participation in the course is basic econometric knowledge, such as that taught in Econometrics I, but no programming knowledge.
The overall grade of the course results from an exam on the computer. Passing the exam no worse than 4.0 is required to pass the course.
The material relevant to the course is provided in the GRIPS Programming with R course. Below is the collection of old codes and data, but these are no longer of immediate relevance to the course.
|01_Introduction||01_Introduction||Data (Folder: PMR.zip)|
| ||02_Objects||Africa.xls ALQ_Kreisebene.xls|
| ||03_Graphics||Growth.txt (Kla. WS10/11) kreise.xls|
|04_Data_Analysis||04_Data_Analysis (Rmarkdown)||Kenya.txt, Wheat.txt, CPS1985.RData|
|05_Flow_Control||05_Flow_Control||Bund09.csv, dataUS.csv, hoabs.txt|
|06_Regression||06_Regression||ophpbs.txt, cnp160v.txt, DAXdaily.csv|
|07_Monte_Carlo||07_Monte_Carlo||MASchools.csv (für Klausur WS12/13)|
btw.rdata (Klausur WS17/18)
|10_Efficiency||10_Efficiency||Scoping, Multivariate Data Analysis|
|K_Packages||ARMA_Bruchtest, Time Series|
|L_Summary||Geomapping, Graphics II|
|WS09/10||Google's R Style Guide|
|WS10/11||100 most downloades R Packages|
|WS11/12||Tutorials for Shiny, ggplot2 and dplyr|
|WS13/14||IQUIT R Video Series|
Appointments and Rooms
|Tutorial||Friday||8:30-12:00||CIP-Pool SG1 (SGBG U25B)||Dominik Ammon|| |
|every second week|