Introduction to Data Analysis Using R

Week 2 (September 23-27, 2019)

Lecturers: Dr. Jan-Philipp Kolb, Shelmith Kariuki

About the Lecturers

Dr. Jan-Philipp Kolb is a researcher at the Leibniz Institute for Social Sciences (GESIS), working as a survey statistician in the GESIS Panel team. He previously worked in the GESIS Survey Statistics team and as a research assistant in the Economic and Social Statistics Department at the University of Trier teaching sampling techniques and applied statistics using R.

Shelmith Nyagathiri Kariuki is a Senior Data Analyst based in Nairobi, Kenya. She is also a Zindi Ambassador for Kenya, where she furthers the Zindi mission of building the data science ecosystem in Africa. Shelmith has previously worked as an assistant lecturer in various Kenyan universities, teaching units in Statistics and Actuarial Science. She holds a Bsc in Actuarial Science and Msc in Applied Statistics from JKUAT. Shelmith has extensive experience in data analysis using R and Python and is at the forefront of the AfricaR initiative, striving to achieve improved representation of Africans in the global R community by encouraging, inspiring, and empowering Africans of all genders.

 

Short Course Description

The open source software package R is free of charge and offers standard data analysis procedures as well as a comprehensive repertoire of highly specialized processes and procedures, even for complex applications. Emphasis in this course will be on methods of graphically-based data analysis as R is particularly suitable for it.

For a full-length syllabus of this course, please click here.

Course Prerequisites

Prior experience with data analysis, basic statistics, and regression. The participants should have already attended an introductory event in statistics. Experience in dealing with other statistics packages is helpful, but not a requirement. Basic familiarity with the use of a computer.

 

Target Group

This course is for people that work with data and want to use R as their first programming language or as an additional tool. Participants will find the course useful to get an overview of the possibilities in using R.

 

Course and Learning Objectives

The workshop provides a hands-on introduction to R and lays the foundations for independently developing your skills in dealing with the programming language R. The participants can expect to receive an overview of the functional scope of R, master the import and export of data, and how to perform basic data analysis in R.

 

Hard- and software requirements

Please bring your own laptops for use in the course and install the following R packages: knitr, Rcmdr, lme4, devtools, ctv, readxl, lattice, xlsx, tibble, haven, foreign, readstata13, org, rio, Hmisc, naniar, memisc, tidyverse, forcats, car, reshape, DT, dplyr, magrittr, jtools, Metrics, visreg, AmesHousing, corrplot, DAAG, caret, stargazer, faraway, ggplot2, pscl, MASS, arm, survey, svyPVpack, mlmRev, vioplot, beanplot, psych, AER, igraph, ggraph, plotly, kknn, maps, dbscan, rvest, rtweet, gapminder, mlbench, purrr, compare, ggmap, leaflet, maptools, raster, rgdal, colorRamps, sp, osmplotr, osmdata, tmap, io, kableExtra, tmaptools before the course.