Data Management and Analysis Using R

About

You will be introduced to concepts, terminology and methods relevant to handling data and spatial information in R. At the course end, you will have a toolbox of scripts to optimize data management procedures by looping through data and using vector-oriented iterative processes. You will work in R studio writing and debugging code for merging datasets, data cleaning and coding different types of variables, as well as overlaying spatial layers. We will use both base R as well as Tidyverse applications.

You will also be introduced to basic procedures for analysis. This includes tabulating basic statistical measures, specifying regression models and interpreting and visualizing results. Throughout the course, the focus will be on writing, adapting and implementing code in R scripts.

The course aims to develop students’ data management and analysis skills through hands-on group work. The last week of the course will be independent (unsupervised) group project work with empirical datasets.

The course mainly uses the free statistical software package R and briefly introduces the geographical information software Q-GIS.

Don’t be a slave to the spreadsheet. Join our course and become part of an ever-increasing vibrant community using the object-oriented programming environment R as their playground

Course leader

Martin Reinhardt Nielsen

Target group

Master students

Course aim

This course aims to provide participants with tools and experience in managing and analyzing data, using cross-sectional and spatial data as examples, that would be required to conduct an MSc thesis project or do research based on quantitative data in social sciences and beyond.

Knowledge:

Knowing codes required to identify different types of datasets and variables (including the nature of maps and geodata) and the implications for the choice of appropriate data management procedure and analysis strategy
Show an overview of principles and procedures for importing, merging, coding, transforming and otherwise preparing data for statistical analysis in R
Know the arguments for using scripts
Possess an overview of basic approaches to quantitative data analysis

Skills:

Apply procedures for managing different types of data in R in preparation for statistical analysis
Ability to combine different data sets and produce composite maps from multiple sets of digital spatial data
Implement statistical analysis in R to derive basic cross-sectional and spatial metrics and estimate linear regression models
Solve coding problems in data management and basic statistical analysis in R using available online support, including ChatGTP
Generate figures and graphs to interpret, visualize and present statistical results in a clear and concise manner

Fee info