BEHAVIORAL DATA ANALYSIS IN R

The course ‘Behavioral data analysis in R’ is aimed at helping students to understand the whole data life cycle and the many aspects of the data computing environment.

This track builds upon the knowledge from the ‚Introduction to R‘ course and focuses on how to use R during the whole data life-cycle. We will show you how to prepare data, how to do exploratory visualization and analysis, how to conducts statistical tests and also how to visualize the results. And above all, how these steps interact and why they usually cannot stand without each other. You can be sure that after finishing the track you will see data-intensive research as less complicated and quicker than ever before.

Prerequisites

During the Introduction to R, we cover the basic concepts of R, such as loading data, subsetting data, running simple exploratory statistics and visualizations. this course REQUIRES you to be able to handle these tasks well. If you are unsure about your skill level, email us and we will send you a set of tasks you should be able to handle easily. In case you are familiar with these essentials, we will allow you to attend the advanced part of the course – please email us in advance though.

Topics

DAY 1
(5 hrs)
Exploratory analysis – descriptive statistics, outliers, duplicate values etc.
Data preprocessing – merging multiple datasets, recoding variables, computing new variables
DAY 2
(5 hrs)
Exploratory analysis – descriptive statistics, outliers, duplicate values etc.
Data preprocessing – merging multiple datasets, recoding variables, computing new variables
DAY 3
(5 hrs)
Exploratory analysis – descriptive statistics, outliers, duplicate values etc.
Data preprocessing – merging multiple datasets, recoding variables, computing new variables
DAY 4
(5 hrs)
Workshop and project

Lecturers

– Lukáš Hejtmánek

Literature

– Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications.

ECTS Requirements

To successfully complete this course and obtain 4 ECTS, students have to:

– submit 4 assignments (1 per day, 12 hrs. of workload in total)
– complete an online final exam (preparation 20 hrs. of workload)
– submit a project (48 hrs. of workload)