Data Analysis and Visualization in R
Preface
Acknowledgments
Prerequisites
Datasets
Feedback
Introduction
I Get
1 R basics
2 Data wrangling
3 Tidy data and combining tables
II Look
4 Low dimensional visualizations
5 High dimensional visualizations
6 Graphically supported hypotheses
III Conclude
7 Resampling-based Statistical Assessment
8 Analytical Statistical Assessment
9 Statistical Assessments for Big Data
10 Linear Regression
11 Logistic Regression
12 Supervised Learning
Appendix
A Importing data
B R programming
C Additonal plotting tools
D Probabilities
Julien Gagneur, TUM
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Data Analysis and Visualization in R (IN2339)

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