Data Visualization with ggplot2: Champions League 2019/2020

This book presents different ways to visualize data in ggplot2.
There is no theoretical explanation of each analysis method.
The analysis uses 2019/20 data for UEFA Champions league.

Table of Contents

1. Visualize the Amount
1.1. Data Import
1.2. Basic Bar Chart
1.3. Switches the x- and y-axes
1.4. Adds a Plot Title
1.5. Specify the Font
1.6. Modifying Order
1.7. Different Colors for Each Group
1.8. Control Legends
1.9. Position of Legend
1.10. Use Theme
1.11. Cleveland Dot
1.12. Mapping the Aesthetics
1.13. Add Annotations to a Plot
1.14. Draw Line and Rectangle
1.15. Visualize Various Indicators
2. Visualize the Distribution (1)
2.1. Histogram
Basic Histogram
Specify the “binwidth”
Specify the “bins”
position=”stack”
position=”identity”
position=”dodge”
geom_freqpoly
2.2. Probability Density Function
2.3. Cumulative Density Function (CDF)
2.4. Visualization of Various Indicators
3. Visualize the Distribution (2)
3.1. Boxplot
3.2. Violinplot
3.3. Density Ridges Plot
3.4. Dotplot
3.5. Beeswarm
3.6. Visualization of Various Indicators
4. Visualize the Relationship Between the Two Items
4.1. Basic Scatterplot
Basic Scatterplot
Regression Line and Confidence Interval
Local Polynomial Regression
Adjusted R Squared
4.2. Visualization of Various Indicators
5. MDS：Multi Dimensional Scaling
5.1. Euclidean Distance
5.2. Mahalanobis Distance
5.3. Pearson Correlation Coefficient
5.4. Cosine Simirality
6. FA：Factor Analysis
6.1. Regression & Varimax
6.2. Cluster Analysis of the Results of FA
6.3. Bartlett & Promax
6.4. Cluster Analysis of the Results of FA
7. PCA：Principal Component Analysis
7.1. Principal Component Analysis
7.2. Cluster Analysis of the Results of PCA
8. ICA：Independent Component Analysis
8.1. Independent Component Analysis
8.2. Cluster Analysis of the Results of ICA