Data Visualization with R Book Club
Welcome
Book club meetings
Pace
1
Data Preparation
1.1
Importing data
1.2
Text files
1.3
Excel spreadsheets
1.4
Stastistical packages
1.5
Databases
1.6
Cleaning data
1.7
Selecting variables
1.8
Selecting observations
1.9
Creating/Recoding variables
1.10
1.11
Summarizing data
1.12
1.13
Using pipes
1.14
Reshaping data
1.15
pivot_wider()
1.16
pivot_longer()
1.17
Missing Data
1.18
Resources
Meeting Videos
Cohort 1
2
Introduction to ggplot2
2.1
Introduction
2.2
Data layer
2.3
Aesthetic Layer
2.4
Geometries Layer
2.5
Statistics Layer
2.6
Coordinates Layer
2.7
grouping
2.8
scales
2.9
Facets Layer
2.10
labels
2.11
themes
2.12
Placing the data and mapping options
2.13
Graphs as objects
2.14
Resources
Meeting Videos
Cohort 1
3
Univariate Graphs
3.1
Categorical vs. Quantitive Variables
3.2
Categorical vs. Quantitive Variables cont.
3.3
Marriage Dataset
3.4
Bar Chart
3.5
Pie Chart
3.6
Caveat: Pie charts
3.7
Tree map
3.8
Tree map cont.
3.9
Histogram
3.10
Kernel Density Plot
3.11
Dot Chart
3.12
Geom options
3.13
Resources
Meeting Videos
Cohort 1
4
Bivariate Graphs
4.1
Introduction
4.2
Stacked bar chart
4.3
Grouped bar chart
4.4
Segmented bar chart
4.5
Improving the color and labeling
4.6
Scatterplot
4.7
Adding best fit lines
4.8
Line plot
4.9
Bar chart (on summary statistics)
4.10
Grouped kernel density plots
4.11
Box plots
4.12
Violin plots
4.13
Ridgeline plots
4.14
Mean/SEM plots
4.15
Strip plots
4.16
Beeswarm Plots
4.17
Cleveland Dot Charts
Meeting Videos
Cohort 1
5
Multivariate Graphs
Introduction
5.1
Grouping
5.2
Faceting
Meeting Videos
Cohort 1
6
Maps
6.1
Introduction
6.2
Dot density maps
6.3
Choropleth maps
6.4
Data by country
6.5
Data by US County
Meeting Videos
Cohort 1
7
Time-dependent graphs
7.1
Time Series
7.2
Economics Dataset
7.3
Simple Line Plot
7.4
Simple Line Plot +
7.5
Modified Simple Line Plot
7.6
scale_*_date Function
7.7
Dumbbell Charts
7.8
Long and Wide Format
7.9
Gapminder Dataset
7.10
Dumbbell Charts
7.11
Dumbbell Charts +
7.12
Modified Dumbbell Charts
7.13
Slope Graphs
7.14
newggslopegraph Function
7.15
Slope Graphs
7.16
Area Charts
7.17
Stacked Area Chart
7.18
Uspopage Dataset
7.19
Stacked Area Chart
7.20
Scientific Notation
7.21
Stacked Area Chart +
7.22
Modified Stacked Area Chart
Meeting Videos
Cohort 1
8
Statistical Models
8.1
Correlation plots
8.2
Saratoga Houses Dataset
8.3
ggcorrplot
package
8.4
ggcorrplot
function
8.5
Linear Regression
8.6
visreg
package
8.7
Another linear regression example
8.8
Logistic Regression
8.9
CPS85 dataset and linear regression
8.10
Visualizing logistic regression
8.11
Visualizing multiple conditional logistic regression plots
8.12
Survival Analysis
8.13
Survival Plots using
ggsurvplot
8.14
Comparing survival probabilities
8.15
Mosaic Plots
8.16
Mosaic plots and the
vcd
package
8.17
Adding color to mosaic plot
8.18
Resources
Meeting Videos
Cohort 1
9
Other Graphs
9.1
3-D Scatterplot
9.2
Modified 3-D Scatterplot
9.3
Modified 3-D Scatterplot (cont.)
9.4
Modified 3-D Scatterplot (cont.)
9.5
Biplots
9.6
Bubble Charts
9.7
Modified Bubble Chart
9.8
Flow Diagrams
9.9
Sankey Diagrams
9.10
Sankey Diagrams (cont.)
9.11
Alluvial Diagrams
9.12
Alternative Alluvial Diagram
9.13
Modified Alluvial Diagram
9.14
Heatmaps
9.15
Sorted Heatmap
9.16
Heatmap for Time Series
9.17
Radar Charts
9.18
Basic Radar Chart
9.19
Scaterplot Matrix
9.20
Customized Scaterplot Matrix
9.21
Waterfall Charts
9.22
Modified Waterfall Chart
9.23
Word Clouds
9.24
Resources
Meeting Videos
Cohort 1
10
Customizing Graphs
SLIDE 1
Meeting Videos
Cohort 1
11
Saving Graphs
SLIDE 1
Meeting Videos
Cohort 1
12
Interactive Graphs
SLIDE 1
Meeting Videos
Cohort 1
13
Advice / Best Practices
SLIDE 1
Meeting Videos
Cohort 1
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Data Visualization with R Book Club
Chapter 8
Statistical Models
Learning objectives:
describe graphs that can help you interpret the results of statistical models, focusing on models that have a single response variable that is either quantitative (a number) or binary (yes/no)