Preface
Four basic ingredients
1 Setting up
2 Data
3 R-Basics
4 R-Markdown
I Data Preprocessing
Introduction
5 Data Cleansing
6 Data Wrangling
7 Missing Values
8 Outliers
II Data Visualization
Introduction
9 Aesthetic Mappings
10 Visualizing Amounts
11 Visualizing Distributions
12 Visualizing Proportions
13 Visualizing Trends
III Univariate Analysis
Introduction
14 Data Tabulation
15 Descriptive Statistics
16 Bivariate Analysis
17 Correlations
IV Multivariate Analysis
Introduction
18 Simple Regression
19 Multiple Regression
V GLM Models
Introduction
20 Reporting Models
VI Time Series Analysis
Introduction
21 Time Series
22 Time Series Smoothing
23 Time Series Models
Appendix
A CSS Crash Course
- A.1 What’s in a name?
- A.2 How do I change the color of….?
B Backlog
C Miscellaneous
D Spillover from distill
E Teaching and Learning with Jupyter Book Review
F R Markdown Themes
G What should I make
H Teaching learners
I What to do on day 1
J Licensing your content
K Remixing licensed content
L Community
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Statistics with R

H Teaching learners

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