Preface
Why
Statistics with R
?
Philosophy
What is in this handbook?
Resources
About me
Four basic ingredients
1
Setting up R
1.1
R
1.2
RStudio
1.2.1
The RStudio IDE
1.2.2
Install packages
1.2.3
RStudio Projects
1.3
Git & GitHub
1.4
Resources
2
Data Import
2.1
Entering data
2.2
From Text
2.3
From Excel
2.4
From SPSS
2.5
From SAS
2.6
From Stata
2.7
From systat
2.8
Data from R packages
3
R-Basics
3.1
Help
3.2
Data structures
3.2.1
Vectors
3.2.2
Sequences
3.2.3
Factors
3.2.4
Data frames
3.2.5
Tibbles
3.2.6
Matrix
3.2.7
List
3.2.8
Array
3.3
Dates
3.3.1
Date Conversion
3.3.2
Date to Character
3.4
Pipes
4
R-Markdown
4.1
Installation
4.2
Resources
4.3
PowerPoint
I Data Preprocessing
Introduction
5
Data Manipulation
6
Data Wrangling
6.1
Wrangling Tutorial
6.2
Wrangling Tutorial 2
6.3
Data Manipulations
7
Missing Values
7.1
Deleting NA’s
7.2
Multiple Imputations
7.3
NA’s tutorial
8
Outliers
8.1
Outliers
8.1.1
Detection by plots
8.1.2
Using statistics
8.1.3
Using MAD
8.1.4
Interquartile Range (IQR)
8.1.5
Grubb’s Test
8.1.6
Tools in R
8.2
Leverage
8.3
Influential
II Data Visualization
Introduction
9
Aesthetic Mappings
10
Visualizing Amounts
11
Visualizing Distributions
12
Visualizing Proportions
13
Visualizing Trends
III Descriptive Statistics
Introduction
14
Data Tabulation
15
Univariate Analysis
16
Bivariate Analysis
IV Multivariate Analysis
Introduction
17
Simple Regression
18
Multiple Regression
19
GLM Regression
20
Reporting Models
V 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
R & SQL
C
Google Colab
D
Jupyter
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Statistics with R
Introduction