Statistics with R
Preface
About Author
1
Getting Started with Statistics and R
1.1
Segment 01
1.1.1
What is Statistics?
1.1.2
Statistics vs Data Science
1.1.3
Statistics vs Mathematics
1.1.4
Why R?
1.1.5
Who Use R?
1.1.6
Who Developed R?
1.1.7
Other Languages and Packages
1.1.8
Installing R and Rstudio
1.1.9
Start Writing R Code (Windows, Linux, and Command Line)
1.1.10
Effectively Using Rstudio
1.1.11
R Script
1.1.12
R Documentation (Help)
1.1.13
Handling Error
1.1.14
R Packages
1.1.15
R Mathematical Operations
1.1.16
Assigning Values
1.1.17
Generating Multiple Numbers
1.1.18
Data Types
1.1.19
Learn More
1.2
Segemnt 02
1.2.1
Vector
1.2.2
Matrix
1.2.3
Data Frame
1.2.4
List
1.2.5
Functions
1.2.6
Loops (Alternatives and Comparison with Other Languages)
1.2.7
Apply family (apply, lapply, sapply, etc.).
2
Data Analysis: Base R
2.1
Session 01: Visualization
2.1.1
Correlation Plot
2.1.2
Pie Chart
2.1.3
Bar Chart
2.1.4
Chart Characteristics (color, title, axes etc.)
2.1.5
How to Use Proper Legends?
2.1.6
Histogram
2.1.7
Ogive (and how to interpret it)
2.1.8
Boxplot
2.1.9
Time Series Plots/Line Chart
2.1.10
Scatter Plot
2.1.11
Equation and Curves
2.1.12
Love Equation and Curve
2.1.13
Different Ways of Coloring Plots
2.1.14
Wordcloud
2.1.15
Comparison of Suitability of Plots.
2.2
Session 02: Analysis
3
Data Analysis: Introduction to The Tidyverse
3.1
Reading and Manipulating Data
3.1.1
Reading Data from Different Files
3.1.2
Concept of Tidy Data
3.1.3
How to Make Data Tidy?
3.1.4
Subsetting/Filtering Data
3.1.5
Pipe Operator
3.1.6
Transforming Data
3.1.7
Summarizing Data
3.1.8
Selecting Rows and Columns
3.2
Vizualizing Data
3.2.1
How ggplot2 works
3.2.2
Different geoms and aesthetics
3.2.3
Scatter Plot
3.2.4
Bar Chart
3.2.5
Histogram
3.2.6
Boxplot
3.2.7
Time Series Plots/Line Chart
3.2.8
Pie Chart
3.2.9
Trends within Plots
3.2.10
Piping Output to ggplot2
3.2.11
Piping Output to Plots
4
Advanced Tidyverse
4.1
Advanced Visualization
4.1.1
Correlogram
4.1.2
Themes and Legends
4.1.3
Doughnut Chart
4.1.4
Density Chart
4.1.5
Violin Chart
4.1.6
Bubble Chart
4.1.7
Spider/Radar Chart
4.1.8
Lollipop Chart
4.1.9
Area Chart
4.1.10
Attaching Texts to Plots
4.1.11
Advanced Customization And Attaining What Seems Improbable
4.1.12
Animation
4.2
Advanced Wrangling
5
Modeling
5.1
Regression
5.1.1
Correlation and Regression
5.1.2
Interpretation of Results
5.1.3
Multiple Linear Regression
5.1.4
Regression with Specified Intercept
5.1.5
Choosing between Models
5.1.6
Poisson Regression
5.1.7
Logistic Regression
5.1.8
Prediction
5.1.9
When to Use Which Regression (with Examples)
5.2
Time Series Analysis
5.3
Test of Hypothesis
5.3.1
Confidence Interval
5.3.2
Z-test
5.3.3
t-test
5.3.4
Chi-squared tests).
6
Publishing
6.1
Introduction of Markdown
6.2
Implementation of Rmarkdown
6.2.1
Report Writing
6.2.2
Presentation
6.2.3
Books
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Statistics with R
Chapter 5
Modeling
5.1
Regression
5.1.1
Correlation and Regression
5.1.2
Interpretation of Results
5.1.3
Multiple Linear Regression
5.1.4
Regression with Specified Intercept
5.1.5
Choosing between Models
5.1.6
Poisson Regression
5.1.7
Logistic Regression
5.1.8
Prediction
5.1.9
When to Use Which Regression (with Examples)
5.2
Time Series Analysis
5.3
Test of Hypothesis
5.3.1
Confidence Interval
5.3.2
Z-test
5.3.3
t-test
5.3.4
Chi-squared tests).