Visual Statistics Use R!

✍ Scribed by Alexey Shipunov

• Year: 2020
• Tongue: English
• Leaves: 451
• Category: Library

✦ Table of Contents

Foreword
I One or two dimensions
The data
Origin of the data
Population and sample
How to obtain the data
What to find in the data
Why do we need the data analysis
What data analysis can do
What data analysis cannot do
Answers to exercises
How to process the data
General purpose software
Statistical software
Graphical systems
Statistical environments
The very short history of the S and R
Use, advantages and disadvantages of the R
How to download and install R
How to start with R
Launching R
First steps
How to type
Overgrown calculator
How to play with R
R and data
How to enter the data from within R
How to name your objects
How to load the text data
How to load data from Internet
How to use read.table() properly
How to load binary data
How to load data from clipboard
How to edit data in R
How to save the results
History and scripts
R graphics
Graphical systems
Graphical devices
Graphical options
Interactive graphics
Answers to exercises
Types of data
Degrees, hours and kilometers: measurement data
Grades and t-shirts: ranked data
Colors, names and sexes: nominal data
Character vectors
Factors
Logical vectors and binary data
Fractions, counts and ranks: secondary data
Missing data
Outliers, and how to find them
Changing data: basics of transformations
How to tell the kind of data
Inside R
Matrices
Lists
Data frames
Overview of data types and modes
Answers to exercises
One-dimensional data
How to estimate general tendencies
Median is the best
Quartiles and quantiles
Variation
1-dimensional plots
Confidence intervals
Normality
How to create your own functions
How good is the proportion?
Answers to exercises
Two-dimensional data: differences
What is a statistical test?
Statistical hypotheses
Statistical errors
Is there a difference? Comparing two samples
Two sample tests
Effect sizes
If there are more than two samples: ANOVA
One way
More then one way
Is there an association? Analysis of tables
Contingency tables
Table tests
Answers to exercises
Exercises on two samples
Exercises on ANOVA
Exercises on tables
Two-dimensional data: models
Analysis of correlation
Plot it first
Correlation
Analysis of regression
Single line
Many lines
More then one way, again
Probability of the success: logistic regression
Answers to exercises
Correlation and linear models
Logistic regression
How to choose the right method
II Many dimensions
Draw
Pictographs
Grouped plots
3D plots
Discover
Discovery with primary data
Shadows of hyper clouds: PCA
Correspondence
Projections, unfolds, t-SNE and UMAP
Non-negative matrix factorization
Discovery with distances
Distances
Making maps: multidimensional scaling
Making trees: hierarchical clustering
How to know the best clustering method
How to compare clusterings
How good are resulted clusters
Making groups: k-means and friends
How to know cluster numbers
Use projection pursuit for clustering
How to compare different ordinations
Answers to exercises
Learn
Learning with regression
Linear discriminant analysis
Recursive partitioning
Ensemble learnig
Random Forest
Gradient boosting
Learning with proximity
Learning with rules
Learning from the black boxes
Support Vector Machines
Neural Networks
Semi-supervised learning
How to choose the right method
Answers to exercises
Appendices
Example of R session
Starting...
Describing...
Plotting...
Testing...
Finishing...
Answers to exercises
Ten Years Later, or use R script
How to make your R script
My R script does not work!
Common pitfalls in R scripting
Advices
Use the Source, Luke!..
Keep it simple
Learn to love errors and warnings
Subselect by names, not numbers
About reserved words, again
The Case-book of Advanced R user
A Case of Were-objects
A Case of Missing Compare
A Case of Outlaw Parameters
A Case of Identity
The Adventure of the Floating Point
A Case of Twin Files
A Case of Bad Grammar
A Case of Double Dipping
A Case of Factor Join
A Case of Bad Font
A Case of Disproportionate Condition
Good, Bad, and Not-too-bad
Good
Bad
Not too bad
Answers to exercises
R fragments
R and databases
R and time
R and bootstrap
R and shape
R and Bayes
R, DNA and evolution
R and reporting
R without graphics
Answers to exercises
Most essential R commands
The short R glossary
References
Reference card