Visual Statistics - cran.r-project.org
Visual Statistics
Use R!
Alexey Shipunov
TONGUE
¡ñ
PEAK
EARLOBE
¡ñ
TOE
¡ñ
¡ñ
¡ñ
¡ñ
PINKY
¡ñ
THUMB
ARM
¡ñ
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CHEEK
March 13, 2019 version
Shipunov, Alexey (and many others). Visual statistics. Use R!
March 13, 2019 version. 429 pp.
URL:
On the cover: R plot illustrating correlations between human phenotypic traits. See
the page 254 for more explanation.
This book is dedicated to the public domain
Contents
Foreword
I
9
One or two dimensions
13
1 The data
1.1 Origin of the data . . . . . . . . . . . . . .
1.2 Population and sample . . . . . . . . . . .
1.3 How to obtain the data . . . . . . . . . . .
1.4 What to ?nd in the data . . . . . . . . . .
1.4.1 Why do we need the data analysis
1.4.2 What data analysis can do . . . . .
1.4.3 What data analysis cannot do . . .
1.5 Answers to exercises . . . . . . . . . . . .
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2 How to process the data
2.1 General purpose software . . . . . . . . . .
2.2 Statistical software . . . . . . . . . . . . . .
2.2.1 Graphical systems . . . . . . . . . .
2.2.2 Statistical environments . . . . . . .
2.3 The very short history of the S and R . . . .
2.4 Use, advantages and disadvantages of the R
2.5 How to download and install R . . . . . . .
2.6 How to start with R . . . . . . . . . . . . . .
2.6.1 Launching R . . . . . . . . . . . . .
2.6.2 First steps . . . . . . . . . . . . . . .
2.6.3 How to type . . . . . . . . . . . . . .
2.6.4 How to play with R . . . . . . . . . .
2.6.5 Overgrown calculator . . . . . . . .
2.7 R and data . . . . . . . . . . . . . . . . . . .
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14
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20
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2.7.1 How to enter the data from within R
2.7.2 How to name your objects . . . . . .
2.7.3 How to load the text data . . . . . .
2.7.4 How to load data from Internet . . .
2.7.5 How to use read.table() . . . . . .
2.7.6 How to load binary data . . . . . . .
2.7.7 How to load data from clipboard . .
2.7.8 How to edit data in R . . . . . . . . .
2.7.9 How to save the results . . . . . . .
2.7.10 History and scripts . . . . . . . . . .
2.8 R graphics . . . . . . . . . . . . . . . . . . .
2.8.1 Graphical systems . . . . . . . . . .
2.8.2 Graphical devices . . . . . . . . . . .
2.8.3 Graphical options . . . . . . . . . .
2.8.4 Interactive graphics . . . . . . . . .
2.9 Answers to exercises . . . . . . . . . . . . .
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3 Types of data
3.1 Degrees, hours and kilometers: measurement data
3.2 Grades and t-shirts: ranked data . . . . . . . . . .
3.3 Colors, names and sexes: nominal data . . . . . .
3.3.1 Character vectors . . . . . . . . . . . . . . .
3.3.2 Factors . . . . . . . . . . . . . . . . . . . .
3.3.3 Logical vectors and binary data . . . . . . .
3.4 Fractions, counts and ranks: secondary data . . . .
3.5 Missing data . . . . . . . . . . . . . . . . . . . . .
3.6 Outliers, and how to ?nd them . . . . . . . . . . .
3.7 Changing data: basics of transformations . . . . .
3.7.1 How to tell the kind of data . . . . . . . . .
3.8 Inside R . . . . . . . . . . . . . . . . . . . . . . . .
3.8.1 Matrices . . . . . . . . . . . . . . . . . . . .
3.8.2 Lists . . . . . . . . . . . . . . . . . . . . . .
3.8.3 Data frames . . . . . . . . . . . . . . . . . .
3.8.4 Overview of data types and modes . . . . .
3.9 Answers to exercises . . . . . . . . . . . . . . . . .
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62
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. 89
. 89
. 89
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. 96
. 103
. 106
4 One-dimensional data
4.1 How to estimate general tendencies
4.1.1 Median is the best . . . . . .
4.1.2 Quartiles and quantiles . . .
4.1.3 Variation . . . . . . . . . . .
4.2 1-dimensional plots . . . . . . . . .
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33
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110
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4.3
4.4
4.5
4.6
4.7
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123
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5 Two-dimensional data: differences
5.1 What is a statistical test? . . . . . . . . . . .
5.1.1 Statistical hypotheses . . . . . . . . .
5.1.2 Statistical errors . . . . . . . . . . . .
5.2 Is there a difference? Comparing two samples
5.2.1 Two sample tests . . . . . . . . . . . .
5.2.2 Effect sizes . . . . . . . . . . . . . . .
5.3 If there are more than two samples: ANOVA .
5.3.1 One way . . . . . . . . . . . . . . . . .
5.3.2 More then one way . . . . . . . . . . .
5.4 Is there an association? Analysis of tables . .
5.4.1 Contingency tables . . . . . . . . . . .
5.4.2 Table tests . . . . . . . . . . . . . . .
5.5 Answers to exercises . . . . . . . . . . . . . .
5.5.1 Two sample tests, effect sizes . . . . .
5.5.2 ANOVA . . . . . . . . . . . . . . . . .
5.5.3 Contingency tables . . . . . . . . . . .
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6 Two-dimensional data: models
6.1 Analysis of correlation . . . . . . . . . . . . .
6.1.1 Plot it ?rst . . . . . . . . . . . . . . .
6.1.2 Correlation . . . . . . . . . . . . . . .
6.2 Analysis of regression . . . . . . . . . . . . .
6.2.1 Single line . . . . . . . . . . . . . . .
6.2.2 Many lines . . . . . . . . . . . . . . .
6.2.3 More then one way, again . . . . . . .
6.3 Probability of the success: logistic regression
6.4 Answers to exercises . . . . . . . . . . . . . .
6.4.1 Correlation and linear models . . . . .
6.4.2 Logistic regression . . . . . . . . . . .
6.5 How to choose the right method . . . . . . .
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220
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271
II
Con?dence intervals . . . . . . . .
Normality . . . . . . . . . . . . . .
How to create your own functions
How good is the proportion? . . .
Answers to exercises . . . . . . . .
Many dimensions
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273
7 Draw
274
5
................
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