Frontmatter
Typography
Interactive Content
Dedication
Data Literacy
I Foundations
1
The R Ecosystem
1.1
Getting R Configured
1.1.1
Packages
1.1.2
CRAN
1.1.3
GitHub
1.1.4
Bioconductor
1.1.5
Troublesome Packages
1.2
Libraries Used
1.3
The RStudio Environment
2
Data Types
2.1
Numeric Data Types
2.2
Distributions
2.2.1
Coercion to Numeric
2.3
Characters
2.3.1
Concatenation of Characters
2.3.2
Coercion to Characters
2.4
Factors
2.4.1
Missing Levels in Factors
2.5
Logical Types
3
Data Containers
3.1
Vectors
3.2
Matrices
3.3
Lists
3.4
Data Frames
3.4.1
Indexing Data Frames
4
Manipulating Data
4.1
Data Import & Export
4.2
Diving Into Data
4.3
Factor & Character Data
4.4
Indexing
4.5
Sorting & Ordering
4.6
Manipulating Data
4.7
Tabulating
5
Programming
5.1
Function Writing
5.2
Variable Scope
5.3
Decision Making
5.3.1
The if Pattern
5.4
The
if-else
Pattern
5.5
Flow Control
5.5.1
The
for()
Loop
5.5.2
Short Circuiting the Loop
6
Markdown
6.1
Markdown
6.2
Marking up Text
6.3
Inserting R Code Chunks
6.3.1
Chunk Options
6.4
Variables in Text
II Data
7
Manipulating Data
8
Graphical Display
9
GGPlot
10
Spatial Data
III Statistical Inferences
11
Statistical Inferences
11.1
An applied example.
11.2
The Binomial
11.3
A Frequentist Approach
11.4
A Bayesian Approach
12
Summary Statistics
12.1
Central Tendency
12.2
Dispersion
12.3
Ordination
12.4
Hierarchical Clustering
13
Correlation
13.1
The Data
13.2
Parametric Correlations
13.3
Non-Parametric Correlation
13.4
Differences
13.5
Permutation
14
Linear Regression
14.1
The Data
14.2
Least Squares Linear Regression
14.3
Fitting a Linear Model
14.4
Multiple Regression
15
Analysis of Variance
15.1
One Sample Hypotheses
15.1.1
Data Variability
15.2
Two Sample Hypotheses
15.3
Many Sample Hypotheses
15.3.1
Post-Hoc Tests
the Dyer Laboratory
Environmental Data Literacy
10
Spatial Data