Preface
0.1
Session info
1
About this module
1.1
About this module
1.2
R programming language
1.3
Suggested schedule
1.4
Reference books
2
R
2.1
R
2.2
Interpreting values
2.3
Basic types
2.4
Numeric operators
2.5
Logical operators
2.6
Variables
2.7
Algorithms and functions
2.8
Functions
2.9
Functions and variables
2.10
Naming
2.11
Coding style
2.12
R libraries
2.13
stringr
2.14
The pipe operator
2.15
Pipe example
2.16
Pipe example
3
Summary
3.1
Summary
3.2
Practical session
3.3
Next lecture
4
Recap @ 102
4.1
Previous lecture
4.2
This lecture
5
Vectors
5.1
Vectors
5.2
Defining vectors
5.3
Creating vectors
5.4
Selection
5.5
Functions on vectors
5.6
Any and all
6
Factors
6.1
Factors
6.2
table
6.3
Specified levels
6.4
(Unordered) Factors
6.5
Ordered Factors
7
Matrices and arrays
7.1
Matrices
7.2
Arrays
7.3
Arrays
7.4
Selection
7.5
apply
8
Lists
8.1
Lists
8.2
Named Lists
8.3
lapply
9
Data frames
9.1
Data Frames
9.2
Selection
9.3
Selection
9.4
Value assignment
9.5
Column processing
9.6
tibble
10
Summary
10.1
Summary
10.2
Practical session
10.3
Next lecture
11
Recap @ 111
11.1
Previous lectures
11.2
This lecture
12
Conditional statements
12.1
If
12.2
Else
12.3
Code blocks
13
Loops
13.1
Loops
13.2
While
13.3
For
13.4
For
13.5
Loops with conditional statements
14
Functions
14.1
Defining functions
14.2
Defining functions
14.3
Defining functions
14.4
More parameters
14.5
Functions and control structures
14.6
Scope
14.7
Example
15
Summary
15.1
Summary
15.2
Practical session
15.3
Next lecture
16
Recap @ 201
16.1
Previous lectures
16.2
This lecture
17
Selection and filtering
17.1
dplyr
17.2
Example dataset
17.3
dplyr::select
17.4
dplyr::select
17.5
Logical filtering
17.6
Conditional filtering
17.7
Filtering data frames
17.8
dplyr::filter
18
Manipulate
18.1
dplyr::arrange
18.2
dplyr::summarise
18.3
dplyr::group_by
18.4
dplyr::mutate
18.5
Full pipe example
19
Summary
19.1
Summary
19.2
Practical session
19.3
Next lecture
20
Recap @ 202
20.1
Previous lectures
20.2
This lecture
21
Join
21.1
Joining data
21.2
Join types
21.3
Example
21.4
Example
21.5
dplyr::full_join
21.6
dplyr::left_join
21.7
dplyr::right_join
21.8
dplyr::inner_join
22
Re-shape
22.1
Wide data
22.2
Long data
22.3
tidyr
22.4
tidyr::gather
22.5
tidyr::spread
23
Read and write
23.1
Comma Separated Values
23.2
Read
23.3
Write
24
Summary
24.1
Summary
24.2
Practical session
24.3
Next lecture
25
Recap @ 301
25.1
Previous lectures
25.2
This lecture
26
Reproducibility
26.1
Reproduciblity
26.2
Why?
26.3
Reproducibility and software engineering
26.4
Reproducibility and “big data”
26.5
Reproducibility in GIScience
26.6
Document everything
26.7
Document well
26.8
Workflow
26.9
Future-proof formats
26.10
Store and share
26.11
This repository
27
RMarkdown
27.1
Markdown
27.2
Markdown example code
27.3
Markdown example output
27.3.1
This is a third level heading
27.4
RMarkdown example code
27.5
Writing RMarkdown docs
28
Git
28.1
What’s git?
28.2
How git works
28.3
Three stages
28.4
Basic git commands
28.5
Git and RStudio
29
Summary
29.1
Summary
29.2
Practical session
29.3
Next lecture
30
Recap @ 501
30.1
Previous lectures
30.2
This lecture
31
Data visualisation
31.1
Visual variables
31.2
Grammar of graphics
31.3
ggplot2
31.4
Histograms
31.5
Histograms
31.6
Boxplots
31.7
Boxplots
31.8
Jittered points
31.9
Jittered points
31.10
Violin plot
31.11
Violin plot
31.12
Lines
31.13
Lines
31.14
Scatterplots
31.15
Scatterplots
31.16
Overlapping points
31.17
Overlapping points
31.18
Bin counts
31.19
Bin counts
32
Descriptive statistics
32.1
Descriptive statistics
32.2
stat.desc output
32.3
stat.desc: basic
32.4
stat.desc: desc
32.5
Sample statistics
32.6
Estimating variation
33
Exploring assumptions
33.1
Normal distribution
33.2
Density histogram
33.3
Q-Q plot
33.4
stat.desc: norm
33.5
Normality
33.6
Significance
33.7
Skewness and kurtosis
33.8
Homogeneity of variance
34
Summary
34.1
Summary
34.2
Practical session
34.3
Next lecture
35
Recap @ 502
35.1
Previous lectures
35.2
This lecture
36
Lecture 502
Comparing means
36.1
Libraries
36.2
Example
36.3
T-test
36.4
Example
36.5
ANOVA
36.6
Example
37
Lecture 502
Correlation
37.1
Correlation
37.2
Example
37.3
Example
37.4
Pearson’s r
37.5
Spearman’s rho
37.6
Kendall’s tau
37.7
Pairs plot
38
Lecture 502
Regression
38.1
Regression analysis
38.2
Least squares
38.3
Example
38.4
Overall fit
38.5
Parameters
38.6
Checking assumptions
38.7
Normality
38.8
Homoscedasticity
38.9
Independence
39
Lecture 502
Summary
39.1
Summary
39.2
Practical session
39.3
Next lecture
40
Recap @ 601
40.1
Previous lectures
40.2
This lecture
41
Lecture 601
Machine Learning
41.1
Definition
41.2
Origines
41.3
Types of machine learning
41.4
Supervised
41.5
Unsupervised
41.6
… more
41.7
Neural networks
41.8
Deep neural networks
41.9
Convolutional neural networks
41.10
Limits
42
Lecture 601
Clustering
42.1
Clustering task
42.2
Example
42.3
k-means
42.4
K-means result
42.5
Fuzzy c-means
42.6
Fuzzy c-means
42.7
Fuzzy c-means result
42.8
Hierarchical clustering
42.9
Clustering tree
42.10
Hierarchical clustering result
42.11
Bagged clustering
42.12
Bagged clustering result
42.13
Density based clustering
42.14
DBSCAN result
42.15
Geodemographic classifications
43
Lecture 601
Summary
43.1
Summary
43.2
Practical session
Geographic data science reproducible teaching resource in R
Lecture materials
4
Recap @ 102
4.1
Previous lecture
An introduction to R
Basic types
Basic operators
Variables
Libraries
The pipe operator
Coding style
4.2
This lecture
More complex data types
Vectors
Factors
Matrices
Arrays
Lists
Data Frames