-
Notifications
You must be signed in to change notification settings - Fork 341
/
spatial-data.Rpres
222 lines (148 loc) · 7.13 KB
/
spatial-data.Rpres
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
An introduction to R for visualising spatial data
========================================================
author: Robin Lovelace
date: 10th October 2014
```{r,echo=FALSE}
# date: 12th June 2014
```
Slides available online from the course's home: [github.com/Robinlovelace/Creating-maps-in-R](https://github.com/Robinlovelace/Creating-maps-in-R).
Old version on CRAN's [contributed docs page](http://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf)
Introduction
========================================================
This course is brought to you by [TALISMAN](http://geotalisman.org/), a
project based at the University of Leeds and UCL. It is
funded by the ESRC's National Centre for Research Methods ([NCRM](http://www.ncrm.ac.uk/))
Course agenda
=======================================================
During this course we will cover these topics
- An introduction to R (9:30 - 10:30)
- Loading and interrogating spatial data (10:30 - 11:30)
- Manipulating spatial objects (11:30 - 12:30)
LUNCH
- Making maps in ggplot2 (13:30 - 14:30)
- Basemaps (14:30 - 15:30)
- More advanced visuals (15:30 - 16:00)
- Taking it further + Q & A (16:00 onwards)
```{r, echo=FALSE}
# During this course we will cover these topics
#
# - An introduction to R (9:30 - 10:30)
# - Loading and interrogating spatial data (10:30 - 11:30)
# - Manipulating spatial objects (11:30 - 12:30)
#
# LUNCH
#
# - Making maps in ggplot2 (13:30 - 14:30)
# - Basemaps (14:30 - 15:30)
# - More advanced visuals (15:30 - 16:00)
#
# - Taking it further + Q & A (16:00 onwards)
```
A bit about R
========================================================
- Named after S and its creators
- De-facto standard for advanced statistical analysis
- A programming language in its own right
- The power of the command line
- Used by an increasing number of organisations
Why R?
========================================================
- **Performace**: stable, light and fast
- **Support network**
- documentation, community, developers
- **Reproducibility**
- anyone anywhere can reproduce results
- enables dissemination (RPubs, RMarkdown, .RPres) - this presentation is a .Rmd file!
- **Versatility**: unified solution to *almost* any numerical problem, graphical capabilities
- **Ethics** removes economic barrier to statistics, is open and democratic
R is up and coming I
=====================
Source: [r4stats.com](http://r4stats.com/articles/popularity/)
II - Increasing popularity in academia
========================
Source: [r4stats.com](http://r4stats.com/articles/popularity/)
III - R vs Python
====================
Source: [Hadley Wickham](https://github.com/hadley/r-python)
IV - employment market
=====================
Source: [revolution analytics](http://blog.revolutionanalytics.com/2013/08/job-trends-for-statistics-packages.html)
Why R for spatial data?
===================================================
- Extensive and rapidly expanding spatial packages
- Interface with 'conventional' GIS software
- The advantages of the command-line:
> "With the advent of “modern” GIS software, most people want
> to point and click their way through life.
> That’s good, but there is a tremendous amount of
> flexibility and power waiting for you with the command
> line. Many times you can do something on the command
> line in a fraction of the time you can do it with
> a GUI (Sherman 2008, p. 283)
Why R for spatial data II
=============================
It can take data in a wide range of formats.
E.g. MySQL database dump gives you this:
LINESTRING(-1.81 52.55,-1.81 52.55, … ) - solved:
```{r, eval=FALSE}
ps <- as.list(ps) # make a list
for(i in 1:length(ps)){
ps[[i]] <- gsub("LINESTRING\\(", "", ps[[i]])
ps[[i]] <- gsub("\\)", "", ps[[i]])
ps[[i]] <- gsub(" ", ",", ps[[i]])
ps[[i]] <- matrix(ps[[i]], ncol=2, byrow=T)
ps[[i]] <- Line(ps[[i]])
}
```
Visualisation
===========================
- R's visualisation capabilities have evolved over time
- Used to create plots in the best academic journals
- ggplot2 has **revolutionised** the visualisation of quantitative
information in R, and (possibly) overall
- Thus there are different camps with different preferences when it comes to maps in R
Why focus on visualisation?
===============
If you cannot visualise your data, it is very difficult to understand your data.
Conversely, visualisation will greatly aid in communicating your results.
> Human beings are remarkably adept at discerning relationships from visual
> representations. A well-crafted graph can help you make meaningful comparisons among thousands of pieces of information, extracting patterns not easily found through other methods. ... Data analysts need to look at their data, and this is one area where R shines.
(Kabacoff, 2009, p. 45).
Maps, the 'base graphics' way
==========
Source: Cheshire and Lovelace (2014) - [available online](https://github.com/geocomPP/sdvwR)
The 'ggplot2' way of doing things
===============
Source: [This tutorial](https://github.com/Robinlovelace/Creating-maps-in-R)!
R in the wild 1: Maps of all census variables for local authorities
===========
R in the wild 2: Global shipping routes in the late 1700s
===========
Source: [R-Bloggers](http://www.r-bloggers.com/mapped-british-and-spanish-shipping-1750-1800/)
R in the wild 3: Reproducible maps of energy use in commuting
============
Energy use of commuting - my [thesis](https://github.com/Robinlovelace/thesis-reproducible) and [RPubs](http://rpubs.com/RobinLovelace/7178)
R in the wild 4: Infographic of housing project finances
================
<!--
-->
<div style="float:center"><img src="https://raw.github.com/Robinlovelace/robinlovelace.github.io/master/figure/unnamed-chunk-53.png" /></div>
Flexibility of ggplot2 - see [robinlovelace.net](http://robinlovelace.net/r/2013/12/27/coxcomb-plots-spiecharts-R.html)
Getting up-and-running for the tutorial
===========
Before progressing further: **Any questions?**
**Course materials** are all available online from a [GitHub repository](https://github.com/Robinlovelace/Creating-maps-in-R). Click "Download ZIP" to download all the test data, ready to procede.
The main document to accompany this tutorial is a [pdf](https://github.com/Robinlovelace/Creating-maps-in-R/raw/master/intro-spatial-rl.pdf) within the main repository. This is to be made available for free worldwide - any comments/corrections welcome.
Plug: [An Introduction to Spatial Microsimulation using R](http://eprints.ncrm.ac.uk/3348/): Course 18th - 19th Sept, Cambridge