-
Notifications
You must be signed in to change notification settings - Fork 0
/
IntroToR.Rpres
295 lines (242 loc) · 8.27 KB
/
IntroToR.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
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
Introduction To R
========================================================
author: Kevin Shook
date: November 23, 2017
autosize: true
css: style.css
Objectives
========================================================
- To explain what R is, and what it can be used for
- Will focus on *why* and *what*, rather than *how*
- Future seminars will cover details of how to use R
- Will be giving a live demonstration of some of the capabilities of R
Typical research workflow:
========================================================
- Reading in data (text files, databases, .xls)
- Data massaging
- Data exploration (trial calculations, plotting)
- Final calculations
- Saving results
- Exporting data for other programs to use
- Creating publication graphs
- Writing a paper/thesis
Reproducible research
========================================================
- Need to know what you did, and to be able to re-do it
- Have to justify your results
- Need to be able to re-do work due to changes or mistakes
What is R?
========================================================
- R began as a statistical programming language
- It's now a general-purpose scientific program
- R allows you to write scripts to automate your work
- Can combine text, equations, R code, output and figures in a single output document
- Creates automatically-updated documents
- Results in self-documenting, reproducible research
Why "R"?
========================================================
- S-plus is a proprietary statistics program
- uses the S language
- R is a Free Open Source implementation of the S language
Why use R?
========================================================
- Excellent for statistics, data manipulation and graphing
- Free Open Source Software
- Can see, test and verify the source code
- Uses standard file formats - no lock-in
- Huge number of packages available
- Works well with other programs
Statistics
========================================================
- R is the standard program for statistical analyses
- Widely used for teaching statistics
- Can do any type of statistical analyses that you need
<img src="https://images-na.ssl-images-amazon.com/images/I/51xMTly1NaL._SX329_BO1,204,203,200_.jpg" width=150 height=200>
Data crunching
========================================================
- R is excellent for massaging for all types of scientific data
- can read data from almost any source including spreadsheets and databases
- time series
- spatial data
- categorical data
- Widely used for "big" data
Graphing
========================================================
- R is arguably the best program for scientific graphing
***
GIS
========================================================
- R can do very sophisticated GIS analyses
Getting R
========================================================
- Download R from
https://www.r-project.org/
- Available for all platforms
- Then, install Rstudio (GUI)
- also FOSS
- https://www.rstudio.com/
Packages
========================================================
- Enormous amount of R-code is available
R demonstration
========================================================
type: prompt
Graphing
========================================================
- Standard (built-in) graphing uses the command plot:
```{r, eval=FALSE}
plot(xvals, yvals, options)
```
- Easy to use from the command line
- Good for quick and dirty plots
- Can get better results for publication using another package
***
```{r}
plot(c(1,2,3), c(4,5,6), type="p", col="red", cex=2, pch=19)
```
ggplot2
========================================================
- R package by Hadley Wickham
- gg = grammar of graphics
- Help available at http://ggplot2.tidyverse.org/reference/
- Book: ggplot2: Elegant Graphics for Data Analysis
Why ggplot2?
========================================================
- Creates amazing publication-quality graphics very easily
- Based on work of Edward Tufte
- Uses a grammar for graphs
- Can change graphs interactively
- Extremely good for categorized data
Grammar of graphing
========================================================
- Graphs are made of
|Definition | Short name |
|------|-----|
| Aesthetics | aes |
| Geometric objects | geom |
| Statistical transformations | stat |
| Scales | scale |
| Faceting | facet |
| Theme | theme |
Creating a ggplot2 graph
========================================================
class: small-code
- Create a ggplot2 object in a variable
````{r, eval=FALSE}
p <- ggplot(dataframe)
```
- Add an aesthetic defining the columns
```{r, eval=FALSE}
p <- p + aes(xvals, yvals)
```
- Add a geometry
```{r, eval=FALSE}
p <- p + geom_point()
```
***
- Add stats, themes, scales, facets
```{r, eval=FALSE}
p <- p + theme_gray(18) +
xlim(0, 5)
```
- Display - type the variable name
```{r, eval=FALSE}
p
```
- Save to a file
```{r, eval=FALSE}
ggsave("graphfile.png")
```
ggplot2 data
========================================================
- ggplot2 requires values to be stored in data frames that are tall, not wide
- Opposite of standard R graphs
- Takes some getting used to
- Worth the effort, as it is *much* more powerful
- Allows you to use categories in your plots
- Tools available to convert your data from wide to tall
Wide data
========================================================
- Like a spreadsheet: each variable's value in a separate column
- Inflexible, doesn't allow for multiple classifications
- Doesn't deal well with differing numbers of values
- Doesn't tell us what the data represents
- not very reproducible
|Time|Saskatoon|Regina|Calgary|
|------|-----|-----|----|
|00:00:00| -7 | -7| -1 |
|01:00:00|-5 | -9| -2 |
|02:00:00| -5 | -9 | -3|
|03:00:00| -6 | -1| -2 |
|04:00:00| -6 | -9| -3 |
|05:00:00| -6 |-11| NA |
Tall data
========================================================
|Time|Temp|Location|
|------|-----|-----|
|00:00:00| -7 | Saskatoon|
|01:00:00|-5 | Saskatoon|
|02:00:00| -5 | Saskatoon|
|03:00:00| -6 | Saskatoon|
|04:00:00| -6 | Saskatoon|
|05:00:00| -6 |Saskatoon|
|00:00:00| -7 | Regina|
|01:00:00| -9 | Regina|
|02:00:00| -9 | Regina|
|03:00:00| -1 | Regina|
|04:00:00| -9 | Regina|
|05:00:00| -11| Regina|
|00:00:00| -1 | Calgary |
...
ggplot2 demonstration
========================================================
type: prompt
Challenges
========================================================
- steep learning curve
- have to learn many new commands
## "R makes easy things hard and hard things easy"
But:
- *lots* of support and information available
- will be doing more training
Resources
========================================================
Rseek (Google for R):
http://rseek.org/
R reference card:
https://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf
Books and manuals:
An Introduction to R
https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
***
R for beginners
https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf
The R guide
https://cran.r-project.org/doc/contrib/Owen-TheRGuide.pdf
The R Reference Index:
https://cran.r-project.org/doc/manuals/r-release/fullrefman.pdf
Centre for Hydrology R packages
========================================================
- There are several R packages developed for accessing/processing data
|package| functions|
|-------|-----|
| CRHMr| pre- and post- processing for CRHM|
| MSCr| reads MSC data|
| Reanalysis| reads gridded reanalysis data|
| WISKIr | reads from WISKI database|
| HYDAT | reads WSC HYDAT data |
- all available at https://github.com/CentreForHydrology
This presentation
========================================================
- All of the files for this presentation can be downloaded from
https://github.com/CentreForHydrology/Introduction_to_R