-
Notifications
You must be signed in to change notification settings - Fork 2
/
data-visualization.Rpres
464 lines (344 loc) · 11.1 KB
/
data-visualization.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
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
data visualization
========================================================
author: katie leap
date: 07-28-18
autosize: true
```{r-setup, echo=FALSE, message = FALSE}
library(tidyverse)
library(ggplot2)
library(scales)
library(gridExtra)
```
overview
========================================================
1. ggplot2
2. base R graphics
3. gridExtra
4. ggsave
graph with communication in mind
========================================================
- when considering making a figure, sometimes we start with a graph in mind and try to make our data fit that graph
- better practice is to consider what we're trying to communicate
- how many variables do you have?
- what type of data are they?
our datasets!
========================================================
```{r}
CO2 %>%
summary()
```
our datasets!
========================================================
```{r}
ToothGrowth %>%
summary()
```
grammar of graphics
========================================================
- ggplot2 is designed with the idea that graphics all share a similar basic setup
- whether it's a scatterplot or a line graph or a histogram, all graphs:
- have data
- are set in a coordinate system
- use visual marks to represent data points
- the `ggplot()` call incorporates these things
univariate analysis
========================================================
- let's say we only want to look at one variable
- based on whether this variable is continuous or discrete, we can choose one of a few different outputs
area plot
========================================================
```{r}
ggplot(CO2, aes(uptake)) + geom_area(stat = "bin")
```
density plot
========================================================
```{r}
ggplot(CO2, aes(conc)) + geom_density(kernel = "gaussian")
```
dotplot
========================================================
```{r}
ggplot(ToothGrowth, aes(len)) + geom_dotplot()
```
frequency polygon
========================================================
```{r}
ggplot(CO2, aes(conc)) + geom_freqpoly(bins = 10)
```
histogram
========================================================
```{r}
ggplot(CO2, aes(uptake)) + geom_histogram(bins = 20) + geom_dotplot()
```
qq plot
========================================================
```{r}
ggplot(ToothGrowth) + geom_qq(aes(sample = len)) +
geom_abline(slope = 1, intercept = 0, color = "red") + ylim(0,)
```
discrete: bar plot
========================================================
```{r}
ggplot(ToothGrowth, aes(supp)) + geom_bar()
```
bivariate analysis
========================================================
continuous x , continuous y
```{r}
e <- ggplot(mpg, aes(cty, hwy))
```
labels
========================================================
```{r}
e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE)
```
jitter
========================================================
```{r}
e + geom_jitter(height = 2, width = 2)
```
scatterplot
========================================================
```{r}
e + geom_point() + geom_rug(sides = "bl")
```
quantile
========================================================
```{r}
e + geom_quantile()
```
rug
========================================================
```{r}
e + geom_rug(sides = "bl")
```
smooth
========================================================
```{r}
e + geom_smooth(method = lm)
e + geom_smooth()
```
text
========================================================
```{r}
e + geom_text(aes(label = cty), nudge_x = 1, nudge_y = 1, check_overlap = TRUE)
```
discrete x , continuous y
========================================================
```{r}
f <- ggplot(mpg, aes(class, hwy))
```
col
========================================================
```{r}
f + geom_col()
mpg %>%
group_by(class) %>%
summarise(hwy = sum(hwy))
```
box plot
========================================================
```{r}
f + geom_boxplot()
```
dot plot
========================================================
```{r}
f + geom_dotplot(binaxis = "y", stackdir = "center")
```
violin plot
========================================================
```{r}
f + geom_violin(scale = "area")
```
discrete x , discrete y
========================================================
```{r}
g <- ggplot(diamonds, aes(cut, color))
```
count
========================================================
```{r}
g + geom_count()
```
more-variate analysis
========================================================
```{r}
ggplot(iris) + geom_point(aes(x = Sepal.Length, y = Sepal.Width, color = Species))
```
pay attention to aesthetics
========================================================
```{r}
ggplot(iris) + geom_point(aes(x = Sepal.Length, y = Sepal.Width), color = "red")
```
pay attention to aesthetics
========================================================
```{r}
ggplot(iris) + geom_point(aes(x = Sepal.Length, y = Sepal.Width, color = "red"))
```
graphic design is hard
========================================================
- intelligent defaults make it easier and harder to screw up
- sometimes it is difficult to do a thing you want to do in ggplot on purpose
- it is difficult to make a pie chart because it's commonly accepted that pie charts are difficult to interpret
- when you experience extreme difficulty doing something, take a second to consider what you *really* want and whether there is a simpler way to do it
customization
========================================================
- labels
- themes
- colors
- legends
- reference lines
scales
========================================================
```{r}
ggplot(USArrests) + geom_point(aes(x = UrbanPop, y = Murder))
```
scales
========================================================
```{r}
ggplot(USArrests) + geom_point(aes(x = UrbanPop, y = Murder)) +
scale_x_continuous(labels = percent)
```
scales
========================================================
```{r}
USArrests %>%
mutate(UrbanPop = UrbanPop/100) %>%
ggplot() + geom_point(aes(x = UrbanPop, y = Murder)) +
scale_x_continuous(labels = percent)
```
scales
========================================================
```{r}
USArrests %>%
mutate(UrbanPop = UrbanPop/100) %>%
ggplot() + geom_point(aes(x = UrbanPop, y = Murder)) +
scale_x_continuous(labels = percent) +
labs(x = "Percent Urban Population", y = "Murder Arrests per 100,000",
title = "Really Good Title",
subtitle = "Some Explanation here")
```
scales
========================================================
```{r}
library(mlbench)
data("BostonHousing")
BostonHousing %>%
mutate(medv = medv * 1000) %>%
ggplot() + geom_point(aes(x = medv, y = ptratio)) +
scale_x_continuous(labels = dollar_format())
```
scales
========================================================
```{r}
library(mlbench)
data("BostonHousing")
BostonHousing %>%
mutate(medv = medv * 1000) %>%
ggplot() + geom_point(aes(x = medv, y = ptratio)) +
scale_x_continuous(labels = dollar_format()) +
labs(x = "Median Value of Owner Occupied Homes",
y = "Student-Teacher Ratio by Town",
title = "Relationship between Property Value and Student-Teacher Ratio",
subtitle = "Data from the 1970 Census in the Boston area")
```
facet
========================================================
```{r}
BostonHousing %>%
mutate(medv = medv * 1000) %>%
ggplot() + geom_point(aes(x = medv, y = ptratio)) +
scale_x_continuous(labels = dollar_format()) +
labs(x = "Median Value of Owner Occupied Homes",
y = "Student-Teacher Ratio by Town",
title = "Relationship between Property Value and Student-Teacher Ratio",
subtitle = "Data from the 1970 Census in the Boston area") +
facet_grid(~chas, labeller=labeller(chas = c("0" = "Non-Riverfront", "1" = "Riverfront")))
```
facet
========================================================
```{r}
data(BostonHousing2)
BostonHousing2 %>%
mutate(age = age / 100) %>%
filter(town == "Boston" | town == "Marblehead" | town == "Salem" | town == "Cambridge") %>%
ggplot() + geom_density(aes(age)) +
labs(x = "Proportion of Owner-Occupied Units Built Prior to 1940",
title = "Age of Properties by Town",
subtitle = "Data from the 1970 Census in the Boston area") +
facet_wrap(~town) +
scale_y_continuous(labels = percent)
```
special characters
========================================================
```{r}
ggplot(USArrests) + geom_point(aes(x = UrbanPop, y = Murder)) +
xlab(c(expression(alpha), "murder"))
```
what are base R graphics?
========================================================
- the graphics system built into R
- the only option for a long time
- difficult to get exactly what you want, but once you've got it, base isn't regularly updated so it won't change
- *default plot methods for objects*
plotting model objects
========================================================
```{r}
summary(lm(uptake ~ conc, data = CO2))
```
plotting model objects
========================================================
```{r}
plot(lm(uptake ~ conc, data = CO2))
```
gridExtra
========================================================
- what if I need to put more than one graph into a layout?
- `gridExtra` to the rescue!
- each plot is called a "grob"
grid.arrange
========================================================
```{r}
library(gridExtra)
p1 <- ggplot(iris) + geom_point(aes(x = Sepal.Length, y = Sepal.Width, color = Species))
p2 <- ggplot(iris) + geom_point(aes(x = Petal.Length, y = Petal.Width, color = Species))
grid.arrange(p1, p2)
```
grid.arrange
========================================================
```{r}
grid.arrange(p1, p2, ncol = 2)
```
grid.table
========================================================
```{r}
a <- anova(lm(Sepal.Length ~ Sepal.Width, data=iris))
grid.table(round(a, digits=3))
```
tableGrob
========================================================
```{r}
```
ggsave
========================================================
- this can be pretty straightforward
- `ggsave("filename.extension")` will save the most recent plot as the extension listed in the place listed
- but you can make it more complicated, especially if you have to do graphics for a publication
```
ggsave(filename, plot = last_plot(), device = NULL, path = NULL,
scale = 1, width = NA, height = NA, units = c("in", "cm", "mm"),
dpi = 300, limitsize = TRUE, ...)
```
ggsave
========================================================
- `filename`: File name to create on disk.
- `plot`: Plot to save, defaults to last plot displayed.
- `device`: Device to use. Can be either be a device function (e.g. png()), or one of "eps", "ps", "tex" (pictex), "pdf", "jpeg", "tiff", "png", "bmp", "svg" or "wmf" (windows only).
- `path`: Path to save plot to (combined with filename).
ggsave
========================================================
- `scale`: Multiplicative scaling factor.
- `width, height, units`: Plot size in units ("in", "cm", or "mm"). If not supplied, uses the size of current graphics device.
- `dpi`: Plot resolution. Also accepts a string input: "retina" (320), "print" (300), or "screen" (72). Applies only to raster output types.
- `limitsize`: When TRUE (the default), ggsave will not save images larger than 50x50 inches, to prevent the common error of specifying dimensions in pixels.