forked from STAT545-UBC/STAT545-UBC-original-website
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathblock014_factors.html
731 lines (682 loc) · 39.7 KB
/
block014_factors.html
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
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<title>Be the boss of your factors</title>
<script src="libs/jquery-1.11.3/jquery.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="libs/bootstrap-3.3.5/css/bootstrap.min.css" rel="stylesheet" />
<script src="libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<script>
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');
ga('create', 'UA-68219208-1', 'auto');
ga('send', 'pageview');
</script>
<style type="text/css">code{white-space: pre;}</style>
<link rel="stylesheet"
href="libs/highlight/default.css"
type="text/css" />
<script src="libs/highlight/highlight.js"></script>
<style type="text/css">
pre:not([class]) {
background-color: white;
}
</style>
<script type="text/javascript">
if (window.hljs && document.readyState && document.readyState === "complete") {
window.setTimeout(function() {
hljs.initHighlighting();
}, 0);
}
</script>
<style type="text/css">
h1 {
font-size: 34px;
}
h1.title {
font-size: 38px;
}
h2 {
font-size: 30px;
}
h3 {
font-size: 24px;
}
h4 {
font-size: 18px;
}
h5 {
font-size: 16px;
}
h6 {
font-size: 12px;
}
.table th:not([align]) {
text-align: left;
}
</style>
<link rel="stylesheet" href="libs/local/main.css" type="text/css" />
<link rel="stylesheet" href="libs/local/nav.css" type="text/css" />
<link rel="stylesheet" href="//netdna.bootstrapcdn.com/font-awesome/4.0.3/css/font-awesome.css" type="text/css" />
</head>
<body>
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
code {
color: inherit;
background-color: rgba(0, 0, 0, 0.04);
}
img {
max-width:100%;
height: auto;
}
.tabbed-pane {
padding-top: 12px;
}
button.code-folding-btn:focus {
outline: none;
}
</style>
<div class="container-fluid main-container">
<!-- tabsets -->
<script src="libs/navigation-1.1/tabsets.js"></script>
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
</script>
<!-- code folding -->
<header>
<div class="nav">
<a class="nav-logo" href="index.html">
<img src="static/img/stat545-logo-s.png" width="70px" height="70px"/>
</a>
<ul>
<li class="home"><a href="index.html">Home</a></li>
<li class="faq"><a href="faq.html">FAQ</a></li>
<li class="syllabus"><a href="syllabus.html">Syllabus</a></li>
<li class="topics"><a href="topics.html">Topics</a></li>
<li class="people"><a href="people.html">People</a></li>
</ul>
</div>
</header>
<div class="fluid-row" id="header">
<h1 class="title toc-ignore">Be the boss of your factors</h1>
</div>
<div id="TOC">
<ul>
<li><a href="#load-the-gapminder-data">Load the Gapminder data</a></li>
<li><a href="#model-life-expectancy-as-a-function-of-year">Model life expectancy as a function of year</a></li>
<li><a href="#get-to-know-the-country-factor">Get to know the country factor</a></li>
<li><a href="#why-the-order-of-factor-levels-matters">Why the order of factor levels matters</a></li>
<li><a href="#droplevels-to-drop-unused-factor-levels"><code>droplevels()</code> to drop unused factor levels</a></li>
<li><a href="#reorder-to-reorder-factor-levels"><code>reorder()</code> to reorder factor levels</a></li>
<li><a href="#reorder-exercise"><code>reorder()</code> exercise</a></li>
<li><a href="#revaluing-factor-levels">Revaluing factor levels</a></li>
<li><a href="#grow-a-factor-object">Grow a factor object</a></li>
<li><a href="#make-a-factor-from-scratch">Make a factor from scratch</a></li>
</ul>
</div>
<p><strong>As of 2016-10-11 this is deprecated. STAT 545 is now using the <a href="https://github.com/tidyverse/forcats">forcats</a> package for factor management, which is covered in the <a href="block029_factors.html">current factor lesson</a>.</strong></p>
<div id="load-the-gapminder-data" class="section level3">
<h3>Load the Gapminder data</h3>
<p>As usual, load the Gapminder excerpt and the <code>ggplot2</code> package. Load <code>plyr</code> and/or <code>dplyr</code>. If you load both, load <code>plyr</code> first.</p>
<pre class="r"><code>library(gapminder)
library(plyr)
suppressPackageStartupMessages(library(dplyr))
library(ggplot2)</code></pre>
</div>
<div id="model-life-expectancy-as-a-function-of-year" class="section level3">
<h3>Model life expectancy as a function of year</h3>
<p>For each country, retain estimated intercept and slope from a linear fit – regressing life expectancy on year. First, we write a function that does the work for one country.</p>
<pre class="r"><code>le_lin_fit <- function(dat, offset = 1952) {
the_fit <- lm(lifeExp ~ I(year - offset), dat)
setNames(data.frame(t(coef(the_fit))), c("intercept", "slope"))
}
gapminder %>%
filter(country == "Canada") %>%
le_lin_fit()
## intercept slope
## 1 68.88385 0.2188692</code></pre>
<p>Now we split the data up by country and apply this function. In both approaches, we include <code>country</code> AND <code>continent</code> in the factors on which to split, so both appear in the result.</p>
<p>First, the <code>dplyr</code> way:</p>
<pre class="r"><code>gcoefs <- gapminder %>%
group_by(country, continent) %>%
do(le_lin_fit(.)) %>%
ungroup()
gcoefs
## # A tibble: 142 × 4
## country continent intercept slope
## <fctr> <fctr> <dbl> <dbl>
## 1 Afghanistan Asia 29.90729 0.2753287
## 2 Albania Europe 59.22913 0.3346832
## 3 Algeria Africa 43.37497 0.5692797
## 4 Angola Africa 32.12665 0.2093399
## 5 Argentina Americas 62.68844 0.2317084
## 6 Australia Oceania 68.40051 0.2277238
## 7 Austria Europe 66.44846 0.2419923
## 8 Bahrain Asia 52.74921 0.4675077
## 9 Bangladesh Asia 36.13549 0.4981308
## 10 Belgium Europe 67.89192 0.2090846
## # ... with 132 more rows</code></pre>
<p>Or, if you wish, the <code>plyr</code> way:</p>
<pre class="r"><code>gcoefs2 <- ddply(gapminder, ~ country + continent, le_lin_fit)
gcoefs2
## country continent intercept slope
## 1 Afghanistan Asia 29.90729 0.27532867
## 2 Albania Europe 59.22913 0.33468322
## 3 Algeria Africa 43.37497 0.56927972
## 4 Angola Africa 32.12665 0.20933986
## 5 Argentina Americas 62.68844 0.23170839
## 6 Australia Oceania 68.40051 0.22772378
## 7 Austria Europe 66.44846 0.24199231
## 8 Bahrain Asia 52.74921 0.46750769
## 9 Bangladesh Asia 36.13549 0.49813077
## 10 Belgium Europe 67.89192 0.20908462
## 11 Benin Africa 39.58851 0.33423287
## 12 Bolivia Americas 38.75645 0.49993217
## 13 Bosnia and Herzegovina Europe 58.08956 0.34975524
## 14 Botswana Africa 52.92912 0.06066853
## 15 Brazil Americas 51.51204 0.39008951
## 16 Bulgaria Europe 65.73731 0.14568881
## 17 Burkina Faso Africa 34.68469 0.36397483
## 18 Burundi Africa 40.57864 0.15413427
## 19 Cambodia Asia 37.01542 0.39590280
## 20 Cameroon Africa 41.24946 0.25014685
## 21 Canada Americas 68.88385 0.21886923
## 22 Central African Republic Africa 38.80951 0.18390559
## 23 Chad Africa 39.80937 0.25324406
## 24 Chile Americas 54.31771 0.47684406
## 25 China Asia 47.19048 0.53071485
## 26 Colombia Americas 53.42712 0.38075035
## 27 Comoros Africa 39.99600 0.45039091
## 28 Congo, Dem. Rep. Africa 41.96108 0.09391538
## 29 Congo, Rep. Africa 47.13678 0.19509580
## 30 Costa Rica Americas 59.10471 0.40278951
## 31 Cote d'Ivoire Africa 44.84586 0.13055664
## 32 Croatia Europe 63.85578 0.22545944
## 33 Cuba Americas 62.21345 0.32115035
## 34 Czech Republic Europe 67.52808 0.14481538
## 35 Denmark Europe 71.03359 0.12133007
## 36 Djibouti Africa 36.27715 0.36740350
## 37 Dominican Republic Americas 48.59781 0.47115245
## 38 Ecuador Americas 49.06537 0.50005315
## 39 Egypt Africa 40.96800 0.55545455
## 40 El Salvador Americas 46.51195 0.47714126
## 41 Equatorial Guinea Africa 34.43031 0.31017063
## 42 Eritrea Africa 35.69527 0.37469021
## 43 Ethiopia Africa 36.02815 0.30718531
## 44 Finland Europe 66.44897 0.23792517
## 45 France Europe 67.79013 0.23850140
## 46 Gabon Africa 38.93535 0.44673287
## 47 Gambia Africa 28.40037 0.58182587
## 48 Germany Europe 67.56813 0.21368322
## 49 Ghana Africa 43.49274 0.32174266
## 50 Greece Europe 67.06721 0.24239860
## 51 Guatemala Americas 42.11940 0.53127343
## 52 Guinea Africa 31.55699 0.42483077
## 53 Guinea-Bissau Africa 31.73704 0.27175315
## 54 Haiti Americas 39.24615 0.39705804
## 55 Honduras Americas 42.99241 0.54285175
## 56 Hong Kong, China Asia 63.42864 0.36597063
## 57 Hungary Europe 65.99282 0.12364895
## 58 Iceland Europe 71.96359 0.16537552
## 59 India Asia 39.26976 0.50532098
## 60 Indonesia Asia 36.88312 0.63464126
## 61 Iran Asia 44.97899 0.49663986
## 62 Iraq Asia 50.11346 0.23521049
## 63 Ireland Europe 67.54146 0.19911958
## 64 Israel Asia 66.30041 0.26710629
## 65 Italy Europe 66.59679 0.26971049
## 66 Jamaica Americas 62.66099 0.22139441
## 67 Japan Asia 65.12205 0.35290420
## 68 Jordan Asia 44.06386 0.57172937
## 69 Kenya Africa 47.00204 0.20650769
## 70 Korea, Dem. Rep. Asia 54.90560 0.31642657
## 71 Korea, Rep. Asia 49.72750 0.55540000
## 72 Kuwait Asia 57.45933 0.41683636
## 73 Lebanon Asia 58.68736 0.26102937
## 74 Lesotho Africa 47.37903 0.09556573
## 75 Liberia Africa 39.83642 0.09599371
## 76 Libya Africa 42.10194 0.62553566
## 77 Madagascar Africa 36.66806 0.40372797
## 78 Malawi Africa 36.91037 0.23422587
## 79 Malaysia Asia 51.50522 0.46452238
## 80 Mali Africa 33.05123 0.37680979
## 81 Mauritania Africa 40.02560 0.44641748
## 82 Mauritius Africa 55.37077 0.34845385
## 83 Mexico Americas 53.00537 0.45103497
## 84 Mongolia Asia 43.82641 0.43868811
## 85 Montenegro Europe 62.24163 0.29300140
## 86 Morocco Africa 42.69083 0.54247273
## 87 Mozambique Africa 34.20615 0.22448531
## 88 Myanmar Asia 41.41155 0.43309510
## 89 Namibia Africa 47.13433 0.23116364
## 90 Nepal Asia 34.43164 0.52926154
## 91 Netherlands Europe 71.88962 0.13668671
## 92 New Zealand Oceania 68.68692 0.19282098
## 93 Nicaragua Americas 43.04513 0.55651958
## 94 Niger Africa 35.15067 0.34210909
## 95 Nigeria Africa 37.85953 0.20806573
## 96 Norway Europe 72.21462 0.13194126
## 97 Oman Asia 37.20774 0.77217902
## 98 Pakistan Asia 43.72296 0.40579231
## 99 Panama Americas 58.06100 0.35420909
## 100 Paraguay Americas 62.48183 0.15735455
## 101 Peru Americas 44.34764 0.52769790
## 102 Philippines Asia 49.40435 0.42046923
## 103 Poland Europe 64.78090 0.19621888
## 104 Portugal Europe 61.14679 0.33720140
## 105 Puerto Rico Americas 66.94853 0.21057483
## 106 Reunion Africa 53.99754 0.45988042
## 107 Romania Europe 63.96213 0.15740140
## 108 Rwanda Africa 42.74195 -0.04583147
## 109 Sao Tome and Principe Africa 48.52756 0.34068252
## 110 Saudi Arabia Asia 40.81412 0.64962308
## 111 Senegal Africa 36.74667 0.50470000
## 112 Serbia Europe 61.53435 0.25515105
## 113 Sierra Leone Africa 30.88321 0.21403497
## 114 Singapore Asia 61.84588 0.34088601
## 115 Slovak Republic Europe 67.00987 0.13404406
## 116 Slovenia Europe 66.08635 0.20052378
## 117 Somalia Africa 34.67540 0.22957343
## 118 South Africa Africa 49.34128 0.16915944
## 119 Spain Europe 66.47782 0.28093077
## 120 Sri Lanka Asia 59.79149 0.24489441
## 121 Sudan Africa 37.87419 0.38277483
## 122 Swaziland Africa 46.38786 0.09507483
## 123 Sweden Europe 71.60500 0.16625455
## 124 Switzerland Europe 69.45372 0.22223147
## 125 Syria Asia 46.10128 0.55435944
## 126 Taiwan Asia 61.33744 0.32724476
## 127 Tanzania Africa 43.10841 0.17468811
## 128 Thailand Asia 52.65642 0.34704825
## 129 Togo Africa 40.97746 0.38259231
## 130 Trinidad and Tobago Americas 62.05231 0.17366154
## 131 Tunisia Africa 44.55531 0.58784336
## 132 Turkey Europe 46.02232 0.49723986
## 133 Uganda Africa 44.27522 0.12158601
## 134 United Kingdom Europe 68.80853 0.18596573
## 135 United States Americas 68.41385 0.18416923
## 136 Uruguay Americas 65.74160 0.18327203
## 137 Venezuela Americas 57.51332 0.32972168
## 138 Vietnam Asia 39.01008 0.67161538
## 139 West Bank and Gaza Asia 43.79840 0.60110070
## 140 Yemen, Rep. Asia 30.13028 0.60545944
## 141 Zambia Africa 47.65803 -0.06042517
## 142 Zimbabwe Africa 55.22124 -0.09302098
all.equal(gcoefs, gcoefs2)
## [1] TRUE</code></pre>
</div>
<div id="get-to-know-the-country-factor" class="section level3">
<h3>Get to know the country factor</h3>
<pre class="r"><code>str(gcoefs, give.attr = FALSE)
## Classes 'tbl_df', 'tbl' and 'data.frame': 142 obs. of 4 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 4 1 1 2 5 4 3 3 4 ...
## $ intercept: num 29.9 59.2 43.4 32.1 62.7 ...
## $ slope : num 0.275 0.335 0.569 0.209 0.232 ...
levels(gcoefs$country)
## [1] "Afghanistan" "Albania"
## [3] "Algeria" "Angola"
## [5] "Argentina" "Australia"
## [7] "Austria" "Bahrain"
## [9] "Bangladesh" "Belgium"
## [11] "Benin" "Bolivia"
## [13] "Bosnia and Herzegovina" "Botswana"
## [15] "Brazil" "Bulgaria"
## [17] "Burkina Faso" "Burundi"
## [19] "Cambodia" "Cameroon"
## [21] "Canada" "Central African Republic"
## [23] "Chad" "Chile"
## [25] "China" "Colombia"
## [27] "Comoros" "Congo, Dem. Rep."
## [29] "Congo, Rep." "Costa Rica"
## [31] "Cote d'Ivoire" "Croatia"
## [33] "Cuba" "Czech Republic"
## [35] "Denmark" "Djibouti"
## [37] "Dominican Republic" "Ecuador"
## [39] "Egypt" "El Salvador"
## [41] "Equatorial Guinea" "Eritrea"
## [43] "Ethiopia" "Finland"
## [45] "France" "Gabon"
## [47] "Gambia" "Germany"
## [49] "Ghana" "Greece"
## [51] "Guatemala" "Guinea"
## [53] "Guinea-Bissau" "Haiti"
## [55] "Honduras" "Hong Kong, China"
## [57] "Hungary" "Iceland"
## [59] "India" "Indonesia"
## [61] "Iran" "Iraq"
## [63] "Ireland" "Israel"
## [65] "Italy" "Jamaica"
## [67] "Japan" "Jordan"
## [69] "Kenya" "Korea, Dem. Rep."
## [71] "Korea, Rep." "Kuwait"
## [73] "Lebanon" "Lesotho"
## [75] "Liberia" "Libya"
## [77] "Madagascar" "Malawi"
## [79] "Malaysia" "Mali"
## [81] "Mauritania" "Mauritius"
## [83] "Mexico" "Mongolia"
## [85] "Montenegro" "Morocco"
## [87] "Mozambique" "Myanmar"
## [89] "Namibia" "Nepal"
## [91] "Netherlands" "New Zealand"
## [93] "Nicaragua" "Niger"
## [95] "Nigeria" "Norway"
## [97] "Oman" "Pakistan"
## [99] "Panama" "Paraguay"
## [101] "Peru" "Philippines"
## [103] "Poland" "Portugal"
## [105] "Puerto Rico" "Reunion"
## [107] "Romania" "Rwanda"
## [109] "Sao Tome and Principe" "Saudi Arabia"
## [111] "Senegal" "Serbia"
## [113] "Sierra Leone" "Singapore"
## [115] "Slovak Republic" "Slovenia"
## [117] "Somalia" "South Africa"
## [119] "Spain" "Sri Lanka"
## [121] "Sudan" "Swaziland"
## [123] "Sweden" "Switzerland"
## [125] "Syria" "Taiwan"
## [127] "Tanzania" "Thailand"
## [129] "Togo" "Trinidad and Tobago"
## [131] "Tunisia" "Turkey"
## [133] "Uganda" "United Kingdom"
## [135] "United States" "Uruguay"
## [137] "Venezuela" "Vietnam"
## [139] "West Bank and Gaza" "Yemen, Rep."
## [141] "Zambia" "Zimbabwe"
head(gcoefs$country)
## [1] Afghanistan Albania Algeria Angola Argentina Australia
## 142 Levels: Afghanistan Albania Algeria Angola Argentina ... Zimbabwe</code></pre>
<p>The levels are in alphabetical order. Why? Because. Just because. Do you have a better idea? THEN STEP UP AND DO SOMETHING ABOUT IT.</p>
</div>
<div id="why-the-order-of-factor-levels-matters" class="section level3">
<h3>Why the order of factor levels matters</h3>
<pre class="r"><code>ggplot(gcoefs, aes(x = slope, y = country)) + geom_point()
ggplot(gcoefs, aes(x = slope, y = reorder(country, slope))) +
geom_point()</code></pre>
<p><img src="block014_factors_files/figure-html/alpha-order-silly-1.png" width="49%" /><img src="block014_factors_files/figure-html/alpha-order-silly-2.png" width="49%" /></p>
<p>Which figure do you find easier to navigate? Which is more interesting? The unsorted, i.e. alphabetical, is an example of visual <a href="http://junkcharts.typepad.com/numbersruleyourworld/2014/09/dont-data-puke-says-avinash-kaushik.html">data puke</a>, because there is no effort to help the viewer learn anything from the plot, even though it is really easy to do so. At the very least, always consider sorting your factor levels in some principled way.</p>
<p>The same point generally applies to tables as well.</p>
<p>Exercise (part of <a href="hw05_factor-boss-files-out-in.html">HW05</a>): Consider <code>post_arrange</code>, <code>post_reorder</code>, and <code>post_both</code> as defined below. State how the objects differ and discuss the differences in terms of utility within an exploratory analysis. If I swapped out <code>arrange(country)</code> for <code>arrange(slope)</code>, would we get the same result? Do you have any preference for one arrange statement over the other?</p>
<pre class="r"><code>post_arrange <- gcoefs %>% arrange(slope)
post_reorder <- gcoefs %>%
mutate(country = reorder(country, slope))
post_both <- gcoefs %>%
mutate(country = reorder(country, slope)) %>%
arrange(country)</code></pre>
</div>
<div id="droplevels-to-drop-unused-factor-levels" class="section level3">
<h3><code>droplevels()</code> to drop unused factor levels</h3>
<p>Many demos will be clearer if we create a smaller dataset with just a few countries.</p>
<p>Let’s look at these five countries: Egypt, Haiti, Romania, Thailand, Venezuela.</p>
<pre class="r"><code>h_countries <- c("Egypt", "Haiti", "Romania", "Thailand", "Venezuela")
hDat <- gapminder %>%
filter(country %in% h_countries)
hDat %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 60 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 39 39 39 39 39 39 39 39 39 39 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 41.9 44.4 47 49.3 51.1 ...
## $ pop : int 22223309 25009741 28173309 31681188 34807417 38783863 45681811 52799062 59402198 66134291 ...
## $ gdpPercap: num 1419 1459 1693 1815 2024 ...</code></pre>
<p>Look at the <code>country</code> factor. Look at it hard.</p>
<pre class="r"><code>#table(hDat$country)
#levels(hDat$country)
nlevels(hDat$country)
## [1] 142</code></pre>
<p>Even though <code>hDat</code> contains data for only 5 countries, the other 137 countries remain as possible levels of the <code>country</code> factor. Sometimes this is exactly what you want but sometimes it’s not.</p>
<p>When you want to drop unused factor levels, use <code>droplevels()</code>.</p>
<pre class="r"><code>iDat <- hDat %>% droplevels()
iDat %>% str()
## Classes 'tbl_df', 'tbl' and 'data.frame': 60 obs. of 6 variables:
## $ country : Factor w/ 5 levels "Egypt","Haiti",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 4 levels "Africa","Americas",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 41.9 44.4 47 49.3 51.1 ...
## $ pop : int 22223309 25009741 28173309 31681188 34807417 38783863 45681811 52799062 59402198 66134291 ...
## $ gdpPercap: num 1419 1459 1693 1815 2024 ...
table(iDat$country)
##
## Egypt Haiti Romania Thailand Venezuela
## 12 12 12 12 12
levels(iDat$country)
## [1] "Egypt" "Haiti" "Romania" "Thailand" "Venezuela"
nlevels(iDat$country)
## [1] 5</code></pre>
</div>
<div id="reorder-to-reorder-factor-levels" class="section level3">
<h3><code>reorder()</code> to reorder factor levels</h3>
<p>Now that we have a more manageable set of 5 countries, let’s compute their max life expectancies, view them, and view life expectancy vs. year.</p>
<pre class="r"><code>i_le_max <- iDat %>%
group_by(country) %>%
summarize(max_le = max(lifeExp))
i_le_max
## # A tibble: 5 × 2
## country max_le
## <fctr> <dbl>
## 1 Egypt 71.338
## 2 Haiti 60.916
## 3 Romania 72.476
## 4 Thailand 70.616
## 5 Venezuela 73.747</code></pre>
<pre class="r"><code>ggplot(i_le_max, aes(x = country, y = max_le, group = 1)) +
geom_path() + geom_point()
ggplot(iDat, aes(x = year, y = lifeExp, group = country)) +
geom_line(aes(color = country))</code></pre>
<p><img src="block014_factors_files/figure-html/factor-order-example-before-1.png" width="49%" /><img src="block014_factors_files/figure-html/factor-order-example-before-2.png" width="49%" /></p>
<p>Here’s a plot of the max life expectancies and a spaghetti plot of life expectancy over time. Notice how the first plot jumps around? Notice how the legend of the second plot is completely out of order with the data?</p>
<p>Use the function <code>reorder()</code> to change the order of factor levels. Read <a href="http://www.rdocumentation.org/packages/stats/functions/reorder.factor">its documentation</a>.</p>
<pre class="r"><code>reorder(your_factor, your_quant_var, your_summarization_function)</code></pre>
<p>Let’s reorder the country factor <strong>logically</strong>, in this case by maximum life expectancy. Even though <code>i_le_max</code> already holds these numbers, I’m going to enact the reordering with the “raw” data to illustrate more about the <code>reorder()</code> function.</p>
<pre class="r"><code>jDat <- iDat %>%
mutate(country = reorder(country, lifeExp, max))
data.frame(before = levels(iDat$country), after = levels(jDat$country))
## before after
## 1 Egypt Haiti
## 2 Haiti Thailand
## 3 Romania Egypt
## 4 Thailand Romania
## 5 Venezuela Venezuela
j_le_max <- i_le_max %>%
mutate(country = reorder(country, max_le))
j_le_max <- i_le_max %>%
mutate(country = factor(country, levels = levels(jDat$country)))</code></pre>
<p>Let’s revisit the two figures to see how much more natural they are.</p>
<pre class="r"><code>ggplot(j_le_max, aes(x = country, y = max_le, group = 1)) +
geom_line() + geom_point()
ggplot(jDat, aes(x = year, y = lifeExp)) +
geom_line(aes(color = country)) +
guides(color = guide_legend(reverse = TRUE))</code></pre>
<p><img src="block014_factors_files/figure-html/factor-order-example-after-1.png" width="49%" /><img src="block014_factors_files/figure-html/factor-order-example-after-2.png" width="49%" /></p>
<p>Conclusion: Use <code>reorder()</code> to reorder a factor according to a quantitative variable. A simple call like this:</p>
<pre class="r"><code>reorder(your_factor, your_quant_var)</code></pre>
<p>implies that the summarization function will default to <code>mean()</code>. If that’s not what you want, specify your own summarization function. It could be built-in, such as <code>max()</code>, or could be written by you on-the-fly or in advance.</p>
<p>You can do this and alter your actual data (or a new copy thereof). Or you can do this reordering on-the-fly, i.e. in an actual plotting or tabulation call, leaving the underlying data untouched.</p>
</div>
<div id="reorder-exercise" class="section level3">
<h3><code>reorder()</code> exercise</h3>
<p>Reorder the <code>continent</code> factor, according to the estimated intercepts.</p>
<p>To review, remember we have computed the estimated intercept and slope for each country:</p>
<pre class="r"><code>head(gcoefs)
## # A tibble: 6 × 4
## country continent intercept slope
## <fctr> <fctr> <dbl> <dbl>
## 1 Afghanistan Asia 29.90729 0.2753287
## 2 Albania Europe 59.22913 0.3346832
## 3 Algeria Africa 43.37497 0.5692797
## 4 Angola Africa 32.12665 0.2093399
## 5 Argentina Americas 62.68844 0.2317084
## 6 Australia Oceania 68.40051 0.2277238</code></pre>
<p>The figure on the left gives a stripplot of estimate intercepts, by continent, with continent in alphabetical order. The line connects continent-specific averages of the intercepts (approx. equal to life expectancy in 1952). The figure on the right gives same plot after the continents have been reordered by average estimated intercept.</p>
<p><img src="block014_factors_files/figure-html/continent-reorder-exercise-1.png" width="49%" /><img src="block014_factors_files/figure-html/continent-reorder-exercise-2.png" width="49%" /></p>
<p>Exercise: Write the <code>reorder()</code> statement to do this.</p>
</div>
<div id="revaluing-factor-levels" class="section level3">
<h3>Revaluing factor levels</h3>
<p>What if you want to recode factor levels? I usually use the <code>revalue()</code> function from <code>plyr</code>; sometime I use <code>plyr::mapvalues()</code> which is a bit more general. In the past I have also used the <code>recode()</code> function from the <code>car</code> package.</p>
<pre class="r"><code>k_countries <- c("Australia", "Korea, Dem. Rep.", "Korea, Rep.")
kDat <- gapminder %>%
filter(country %in% k_countries, year > 2000) %>%
droplevels()
kDat
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 2002 80.370 19546792 30687.755
## 2 Australia Oceania 2007 81.235 20434176 34435.367
## 3 Korea, Dem. Rep. Asia 2002 66.662 22215365 1646.758
## 4 Korea, Dem. Rep. Asia 2007 67.297 23301725 1593.065
## 5 Korea, Rep. Asia 2002 77.045 47969150 19233.988
## 6 Korea, Rep. Asia 2007 78.623 49044790 23348.140
levels(kDat$country)
## [1] "Australia" "Korea, Dem. Rep." "Korea, Rep."
kDat <- kDat %>%
mutate(new_country = revalue(country,
c("Australia" = "Oz",
"Korea, Dem. Rep." = "North Korea",
"Korea, Rep." = "South Korea")))
data_frame(levels(kDat$country), levels(kDat$new_country))
## # A tibble: 3 × 2
## `levels(kDat$country)` `levels(kDat$new_country)`
## <chr> <chr>
## 1 Australia Oz
## 2 Korea, Dem. Rep. North Korea
## 3 Korea, Rep. South Korea
kDat
## # A tibble: 6 × 7
## country continent year lifeExp pop gdpPercap new_country
## <fctr> <fctr> <int> <dbl> <int> <dbl> <fctr>
## 1 Australia Oceania 2002 80.370 19546792 30687.755 Oz
## 2 Australia Oceania 2007 81.235 20434176 34435.367 Oz
## 3 Korea, Dem. Rep. Asia 2002 66.662 22215365 1646.758 North Korea
## 4 Korea, Dem. Rep. Asia 2007 67.297 23301725 1593.065 North Korea
## 5 Korea, Rep. Asia 2002 77.045 47969150 19233.988 South Korea
## 6 Korea, Rep. Asia 2007 78.623 49044790 23348.140 South Korea</code></pre>
</div>
<div id="grow-a-factor-object" class="section level3">
<h3>Grow a factor object</h3>
<p>Try to avoid this. If you must <code>rbind()</code>ing data.frames works much better than <code>c()</code>ing vectors.</p>
<pre class="r"><code>usa <- gapminder %>%
filter(country == "United States", year > 2000) %>%
droplevels()
mex <- gapminder %>%
filter(country == "Mexico", year > 2000) %>%
droplevels()
str(usa)
## Classes 'tbl_df', 'tbl' and 'data.frame': 2 obs. of 6 variables:
## $ country : Factor w/ 1 level "United States": 1 1
## $ continent: Factor w/ 1 level "Americas": 1 1
## $ year : int 2002 2007
## $ lifeExp : num 77.3 78.2
## $ pop : int 287675526 301139947
## $ gdpPercap: num 39097 42952
str(mex)
## Classes 'tbl_df', 'tbl' and 'data.frame': 2 obs. of 6 variables:
## $ country : Factor w/ 1 level "Mexico": 1 1
## $ continent: Factor w/ 1 level "Americas": 1 1
## $ year : int 2002 2007
## $ lifeExp : num 74.9 76.2
## $ pop : int 102479927 108700891
## $ gdpPercap: num 10742 11978
usa_mex <- rbind(usa, mex)
## rbinding data.frames works!
str(usa_mex)
## Classes 'tbl_df', 'tbl' and 'data.frame': 4 obs. of 6 variables:
## $ country : Factor w/ 2 levels "United States",..: 1 1 2 2
## $ continent: Factor w/ 1 level "Americas": 1 1 1 1
## $ year : int 2002 2007 2002 2007
## $ lifeExp : num 77.3 78.2 74.9 76.2
## $ pop : int 287675526 301139947 102479927 108700891
## $ gdpPercap: num 39097 42952 10742 11978
## simply catenating factors does not work!
(oops <- c(usa$country, mex$country))
## [1] 1 1 1 1
## workaround
(yeah <- factor(c(levels(usa$country)[usa$country],
levels(mex$country)[mex$country])))
## [1] United States United States Mexico Mexico
## Levels: Mexico United States</code></pre>
<p>If you really want to catenate factors with different levels, you must first convert to their levels as character data, combine, then re-convert to factor.</p>
</div>
<div id="make-a-factor-from-scratch" class="section level3">
<h3>Make a factor from scratch</h3>
<p>The <code>factor()</code> function will explicitly create a factor <em>de novo</em>. Let’s start with an example we are familiar with. Pretend the continent variable in gapminder was a not a factor, but character.</p>
<pre class="r"><code>gapminder$continent <- as.character(gapminder$continent)
#prove it
str(gapminder)
## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: chr "Asia" "Asia" "Asia" "Asia" ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <chr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134</code></pre>
<p>We can now turn it back into a factor by calling factor. The first argument is the input (usually character), followed by factor levels, which will default to the unique input values, in alphabetical order.</p>
<pre class="r"><code>gapminder$continent <- factor(gapminder$continent)
#prove it
str(gapminder)
## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134</code></pre>
</div>
<div class="footer">
This work is licensed under the <a href="http://creativecommons.org/licenses/by-nc/3.0/">CC BY-NC 3.0 Creative Commons License</a>.
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
$(document).ready(function () {
$('tr.header').parent('thead').parent('table').addClass('table table-condensed');
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>