-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlongitudinal_eda.html
1019 lines (823 loc) · 94.4 KB
/
longitudinal_eda.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
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Exploratory Data Visualization for Time-Series and Longitudinal Data</title>
<meta charset="utf-8" />
<meta name="author" content="Christian Testa [email protected] @ctestaa" />
<script src="libs/header-attrs-2.23/header-attrs.js"></script>
<link href="libs/remark-css-0.0.1/default.css" rel="stylesheet" />
<link href="libs/remark-css-0.0.1/default-fonts.css" rel="stylesheet" />
<script src="libs/htmlwidgets-1.6.2/htmlwidgets.js"></script>
<script src="libs/plotly-binding-4.10.4/plotly.js"></script>
<script src="libs/typedarray-0.1/typedarray.min.js"></script>
<script src="libs/jquery-3.5.1/jquery.min.js"></script>
<link href="libs/crosstalk-1.2.0/css/crosstalk.min.css" rel="stylesheet" />
<script src="libs/crosstalk-1.2.0/js/crosstalk.min.js"></script>
<link href="libs/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css" rel="stylesheet" />
<script src="libs/plotly-main-2.11.1/plotly-latest.min.js"></script>
</head>
<body>
<textarea id="source">
name: xaringan-title
class: left, middle, inverse
background-color: '#BBB'
background-size: cover
<style type="text/css">
.remark-code-line-highlighted { background-color: rgba(97, 172, 240, .5) !important; }
.huge .remark-code { /*Change made here*/
font-size: 125% !important;
}
.tiny .remark-code { /*Change made here*/
font-size: 50% !important;
}
</style>
# Exploratory Data Visualization for Time-Series and Longitudinal Data
<div style='font-size: 25px;'>
Christian Testa <br>
<a href='mailto:[email protected]'><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#F92672;" xmlns="http://www.w3.org/2000/svg"> <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path></svg> [email protected]</a> <br>
<a style='color: #55acee;' href='https://fediscience.org/@ctesta'><svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#55acee;" xmlns="http://www.w3.org/2000/svg"> <path d="M433 179.11c0-97.2-63.71-125.7-63.71-125.7-62.52-28.7-228.56-28.4-290.48 0 0 0-63.72 28.5-63.72 125.7 0 115.7-6.6 259.4 105.63 289.1 40.51 10.7 75.32 13 103.33 11.4 50.81-2.8 79.32-18.1 79.32-18.1l-1.7-36.9s-36.31 11.4-77.12 10.1c-40.41-1.4-83-4.4-89.63-54a102.54 102.54 0 0 1-.9-13.9c85.63 20.9 158.65 9.1 178.75 6.7 56.12-6.7 105-41.3 111.23-72.9 9.8-49.8 9-121.5 9-121.5zm-75.12 125.2h-46.63v-114.2c0-49.7-64-51.6-64 6.9v62.5h-46.33V197c0-58.5-64-56.6-64-6.9v114.2H90.19c0-122.1-5.2-147.9 18.41-175 25.9-28.9 79.82-30.8 103.83 6.1l11.6 19.5 11.6-19.5c24.11-37.1 78.12-34.8 103.83-6.1 23.71 27.3 18.4 53 18.4 175z"></path></svg> @ctesta</a> <br>
<a style='color: #636e72;' href='https://github.com/ctesta01'><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#636e72;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> ctesta01</a> <br> <br>
January 22nd, 2025
</div>
<img src="images/osha_covid.png" alt="Research Figure Showing correlations in COVID and OSHA complaints"/>
<span style='font-family: Open Sans; font-size: 12px;'>Hanage, W.P., Testa, C., Chen, J.T. et al. [COVID-19: US federal accountability for entry, spread, and inequities—lessons for the future.](https://link.springer.com/article/10.1007/s10654-020-00689-2) Eur J Epidemiol 35, 995–1006 (2020). https://doi.org/10.1007/s10654-020-00689-2</span>
---
# Motivations
<span style='font-size: 25px;'>
> "The greatest value of a picture is when it forces us to notice what we never expected to see." –John Tukey
--
> "Visualization is often used for evil - twisting insignificant data changes
and making them look meaningful. Don't do that crap if you want to be my friend.
Present results clearly and honestly. If something isn't working - those
reviewing results need to know." —John Tukey
</span>
--
<img src='images/EleanorLutz.png' height='400px'>
---
# Aims
- Learn how to use data manipulation tools such as `dplyr` and `tidyr`
- Learn how to use `ggplot2`, a powerful, flexible framework for visualizing data in R
- Learn where to find more resources
<img src='images/R-for-Data-Science.jpg'/>
---
# Before we get started
There are some packages you'll want to make sure you have installed.
```r
install.packages("tidyverse")
```
```r
library(tidyverse, quietly = F, warn.conflicts = T)
```
```
## ── Attaching core tidyverse packages ──────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
```
---
# Example Data Set
```r
df <- readr::read_csv("example_data/example_dataset_1.csv")
```
```
## Rows: 400 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): strata, gender
## dbl (4): X2005, X2010, X2015, X2020
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```
--
Why use `readr::read_csv`?
- Reports on the assumed column types, with options to override
- Loads faster
- Loads into a tibble, which are faster, better data.frames.
---
# Check out the data
```r
knitr::kable(head(df,4))
```
<table>
<thead>
<tr>
<th style="text-align:left;"> strata </th>
<th style="text-align:left;"> gender </th>
<th style="text-align:right;"> X2005 </th>
<th style="text-align:right;"> X2010 </th>
<th style="text-align:right;"> X2015 </th>
<th style="text-align:right;"> X2020 </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 10.509583 </td>
<td style="text-align:right;"> 15.796463 </td>
<td style="text-align:right;"> 14.918578 </td>
<td style="text-align:right;"> 25.42652 </td>
</tr>
<tr>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 19.413696 </td>
<td style="text-align:right;"> 25.082179 </td>
<td style="text-align:right;"> 25.200332 </td>
<td style="text-align:right;"> 30.43139 </td>
</tr>
<tr>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 12.475157 </td>
<td style="text-align:right;"> 19.262194 </td>
<td style="text-align:right;"> 19.930757 </td>
<td style="text-align:right;"> 20.63809 </td>
</tr>
<tr>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> F </td>
<td style="text-align:right;"> 1.284895 </td>
<td style="text-align:right;"> 5.251643 </td>
<td style="text-align:right;"> 8.746856 </td>
<td style="text-align:right;"> 10.24585 </td>
</tr>
</tbody>
</table>
Note that:
- Data is in a wide format
- Column names need cleaning
- We have groups of participants
---
# Examine the categorical variables
Let's check what those groups are:
```r
unique(df$strata)
```
```
## [1] "A" "B" "C" "D"
```
--
```r
table(df$gender)
```
```
##
## F M
## 210 190
```
---
# Summarize quantitative variables
--
```r
df %>% select(-c(strata, gender)) %>%
summary()
```
```
## X2005 X2010 X2015 X2020
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 5.217 1st Qu.: 6.765 1st Qu.: 6.135 1st Qu.: 7.621
## Median : 9.292 Median :10.592 Median :10.280 Median :12.065
## Mean : 9.537 Mean :11.260 Mean :10.677 Mean :12.678
## 3rd Qu.:12.851 3rd Qu.:15.073 3rd Qu.:14.331 3rd Qu.:17.638
## Max. :34.977 Max. :34.707 Max. :33.236 Max. :39.052
```
--
... Let's break this one down.
---
<img src="images/MagrittrPipe.png" align='right' width='20%'
style='padding: 50px;'><br>
# Intro to `%>%` and dplyr
```r
df %>% select(-c(strata, gender)) %>%
summary()
```
--
`%>%`, the pipe operator, comes from the `magrittr` package, but is also included
in `dplyr`.
--
`x %>% f()` is equivalent to `f(x)` <br>
`x %>% f(y)` is equivalent to `f(x,y)`
--
`df %>% select(-c(strata, gender))` is equivalent to <br>`select(df, -c(strata, gender))`
--
Read `x %>% f(y)` as "`x` gets passed to `f` with additional
argument `y`."
--
Using pipes helps to:
1. chain several commands together,
2. without creating unnecessarily nested one-liners, e.g. <br>
`summary(select(df, -strata))`
---
# What's the deal with select?
```r
df %>% select(-c(strata, gender)) %>%
summary()
```
`select` is the command for subsetting the columns of a data.frame or
tibble.
--
Notice that `strata` and `gender` are not in quotes. This is because `dplyr` and
many of the functions in the tidyverse use tidy-evaluation, which allows users
to reference column names of data.frames and tibbles as if they are variables
within tidyverse functions.
--
The minus sign is saying that we want to remove strata and gender, or
equivalently to select all of the columns except for strata and gender.
```
## X2005 X2010 X2015 X2020
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 5.217 1st Qu.: 6.765 1st Qu.: 6.135 1st Qu.: 7.621
## Median : 9.292 Median :10.592 Median :10.280 Median :12.065
## Mean : 9.537 Mean :11.260 Mean :10.677 Mean :12.678
## 3rd Qu.:12.851 3rd Qu.:15.073 3rd Qu.:14.331 3rd Qu.:17.638
## Max. :34.977 Max. :34.707 Max. :33.236 Max. :39.052
```
---
# Let's add participant ID numbers
```r
df <- df %>%
mutate(id = 1:nrow(.)) %>%
select(id, everything())
knitr::kable(head(df,3))
```
<table>
<thead>
<tr>
<th style="text-align:right;"> id </th>
<th style="text-align:left;"> strata </th>
<th style="text-align:left;"> gender </th>
<th style="text-align:right;"> X2005 </th>
<th style="text-align:right;"> X2010 </th>
<th style="text-align:right;"> X2015 </th>
<th style="text-align:right;"> X2020 </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 10.50958 </td>
<td style="text-align:right;"> 15.79646 </td>
<td style="text-align:right;"> 14.91858 </td>
<td style="text-align:right;"> 25.42652 </td>
</tr>
<tr>
<td style="text-align:right;"> 2 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 19.41370 </td>
<td style="text-align:right;"> 25.08218 </td>
<td style="text-align:right;"> 25.20033 </td>
<td style="text-align:right;"> 30.43139 </td>
</tr>
<tr>
<td style="text-align:right;"> 3 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 12.47516 </td>
<td style="text-align:right;"> 19.26219 </td>
<td style="text-align:right;"> 19.93076 </td>
<td style="text-align:right;"> 20.63809 </td>
</tr>
</tbody>
</table>
--
Note the use of `.` here, which refers to the argument
passed using `%>%`.
--
Equivalently, we could have written `mutate(df, id = 1:nrow(df))`.
--
`everything()` is part of the `tidyselect` package and system which
helps with the programmatic selection of columns and offers other
helpful functions like `starts_with` or `contains`.
---
# A note about `|>` and `%>%`
<small>
`|>` is a pipe now built into base R starting from version 4.1.0.
Basically the main differences is that `%>%` uses
the `.` notation to refer to the left-hand-side of the pipe,
while `|>` uses `_` instead. There are some subtle differences
in what you can do with the left-hand-side (like `%>%` supports `.$var`
while `|>` does not).
In many regards, the two pipes are similar, and you will start to
see more and more code using `|>` because it is new and
hopefully faster than `%>%`.
</small>
.pull-left[
<a href="https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/"><img src="images/pipe_article.png" width='300px' /> </a>
]
.pull-right[
Read more here: <https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/>
]
---
# Let's convert to a tidy format
```r
df <- df %>% tidyr::pivot_longer(
cols = starts_with('X'),
names_to = 'year',
values_to = 'rate')
knitr::kable(head(df, 4))
```
<table>
<thead>
<tr>
<th style="text-align:right;"> id </th>
<th style="text-align:left;"> strata </th>
<th style="text-align:left;"> gender </th>
<th style="text-align:left;"> year </th>
<th style="text-align:right;"> rate </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> X2005 </td>
<td style="text-align:right;"> 10.50958 </td>
</tr>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> X2010 </td>
<td style="text-align:right;"> 15.79646 </td>
</tr>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> X2015 </td>
<td style="text-align:right;"> 14.91858 </td>
</tr>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> X2020 </td>
<td style="text-align:right;"> 25.42652 </td>
</tr>
</tbody>
</table>
---
# Get rid of "X"
```r
df <- df %>%
mutate(year = stringr::str_remove(year, "X"))
knitr::kable(head(df, 4))
```
<table>
<thead>
<tr>
<th style="text-align:right;"> id </th>
<th style="text-align:left;"> strata </th>
<th style="text-align:left;"> gender </th>
<th style="text-align:left;"> year </th>
<th style="text-align:right;"> rate </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> 2005 </td>
<td style="text-align:right;"> 10.50958 </td>
</tr>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> 2010 </td>
<td style="text-align:right;"> 15.79646 </td>
</tr>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> 2015 </td>
<td style="text-align:right;"> 14.91858 </td>
</tr>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:left;"> 2020 </td>
<td style="text-align:right;"> 25.42652 </td>
</tr>
</tbody>
</table>
---
# Now we can do some plotting
.pull-left[
```r
ggplot(data = df,
aes(x = year, y = rate)) +
geom_point()
```
]
.pull-right[
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-17-1.png" width="100%" />
]
---
# Now let's try geom_line
.pull-left[
```r
ggplot(data = df,
aes(x = year, y = rate,
* group = id)) +
geom_line()
```
]
.pull-right[
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-18-1.png" width="100%" />
]
---
# Adding color
.pull-left[
```r
ggplot(data = df,
aes(x = year, y = rate,
group = id,
* color = strata)) +
* geom_line(alpha=0.5)
```
]
.pull-right[
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-19-1.png" width="100%" />
]
---
# Facet Wrapping
.tiny[
```r
ggplot(data = df, aes(x = year, y = rate, group = id, color = strata)) +
geom_line(alpha=0.5) +
* facet_wrap(~strata)
```
]
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-20-1.png" width="576" height="100%" style="display: block; margin: auto;" />
---
# Facet Grid
.tiny[
```r
ggplot(data = df, aes(x = year, y = rate, group = id, color = strata)) +
geom_line(alpha=0.5) +
* facet_grid(gender~strata) +
ggtitle("Different strata had different trajectories")
```
]
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-21-1.png" width="576" height="100%" style="display: block; margin: auto;" />
---
# Using Stat Summaries
.tiny[
```r
ggplot(data = df, aes(x = year, y = rate, group = id, color = gender)) +
geom_line(alpha=0.5) +
facet_wrap(~strata) +
stat_summary(aes(group = interaction(strata, gender)), fun = mean, geom='line', color = 'black') +
stat_summary(aes(group = interaction(strata, gender), shape=gender), fun = mean, geom='point', size=2, color = 'black') +
labs(shape = 'Gender and Strata\nLevel Average', color = 'Gender') +
ggtitle("Men have higher rates than women")
```
]
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-22-1.png" width="576" height="100%" style="display: block; margin: auto;" />
---
# Another way using boxplots
.tiny[
```r
ggplot(data = df, aes(x = year, y = rate, color = gender)) +
geom_boxplot(alpha=0.5) +
stat_summary(aes(group = interaction(strata, gender), shape=''), position = position_dodge(width=0.75),
fun = mean, geom='point', color = 'grey10', alpha=0.8) +
facet_wrap(~strata) +
labs(color = "Gender", shape = "Gender + Strata\nLevel Average") +
ggtitle("Boxplots allow us to see the interquartile range clearly")
```
]
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-23-1.png" width="576" height="100%" style="display: block; margin: auto;" />
---
# Using `geom_ribbon`
.tiny[
```r
df %>% group_by(strata, gender, year) %>%
summarize(
percentile_97.5 = quantile(rate, 0.975),
percentile_2.5 = quantile(rate, 0.025),
mean = mean(rate),
.groups = 'keep') %>%
ggplot(aes(x = year, y = mean, ymax = percentile_97.5,
ymin = percentile_2.5, group = gender, fill = gender, color = gender)) +
geom_ribbon(alpha=0.5, linewidth = 0) +
geom_line(aes(linetype='')) +
facet_wrap(~strata) +
scale_color_manual(values = c('M' = '#2980b9', 'F' = '#c0392b')) +
labs(linetype = 'Gender+Strata\nLevel Average', fill = 'Gender', color = 'Gender', y = 'Rate') +
ggtitle(paste0("The difference between men and women was consistent over time"))
```
]
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-24-1.png" width="432" height="100%" style="display: block; margin: auto;" />
---
# Using plotly for interactive graphics
.tiny[
```r
suppressMessages(library(plotly))
ggplotly() %>% layout(width = 8, height = 3.5)
```
<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-b9236b4875a862d2f564" style="width:504px;height:504px;"></div>
<script type="application/json" data-for="htmlwidget-b9236b4875a862d2f564">{"x":{"data":[{"x":[1,2,3,4,4,3,2,1,1],"y":[0.15944384779508064,3.0387830915421765,6.3389244118795549,8.385279481257653,30.694048988616057,21.94356444316869,22.443381232513801,13.721880058389864,0.15944384779508064],"text":["year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 8.103492<br />percentile_97.5: 22.44338<br />percentile_2.5: 3.03878309<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 11.726415<br />percentile_97.5: 21.94356<br />percentile_2.5: 6.33892441<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 14.544723<br />percentile_97.5: 30.69405<br />percentile_2.5: 8.38527948<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 14.544723<br />percentile_97.5: 30.69405<br />percentile_2.5: 8.38527948<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 11.726415<br />percentile_97.5: 21.94356<br />percentile_2.5: 6.33892441<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 8.103492<br />percentile_97.5: 22.44338<br />percentile_2.5: 3.03878309<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,1)","legendgroup":"(F,1)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[0.42472341838013028,0.35666507405595671,0,0.11828104578049344,18.130757566877488,17.248324050288311,19.440812875920024,18.520926916008811,0.42472341838013028],"text":["year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.346918<br />percentile_97.5: 19.44081<br />percentile_2.5: 0.35666507<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.765133<br />percentile_97.5: 17.24832<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 6.167488<br />percentile_97.5: 18.13076<br />percentile_2.5: 0.11828105<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 6.167488<br />percentile_97.5: 18.13076<br />percentile_2.5: 0.11828105<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.765133<br />percentile_97.5: 17.24832<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.346918<br />percentile_97.5: 19.44081<br />percentile_2.5: 0.35666507<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,2)","legendgroup":"(F,2)","showlegend":true,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[0.053466760163304351,4.965760261352167,0.22485584758449795,6.7111902190070687,23.15981707547493,15.305294172688725,21.319821512747897,14.778189881749258,0.053466760163304351],"text":["year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 10.944576<br />percentile_97.5: 21.31982<br />percentile_2.5: 4.96576026<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 7.009228<br />percentile_97.5: 15.30529<br />percentile_2.5: 0.22485585<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 12.845895<br />percentile_97.5: 23.15982<br />percentile_2.5: 6.71119022<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 12.845895<br />percentile_97.5: 23.15982<br />percentile_2.5: 6.71119022<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 7.009228<br />percentile_97.5: 15.30529<br />percentile_2.5: 0.22485585<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 10.944576<br />percentile_97.5: 21.31982<br />percentile_2.5: 4.96576026<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,3)","legendgroup":"(F,3)","showlegend":true,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[0.61719762387025068,0.012301143588057699,0,0,16.423673254148444,13.606579742071446,16.756253402141212,18.606696163001747,0.61719762387025068],"text":["year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.582965<br />percentile_97.5: 16.75625<br />percentile_2.5: 0.01230114<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.108767<br />percentile_97.5: 13.60658<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 4.280819<br />percentile_97.5: 16.42367<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 4.280819<br />percentile_97.5: 16.42367<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.108767<br />percentile_97.5: 13.60658<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.582965<br />percentile_97.5: 16.75625<br />percentile_2.5: 0.01230114<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,4)","legendgroup":"(F,4)","showlegend":true,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[7.1900421015077214,9.1686947902909033,13.325133821664501,14.046845780249361,30.335173709265501,26.487603391200388,24.66205762258954,19.28667855263485,7.1900421015077214],"text":["year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 16.253586<br />percentile_97.5: 24.66206<br />percentile_2.5: 9.16869479<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 18.502823<br />percentile_97.5: 26.48760<br />percentile_2.5: 13.32513382<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 21.329844<br />percentile_97.5: 30.33517<br />percentile_2.5: 14.04684578<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 21.329844<br />percentile_97.5: 30.33517<br />percentile_2.5: 14.04684578<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 18.502823<br />percentile_97.5: 26.48760<br />percentile_2.5: 13.32513382<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 16.253586<br />percentile_97.5: 24.66206<br />percentile_2.5: 9.16869479<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,1)","legendgroup":"(M,1)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[6.1799531984469427,6.8321615459700276,8.014118023374154,7.9595542155274046,25.863923800295716,25.097873054944248,22.432236000146009,25.411280373218901,6.1799531984469427],"text":["year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 13.713107<br />percentile_97.5: 22.43224<br />percentile_2.5: 6.83216155<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 13.811596<br />percentile_97.5: 25.09787<br />percentile_2.5: 8.01411802<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 13.960148<br />percentile_97.5: 25.86392<br />percentile_2.5: 7.95955422<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 13.960148<br />percentile_97.5: 25.86392<br />percentile_2.5: 7.95955422<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 13.811596<br />percentile_97.5: 25.09787<br />percentile_2.5: 8.01411802<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 13.713107<br />percentile_97.5: 22.43224<br />percentile_2.5: 6.83216155<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,2)","legendgroup":"(M,2)","showlegend":true,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[7.6191482482537651,11.17731523081488,7.0992393115585273,13.185690546664805,32.102698958986664,25.216910959717616,27.749722965597254,22.556376479097064,7.6191482482537651],"text":["year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 17.705141<br />percentile_97.5: 27.74972<br />percentile_2.5: 11.17731523<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 14.341442<br />percentile_97.5: 25.21691<br />percentile_2.5: 7.09923931<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 19.539365<br />percentile_97.5: 32.10270<br />percentile_2.5: 13.18569055<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 19.539365<br />percentile_97.5: 32.10270<br />percentile_2.5: 13.18569055<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 14.341442<br />percentile_97.5: 25.21691<br />percentile_2.5: 7.09923931<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 17.705141<br />percentile_97.5: 27.74972<br />percentile_2.5: 11.17731523<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,3)","legendgroup":"(M,3)","showlegend":true,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,4,3,2,1,1],"y":[7.6307388341178051,5.6602833685091083,5.957267033323479,6.1328340559156009,6.1328340559156009,15.544909293772228,19.424192187140822,19.038225520543911,21.66653650976415,7.6307388341178051],"text":["year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 11.814473<br />percentile_97.5: 19.03823<br />percentile_2.5: 5.66028337<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 10.457162<br />percentile_97.5: 19.42419<br />percentile_2.5: 5.95726703<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 10.457162<br />percentile_97.5: 19.42419<br />percentile_2.5: 5.95726703<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 11.814473<br />percentile_97.5: 19.03823<br />percentile_2.5: 5.66028337<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,4)","legendgroup":"(M,4)","showlegend":true,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[5.3273934495184019,8.1034922134170149,11.726414848576409,14.544723146618084],"text":["year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 8.103492<br />percentile_97.5: 22.44338<br />percentile_2.5: 3.03878309<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 11.726415<br />percentile_97.5: 21.94356<br />percentile_2.5: 6.33892441<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 14.544723<br />percentile_97.5: 30.69405<br />percentile_2.5: 8.38527948<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[6.2951767433215311,6.3469177228192253,5.7651327617218211,6.1674881743824077],"text":["year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 6.346918<br />percentile_97.5: 19.44081<br />percentile_2.5: 0.35666507<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 5.765133<br />percentile_97.5: 17.24832<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 6.167488<br />percentile_97.5: 18.13076<br />percentile_2.5: 0.11828105<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":false,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[6.0269653117263298,10.944575584138081,7.0092276216609655,12.845895439423304],"text":["year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 10.944576<br />percentile_97.5: 21.31982<br />percentile_2.5: 4.96576026<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 7.009228<br />percentile_97.5: 15.30529<br />percentile_2.5: 0.22485585<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 12.845895<br />percentile_97.5: 23.15982<br />percentile_2.5: 6.71119022<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":false,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[7.711203428266999,6.5829653492869662,5.1087669583250221,4.2808186649191384],"text":["year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 6.582965<br />percentile_97.5: 16.75625<br />percentile_2.5: 0.01230114<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 5.108767<br />percentile_97.5: 13.60658<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 4.280819<br />percentile_97.5: 16.42367<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":false,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[12.149497959716998,16.253586080226828,18.502822784263497,21.32984388588028],"text":["year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 16.253586<br />percentile_97.5: 24.66206<br />percentile_2.5: 9.16869479<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 18.502823<br />percentile_97.5: 26.48760<br />percentile_2.5: 13.32513382<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 21.329844<br />percentile_97.5: 30.33517<br />percentile_2.5: 14.04684578<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[13.50146436733943,13.713106840324878,13.811595540248842,13.960147870405367],"text":["year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 13.713107<br />percentile_97.5: 22.43224<br />percentile_2.5: 6.83216155<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 13.811596<br />percentile_97.5: 25.09787<br />percentile_2.5: 8.01411802<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 13.960148<br />percentile_97.5: 25.86392<br />percentile_2.5: 7.95955422<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":false,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[13.125231487618519,17.705141044354569,14.341442430615494,19.539365277157756],"text":["year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 17.705141<br />percentile_97.5: 27.74972<br />percentile_2.5: 11.17731523<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 14.341442<br />percentile_97.5: 25.21691<br />percentile_2.5: 7.09923931<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 19.539365<br />percentile_97.5: 32.10270<br />percentile_2.5: 13.18569055<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":false,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[13.431699684721654,11.814472805553699,10.457161962772529,10.148827669080109],"text":["year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 11.814473<br />percentile_97.5: 19.03823<br />percentile_2.5: 5.66028337<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 10.457162<br />percentile_97.5: 19.42419<br />percentile_2.5: 5.95726703<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":false,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":52.529680365296812,"r":7.3059360730593621,"b":37.260273972602747,"l":37.260273972602747},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"title":{"text":"The difference between men and women was consistent over time","font":{"color":"rgba(0,0,0,1)","family":"","size":17.534246575342468},"x":0,"xref":"paper"},"xaxis":{"domain":[0,0.48912807131985214],"automargin":true,"type":"linear","autorange":false,"range":[0.40000000000000002,4.5999999999999996],"tickmode":"array","ticktext":["2005","2010","2015","2020"],"tickvals":[1,2,3,4],"categoryorder":"array","categoryarray":["2005","2010","2015","2020"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"y2","title":"","hoverformat":".2f"},"annotations":[{"text":"year","x":0.5,"y":0,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"top","annotationType":"axis","yshift":-21.917808219178088},{"text":"Rate","x":0,"y":0.5,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"xref":"paper","yref":"paper","textangle":-90,"xanchor":"right","yanchor":"center","annotationType":"axis","xshift":-21.917808219178088},{"text":"A","x":0.24456403565992607,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"B","x":0.75543596434007387,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"C","x":0.24456403565992607,"y":0.47173298543161557,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"D","x":0.75543596434007387,"y":0.47173298543161557,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"}],"yaxis":{"domain":[0.52826701456838443,1],"automargin":true,"type":"linear","autorange":false,"range":[-1.6051349479493333,33.707833906935996],"tickmode":"array","ticktext":["0","10","20","30"],"tickvals":[0,10,20,30],"categoryorder":"array","categoryarray":["0","10","20","30"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"x","title":"","hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0.52826701456838443,"y1":1},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0,"y1":23.37899543378996,"yanchor":1,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0.52826701456838443,"y1":1},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0,"y1":23.37899543378996,"yanchor":1,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0,"y1":0.47173298543161557},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0,"y1":23.37899543378996,"yanchor":0.47173298543161557,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0,"y1":0.47173298543161557},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0,"y1":23.37899543378996,"yanchor":0.47173298543161557,"ysizemode":"pixel"}],"xaxis2":{"type":"linear","autorange":false,"range":[0.40000000000000002,4.5999999999999996],"tickmode":"array","ticktext":["2005","2010","2015","2020"],"tickvals":[1,2,3,4],"categoryorder":"array","categoryarray":["2005","2010","2015","2020"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"domain":[0.51087192868014786,1],"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"y2","title":"","hoverformat":".2f"},"yaxis2":{"type":"linear","autorange":false,"range":[-1.6051349479493333,33.707833906935996],"tickmode":"array","ticktext":["0","10","20","30"],"tickvals":[0,10,20,30],"categoryorder":"array","categoryarray":["0","10","20","30"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"domain":[0,0.47173298543161557],"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"x","title":"","hoverformat":".2f"},"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.8897637795275593,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.68949771689498},"title":{"text":"Gender<br />Gender+Strata<br />Level Average","font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724}}},"hovermode":"closest","barmode":"relative","width":8,"height":3.5},"config":{"doubleClick":"reset","modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"source":"A","attrs":{"4637765c232f":{"x":{},"y":{},"ymax":{},"ymin":{},"fill":{},"colour":{},"type":"scatter"},"46371b0a1f5e":{"x":{},"y":{},"ymax":{},"ymin":{},"fill":{},"colour":{},"linetype":{}}},"cur_data":"4637765c232f","visdat":{"4637765c232f":["function (y) ","x"],"46371b0a1f5e":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
]
```
## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()
```
<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-e867937eb4e8125bcc9c" style="width:100%;height:504px;"></div>
<script type="application/json" data-for="htmlwidget-e867937eb4e8125bcc9c">{"x":{"data":[{"x":[1,2,3,4,4,3,2,1,1],"y":[0.15944384779508064,3.0387830915421765,6.3389244118795549,8.385279481257653,30.694048988616057,21.94356444316869,22.443381232513801,13.721880058389864,0.15944384779508064],"text":["year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 8.103492<br />percentile_97.5: 22.44338<br />percentile_2.5: 3.03878309<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 11.726415<br />percentile_97.5: 21.94356<br />percentile_2.5: 6.33892441<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 14.544723<br />percentile_97.5: 30.69405<br />percentile_2.5: 8.38527948<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 14.544723<br />percentile_97.5: 30.69405<br />percentile_2.5: 8.38527948<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 11.726415<br />percentile_97.5: 21.94356<br />percentile_2.5: 6.33892441<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 8.103492<br />percentile_97.5: 22.44338<br />percentile_2.5: 3.03878309<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,1)","legendgroup":"(F,1)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[0.42472341838013028,0.35666507405595671,0,0.11828104578049344,18.130757566877488,17.248324050288311,19.440812875920024,18.520926916008811,0.42472341838013028],"text":["year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.346918<br />percentile_97.5: 19.44081<br />percentile_2.5: 0.35666507<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.765133<br />percentile_97.5: 17.24832<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 6.167488<br />percentile_97.5: 18.13076<br />percentile_2.5: 0.11828105<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 6.167488<br />percentile_97.5: 18.13076<br />percentile_2.5: 0.11828105<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.765133<br />percentile_97.5: 17.24832<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.346918<br />percentile_97.5: 19.44081<br />percentile_2.5: 0.35666507<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,2)","legendgroup":"(F,2)","showlegend":true,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[0.053466760163304351,4.965760261352167,0.22485584758449795,6.7111902190070687,23.15981707547493,15.305294172688725,21.319821512747897,14.778189881749258,0.053466760163304351],"text":["year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 10.944576<br />percentile_97.5: 21.31982<br />percentile_2.5: 4.96576026<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 7.009228<br />percentile_97.5: 15.30529<br />percentile_2.5: 0.22485585<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 12.845895<br />percentile_97.5: 23.15982<br />percentile_2.5: 6.71119022<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 12.845895<br />percentile_97.5: 23.15982<br />percentile_2.5: 6.71119022<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 7.009228<br />percentile_97.5: 15.30529<br />percentile_2.5: 0.22485585<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 10.944576<br />percentile_97.5: 21.31982<br />percentile_2.5: 4.96576026<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,3)","legendgroup":"(F,3)","showlegend":true,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[0.61719762387025068,0.012301143588057699,0,0,16.423673254148444,13.606579742071446,16.756253402141212,18.606696163001747,0.61719762387025068],"text":["year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.582965<br />percentile_97.5: 16.75625<br />percentile_2.5: 0.01230114<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.108767<br />percentile_97.5: 13.60658<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 4.280819<br />percentile_97.5: 16.42367<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2020<br />mean: 4.280819<br />percentile_97.5: 16.42367<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2015<br />mean: 5.108767<br />percentile_97.5: 13.60658<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F","year: 2010<br />mean: 6.582965<br />percentile_97.5: 16.75625<br />percentile_2.5: 0.01230114<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F","year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(192,57,43,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(248,118,109,0.5)","hoveron":"points","name":"(F,4)","legendgroup":"(F,4)","showlegend":true,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[7.1900421015077214,9.1686947902909033,13.325133821664501,14.046845780249361,30.335173709265501,26.487603391200388,24.66205762258954,19.28667855263485,7.1900421015077214],"text":["year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 16.253586<br />percentile_97.5: 24.66206<br />percentile_2.5: 9.16869479<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 18.502823<br />percentile_97.5: 26.48760<br />percentile_2.5: 13.32513382<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 21.329844<br />percentile_97.5: 30.33517<br />percentile_2.5: 14.04684578<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 21.329844<br />percentile_97.5: 30.33517<br />percentile_2.5: 14.04684578<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 18.502823<br />percentile_97.5: 26.48760<br />percentile_2.5: 13.32513382<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 16.253586<br />percentile_97.5: 24.66206<br />percentile_2.5: 9.16869479<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,1)","legendgroup":"(M,1)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[6.1799531984469427,6.8321615459700276,8.014118023374154,7.9595542155274046,25.863923800295716,25.097873054944248,22.432236000146009,25.411280373218901,6.1799531984469427],"text":["year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 13.713107<br />percentile_97.5: 22.43224<br />percentile_2.5: 6.83216155<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 13.811596<br />percentile_97.5: 25.09787<br />percentile_2.5: 8.01411802<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 13.960148<br />percentile_97.5: 25.86392<br />percentile_2.5: 7.95955422<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 13.960148<br />percentile_97.5: 25.86392<br />percentile_2.5: 7.95955422<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 13.811596<br />percentile_97.5: 25.09787<br />percentile_2.5: 8.01411802<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 13.713107<br />percentile_97.5: 22.43224<br />percentile_2.5: 6.83216155<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,2)","legendgroup":"(M,2)","showlegend":true,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,3,2,1,1],"y":[7.6191482482537651,11.17731523081488,7.0992393115585273,13.185690546664805,32.102698958986664,25.216910959717616,27.749722965597254,22.556376479097064,7.6191482482537651],"text":["year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 17.705141<br />percentile_97.5: 27.74972<br />percentile_2.5: 11.17731523<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 14.341442<br />percentile_97.5: 25.21691<br />percentile_2.5: 7.09923931<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 19.539365<br />percentile_97.5: 32.10270<br />percentile_2.5: 13.18569055<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 19.539365<br />percentile_97.5: 32.10270<br />percentile_2.5: 13.18569055<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 14.341442<br />percentile_97.5: 25.21691<br />percentile_2.5: 7.09923931<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 17.705141<br />percentile_97.5: 27.74972<br />percentile_2.5: 11.17731523<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,3)","legendgroup":"(M,3)","showlegend":true,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4,4,4,3,2,1,1],"y":[7.6307388341178051,5.6602833685091083,5.957267033323479,6.1328340559156009,6.1328340559156009,15.544909293772228,19.424192187140822,19.038225520543911,21.66653650976415,7.6307388341178051],"text":["year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 11.814473<br />percentile_97.5: 19.03823<br />percentile_2.5: 5.66028337<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 10.457162<br />percentile_97.5: 19.42419<br />percentile_2.5: 5.95726703<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M","year: 2015<br />mean: 10.457162<br />percentile_97.5: 19.42419<br />percentile_2.5: 5.95726703<br />gender: M<br />gender: M<br />gender: M","year: 2010<br />mean: 11.814473<br />percentile_97.5: 19.03823<br />percentile_2.5: 5.66028337<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M","year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M"],"type":"scatter","mode":"lines","line":{"width":0,"color":"rgba(41,128,185,0.5)","dash":"solid"},"fill":"toself","fillcolor":"rgba(0,191,196,0.5)","hoveron":"points","name":"(M,4)","legendgroup":"(M,4)","showlegend":true,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[5.3273934495184019,8.1034922134170149,11.726414848576409,14.544723146618084],"text":["year: 2005<br />mean: 5.327393<br />percentile_97.5: 13.72188<br />percentile_2.5: 0.15944385<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 8.103492<br />percentile_97.5: 22.44338<br />percentile_2.5: 3.03878309<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 11.726415<br />percentile_97.5: 21.94356<br />percentile_2.5: 6.33892441<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 14.544723<br />percentile_97.5: 30.69405<br />percentile_2.5: 8.38527948<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[6.2951767433215311,6.3469177228192253,5.7651327617218211,6.1674881743824077],"text":["year: 2005<br />mean: 6.295177<br />percentile_97.5: 18.52093<br />percentile_2.5: 0.42472342<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 6.346918<br />percentile_97.5: 19.44081<br />percentile_2.5: 0.35666507<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 5.765133<br />percentile_97.5: 17.24832<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 6.167488<br />percentile_97.5: 18.13076<br />percentile_2.5: 0.11828105<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":false,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[6.0269653117263298,10.944575584138081,7.0092276216609655,12.845895439423304],"text":["year: 2005<br />mean: 6.026965<br />percentile_97.5: 14.77819<br />percentile_2.5: 0.05346676<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 10.944576<br />percentile_97.5: 21.31982<br />percentile_2.5: 4.96576026<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 7.009228<br />percentile_97.5: 15.30529<br />percentile_2.5: 0.22485585<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 12.845895<br />percentile_97.5: 23.15982<br />percentile_2.5: 6.71119022<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":false,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[7.711203428266999,6.5829653492869662,5.1087669583250221,4.2808186649191384],"text":["year: 2005<br />mean: 7.711203<br />percentile_97.5: 18.60670<br />percentile_2.5: 0.61719762<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2010<br />mean: 6.582965<br />percentile_97.5: 16.75625<br />percentile_2.5: 0.01230114<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2015<br />mean: 5.108767<br />percentile_97.5: 13.60658<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F<br />linetype: ","year: 2020<br />mean: 4.280819<br />percentile_97.5: 16.42367<br />percentile_2.5: 0.00000000<br />gender: F<br />gender: F<br />gender: F<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(192,57,43,1)","dash":"solid"},"hoveron":"points","name":"(F,)","legendgroup":"(F,)","showlegend":false,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[12.149497959716998,16.253586080226828,18.502822784263497,21.32984388588028],"text":["year: 2005<br />mean: 12.149498<br />percentile_97.5: 19.28668<br />percentile_2.5: 7.19004210<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 16.253586<br />percentile_97.5: 24.66206<br />percentile_2.5: 9.16869479<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 18.502823<br />percentile_97.5: 26.48760<br />percentile_2.5: 13.32513382<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 21.329844<br />percentile_97.5: 30.33517<br />percentile_2.5: 14.04684578<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":true,"xaxis":"x","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[13.50146436733943,13.713106840324878,13.811595540248842,13.960147870405367],"text":["year: 2005<br />mean: 13.501464<br />percentile_97.5: 25.41128<br />percentile_2.5: 6.17995320<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 13.713107<br />percentile_97.5: 22.43224<br />percentile_2.5: 6.83216155<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 13.811596<br />percentile_97.5: 25.09787<br />percentile_2.5: 8.01411802<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 13.960148<br />percentile_97.5: 25.86392<br />percentile_2.5: 7.95955422<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":false,"xaxis":"x2","yaxis":"y","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[13.125231487618519,17.705141044354569,14.341442430615494,19.539365277157756],"text":["year: 2005<br />mean: 13.125231<br />percentile_97.5: 22.55638<br />percentile_2.5: 7.61914825<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 17.705141<br />percentile_97.5: 27.74972<br />percentile_2.5: 11.17731523<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 14.341442<br />percentile_97.5: 25.21691<br />percentile_2.5: 7.09923931<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 19.539365<br />percentile_97.5: 32.10270<br />percentile_2.5: 13.18569055<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":false,"xaxis":"x","yaxis":"y2","hoverinfo":"text","frame":null},{"x":[1,2,3,4],"y":[13.431699684721654,11.814472805553699,10.457161962772529,10.148827669080109],"text":["year: 2005<br />mean: 13.431700<br />percentile_97.5: 21.66654<br />percentile_2.5: 7.63073883<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2010<br />mean: 11.814473<br />percentile_97.5: 19.03823<br />percentile_2.5: 5.66028337<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2015<br />mean: 10.457162<br />percentile_97.5: 19.42419<br />percentile_2.5: 5.95726703<br />gender: M<br />gender: M<br />gender: M<br />linetype: ","year: 2020<br />mean: 10.148828<br />percentile_97.5: 15.54491<br />percentile_2.5: 6.13283406<br />gender: M<br />gender: M<br />gender: M<br />linetype: "],"type":"scatter","mode":"lines","line":{"width":1.8897637795275593,"color":"rgba(41,128,185,1)","dash":"solid"},"hoveron":"points","name":"(M,)","legendgroup":"(M,)","showlegend":false,"xaxis":"x2","yaxis":"y2","hoverinfo":"text","frame":null}],"layout":{"margin":{"t":52.529680365296812,"r":7.3059360730593621,"b":37.260273972602747,"l":37.260273972602747},"plot_bgcolor":"rgba(235,235,235,1)","paper_bgcolor":"rgba(255,255,255,1)","font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"title":{"text":"The difference between men and women was consistent over time","font":{"color":"rgba(0,0,0,1)","family":"","size":17.534246575342468},"x":0,"xref":"paper"},"xaxis":{"domain":[0,0.48912807131985214],"automargin":true,"type":"linear","autorange":false,"range":[0.40000000000000002,4.5999999999999996],"tickmode":"array","ticktext":["2005","2010","2015","2020"],"tickvals":[1,2,3,4],"categoryorder":"array","categoryarray":["2005","2010","2015","2020"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"y2","title":"","hoverformat":".2f"},"annotations":[{"text":"year","x":0.5,"y":0,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"top","annotationType":"axis","yshift":-21.917808219178088},{"text":"Rate","x":0,"y":0.5,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724},"xref":"paper","yref":"paper","textangle":-90,"xanchor":"right","yanchor":"center","annotationType":"axis","xshift":-21.917808219178088},{"text":"A","x":0.24456403565992607,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"B","x":0.75543596434007387,"y":1,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"C","x":0.24456403565992607,"y":0.47173298543161557,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"},{"text":"D","x":0.75543596434007387,"y":0.47173298543161557,"showarrow":false,"ax":0,"ay":0,"font":{"color":"rgba(26,26,26,1)","family":"","size":11.68949771689498},"xref":"paper","yref":"paper","textangle":-0,"xanchor":"center","yanchor":"bottom"}],"yaxis":{"domain":[0.52826701456838443,1],"automargin":true,"type":"linear","autorange":false,"range":[-1.6051349479493333,33.707833906935996],"tickmode":"array","ticktext":["0","10","20","30"],"tickvals":[0,10,20,30],"categoryorder":"array","categoryarray":["0","10","20","30"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"x","title":"","hoverformat":".2f"},"shapes":[{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0.52826701456838443,"y1":1},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0,"y1":23.37899543378996,"yanchor":1,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0.52826701456838443,"y1":1},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0,"y1":23.37899543378996,"yanchor":1,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0,"y1":0.47173298543161557},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0,"x1":0.48912807131985214,"y0":0,"y1":23.37899543378996,"yanchor":0.47173298543161557,"ysizemode":"pixel"},{"type":"rect","fillcolor":null,"line":{"color":null,"width":0,"linetype":[]},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0,"y1":0.47173298543161557},{"type":"rect","fillcolor":"rgba(217,217,217,1)","line":{"color":"transparent","width":0.66417600664176002,"linetype":"solid"},"yref":"paper","xref":"paper","x0":0.51087192868014786,"x1":1,"y0":0,"y1":23.37899543378996,"yanchor":0.47173298543161557,"ysizemode":"pixel"}],"xaxis2":{"type":"linear","autorange":false,"range":[0.40000000000000002,4.5999999999999996],"tickmode":"array","ticktext":["2005","2010","2015","2020"],"tickvals":[1,2,3,4],"categoryorder":"array","categoryarray":["2005","2010","2015","2020"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"domain":[0.51087192868014786,1],"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"y2","title":"","hoverformat":".2f"},"yaxis2":{"type":"linear","autorange":false,"range":[-1.6051349479493333,33.707833906935996],"tickmode":"array","ticktext":["0","10","20","30"],"tickvals":[0,10,20,30],"categoryorder":"array","categoryarray":["0","10","20","30"],"nticks":null,"ticks":"outside","tickcolor":"rgba(51,51,51,1)","ticklen":3.6529680365296811,"tickwidth":0.66417600664176002,"showticklabels":true,"tickfont":{"color":"rgba(77,77,77,1)","family":"","size":11.68949771689498},"tickangle":-0,"showline":false,"linecolor":null,"linewidth":0,"showgrid":true,"domain":[0,0.47173298543161557],"gridcolor":"rgba(255,255,255,1)","gridwidth":0.66417600664176002,"zeroline":false,"anchor":"x","title":"","hoverformat":".2f"},"showlegend":true,"legend":{"bgcolor":"rgba(255,255,255,1)","bordercolor":"transparent","borderwidth":1.8897637795275593,"font":{"color":"rgba(0,0,0,1)","family":"","size":11.68949771689498},"title":{"text":"Gender<br />Gender+Strata<br />Level Average","font":{"color":"rgba(0,0,0,1)","family":"","size":14.611872146118724}}},"hovermode":"closest","barmode":"relative","width":8,"height":3.5},"config":{"doubleClick":"reset","modeBarButtonsToAdd":["hoverclosest","hovercompare"],"showSendToCloud":false},"source":"A","attrs":{"46377ef60109":{"x":{},"y":{},"ymax":{},"ymin":{},"fill":{},"colour":{},"type":"scatter"},"463748be36fd":{"x":{},"y":{},"ymax":{},"ymin":{},"fill":{},"colour":{},"linetype":{}}},"cur_data":"46377ef60109","visdat":{"46377ef60109":["function (y) ","x"],"463748be36fd":["function (y) ","x"]},"highlight":{"on":"plotly_click","persistent":false,"dynamic":false,"selectize":false,"opacityDim":0.20000000000000001,"selected":{"opacity":1},"debounce":0},"shinyEvents":["plotly_hover","plotly_click","plotly_selected","plotly_relayout","plotly_brushed","plotly_brushing","plotly_clickannotation","plotly_doubleclick","plotly_deselect","plotly_afterplot","plotly_sunburstclick"],"base_url":"https://plot.ly"},"evals":[],"jsHooks":[]}</script>
---
# Widening Data for Correlation Analysis
Before we can look at correlation across the years, we need
to widen the dataframe (similar to how it was originally formatted).
```r
df_wide <- df %>%
tidyr::pivot_wider(id_cols = c(id, strata, gender),
names_from = year,
values_from = rate)
knitr::kable(head(df_wide, 3))
```
<table>
<thead>
<tr>
<th style="text-align:right;"> id </th>
<th style="text-align:left;"> strata </th>
<th style="text-align:left;"> gender </th>
<th style="text-align:right;"> 2005 </th>
<th style="text-align:right;"> 2010 </th>
<th style="text-align:right;"> 2015 </th>
<th style="text-align:right;"> 2020 </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;"> 1 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 10.50958 </td>
<td style="text-align:right;"> 15.79646 </td>
<td style="text-align:right;"> 14.91858 </td>
<td style="text-align:right;"> 25.42652 </td>
</tr>
<tr>
<td style="text-align:right;"> 2 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 19.41370 </td>
<td style="text-align:right;"> 25.08218 </td>
<td style="text-align:right;"> 25.20033 </td>
<td style="text-align:right;"> 30.43139 </td>
</tr>
<tr>
<td style="text-align:right;"> 3 </td>
<td style="text-align:left;"> A </td>
<td style="text-align:left;"> M </td>
<td style="text-align:right;"> 12.47516 </td>
<td style="text-align:right;"> 19.26219 </td>
<td style="text-align:right;"> 19.93076 </td>
<td style="text-align:right;"> 20.63809 </td>
</tr>
</tbody>
</table>
---
# Correlation Plot
.tiny[
```r
# install.packages(GGally)
library(GGally)
ggpairs(df_wide, aes(color = strata, alpha=0.25), columns = c('2005', '2010', '2015', '2020'), progress=F)
```
]
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-27-1.png" width="576" height="100%" style="display: block; margin: auto;" />
---
# Bivariate Pairs Plots
```r
ggbivariate(df, outcome = 'gender', explanatory = 'strata') +
theme(legend.position = 'bottom') +
ggtitle("Strata were about evenly split across gender")
```
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-28-1.png" width="432" height="100%" style="display: block; margin: auto;" />
---
<img src="images/tt_logo.png" align='right' width='30%'
style='padding: 0px;'>
# Where you can learn more
For data manipulation and visualization:
<img src="images/ggplot2-cheatsheet.png" align='right' width='30%'
style='padding: 0px;'>
- R for Data Science, by Garrett Grolemund and Hadley Wickham, [r4ds.had.co.nz](https://r4ds.had.co.nz)
- The ggplot2 Website, [ggplot2.tidyverse.org](https://ggplot2.tidyverse.org)
- The [RStudio Cheatsheets](https://rstudio.com/resources/cheatsheets/) (I suggest starting with [dplyr](https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf) and [ggplot2](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf))
- Watch the [TidyTuesday tutorials on YouTube](https://www.youtube.com/results?search_query=tidytuesday) or check out [TidyTuesday
on GitHub](https://github.com/rfordatascience/tidytuesday/)
--
For longitudinal data analysis:
- [Applied Longitudinal Analysis by Garrett Fitzmaurice, Nan Laird, and James Ware](https://content.sph.harvard.edu/fitzmaur/ala2e/)
- [Marie Davidian's Slides on Modeling and Analysis of Longitudinal Data](https://www4.stat.ncsu.edu/~davidian/enar06_handout.pdf)
- [Patrick Hagearty's notes on Longitudinal Data Analysis](https://faculty.washington.edu/heagerty/Courses/VA-longitudinal/private/LDAchapter.pdf) (fairly technical)
- Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models, by Ikuko FunatogawaTakashi Funatogawa (very technical)
---
# How to take your longitudinal analysis further
.pull-left[
<!-- <div style='font-size: 75% !important;'> -->
<ul>
Use models to make inferences about your data. Models for longitudinal data
often include the following features: <br> <br>
<li> Multi-level or random effects design</li>
<li> Generalized Linear Models</li>
<li> Auto-regressive</li>
<li> Treatment of missing data</li>
</ul>
<!-- </div> -->
]
.pull-right[
<img src='images/multilevel-reaction.png'/>
<span style='font-size: 16px;'>
`library(lme4)` <br>
`lmer(Reaction ~ Days + (Days|Subject), sleepstudy)`
</span>
]
---
# Get More Inspiration
.pull-left[
* [Georgios Karamanis](https://karaman.is/)
* [Cedric Scherer](https://cedricscherer.com)
* [Eleanor Lutz](https://eleanorlutz.com/)
* [Flowing Data](https://flowingdata.com/)
<img src="images/Kaashoek.png" alt="A figure showing effectiveness of statewide interventions as an effect modifier" />
<span style='font-size: 12px;'>Kaashoek J, Testa C, Chen JT, Stolerman LM, Krieger N, Hanage WP, et al. [The evolving roles of US political partisanship and social vulnerability in the COVID-19 pandemic from February 2020–February 2021.](https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000557) PLOS Global Public Health. 2022
</span>
]
.pull-right[
<img src="images/EscalatingDrought.jpg" height='500px' alt='A figure on drought from Cedric Scherer' />
]
---
# Join the R User Group! &nbsp; rug-at-hdsi.org
.pull-left[
<img src="images/rug.png" alt='screenshot of our RUG YouTube'><br>
Join at <https://rug-at-hdsi.org> and check out our [YouTube](https://www.youtube.com/@rusergroupatharvarddatasci7232/videos)
]
<!-- .pull-right[
<img src="images/tidymodels.png" style='max-height: 450px;' alt="poster for our upcoming talk on Tidymodels" />
] -->
---
# Find this talk on my github
<img src='screenshot.png' height='450px' />
<img src='images/github_qr.png' style = 'vertical-align: top;' height='175px' />
<https://github.com/ctesta01/longitudinal_eda_talk>
---
# Image Credits
<div style='font-size:small'>
<ul>
<li>R for Data Science: https://www.dataoptimal.com/wp-content/uploads/R-for-Data-Science.jpg </li>
<li>Magrittr Logo: https://magrittr.tidyverse.org/logo.png</li>
<li>Tidy Tuesday Logo: https://github.com/rfordatascience/tidytuesday/</li>
<li>Escalating Drought: https://www.scientificamerican.com/article/climate-change-drives-escalating-drought/</li>
</ul>
</div>
</textarea>
<style data-target="print-only">@media screen {.remark-slide-container{display:block;}.remark-slide-scaler{box-shadow:none;}}</style>
<script src="https://remarkjs.com/downloads/remark-latest.min.js"></script>
<script>var slideshow = remark.create({
"highlightStyle": "github",
"highlightLines": true
});
if (window.HTMLWidgets) slideshow.on('afterShowSlide', function (slide) {
window.dispatchEvent(new Event('resize'));
});
(function(d) {
var s = d.createElement("style"), r = d.querySelector(".remark-slide-scaler");
if (!r) return;
s.type = "text/css"; s.innerHTML = "@page {size: " + r.style.width + " " + r.style.height +"; }";
d.head.appendChild(s);
})(document);
(function(d) {
var el = d.getElementsByClassName("remark-slides-area");
if (!el) return;
var slide, slides = slideshow.getSlides(), els = el[0].children;
for (var i = 1; i < slides.length; i++) {
slide = slides[i];
if (slide.properties.continued === "true" || slide.properties.count === "false") {
els[i - 1].className += ' has-continuation';
}
}
var s = d.createElement("style");
s.type = "text/css"; s.innerHTML = "@media print { .has-continuation { display: none; } }";
d.head.appendChild(s);
})(document);
// delete the temporary CSS (for displaying all slides initially) when the user
// starts to view slides
(function() {
var deleted = false;
slideshow.on('beforeShowSlide', function(slide) {
if (deleted) return;
var sheets = document.styleSheets, node;
for (var i = 0; i < sheets.length; i++) {
node = sheets[i].ownerNode;
if (node.dataset["target"] !== "print-only") continue;
node.parentNode.removeChild(node);
}
deleted = true;
});
})();
// add `data-at-shortcutkeys` attribute to <body> to resolve conflicts with JAWS
// screen reader (see PR #262)
(function(d) {
let res = {};
d.querySelectorAll('.remark-help-content table tr').forEach(tr => {
const t = tr.querySelector('td:nth-child(2)').innerText;
tr.querySelectorAll('td:first-child .key').forEach(key => {
const k = key.innerText;
if (/^[a-z]$/.test(k)) res[k] = t; // must be a single letter (key)
});
});
d.body.setAttribute('data-at-shortcutkeys', JSON.stringify(res));
})(document);
(function() {
"use strict"
// Replace <script> tags in slides area to make them executable
var scripts = document.querySelectorAll(
'.remark-slides-area .remark-slide-container script'
);
if (!scripts.length) return;
for (var i = 0; i < scripts.length; i++) {
var s = document.createElement('script');
var code = document.createTextNode(scripts[i].textContent);
s.appendChild(code);
var scriptAttrs = scripts[i].attributes;
for (var j = 0; j < scriptAttrs.length; j++) {
s.setAttribute(scriptAttrs[j].name, scriptAttrs[j].value);
}
scripts[i].parentElement.replaceChild(s, scripts[i]);
}
})();
(function() {
var links = document.getElementsByTagName('a');
for (var i = 0; i < links.length; i++) {
if (/^(https?:)?\/\//.test(links[i].getAttribute('href'))) {
links[i].target = '_blank';
}
}
})();
// adds .remark-code-has-line-highlighted class to <pre> parent elements
// of code chunks containing highlighted lines with class .remark-code-line-highlighted
(function(d) {
const hlines = d.querySelectorAll('.remark-code-line-highlighted');
const preParents = [];
const findPreParent = function(line, p = 0) {
if (p > 1) return null; // traverse up no further than grandparent
const el = line.parentElement;
return el.tagName === "PRE" ? el : findPreParent(el, ++p);
};
for (let line of hlines) {
let pre = findPreParent(line);
if (pre && !preParents.includes(pre)) preParents.push(pre);
}
preParents.forEach(p => p.classList.add("remark-code-has-line-highlighted"));
})(document);</script>
<script>
slideshow._releaseMath = function(el) {
var i, text, code, codes = el.getElementsByTagName('code');
for (i = 0; i < codes.length;) {
code = codes[i];
if (code.parentNode.tagName !== 'PRE' && code.childElementCount === 0) {
text = code.textContent;
if (/^\\\((.|\s)+\\\)$/.test(text) || /^\\\[(.|\s)+\\\]$/.test(text) ||
/^\$\$(.|\s)+\$\$$/.test(text) ||
/^\\begin\{([^}]+)\}(.|\s)+\\end\{[^}]+\}$/.test(text)) {
code.outerHTML = code.innerHTML; // remove <code></code>
continue;
}