-
Notifications
You must be signed in to change notification settings - Fork 186
/
app.R
1012 lines (829 loc) · 58.4 KB
/
app.R
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
## COVID-2019 interactive mapping tool
## Edward Parker, London School of Hygiene & Tropical Medicine ([email protected]), last updated April 2020
## includes code adapted from the following sources:
# https://github.com/rstudio/shiny-examples/blob/master/087-crandash/
# https://rviews.rstudio.com/2019/10/09/building-interactive-world-maps-in-shiny/
# https://github.com/rstudio/shiny-examples/tree/master/063-superzip-example
# update data with automated script
#source("jhu_data_daily_cases.R") # option to update daily cases
source("jhu_data_weekly_cases.R") # run locally to update numbers, but not live on Rstudio server /Users/epp11/Dropbox (VERG)/GitHub/nCoV_tracker/app.R(to avoid possible errors on auto-updates)
source("ny_data_us.R") # run locally to update numbers, but not live on Rstudio server (to avoid possible errors on auto-updates)
# load required packages
if(!require(magrittr)) install.packages("magrittr", repos = "http://cran.us.r-project.org")
if(!require(rvest)) install.packages("rvest", repos = "http://cran.us.r-project.org")
if(!require(readxl)) install.packages("readxl", repos = "http://cran.us.r-project.org")
if(!require(dplyr)) install.packages("dplyr", repos = "http://cran.us.r-project.org")
if(!require(maps)) install.packages("maps", repos = "http://cran.us.r-project.org")
if(!require(ggplot2)) install.packages("ggplot2", repos = "http://cran.us.r-project.org")
if(!require(reshape2)) install.packages("reshape2", repos = "http://cran.us.r-project.org")
if(!require(ggiraph)) install.packages("ggiraph", repos = "http://cran.us.r-project.org")
if(!require(RColorBrewer)) install.packages("RColorBrewer", repos = "http://cran.us.r-project.org")
if(!require(leaflet)) install.packages("leaflet", repos = "http://cran.us.r-project.org")
if(!require(plotly)) install.packages("plotly", repos = "http://cran.us.r-project.org")
if(!require(geojsonio)) install.packages("geojsonio", repos = "http://cran.us.r-project.org")
if(!require(shiny)) install.packages("shiny", repos = "http://cran.us.r-project.org")
if(!require(shinyWidgets)) install.packages("shinyWidgets", repos = "http://cran.us.r-project.org")
if(!require(shinydashboard)) install.packages("shinydashboard", repos = "http://cran.us.r-project.org")
if(!require(shinythemes)) install.packages("shinythemes", repos = "http://cran.us.r-project.org")
# set mapping colour for each outbreak
covid_col = "#cc4c02"
covid_other_col = "#662506"
sars_col = "#045a8d"
h1n1_col = "#4d004b"
ebola_col = "#016c59"
# import data
cv_cases = read.csv("input_data/coronavirus.csv")
sars_cases = read.csv("input_data/sars.csv")
countries = read.csv("input_data/countries_codes_and_coordinates.csv")
ebola_cases = read.csv("input_data/ebola.csv")
h1n1_cases = read.csv("input_data/h1n1.csv")
worldcountry = geojson_read("input_data/50m.geojson", what = "sp")
country_geoms = read.csv("input_data/country_geoms.csv")
cv_states = read.csv("input_data/coronavirus_states.csv")
### MAP FUNCTIONS ###
# function to plot cumulative COVID cases by date
cumulative_plot = function(cv_aggregated, plot_date) {
plot_df = subset(cv_aggregated, date<=plot_date)
g1 = ggplot(plot_df, aes(x = date, y = cases, color = region)) + geom_line() + geom_point(size = 1, alpha = 0.8) +
ylab("Cumulative cases") + xlab("Date") + theme_bw() +
scale_colour_manual(values=c(covid_col)) +
scale_y_continuous(labels = function(l) {trans = l / 1000000; paste0(trans, "M")}) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10),
plot.margin = margin(5, 12, 5, 5))
g1
}
# function to plot new COVID cases by date
new_cases_plot = function(cv_aggregated, plot_date) {
plot_df_new = subset(cv_aggregated, date<=plot_date)
g1 = ggplot(plot_df_new, aes(x = date, y = new, colour = region)) + geom_line() + geom_point(size = 1, alpha = 0.8) +
# geom_bar(position="stack", stat="identity") +
ylab("New cases (weekly)") + xlab("Date") + theme_bw() +
scale_colour_manual(values=c(covid_col)) +
scale_y_continuous(labels = function(l) {trans = l / 1000000; paste0(trans, "M")}) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10),
plot.margin = margin(5, 12, 5, 5))
g1
}
# test function
#cumulative_plot(cv_aggregated, current_date)
#new_cases_plot(cv_aggregated, current_date)
# function to plot cumulative sars cases by date
sars_cumulative_plot = function(sars_aggregated, sars_date) {
plot_df = subset(sars_aggregated, date<=as.Date(sars_date, format="%Y-%m-%d"))
ggplot(plot_df, aes(x = date, y = cases)) + geom_line(colour = sars_col) + geom_point(size = 1, alpha = 0.8, colour = sars_col) +
ylab("Cumulative cases") + xlab("Date") + theme_bw() +
scale_colour_manual(values=c(sars_col)) + scale_x_date(date_labels = "%b", limits=c(sars_min_date,sars_max_date)) +
scale_y_continuous(limits=c(0,10000), labels = function(l) {trans = l / 1000000; paste0(trans, "M")}) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10),
plot.margin = margin(5, 5, 5, 5))
}
# function to plot new cases by date
sars_new_cases_plot = function(sars_aggregated, plot_date) {
plot_df_new = subset(sars_aggregated, date<=plot_date)
ggplot(plot_df_new, aes(x = date, y = new)) +
geom_bar(position="stack", stat="identity", fill = sars_col) +
ylab("New cases") + xlab("Date") + theme_bw() + ylim(0,2000) +
scale_fill_manual(values=c(sars_col)) +
xlim(c(sars_min_date,sars_max_date)) + scale_x_date(date_labels = "%b", limits=c(sars_min_date,sars_max_date)) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10),
plot.margin = margin(5, 5, 5, 5))
}
# function to plot new cases by region
country_cases_plot = function(cv_cases, start_point=c("Date", "Week of 100th confirmed case", "Week of 10th death"), plot_start_date) {
if (start_point=="Date") {
g = ggplot(cv_cases, aes(x = date, y = new_outcome, fill = region, group = 1,
text = paste0(format(date, "%d %B %Y"), "\n", region, ": ",new_outcome))) +
xlim(c(plot_start_date,(current_date+5))) + xlab("Date")
}
if (start_point=="Week of 100th confirmed case") {
cv_cases = subset(cv_cases, weeks_since_case100>0)
g = ggplot(cv_cases, aes(x = weeks_since_case100, y = new_outcome, fill = region, group = 1,
text = paste0("Week ",weeks_since_case100, "\n", region, ": ",new_outcome)))+
xlab("Weeks since 100th confirmed case") #+ xlim(c(plot_start_date,(current_date+5)))
}
if (start_point=="Week of 10th death") {
cv_cases = subset(cv_cases, weeks_since_death10>0)
g = ggplot(cv_cases, aes(x = weeks_since_death10, y = new_outcome, fill = region, group = 1,
text = paste0("Week ",weeks_since_death10, "\n", region, ": ",new_outcome))) +
xlab("Weeks since 10th death") #+ xlim(c(plot_start_date,(current_date+5)))
}
g1 = g +
geom_bar(position="stack", stat="identity") +
ylab("New (weekly)") + theme_bw() +
scale_fill_manual(values=country_cols) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10))
ggplotly(g1, tooltip = c("text")) %>% layout(legend = list(font = list(size=11)))
}
# function to plot cumulative cases by region
country_cases_cumulative = function(cv_cases, start_point=c("Date", "Week of 100th confirmed case", "Week of 10th death"), plot_start_date) {
if (start_point=="Date") {
g = ggplot(cv_cases, aes(x = date, y = outcome, colour = region, group = 1,
text = paste0(format(date, "%d %B %Y"), "\n", region, ": ",outcome))) +
xlim(c(plot_start_date,(current_date+1))) + xlab("Date")
}
if (start_point=="Week of 100th confirmed case") {
cv_cases = subset(cv_cases, weeks_since_case100>0)
g = ggplot(cv_cases, aes(x = weeks_since_case100, y = outcome, colour = region, group = 1,
text = paste0("Week ", weeks_since_case100,"\n", region, ": ",outcome))) +
xlab("Weeks since 100th confirmed case")
}
if (start_point=="Week of 10th death") {
cv_cases = subset(cv_cases, weeks_since_death10>0)
g = ggplot(cv_cases, aes(x = weeks_since_death10, y = outcome, colour = region, group = 1,
text = paste0("Week ", weeks_since_death10,"\n", region, ": ",outcome))) +
xlab("Weeks since 10th death")
}
g1 = g + geom_line(alpha=0.8) + geom_point(size = 1, alpha = 0.8) +
ylab("Cumulative") + theme_bw() +
scale_colour_manual(values=country_cols) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10))
ggplotly(g1, tooltip = c("text")) %>% layout(legend = list(font = list(size=11)))
}
# function to plot cumulative cases by region on log10 scale
country_cases_cumulative_log = function(cv_cases, start_point=c("Date", "Week of 100th confirmed case", "Week of 10th death"), plot_start_date) {
if (start_point=="Date") {
g = ggplot(cv_cases, aes(x = date, y = outcome, colour = region, group = 1,
text = paste0(format(date, "%d %B %Y"), "\n", region, ": ",outcome))) +
xlim(c(plot_start_date,(current_date+1))) + xlab("Date")
}
if (start_point=="Week of 100th confirmed case") {
cv_cases = subset(cv_cases, weeks_since_case100>0)
g = ggplot(cv_cases, aes(x = weeks_since_case100, y = outcome, colour = region, group = 1,
text = paste0("Week ",weeks_since_case100, "\n", region, ": ",outcome))) +
xlab("Weeks since 100th confirmed case")
}
if (start_point=="Week of 10th death") {
cv_cases = subset(cv_cases, weeks_since_death10>0)
g = ggplot(cv_cases, aes(x = weeks_since_death10, y = outcome, colour = region, group = 1,
text = paste0("Week ",weeks_since_death10, "\n", region, ": ",outcome))) +
xlab("Weeks since 10th death")
}
g1 = g + geom_line(alpha=0.8) + geom_point(size = 1, alpha = 0.8) +
ylab("Cumulative (log10)") + theme_bw() +
scale_y_continuous(trans="log10") +
scale_colour_manual(values=country_cols) +
theme(legend.title = element_blank(), legend.position = "", plot.title = element_text(size=10))
ggplotly(g1, tooltip = c("text")) %>% layout(legend = list(font = list(size=11)))
}
# function to render plotly of epidemic comparison depending on selected outcome
comparison_plot = function(epi_comp, comparison) {
epi_comp$outcome = epi_comp[,comparison]
epi_comp = epi_comp[order(epi_comp$outcome),]
epi_comp$outbreak = factor(epi_comp$outbreak, levels=epi_comp$outbreak)
p1 <- ggplot(epi_comp, aes(x = outbreak, y = outcome, fill=outbreak, text = paste0(outbreak, ": ",outcome))) + geom_bar(alpha = 0.8, stat="identity") +
ylab("N") + xlab("") + theme_bw() +
scale_fill_manual(values=c("2019-COVID"=covid_col, "2003-SARS"=sars_col, "2014-Ebola"=ebola_col,"2009-H1N1 (swine flu)"=h1n1_col)) +
theme(legend.position = "")
# if(comparison == "cfr") { p1 = p1 + ylab("%") }
if(comparison == "deaths") { p1 = p1 + scale_y_continuous(trans='log10', limits = c(1,1e7), breaks=c(1,1000,1e6), labels = function(l) {trans = l / 1000; paste0(trans, "K")}) }
if(comparison == "cases") { p1 = p1 + scale_y_continuous(trans='log10', limits = c(1,1e10), breaks=c(1,1000,1e6,1e9), labels = function(l) {trans = l / 1000; paste0(trans, "K")}) }
ggplotly(p1 + coord_flip(), tooltip = c("text")) %>% layout(showlegend = FALSE)
}
### DATA PROCESSING: COVID-19 ###
# extract time stamp from cv_cases
update = tail(cv_cases$last_update,1)
# check consistency of country names across datasets
if (all(unique(cv_cases$country) %in% unique(countries$country))==FALSE) { print("Error: inconsistent country names")}
# extract dates from cv data
if (any(grepl("/", cv_cases$date))) {
cv_cases$date = format(as.Date(cv_cases$date, format="%d/%m/%Y"),"%Y-%m-%d")
} else { cv_cases$date = as.Date(cv_cases$date, format="%Y-%m-%d") }
cv_cases$date = as.Date(cv_cases$date)
cv_min_date = as.Date(min(cv_cases$date),"%Y-%m-%d")
current_date = as.Date(max(cv_cases$date),"%Y-%m-%d")
cv_max_date_clean = format(as.POSIXct(current_date),"%d %B %Y")
# merge cv data with country data and extract key summary variables
cv_cases = merge(cv_cases, countries, by = "country")
cv_cases = cv_cases[order(cv_cases$date),]
cv_cases$cases_per_million = as.numeric(format(round(cv_cases$cases/(cv_cases$population/1000000),1),nsmall=1))
cv_cases$new_cases_per_million = as.numeric(format(round(cv_cases$new_cases/(cv_cases$population/1000000),1),nsmall=1))
cv_cases$million_pop = as.numeric(cv_cases$population>1e6)
cv_cases$deaths_per_million = as.numeric(format(round(cv_cases$deaths/(cv_cases$population/1000000),1),nsmall=1))
cv_cases$new_deaths_per_million = as.numeric(format(round(cv_cases$new_deaths/(cv_cases$population/1000000),1),nsmall=1))
# add variable for weeks since 100th case and 10th death
cv_cases$weeks_since_case100 = cv_cases$weeks_since_death10 = 0
for (i in 1:length(unique(cv_cases$country))) {
country_name = as.character(unique(cv_cases$country))[i]
country_db = subset(cv_cases, country==country_name)
country_db$weeks_since_case100[country_db$cases>=100] = 0:(sum(country_db$cases>=100)-1)
country_db$weeks_since_death10[country_db$deaths>=10] = 0:(sum(country_db$deaths>=10)-1)
cv_cases$weeks_since_case100[cv_cases$country==country_name] = country_db$weeks_since_case100
cv_cases$weeks_since_death10[cv_cases$country==country_name] = country_db$weeks_since_death10
}
# creat variable for today's data
cv_today = subset(cv_cases, date==current_date)
current_case_count = sum(cv_today$cases)
current_case_count_China = sum(cv_today$cases[cv_today$country=="Mainland China"])
current_case_count_other = sum(cv_today$cases[cv_today$country!="Mainland China"])
current_death_count = sum(cv_today$deaths)
# create subset of state data for today's data
if (any(grepl("/", cv_states$date))) {
cv_states$date = format(as.Date(cv_states$date, format="%d/%m/%Y"),"%Y-%m-%d")
} else { cv_states$date = as.Date(cv_states$date, format="%Y-%m-%d") }
cv_states_today = subset(cv_states, date==max(cv_states$date))
# create subset for countries with at least 1000 cases
cv_today_reduced = subset(cv_today, cases>=1000)
# write current day's data
write.csv(cv_today %>% select(c(country, date, update, cases, new_cases, deaths, new_deaths,
cases_per_million, new_cases_per_million,
deaths_per_million, new_deaths_per_million,
weeks_since_case100, weeks_since_death10)), "input_data/coronavirus_today.csv")
# aggregate at continent level
cv_cases_continent = subset(cv_cases, !is.na(continent_level)) %>% select(c(cases, new_cases, deaths, new_deaths, date, continent_level)) %>% group_by(continent_level, date) %>% summarise_each(funs(sum)) %>% data.frame()
# add variable for weeks since 100th case and 10th death
cv_cases_continent$weeks_since_case100 = cv_cases_continent$weeks_since_death10 = 0
cv_cases_continent$continent = cv_cases_continent$continent_level
for (i in 1:length(unique(cv_cases_continent$continent))) {
continent_name = as.character(unique(cv_cases_continent$continent))[i]
continent_db = subset(cv_cases_continent, continent==continent_name)
continent_db$weeks_since_case100[continent_db$cases>=100] = 0:(sum(continent_db$cases>=100)-1)
continent_db$weeks_since_death10[continent_db$deaths>=10] = 0:(sum(continent_db$deaths>=10)-1)
cv_cases_continent$weeks_since_case100[cv_cases_continent$continent==continent_name] = continent_db$weeks_since_case100
cv_cases_continent$weeks_since_death10[cv_cases_continent$continent==continent_name] = continent_db$weeks_since_death10
}
# add continent populations
cv_cases_continent$pop = NA
cv_cases_continent$pop[cv_cases_continent$continent=="Africa"] = 1.2e9
cv_cases_continent$pop[cv_cases_continent$continent=="Asia"] = 4.5e9
cv_cases_continent$pop[cv_cases_continent$continent=="Europe"] = 7.4e8
cv_cases_continent$pop[cv_cases_continent$continent=="North America"] = 5.8e8
cv_cases_continent$pop[cv_cases_continent$continent=="Oceania"] = 3.8e7
cv_cases_continent$pop[cv_cases_continent$continent=="South America"] = 4.2e8
# add normalised counts
cv_cases_continent$cases_per_million = as.numeric(format(round(cv_cases_continent$cases/(cv_cases_continent$pop/1000000),1),nsmall=1))
cv_cases_continent$new_cases_per_million = as.numeric(format(round(cv_cases_continent$new_cases/(cv_cases_continent$pop/1000000),1),nsmall=1))
cv_cases_continent$deaths_per_million = as.numeric(format(round(cv_cases_continent$deaths/(cv_cases_continent$pop/1000000),1),nsmall=1))
cv_cases_continent$new_deaths_per_million = as.numeric(format(round(cv_cases_continent$new_deaths/(cv_cases_continent$pop/1000000),1),nsmall=1))
write.csv(cv_cases_continent, "input_data/coronavirus_continent.csv")
# aggregate at global level
cv_cases_global = cv_cases %>% select(c(cases, new_cases, deaths, new_deaths, date, global_level)) %>% group_by(global_level, date) %>% summarise_each(funs(sum)) %>% data.frame()
cv_cases_global$weeks_since_case100 = cv_cases_global$weeks_since_death10 = 0:(nrow(cv_cases_global)-1)
# add normalised counts
cv_cases_global$pop = 7.6e9
cv_cases_global$cases_per_million = as.numeric(format(round(cv_cases_global$cases/(cv_cases_global$pop/1000000),1),nsmall=1))
cv_cases_global$new_cases_per_million = as.numeric(format(round(cv_cases_global$new_cases/(cv_cases_global$pop/1000000),1),nsmall=1))
cv_cases_global$deaths_per_million = as.numeric(format(round(cv_cases_global$deaths/(cv_cases_global$pop/1000000),1),nsmall=1))
cv_cases_global$new_deaths_per_million = as.numeric(format(round(cv_cases_global$new_deaths/(cv_cases_global$pop/1000000),1),nsmall=1))
write.csv(cv_cases_global, "input_data/coronavirus_global.csv")
# select large countries for mapping polygons
cv_large_countries = cv_today %>% filter(alpha3 %in% worldcountry$ADM0_A3)
if (all(cv_large_countries$alpha3 %in% worldcountry$ADM0_A3)==FALSE) { print("Error: inconsistent country names")}
cv_large_countries = cv_large_countries[order(cv_large_countries$alpha3),]
# create plotting parameters for map
bins = c(0,10,50,100,500,1000,Inf)
cv_pal <- colorBin("Oranges", domain = cv_large_countries$cases_per_million, bins = bins)
plot_map <- worldcountry[worldcountry$ADM0_A3 %in% cv_large_countries$alpha3, ]
# creat cv base map
basemap = leaflet(plot_map) %>%
addTiles() %>%
addLayersControl(
position = "bottomright",
overlayGroups = c("2019-COVID (new)", "2019-COVID (cumulative)", "2003-SARS", "2009-H1N1 (swine flu)", "2014-Ebola"),
options = layersControlOptions(collapsed = FALSE)) %>%
hideGroup(c("2019-COVID (cumulative)", "2003-SARS", "2009-H1N1 (swine flu)", "2014-Ebola")) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
fitBounds(~-100,-60,~60,70) %>%
addLegend("bottomright", pal = cv_pal, values = ~cv_large_countries$deaths_per_million,
title = "<small>Deaths per million</small>")
# sum cv case counts by date
cv_aggregated = aggregate(cv_cases$cases, by=list(Category=cv_cases$date), FUN=sum)
names(cv_aggregated) = c("date", "cases")
# add variable for new cases in last 7 days
for (i in 1:nrow(cv_aggregated)) {
if (i==1) { cv_aggregated$new[i] = 0 }
if (i>1) { cv_aggregated$new[i] = cv_aggregated$cases[i] - cv_aggregated$cases[i-1] }
}
# add plotting region
cv_aggregated$region = "Global"
cv_aggregated$date = as.Date(cv_aggregated$date,"%Y-%m-%d")
# assign colours to countries to ensure consistency between plots
cls = rep(c(brewer.pal(8,"Dark2"), brewer.pal(10, "Paired"), brewer.pal(12, "Set3"), brewer.pal(8,"Set2"), brewer.pal(9, "Set1"), brewer.pal(8, "Accent"), brewer.pal(9, "Pastel1"), brewer.pal(8, "Pastel2")),4)
cls_names = c(as.character(unique(cv_cases$country)), as.character(unique(cv_cases_continent$continent)), as.character(unique(cv_states$state)),"Global")
country_cols = cls[1:length(cls_names)]
names(country_cols) = cls_names
### DATA PROCESSING: SARS ###
# extract dates from sars data
sars_cases$date = as.Date(sars_cases$date, format="%d/%m/%Y")
sars_min_date = min(sars_cases$date)
sars_max_date = max(sars_cases$date)
sars_max_date_clean = format(as.POSIXct(sars_max_date),"%d %B %Y")
# merge sars data with country data and extract key summary variables
sars_cases = merge(sars_cases, countries, by = "country")
sars_cases = sars_cases[order(sars_cases$date),]
sars_cases$cases_per_million = as.numeric(format(round(sars_cases$cases/(sars_cases$population/1000000),1),nsmall=1))
sars_final = subset(sars_cases, date==sars_max_date)
sars_final_case_count = sum(sars_final$cases)
# select polygons for sars base map
sars_large_countries = sars_final %>% filter(country %in% country_geoms$countries_present)
sars_large_countries = sars_large_countries[order(sars_large_countries$alpha3),]
sars_plot_map <- worldcountry[worldcountry$ADM0_A3 %in% sars_large_countries$alpha3, ]
# create plotting parameters for sars map
sars_pal <- colorBin("Blues", domain = sars_large_countries$cases_per_million, bins = bins)
# creat sars interactive map (needs to include polygons and circles as slider input not recognised upon initial loading)
sars_basemap = leaflet(sars_plot_map) %>%
addTiles() %>%
addLayersControl(
position = "bottomright",
overlayGroups = c("2003-SARS (cumulative)", "2019-COVID", "2009-H1N1 (swine flu)", "2014-Ebola"),
options = layersControlOptions(collapsed = FALSE)) %>%
hideGroup(c("2019-COVID", "2009-H1N1 (swine flu)", "2014-Ebola")) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
fitBounds(~-100,-60,~60,70) %>%
addPolygons(stroke = FALSE, smoothFactor = 0.2, fillOpacity = 0.4, fillColor = ~sars_pal(sars_large_countries$cases_per_million), group = "2003-SARS (cumulative)",
label = sprintf("<strong>%s</strong><br/>SARS cases: %g<br/>Deaths: %d<br/>Cases per million: %g", sars_large_countries$country, sars_large_countries$cases, sars_large_countries$deaths, sars_large_countries$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = sars_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = sars_final, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/4),
fillOpacity = 0.2, color = sars_col, group = "2003-SARS (cumulative)",
label = sprintf("<strong>%s</strong><br/>SARS cases: %g<br/>Deaths: %d<br/>Cases per million: %g", sars_final$country, sars_final$cases, sars_final$deaths, sars_final$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = sars_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = cv_today, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/5.5),
fillOpacity = 0.2, color = covid_col, group = "2019-COVID",
label = sprintf("<strong>%s (cumulative)</strong><br/>Confirmed cases: %g<br/>Deaths: %d<br/>Cases per million: %g", cv_today$country, cv_today$cases, cv_today$deaths, cv_today$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = covid_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = h1n1_cases, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(projected_deaths)^(1/4),
fillOpacity = 0.2, color = h1n1_col, group = "2009-H1N1 (swine flu)",
label = sprintf("<strong>%s</strong><br/>H1N1 deaths (confirmed): %g<br/>H1N1 deaths (estimated): %g", h1n1_cases$region, h1n1_cases$deaths, h1n1_cases$projected_deaths) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = h1n1_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = ebola_cases, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/4),
fillOpacity = 0.2, color = ebola_col, group = "2014-Ebola",
label = sprintf("<strong>%s</strong><br/>Ebola cases: %g<br/>Deaths: %d", ebola_cases$country, ebola_cases$cases, ebola_cases$deaths) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = ebola_col),
textsize = "15px", direction = "auto"))
# sum sars case counts by date
sars_aggregated = aggregate(sars_cases$cases, by=list(Category=sars_cases$date), FUN=sum)
names(sars_aggregated) = c("date", "cases")
# add variable for new sars cases in last 7 days
for (i in 1:nrow(sars_aggregated)) {
if (i==1) { sars_aggregated$new[i] = NA }
if (i>1) {
sars_aggregated$new[i] = sars_aggregated$cases[i] - sars_aggregated$cases[i-1]
}
}
sars_aggregated$new[sars_aggregated$new<0] = 0
### OUTBREAK COMPARISON DATA ###
# load epidemic comparison data
epi_comp = as.data.frame(data.table::fread("input_data/epi_comp.csv"))
epi_comp$outbreak = factor(epi_comp$outbreak, levels = epi_comp$outbreak)
epi_comp$cases[1] = current_case_count
epi_comp$deaths[1] = current_death_count
epi_comp$countries[1] = nrow(subset(cv_today, country!="Diamond Princess Cruise Ship"))
#epi_comp$cfr[1] = round(epi_comp$deaths[1]/epi_comp$cases[1]*100,1)
#epi_comp$cfr = round(epi_comp$cfr,2)
### SHINY UI ###
ui <- bootstrapPage(
tags$head(includeHTML("gtag.html")),
navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
HTML('<a style="text-decoration:none;cursor:default;color:#FFFFFF;" class="active" href="#">COVID-19 tracker</a>'), id="nav",
windowTitle = "COVID-19 tracker",
tabPanel("COVID-19 mapper",
div(class="outer",
tags$head(includeCSS("styles.css")),
leafletOutput("mymap", width="100%", height="100%"),
absolutePanel(id = "controls", class = "panel panel-default",
top = 75, left = 55, width = 250, fixed=TRUE,
draggable = TRUE, height = "auto",
span(tags$i(h6("Reported cases are subject to significant variation in testing policy and capacity between countries.")), style="color:#045a8d"),
h3(textOutput("reactive_case_count"), align = "right"),
h4(textOutput("reactive_death_count"), align = "right"),
h6(textOutput("clean_date_reactive"), align = "right"),
h6(textOutput("reactive_country_count"), align = "right"),
plotOutput("epi_curve", height="130px", width="100%"),
plotOutput("cumulative_plot", height="130px", width="100%"),
sliderTextInput("plot_date",
label = h5("Select mapping date"),
choices = format(unique(cv_cases$date), "%d %b %y"),
selected = format(current_date, "%d %b %y"),
grid = FALSE,
animate=animationOptions(interval = 3000, loop = FALSE))
),
absolutePanel(id = "logo", class = "card", bottom = 20, left = 60, width = 80, fixed=TRUE, draggable = FALSE, height = "auto",
tags$a(href='https://www.lshtm.ac.uk', tags$img(src='lshtm_dark.png',height='40',width='80'))),
absolutePanel(id = "logo", class = "card", bottom = 20, left = 20, width = 30, fixed=TRUE, draggable = FALSE, height = "auto",
actionButton("twitter_share", label = "", icon = icon("twitter"),style='padding:5px',
onclick = sprintf("window.open('%s')",
"https://twitter.com/intent/tweet?text=%20@LSHTM_Vaccines%20outbreak%20mapper&url=https://bit.ly/2uBvnds&hashtags=coronavirus")))
)
),
tabPanel("Region plots",
sidebarLayout(
sidebarPanel(
span(tags$i(h6("Reported cases are subject to significant variation in testing policy and capacity between countries.")), style="color:#045a8d"),
span(tags$i(h6("Occasional anomalies (e.g. spikes in daily case counts) are generally caused by changes in case definitions.")), style="color:#045a8d"),
pickerInput("level_select", "Level:",
choices = c("Global", "Continent", "Country", "US state"),
selected = c("Country"),
multiple = FALSE),
pickerInput("region_select", "Country/Region:",
choices = as.character(cv_today_reduced[order(-cv_today_reduced$cases),]$country),
options = list(`actions-box` = TRUE, `none-selected-text` = "Please make a selection!"),
selected = as.character(cv_today_reduced[order(-cv_today_reduced$cases),]$country)[1:10],
multiple = TRUE),
pickerInput("outcome_select", "Outcome:",
choices = c("Deaths per million", "Cases per million", "Cases (total)", "Deaths (total)"),
selected = c("Deaths per million"),
multiple = FALSE),
pickerInput("start_date", "Plotting start date:",
choices = c("Date", "Week of 100th confirmed case", "Week of 10th death"),
options = list(`actions-box` = TRUE),
selected = "Date",
multiple = FALSE),
sliderInput("minimum_date",
"Minimum date:",
min = as.Date(cv_min_date,"%Y-%m-%d"),
max = as.Date(current_date,"%Y-%m-%d"),
value=as.Date(cv_min_date),
timeFormat="%d %b"),
"Select outcome, regions, and plotting start date from drop-down menues to update plots. Countries with at least 1000 confirmed cases are included."
),
mainPanel(
tabsetPanel(
tabPanel("Cumulative", plotlyOutput("country_plot_cumulative")),
tabPanel("New", plotlyOutput("country_plot")),
tabPanel("Cumulative (log10)", plotlyOutput("country_plot_cumulative_log"))
)
)
)
),
tabPanel("SARS mapper",
div(class="outer",
tags$head(includeCSS("styles.css")),
leafletOutput("sars_map", width="100%", height="100%"),
absolutePanel(id = "controls", class = "panel panel-default",
top = 75, left = 55, width = 250, fixed=TRUE,
draggable = TRUE, height = "auto",
h3(textOutput("sars_reactive_case_count"), align = "right"),
h4(textOutput("sars_reactive_death_count"), align = "right"),
h6(textOutput("sars_clean_date_reactive"), align = "right"),
h6(textOutput("sars_reactive_country_count"), align = "right"),
plotOutput("sars_epi_curve", height="130px", width="100%"),
plotOutput("sars_cumulative_plot", height="130px", width="100%"),
span(("The final count appears to decrease as several cases initially classified as SARS were later re-assigned."),align = "left", style = "font-size:80%"),#tags$br(),
span(("Circles show confirmed cases for COVID, SARS, and Ebola, and estimated deaths for H1N1."),align = "left", style = "font-size:80%"),
sliderTextInput("sars_plot_date",
label = h5("Select mapping date"),
choices = format(unique(sars_cases$date), "%d %b %y"),
selected = format(sars_max_date, "%d %b %y"),
grid = FALSE,
animate=animationOptions(interval = 3000, loop = FALSE))
),
absolutePanel(id = "logo", class = "card", bottom = 15, left = 60, width = 80, fixed=TRUE, draggable = FALSE, height = "auto",
tags$a(href='https://www.lshtm.ac.uk', tags$img(src='lshtm_dark.png',height='40',width='80'))),
absolutePanel(id = "logo", class = "card", bottom = 15, left = 20, width = 30, fixed=TRUE, draggable = FALSE, height = "auto",
actionButton("twitter_share", label = "", icon = icon("twitter"),style='padding:5px',
onclick = sprintf("window.open('%s')",
"https://twitter.com/intent/tweet?text=%20@LSHTM_Vaccines%20outbreak%20mapper&url=https://bit.ly/2uBvnds&hashtags=coronavirus")))
)
),
tabPanel("Outbreak comparisons",
sidebarLayout(
sidebarPanel(
radioButtons("comparison_metric", h3("Select comparison:"),
c("Cases" = "cases",
"Deaths" = "deaths",
"Countries/regions affected" = "countries")),
textOutput("epi_notes_1"),
textOutput("epi_notes_2")
),
mainPanel(plotlyOutput("comparison_plot"), width = 6)
)
),
tabPanel("Data",
numericInput("maxrows", "Rows to show", 25),
verbatimTextOutput("rawtable"),
downloadButton("downloadCsv", "Download as CSV"),tags$br(),tags$br(),
"Adapted from timeline data published by ", tags$a(href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series",
"Johns Hopkins Center for Systems Science and Engineering.")
),
tabPanel("About this site",
tags$div(
tags$h4("Last update"),
h6(paste0(update)),
"This site is updated once daily. There are several other excellent COVID mapping tools available, including those run by",
tags$a(href="https://experience.arcgis.com/experience/685d0ace521648f8a5beeeee1b9125cd", "the WHO,"),
tags$a(href="https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6", "Johns Hopkins University,"),"and",
tags$a(href="https://ourworldindata.org/coronavirus-data-explorer?zoomToSelection=true&time=2020-03-01..latest&country=IND~USA~GBR~CAN~DEU~FRA®ion=World&casesMetric=true&interval=smoothed&perCapita=true&smoothing=7&pickerMetric=total_cases&pickerSort=desc", "Our World in Data."),
"Our aim is to complement these resources with several interactive features, including the timeline function and the ability to overlay past outbreaks.",
tags$br(),tags$br(),tags$h4("Background"),
"In December 2019, cases of severe respiratory illness began to be reported across the city of Wuhan in China.
These were caused by a new type of coronavirus, and the disease is now commonly referred to as COVID-19.
The number of COVID-19 cases started to escalate more quickly in mid-January and the virus soon spread beyond China's borders.
This story has been rapidly evolving ever since, and each day we are faced by worrying headlines regarding the current state of the outbreak.",
tags$br(),tags$br(),
"In isolation, these headlines can be hard to interpret.
How fast is the virus spreading? Are efforts to control the disease working? How does the situation compare with previous epidemics?
This site is updated daily based on data published by Johns Hopkins University.
By looking beyond the headlines, we hope it is possible to get a deeper understanding of this unfolding pandemic.",
tags$br(),tags$br(),
"An article discussing this site was published in ",tags$a(href="https://theconversation.com/coronavirus-outbreak-a-new-mapping-tool-that-lets-you-scroll-through-timeline-131422", "The Conversation. "),
"The map was also featured on the BBC World Service program",tags$a(href="https://www.bbc.co.uk/programmes/w3csym33", "Science in Action."),
tags$br(),tags$br(),tags$h4("Code"),
"Code and input data used to generate this Shiny mapping tool are available on ",tags$a(href="https://github.com/eparker12/nCoV_tracker", "Github."),
tags$br(),tags$br(),tags$h4("Sources"),
tags$b("2019-COVID cases: "), tags$a(href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", "Johns Hopkins Center for Systems Science and Engineering github page,")," with additional information from the ",tags$a(href="https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports", "WHO's COVID-19 situation reports."),
" In previous versions of this site (up to 17th March 2020), updates were based solely on the WHO's situation reports.",tags$br(),
tags$b("US state-level case data: "), tags$a(href="https://github.com/nytimes/covid-19-data", "New York Times github page,"),
tags$b("2003-SARS cases: "), tags$a(href="https://www.who.int/csr/sars/country/en/", "WHO situation reports"),tags$br(),
tags$b("2009-H1N1 confirmed deaths: "), tags$a(href="https://www.who.int/csr/disease/swineflu/updates/en/", "WHO situation reports"),tags$br(),
tags$b("2009-H1N1 projected deaths: "), "Model estimates from ", tags$a(href="https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001558", "GLaMOR Project"),tags$br(),
tags$b("2009-H1N1 cases: "), tags$a(href="https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html", "CDC"),tags$br(),
tags$b("2009-H1N1 case fatality rate: "), "a systematic review by ", tags$a(href="https://www.ncbi.nlm.nih.gov/pubmed/24045719", "Wong et al (2009)"), "identified
substantial variation in case fatality rate estimates for the H1N1 pandemic. However, most were in the range of 10 to 100 per 100,000 symptomatic cases (0.01 to 0.1%).
The upper limit of this range is used for illustrative purposes in the Outbreak comarisons tab.",tags$br(),
tags$b("2014-Ebola cases: "), tags$a(href="https://www.cdc.gov/flu/pandemic-resources/2009-h1n1-pandemic.html", "CDC"),tags$br(),
tags$b("Country mapping coordinates: "), tags$a(href="https://github.com/martynafford/natural-earth-geojson", "Martyn Afford's Github repository"),
tags$br(),tags$br(),tags$h4("Authors"),
"Dr Edward Parker, The Vaccine Centre, London School of Hygiene & Tropical Medicine",tags$br(),
"Quentin Leclerc, Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine",tags$br(),
tags$br(),tags$br(),tags$h4("Contact"),
"[email protected]",tags$br(),tags$br(),
tags$img(src = "vac_dark.png", width = "150px", height = "75px"), tags$img(src = "lshtm_dark.png", width = "150px", height = "75px")
)
)
)
)
### SHINY SERVER ###
server = function(input, output, session) {
# covid tab
formatted_date = reactive({
format(as.Date(input$plot_date, format="%d %b %y"), "%Y-%m-%d")
})
output$clean_date_reactive <- renderText({
format(as.POSIXct(formatted_date()),"%d %B %Y")
})
reactive_db = reactive({
cv_cases %>% filter(date == formatted_date())
})
reactive_db_last7d = reactive({
cv_cases %>% filter(date == formatted_date() & new_cases>0)
})
reactive_db_large = reactive({
large_countries = reactive_db() %>% filter(alpha3 %in% worldcountry$ADM0_A3)
#large_countries = reactive %>% filter(alpha3 %in% worldcountry$ADM0_A3)
worldcountry_subset = worldcountry[worldcountry$ADM0_A3 %in% large_countries$alpha3, ]
large_countries = large_countries[match(worldcountry_subset$ADM0_A3, large_countries$alpha3),]
large_countries
})
reactive_db_large_last7d = reactive({
large_countries = reactive_db_last7d() %>% filter(alpha3 %in% worldcountry$ADM0_A3)
large_countries = large_countries[order(large_countries$alpha3),]
large_countries
})
reactive_polygons = reactive({
worldcountry[worldcountry$ADM0_A3 %in% reactive_db_large()$alpha3, ]
})
reactive_polygons_last7d = reactive({
worldcountry[worldcountry$ADM0_A3 %in% reactive_db_large_last7d()$alpha3, ]
})
output$reactive_case_count <- renderText({
paste0(prettyNum(sum(reactive_db()$cases), big.mark=","), " cases")
})
output$reactive_death_count <- renderText({
paste0(prettyNum(sum(reactive_db()$deaths), big.mark=","), " deaths")
})
output$reactive_country_count <- renderText({
paste0(nrow(subset(reactive_db(), country!="Diamond Princess Cruise Ship")), " countries/regions affected")
})
output$reactive_new_cases_7d <- renderText({
paste0(round((cv_aggregated %>% filter(date == formatted_date() & region=="Global"))$new/7,0), " 7-day average")
})
output$mymap <- renderLeaflet({
basemap
})
observeEvent(input$plot_date, {
leafletProxy("mymap") %>%
clearMarkers() %>%
clearShapes() %>%
addCircleMarkers(data = reactive_db(), lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/5.5),
fillOpacity = 0.1, color = covid_col, group = "2019-COVID (cumulative)",
label = sprintf("<strong>%s (cumulative)</strong><br/>Confirmed cases: %g<br/>Deaths: %d<br/>Cases per million: %g<br/>Deaths per million: %g", reactive_db()$country, reactive_db()$cases, reactive_db()$deaths, reactive_db()$cases_per_million, reactive_db()$deaths_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = covid_col),
textsize = "15px", direction = "auto")) %>%
addPolygons(data = reactive_polygons(), stroke = FALSE, smoothFactor = 0.1, fillOpacity = 0.15, fillColor = ~cv_pal(reactive_db_large()$deaths_per_million)) %>%
addCircleMarkers(data = reactive_db_last7d(), lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(new_cases)^(1/5.5),
fillOpacity = 0.1, color = covid_col, group = "2019-COVID (new)",
label = sprintf("<strong>%s (7-day average)</strong><br/>Confirmed cases: %g<br/>Deaths: %d<br/>Cases per million: %g<br/>Deaths per million: %g", reactive_db_last7d()$country, round(reactive_db_last7d()$new_cases/7,0), round(reactive_db_last7d()$new_deaths/7,0), round(reactive_db_last7d()$new_cases_per_million/7,1), round(reactive_db_last7d()$new_deaths_per_million/7,1)) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = covid_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = sars_final, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/4),
fillOpacity = 0.2, color = sars_col, group = "2003-SARS",
label = sprintf("<strong>%s</strong><br/>SARS cases: %g<br/>Deaths: %d<br/>Cases per million: %g", sars_final$country, sars_final$cases, sars_final$deaths, sars_final$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = sars_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = h1n1_cases, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(projected_deaths)^(1/4),
fillOpacity = 0.2, color = h1n1_col, group = "2009-H1N1 (swine flu)",
label = sprintf("<strong>%s</strong><br/>H1N1 deaths (confirmed): %g<br/>H1N1 deaths (estimated): %g", h1n1_cases$region, h1n1_cases$deaths, h1n1_cases$projected_deaths) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = h1n1_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = ebola_cases, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/4),
fillOpacity = 0.2, color = ebola_col, group = "2014-Ebola",
label = sprintf("<strong>%s</strong><br/>Ebola cases: %g<br/>Deaths: %d", ebola_cases$country, ebola_cases$cases, ebola_cases$deaths) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = ebola_col),
textsize = "15px", direction = "auto"))
})
output$cumulative_plot <- renderPlot({
cumulative_plot(cv_aggregated, formatted_date())
})
output$epi_curve <- renderPlot({
new_cases_plot(cv_aggregated, formatted_date())
})
# sars tab
sars_mod_date = reactive({
format(as.Date(input$sars_plot_date, format="%d %b %y"), "%Y-%m-%d")
})
output$sars_clean_date_reactive <- renderText({
format(as.POSIXct(sars_mod_date()),"%d %B %Y")
})
sars_reactive_db = reactive({
sars_cases %>% filter(date == sars_mod_date())
})
sars_reactive_db_large = reactive({
large_countries = sars_reactive_db() %>% filter(country!="Singapore" & country!="Diamond Princess Cruise Ship" & country!="Hong Kong" & country!="Macao")
large_countries = large_countries[order(large_countries$alpha3),]
large_countries
})
sars_reactive_polygons = reactive({
worldcountry[worldcountry$ADM0_A3 %in% sars_reactive_db_large()$alpha3, ]
})
output$sars_reactive_case_count <- renderText({
paste0(sum(sars_reactive_db()$cases), " cases")
})
output$sars_reactive_death_count <- renderText({
paste0(sum(sars_reactive_db()$deaths), " deaths")
})
output$sars_reactive_country_count <- renderText({
paste0(length(unique(sars_reactive_db()$country_group)), " countries/territories affected")
})
output$sars_map <- renderLeaflet({
sars_basemap
})
observeEvent(input$sars_plot_date, {
leafletProxy("sars_map") %>%
clearMarkers() %>%
clearShapes() %>%
addPolygons(data = sars_reactive_polygons(), stroke = FALSE, smoothFactor = 0.2, fillOpacity = 0.1, fillColor = ~sars_pal(sars_reactive_db_large()$cases_per_million), group = "2003-SARS (cumulative)",
label = sprintf("<strong>%s</strong><br/>SARS cases: %g<br/>Deaths: %d<br/>Cases per million: %g", sars_reactive_db_large()$country, sars_reactive_db_large()$cases, sars_reactive_db_large()$deaths, sars_reactive_db_large()$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = sars_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = sars_reactive_db(), lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/4),
fillOpacity = 0.2, color = sars_col, group = "2003-SARS (cumulative)",
label = sprintf("<strong>%s</strong><br/>SARS cases: %g<br/>Deaths: %d<br/>Cases per million: %g", sars_reactive_db()$country, sars_reactive_db()$cases, sars_reactive_db()$deaths, sars_reactive_db()$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = sars_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = cv_today, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/5.5),
fillOpacity = 0.1, color = covid_col, group = "2019-COVID",
label = sprintf("<strong>%s (cumulative)</strong><br/>Confirmed cases: %g<br/>Deaths: %d<br/>Cases per million: %g", cv_today$country, cv_today$cases, cv_today$deaths, cv_today$cases_per_million) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = covid_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = h1n1_cases, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(projected_deaths)^(1/4),
fillOpacity = 0.2, color = h1n1_col, group = "2009-H1N1 (swine flu)",
label = sprintf("<strong>%s</strong><br/>H1N1 deaths (confirmed): %g<br/>H1N1 deaths (estimated): %g", h1n1_cases$region, h1n1_cases$deaths, h1n1_cases$projected_deaths) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = h1n1_col),
textsize = "15px", direction = "auto")) %>%
addCircleMarkers(data = ebola_cases, lat = ~ latitude, lng = ~ longitude, weight = 1, radius = ~(cases)^(1/4),
fillOpacity = 0.2, color = ebola_col, group = "2014-Ebola",
label = sprintf("<strong>%s</strong><br/>Ebola cases: %g<br/>Deaths: %d", ebola_cases$country, ebola_cases$cases, ebola_cases$deaths) %>% lapply(htmltools::HTML),
labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px", "color" = ebola_col),
textsize = "15px", direction = "auto"))
})
output$sars_cumulative_plot <- renderPlot({
sars_cumulative_plot(sars_aggregated, sars_mod_date())
})
output$sars_epi_curve <- renderPlot({
sars_new_cases_plot(sars_aggregated, sars_mod_date())
})
# comparison plot
output$comparison_plot <- renderPlotly({
comparison_plot(epi_comp, input$comparison_metric)
})
# add footnote for cases
output$epi_notes_1 <- renderText({
if(input$comparison_metric=="cases") { paste0("Note that the axis is on a log10 scale so moves in 10-fold increments.") }
})
# add footnote for deaths
output$epi_notes_2 <- renderText({
if(input$comparison_metric=="deaths") {
paste0("Note that the axis is on a log10 scale so moves in 10-fold increments. For H1N1, the number of laboratory-confirmed deaths reported by the WHO is displayed. Subsequent modelling studies have estimated the actual number to be in the range of 123,000 to 203,000.")
}
})
# add note for cfr
# output$epi_notes_3 <- renderText({
# if(input$comparison_metric=="cfr") {
# paste0("For COVID-19, this displays the proportion of confirmed cases who have subsequently died. When factoring in mild or asymptomatic infections that are not picked up by case surveillance efforts, current estimates place the case fatality rate in the range of 0.3-1%.")
# }
# })
# update region selections
observeEvent(input$level_select, {
if (input$level_select=="Global") {
updatePickerInput(session = session, inputId = "region_select",
choices = "Global", selected = "Global")
}
if (input$level_select=="Continent") {
updatePickerInput(session = session, inputId = "region_select",
choices = c("Africa", "Asia", "Europe", "North America", "South America"),
selected = c("Africa", "Asia", "Europe", "North America", "South America"))
}
if (input$level_select=="US state") {
updatePickerInput(session = session, inputId = "region_select",
choices = as.character(cv_states_today[order(-cv_states_today$cases),]$state),
selected = as.character(cv_states_today[order(-cv_states_today$cases),]$state)[1:10])
}
if (input$level_select=="Country") {
updatePickerInput(session = session, inputId = "region_select",
choices = as.character(cv_today_reduced[order(-cv_today_reduced$cases),]$country),
selected = as.character(cv_states_today[order(-cv_states_today$cases),]$state)[1:10])
}
}, ignoreInit = TRUE)
# create dataframe with selected countries
country_reactive_db = reactive({
if (input$level_select=="Global") {
db = cv_cases_global
db$region = db$global_level
}
if (input$level_select=="Continent") {
db = cv_cases_continent
db$region = db$continent
}
if (input$level_select=="Country") {
db = cv_cases
db$region = db$country
}
if (input$level_select=="US state") {
db = cv_states
db$region = db$state
}
if (input$outcome_select=="Cases (total)") {
db$outcome = db$cases
db$new_outcome = db$new_cases
}
if (input$outcome_select=="Deaths (total)") {
db$outcome = db$deaths
db$new_outcome = db$new_deaths
}
if (input$outcome_select=="Cases per million") {
db$outcome = db$cases_per_million
db$new_outcome = db$new_cases_per_million
}
if (input$outcome_select=="Deaths per million") {
db$outcome = db$deaths_per_million
db$new_outcome = db$new_deaths_per_million
}
db %>% filter(region %in% input$region_select)
})
# country-specific plots
output$country_plot <- renderPlotly({
country_cases_plot(country_reactive_db(), start_point=input$start_date, input$minimum_date)
})
# country-specific plots
output$country_plot_cumulative <- renderPlotly({
country_cases_cumulative(country_reactive_db(), start_point=input$start_date, input$minimum_date)
})
# country-specific plots
output$country_plot_cumulative_log <- renderPlotly({
country_cases_cumulative_log(country_reactive_db(), start_point=input$start_date, input$minimum_date)
})
# output to download data
output$downloadCsv <- downloadHandler(
filename = function() {
paste("COVID_data_", cv_today$date[1], ".csv", sep="")
},
content = function(file) {
cv_cases_sub = cv_cases %>% select(c(country, date, cases, new_cases, deaths, new_deaths,
cases_per_million, new_cases_per_million, deaths_per_million, new_deaths_per_million))
names(cv_cases_sub) = c("country", "date", "cumulative_cases", "new_cases_past_week", "cumulative_deaths", "new_deaths_past_week",
"cumulative_cases_per_million", "new_cases_per_million_past_week", "cumulative_deaths_per_million", "new_deaths_per_million_past_week")
write.csv(cv_cases_sub, file)
}
)
output$rawtable <- renderPrint({
cv_cases_sub = cv_cases %>% select(c(country, date, cases, new_cases, deaths, new_deaths,
cases_per_million, new_cases_per_million, deaths_per_million, new_deaths_per_million))
names(cv_cases_sub) = c("country", "date", "cumulative_cases", "new_cases_past_week", "cumulative_deaths", "new_deaths_past_week",
"cumulative_cases_per_million", "new_cases_per_million_past_week", "cumulative_deaths_per_million", "new_deaths_per_million_past_week")