-
Notifications
You must be signed in to change notification settings - Fork 633
/
Copy pathggplotly.R
1567 lines (1419 loc) · 58.6 KB
/
ggplotly.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
#' Convert ggplot2 to plotly
#'
#' This function converts a [ggplot2::ggplot()] object to a
#' plotly object.
#'
#' @details Conversion of relative sizes depends on the size of the current
#' graphics device (if no device is open, width/height of a new (off-screen)
#' device defaults to 640/480). In other words, `height` and
#' `width` must be specified at runtime to ensure sizing is correct.
#' For examples on how to specify the output container's `height`/`width` in a
#' shiny app, see `plotly_example("shiny", "ggplotly_sizing")`.
#'
#'
#' @param p a ggplot object.
#' @param width Width of the plot in pixels (optional, defaults to automatic sizing).
#' @param height Height of the plot in pixels (optional, defaults to automatic sizing).
#' @param tooltip a character vector specifying which aesthetic mappings to show
#' in the tooltip. The default, "all", means show all the aesthetic mappings
#' (including the unofficial "text" aesthetic). The order of variables here will
#' also control the order they appear. For example, use
#' `tooltip = c("y", "x", "colour")` if you want y first, x second, and
#' colour last.
#' @param dynamicTicks should plotly.js dynamically generate axis tick labels?
#' Dynamic ticks are useful for updating ticks in response to zoom/pan
#' interactions; however, they can not always reproduce labels as they
#' would appear in the static ggplot2 image.
#' @param layerData data from which layer should be returned?
#' @param originalData should the "original" or "scaled" data be returned?
#' @param source a character string of length 1. Match the value of this string
#' with the source argument in [event_data()] to retrieve the
#' event data corresponding to a specific plot (shiny apps can have multiple plots).
#' @param ... arguments passed onto methods.
#' @export
#' @author Carson Sievert
#' @references \url{https://plotly.com/ggplot2/}
#' @seealso [plot_ly()]
#' @examples \dontrun{
#' # simple example
#' ggpenguins <- qplot(bill_length_mm , body_mass_g,
#' data = palmerpenguins::penguins, color = species)
#' ggplotly(ggpenguins)
#'
#' data(canada.cities, package = "maps")
#' viz <- ggplot(canada.cities, aes(long, lat)) +
#' borders(regions = "canada") +
#' coord_equal() +
#' geom_point(aes(text = name, size = pop), colour = "red", alpha = 1/2)
#' ggplotly(viz, tooltip = c("text", "size"))
#'
#' # linked scatterplot brushing
#' d <- highlight_key(mtcars)
#' qplot(data = d, x = mpg, y = wt) %>%
#' subplot(qplot(data = d, x = mpg, y = vs)) %>%
#' layout(title = "Click and drag to select points") %>%
#' highlight("plotly_selected")
#'
#'
#' # more brushing (i.e. highlighting) examples
#' demo("crosstalk-highlight-ggplotly", package = "plotly")
#'
#' # client-side linked brushing in a scatterplot matrix
#' highlight_key(palmerpenguins::penguins) %>%
#' GGally::ggpairs(aes(colour = Species), columns = 1:4) %>%
#' ggplotly(tooltip = c("x", "y", "colour")) %>%
#' highlight("plotly_selected")
#' }
#'
ggplotly <- function(p = ggplot2::last_plot(), width = NULL, height = NULL,
tooltip = "all", dynamicTicks = FALSE,
layerData = 1, originalData = TRUE, source = "A", ...) {
UseMethod("ggplotly", p)
}
#' @export
ggplotly.NULL <- function(...) {
htmltools::browsable(htmltools::div(...))
}
#' @export
ggplotly.plotly <- function(p = ggplot2::last_plot(), width = NULL, height = NULL,
tooltip = "all", dynamicTicks = FALSE,
layerData = 1, originalData = TRUE, source = "A", ...) {
p
}
#' @export
ggplotly.ggmatrix <- function(p = ggplot2::last_plot(), width = NULL,
height = NULL, tooltip = "all", dynamicTicks = FALSE,
layerData = 1, originalData = TRUE, source = "A", ...) {
dots <- list(...)
# provide a sensible crosstalk if none is already provided (makes ggnostic() work at least)
if (!crosstalk_key() %in% names(p$data)) {
p$data[[crosstalk_key()]] <- p$data[[".rownames"]] %||% seq_len(nrow(p$data))
attr(p$data, "set") <- dots[["set"]] %||% new_id()
}
subplotList <- list()
for (i in seq_len(p$ncol)) {
columnList <- list()
for (j in seq_len(p$nrow)) {
thisPlot <- p[j, i]
if (i == 1) {
# should the first column contain axis labels?
if (p$showYAxisPlotLabels %||% TRUE) thisPlot <- thisPlot + ylab(p$yAxisLabels[j])
} else {
# y-axes are never drawn on the interior, and diagonal plots are densities,
# so it doesn't make sense to synch zoom actions on y
thisPlot <- thisPlot + ylab(NULL) +
theme(
axis.ticks.y = element_blank(),
axis.text.y = element_blank()
)
}
columnList <- c(
columnList, list(ggplotly(
thisPlot, tooltip = tooltip, dynamicTicks = dynamicTicks,
layerData = layerData, originalData = originalData, source = source,
width = width, height = height
))
)
}
# conditioned on a column in a ggmatrix, the x-axis should be on the
# same scale.
s <- subplot(columnList, nrows = p$nrow, margin = 0.01, shareX = TRUE,
titleY = TRUE, titleX = TRUE)
subplotList <- c(subplotList, list(s))
}
s <- subplot(subplotList, nrows = 1, margin = 0.01,
titleY = TRUE, titleX = TRUE) %>%
hide_legend() %>%
layout(dragmode = "select")
if (robust_nchar(p$title) > 0) {
s <- layout(s, title = p$title)
}
for (i in seq_along(p$xAxisLabels)) {
s$x$layout[[sub("^xaxis1$", "xaxis", paste0("xaxis", i))]]$title <- p$xAxisLabels[[i]]
}
if (length(p$yAxisLabels)) {
s$x$layout$margin$l <- s$x$layout$margin$l + 50
}
config(s)
}
#' @export
ggplotly.ggplot <- function(p = ggplot2::last_plot(), width = NULL,
height = NULL, tooltip = "all", dynamicTicks = FALSE,
layerData = 1, originalData = TRUE, source = "A", ...) {
l <- gg2list(p, width = width, height = height, tooltip = tooltip,
dynamicTicks = dynamicTicks, layerData = layerData,
originalData = originalData, source = source, ...)
config(as_widget(l))
}
#' Convert a ggplot to a list.
#' @param p ggplot2 plot.
#' @param width Width of the plot in pixels (optional, defaults to automatic sizing).
#' @param height Height of the plot in pixels (optional, defaults to automatic sizing).
#' @param tooltip a character vector specifying which aesthetic tooltips to show in the
#' tooltip. The default, "all", means show all the aesthetic tooltips
#' (including the unofficial "text" aesthetic).
#' @param dynamicTicks accepts the following values: `FALSE`, `TRUE`, `"x"`, or `"y"`.
#' Dynamic ticks are useful for updating ticks in response to zoom/pan/filter
#' interactions; however, there is no guarantee they reproduce axis tick text
#' as they would appear in the static ggplot2 image.
#' @param layerData data from which layer should be returned?
#' @param originalData should the "original" or "scaled" data be returned?
#' @param source a character string of length 1. Match the value of this string
#' with the source argument in [event_data()] to retrieve the
#' event data corresponding to a specific plot (shiny apps can have multiple plots).
#' @param ... currently not used
#' @return a 'built' plotly object (list with names "data" and "layout").
#' @export
gg2list <- function(p, width = NULL, height = NULL,
tooltip = "all", dynamicTicks = FALSE,
layerData = 1, originalData = TRUE, source = "A", ...) {
# To convert relative sizes correctly, we use grid::convertHeight(),
# which requires a known output (device) size.
dev_fun <- if (capabilities("aqua") || capabilities("png")) {
grDevices::png
} else if (capabilities("jpeg")) {
grDevices::jpeg
} else if (is_installed("Cairo")) {
function(filename, ...) Cairo::Cairo(file = filename, ...)
} else {
stop(
"No Cairo or bitmap device is available. Such a graphics device is required to convert sizes correctly in ggplotly().\n\n",
" You have two options:\n",
" (1) install.packages('Cairo')\n",
" (2) compile R to use a bitmap device (png or jpeg)",
call. = FALSE
)
}
# if a device (or RStudio) is already open, use the device size as default size
if (!is.null(grDevices::dev.list()) || is_rstudio()) {
width <- width %||% default(grDevices::dev.size("px")[1])
height <- height %||% default(grDevices::dev.size("px")[2])
}
# open the device and make sure it closes on exit
dev_fun(filename = tempfile(), width = width %||% 640, height = height %||% 480)
on.exit(grDevices::dev.off(), add = TRUE)
# check the value of dynamicTicks
dynamicValues <- c(FALSE, TRUE, "x", "y")
if (length(setdiff(dynamicTicks, dynamicValues))) {
stop(
sprintf(
"`dynamicValues` accepts the following values: '%s'",
paste(dynamicValues, collapse = "', '")
), call. = FALSE
)
}
# ------------------------------------------------------------------------
# Our internal version of ggplot2::ggplot_build(). Modified from
# https://github.com/hadley/ggplot2/blob/0cd0ba/R/plot-build.r#L18-L92
# ------------------------------------------------------------------------
ggplotly_build <- function(p) {
plot <- ggfun("plot_clone")(p)
if (length(plot$layers) == 0) {
plot <- plot + geom_blank()
}
layers <- plot$layers
layer_data <- lapply(layers, function(y) y$layer_data(plot$data))
# save crosstalk sets before this attribute gets squashed
sets <- lapply(layer_data, function(y) attr(y, "set"))
scales <- plot$scales
# Apply function to layer and matching data
by_layer <- function(f) {
out <- vector("list", length(data))
for (i in seq_along(data)) {
out[[i]] <- f(l = layers[[i]], d = data[[i]])
}
out
}
# ggplot2 3.1.0.9000 introduced a Layer method named setup_layer()
# currently, LayerSf is the only core-ggplot2 Layer that makes use
# of it https://github.com/tidyverse/ggplot2/pull/2875#issuecomment-438708426
data <- layer_data
if (get_package_version("ggplot2") > "3.1.0") {
data <- by_layer(function(l, d) if (is.function(l$setup_layer)) l$setup_layer(d, plot) else d)
}
# Initialise panels, add extra data for margins & missing facetting
# variables, and add on a PANEL variable to data
layout <- ggfun("create_layout")(plot$facet, plot$coordinates)
data <- layout$setup(data, plot$data, plot$plot_env)
# save the domain of the group for display in tooltips
groupDomains <- Map(function(x, y) {
aes_g <- y$mapping[["group"]] %||% plot$mapping[["group"]]
tryNULL(rlang::eval_tidy(aes_g, x))
}, data, layers)
# for simple (StatIdentity) geoms, add crosstalk key to aes mapping
# (effectively adding it as a group)
# later on, for more complicated geoms (w/ non-trivial summary statistics),
# we construct a nested key mapping (within group)
layers <- Map(function(x, y) {
if (crosstalk_key() %in% names(y) && !"key" %in% names(x[["mapping"]]) &&
inherits(x[["stat"]], "StatIdentity")) {
# ggplot2 v3.3.4 started using the computed_mapping (instead of mapping)
# field to inform the compute_aesthetics() method, so in order to add
# the crosstalk key, we need to add to that field (when present)
# https://github.com/tidyverse/ggplot2/pull/4475
nm <- if ("computed_mapping" %in% names(x)) "computed_mapping" else "mapping"
x[[nm]] <- c(x[[nm]], key = as.name(crosstalk_key()))
}
x
}, layers, layer_data)
# Compute aesthetics to produce data with generalised variable names
data <- by_layer(function(l, d) l$compute_aesthetics(d, plot))
# add frame to group if it exists
data <- lapply(data, function(d) {
if (!"frame" %in% names(d)) return(d)
d$group <- with(d, paste(group, frame, sep = "-"))
d
})
# The computed aesthetic codes the groups as integers
# Here we build a map each of the integer values to the group label
group_maps <- Map(function(x, y) {
tryCatch({
x_group <- x[["group"]]
names(x_group) <- y
x_group <- x_group[!duplicated(x_group)]
x_group
}, error = function(e) NULL
)
}, data, groupDomains)
# Before mapping x/y position, save the domain (for discrete scales)
# to display in tooltip.
data <- lapply(data, function(d) {
d[["x_plotlyDomain"]] <- d[["x"]]
d[["y_plotlyDomain"]] <- d[["y"]]
d
})
# And since we're essentially adding an "unknown" (to ggplot2)
# aesthetic, add it to the dropped_aes field to avoid fals positive
# warnings (https://github.com/tidyverse/ggplot2/pull/4866)
layers <- lapply(layers, function(l) {
l$stat$dropped_aes <- c(l$stat$dropped_aes, "x_plotlyDomain")
l$stat$dropped_aes <- c(l$stat$dropped_aes, "y_plotlyDomain")
l
})
# Transform all scales
data <- lapply(data, scales_transform_df, scales = scales)
# Map and train positions so that statistics have access to ranges
# and all positions are numeric
scale_x <- function() scales$get_scales("x")
scale_y <- function() scales$get_scales("y")
layout$train_position(data, scale_x(), scale_y())
data <- layout$map_position(data)
# build a mapping between group and key
# if there are multiple keys within a group, the key is a list-column
reComputeGroup <- function(x, layer = NULL) {
# 1-to-1 link between data & visual marks -- group == key
if (inherits(layer$geom, "GeomDotplot")) {
x <- split(x, x[["PANEL"]])
x <- lapply(x, function(d) {
d[["group"]] <- do.call("order", d[c("x", "group")])
d
})
x <- dplyr::bind_rows(x)
}
if (inherits(layer$geom, "GeomSf")) {
x <- split(x, x[["PANEL"]])
x <- lapply(x, function(d) {
d[["group"]] <- seq_len(nrow(d))
d
})
# I think this is safe?
x <- suppressWarnings(dplyr::bind_rows(x))
}
x
}
nestedKeys <- Map(function(x, y, z) {
key <- y[[crosstalk_key()]]
if (is.null(key) || inherits(z[["stat"]], "StatIdentity")) return(NULL)
x <- reComputeGroup(x, z)
tib <- tibble::as_tibble(x[c("PANEL", "group")])
tib[["key"]] <- key
nested <- tidyr::nest(tib, key = key)
# reduce the dimensions of list column elements from 2 to 1
nested$key <- lapply(nested$key, function(x) x[[1]])
nested
}, data, layer_data, layers)
# for some geoms (e.g. boxplots) plotly.js needs the "pre-statistics" data
# we also now provide the option to return one of these two
prestats_data <- data
data <- by_layer(function(l, d) l$compute_statistic(d, layout))
data <- by_layer(function(l, d) l$map_statistic(d, plot))
# Make sure missing (but required) aesthetics are added
scales_add_missing(plot, c("x", "y"))
# Reparameterise geoms from (e.g.) y and width to ymin and ymax
data <- by_layer(function(l, d) l$compute_geom_1(d))
# compute_geom_1 can reorder the rows from `data`, making groupDomains
# invalid. We rebuild groupDomains based on the current `data` and the
# group map we built before.
groupDomains <- Map(function(x, y) {
tryCatch({
names(y)[match(x$group, y)]
}, error = function(e) NULL
)
}, data, group_maps)
# there are some geoms (e.g. geom_dotplot()) where attaching the key
# before applying the statistic can cause problems, but there is still a
# 1-to-1 corresponding between graphical marks and
# Apply position adjustments
data <- by_layer(function(l, d) l$compute_position(d, layout))
# Reset position scales, then re-train and map. This ensures that facets
# have control over the range of a plot: is it generated from what's
# displayed, or does it include the range of underlying data
layout$reset_scales()
layout$train_position(data, scale_x(), scale_y())
layout$setup_panel_params()
data <- layout$map_position(data)
# Train and map non-position scales
npscales <- scales$non_position_scales()
if (npscales$n() > 0) {
lapply(data, scales_train_df, scales = npscales)
# this for loop is unique to plotly -- it saves the "domain"
# of each non-positional scale for display in tooltips
for (sc in npscales$scales) {
data <- lapply(data, function(d) {
# scale may not be relevant for every layer data
if (any(names(d) %in% sc$aesthetics)) {
d[paste0(sc$aesthetics, "_plotlyDomain")] <- d[sc$aesthetics]
}
d
})
}
data <- lapply(data, scales_map_df, scales = npscales)
}
# Fill in defaults etc.
data <- by_layer(function(l, d) l$compute_geom_2(d))
# Let layer stat have a final say before rendering
data <- by_layer(function(l, d) l$finish_statistics(d))
# Let Layout modify data before rendering
data <- layout$finish_data(data)
# if necessary, attach key
data <- Map(function(x, y, z) {
if (!length(y)) return(x)
x <- reComputeGroup(x, z)
# dplyr issue??? https://github.com/tidyverse/dplyr/issues/2701
attr(y$group, "n") <- NULL
# https://github.com/plotly/plotly.R/issues/2013
if (!identical(class(x$group), class(y$group))) {
x$group <- as.character(x$group)
y$group <- as.character(y$group)
}
suppressMessages(dplyr::left_join(x, y))
}, data, nestedKeys, layers)
structure(
list(
data = data, layout = layout, plot = plot,
env = environment()
),
class = "ggplot_built"
)
}
# Allow thematic to add new defaults to the plot object based on it's theme
built <- if (isNamespaceLoaded("thematic")) {
tns <- asNamespace("thematic")
tns$ggthematic_build(p, ggplotly_build, tns$thematic_get_theme(resolve = TRUE))
} else {
ggplotly_build(p)
}
# Assign all the objects available to ggplotly_build() to this functions environment
built_env <- built$env
envir <- environment()
for (var in ls(built_env)) {
assign(var, built_env[[var]], envir = envir)
}
theme <- calculated_theme_elements(plot)
# Translate plot wide theme elements to plotly.js layout
pm <- unitConvert(theme$plot.margin, "pixels")
gglayout <- list(
margin = list(t = pm[[1]], r = pm[[2]], b = pm[[3]], l = pm[[4]]),
plot_bgcolor = toRGB(theme$panel.background$fill),
paper_bgcolor = toRGB(theme$plot.background$fill),
font = text2font(theme$text)
)
# main plot title
if (robust_nchar(plot$labels$title) > 0) {
gglayout$title <- list(
text = faced(plot$labels$title, theme$plot.title$face),
font = text2font(theme$plot.title),
# don't translate vjust to y since they since have very different meaning...
# y is allowed to span the paper coordinate whereas vjust it local to it's grob
x = theme$plot.title$hjust,
xref = "paper"
)
gglayout$margin$t <- gglayout$margin$t + gglayout$title$font$size
}
# ensure there's enough space for the modebar (this is based on a height of 1em)
# https://github.com/plotly/plotly.js/blob/dd1547/src/components/modebar/index.js#L171
gglayout$margin$t <- gglayout$margin$t + 16
# important stuff like layout$panel_params is already flipped, but
# plot$scales/plot$labels/data aren't. We flip x/y trace data at the very end
# and scales in the axis loop below.
if (inherits(plot$coordinates, "CoordFlip")) {
plot$labels[c("x", "y")] <- plot$labels[c("y", "x")]
}
# important panel summary stats
nPanels <- nrow(layout$layout)
nRows <- max(layout$layout$ROW)
nCols <- max(layout$layout$COL)
# panel -> plotly.js axis/anchor info
# (assume a grid layout by default)
layout$layout <- dplyr::mutate(
layout$layout,
xaxis = COL,
yaxis = ROW,
xanchor = nRows,
yanchor = 1L
)
if (inherits(plot$facet, "FacetWrap")) {
if (plot$facet$params$free$x && plot$facet$params$free$y) {
layout$layout <- dplyr::mutate(
layout$layout,
xaxis = PANEL,
yaxis = PANEL,
xanchor = PANEL,
yanchor = PANEL
)
} else if (plot$facet$params$free$x) {
layout$layout <- dplyr::mutate(
layout$layout,
xaxis = PANEL,
xanchor = ROW
)
} else if (plot$facet$params$free$y) {
layout$layout <- dplyr::mutate(
layout$layout,
yaxis = PANEL,
yanchor = COL
)
}
# anchor X axis to the lowest plot in its column
layout$layout <- dplyr::group_by(layout$layout, !!rlang::sym("xaxis"))
layout$layout <- dplyr::mutate(layout$layout, xanchor = max(as.integer(yaxis)))
}
layout$layout <- as.data.frame(layout$layout)
# format the axis/anchor to a format plotly.js respects
layout$layout$xaxis <- paste0("xaxis", sub("^1$", "", layout$layout$xaxis))
layout$layout$yaxis <- paste0("yaxis", sub("^1$", "", layout$layout$yaxis))
layout$layout$xanchor <- paste0("y", sub("^1$", "", layout$layout$xanchor))
layout$layout$yanchor <- paste0("x", sub("^1$", "", layout$layout$yanchor))
# for some layers2traces computations, we need the range of each panel
layout$layout$x_min <- sapply(layout$panel_params, function(z) { min(z[["x"]]$dimension %()% z$x.range %||% z$x_range) })
layout$layout$x_max <- sapply(layout$panel_params, function(z) { max(z[["x"]]$dimension %()% z$x.range %||% z$x_range) })
layout$layout$y_min <- sapply(layout$panel_params, function(z) { min(z[["y"]]$dimension %()% z$y.range %||% z$y_range) })
layout$layout$y_max <- sapply(layout$panel_params, function(z) { max(z[["y"]]$dimension %()% z$y.range %||% z$y_range) })
# layers -> plotly.js traces
plot$tooltip <- tooltip
data <- Map(function(x, y) {
tryCatch({ x$group_plotlyDomain <- y; x }, error = function(e) x)
}, data, groupDomains)
# reattach crosstalk key-set attribute
data <- Map(function(x, y) structure(x, set = y), data, sets)
traces <- layers2traces(data, prestats_data, layout, plot)
gglayout <- layers2layout(gglayout, layers, layout$layout)
# default to just the text in hover info, mainly because of this
# https://github.com/plotly/plotly.js/issues/320
traces <- lapply(traces, function(tr) {
tr$hoverinfo <- tr$hoverinfo %||%"text"
tr
})
# show only one legend entry per legendgroup
grps <- sapply(traces, "[[", "legendgroup")
traces <- Map(function(x, y) {
if (!is.null(x[["frame"]])) return(x)
x$showlegend <- isTRUE(x$showlegend) && y
x
}, traces, !duplicated(grps))
# ------------------------------------------------------------------------
# axis/facet/margin conversion
# ------------------------------------------------------------------------
# panel margins must be computed before panel/axis loops
# (in order to use get_domains())
panelMarginX <- unitConvert(
theme[["panel.spacing.x"]] %||% theme[["panel.spacing"]],
"npc", "width"
)
panelMarginY <- unitConvert(
theme[["panel.spacing.y"]] %||% theme[["panel.spacing"]],
"npc", "height"
)
# space for _interior_ facet strips
if (inherits(plot$facet, "FacetWrap")) {
stripSize <- unitConvert(
theme[["strip.text.x"]] %||% theme[["strip.text"]],
"npc", "height"
)
panelMarginY <- panelMarginY + stripSize
# space for ticks/text in free scales
if (plot$facet$params$free$x) {
axisTicksX <- unitConvert(
theme[["axis.ticks.x"]] %||% theme[["axis.ticks"]],
"npc", "height"
)
# allocate enough space for the _longest_ text label
axisTextX <- theme[["axis.text.x"]] %||% theme[["axis.text"]]
labz <- unlist(lapply(layout$panel_params, function(pp) { pp[["x"]]$get_labels %()% pp$x.labels }))
lab <- longest_element(labz)
panelMarginY <- panelMarginY + axisTicksX +
bbox(lab, axisTextX$angle, unitConvert(axisTextX, "npc", "height"))[["height"]]
}
if (plot$facet$params$free$y) {
axisTicksY <- unitConvert(
theme[["axis.ticks.y"]] %||% theme[["axis.ticks"]],
"npc", "width"
)
# allocate enough space for the _longest_ text label
axisTextY <- theme[["axis.text.y"]] %||% theme[["axis.text"]]
labz <- unlist(lapply(layout$panel_params, function(pp) { pp[["y"]]$get_labels %()% pp$y.labels }))
lab <- longest_element(labz)
panelMarginX <- panelMarginX + axisTicksY +
bbox(lab, axisTextY$angle, unitConvert(axisTextY, "npc", "width"))[["width"]]
}
}
margins <- c(
rep(panelMarginX, 2),
rep(panelMarginY, 2)
)
doms <- get_domains(nPanels, nRows, margins)
for (i in seq_len(nPanels)) {
lay <- layout$layout[i, ]
for (xy in c("x", "y")) {
# find axis specific theme elements that inherit from their parent
theme_el <- function(el) {
theme[[paste0(el, ".", xy)]] %||% theme[[el]]
}
axisTicks <- theme_el("axis.ticks")
axisText <- theme_el("axis.text")
axisTitle <- theme_el("axis.title")
axisLine <- theme_el("axis.line")
panelGrid <- theme_el("panel.grid.major") %||% theme_el("panel.grid")
stripText <- theme_el("strip.text")
axisName <- lay[, paste0(xy, "axis")]
anchor <- lay[, paste0(xy, "anchor")]
rng <- layout$panel_params[[i]]
# panel_params is quite different for "CoordSf"
if ("CoordSf" %in% class(p$coordinates)) {
# see CoordSf$render_axis_v
direction <- if (xy == "x") "E" else "N"
idx <- rng$graticule$type == direction &
!is.na(rng$graticule$degree_label) &
# Respect the logical 'plot12' column which sf constructs for
# determining which tick labels should be drawn
# https://github.com/r-spatial/sf/blob/b49d37/R/graticule.R#L199
# https://github.com/r-spatial/sf/blob/52a8351/R/plot.R#L580
(rng$graticule$plot12 %||% TRUE)
tickData <- rng$graticule[idx, ]
# TODO: how to convert a language object to unicode character string?
rng[[paste0(xy, ".labels")]] <- sub(
"\\*\\s+degree[ ]?[\\*]?", "°",
gsub("\"", "", tickData[["degree_label"]])
)
rng[[paste0(xy, ".major")]] <- tickData[[paste0(xy, "_start")]]
# If it doesn't already exist (for this panel),
# generate graticule (as done in, CoordSf$render_bg)
isGrill <- vapply(traces, function(tr) {
identical(tr$xaxis, lay$xaxis) &&
identical(tr$yaxis, lay$yaxis) &&
isTRUE(tr$`_isGraticule`)
}, logical(1))
if (sum(isGrill) == 0) {
# TODO: reduce the number of points (via coord_munch?)
d <- fortify_sf(rng$graticule)
d$x <- scales::rescale(d$x, rng$x_range, from = c(0, 1))
d$y <- scales::rescale(d$y, rng$y_range, from = c(0, 1))
params <- list(
colour = panelGrid$colour,
linetype = panelGrid$linetype
)
nm <- linewidth_or_size(panelGrid)
params[[nm]] <- panelGrid[[nm]]
grill <- geom2trace.GeomPath(d, params)
grill$hoverinfo <- "none"
grill$showlegend <- FALSE
grill$`_isGraticule` <- TRUE
grill$xaxis <- sub("axis", "", lay$xaxis)
grill$yaxis <- sub("axis", "", lay$yaxis)
traces <- c(list(grill), traces)
}
# if labels are empty, don't show axis ticks
tickExists <- with(rng$graticule, sapply(degree_label, is.language))
if (sum(tickExists) == 0) {
theme$axis.ticks.length <- 0
}
}
# stuff like layout$panel_params is already flipped, but scales aren't
sc <- if (inherits(plot$coordinates, "CoordFlip")) {
scales$get_scales(setdiff(c("x", "y"), xy))
} else {
scales$get_scales(xy)
}
# type of unit conversion
type <- if (xy == "x") "height" else "width"
# get axis title
axisTitleText <- sc$name %||% plot$labels[[xy]] %||% ""
if (is_blank(axisTitle)) axisTitleText <- ""
# is this axis dynamic?
isDynamic <- isTRUE(dynamicTicks) || identical(dynamicTicks, xy)
if (isDynamic && !p$coordinates$is_linear()) {
warning(
"`dynamicTicks` is only supported for linear (i.e., cartesian) coordinates",
call. = FALSE
)
}
# determine axis types
isDate <- inherits(sc, c("ScaleContinuousDatetime", "ScaleContinuousDate"))
isDateType <- isDynamic && isDate
isDiscrete <- inherits(sc, "ScaleDiscretePosition")
isDiscreteType <- isDynamic && isDiscrete
# In 3.2.x .major disappeared in favor of break_positions()
# (tidyverse/ggplot2#3436), but with 3.4.x break_positions() no longer
# yields the actual final positions on a 0-1 scale, but .major does
# (tidyverse/ggplot2#5029)
ticktext <- rng[[paste0(xy, ".labels")]] %||% rng[[xy]]$get_labels()
tickvals <- rng[[paste0(xy, ".major")]] %||% rng[[xy]]$break_positions()
# https://github.com/tidyverse/ggplot2/pull/3566#issuecomment-565085809
hasTickText <- !(is.na(ticktext) | is.na(tickvals))
ticktext <- ticktext[hasTickText]
tickvals <- tickvals[hasTickText]
axisObj <- list(
# TODO: log type?
type = if (isDateType) "date" else if (isDiscreteType) "category" else "linear",
autorange = isDynamic,
range = rng[[xy]]$dimension %()% rng[[paste0(xy, ".range")]] %||% rng[[paste0(xy, "_range")]],
tickmode = if (isDynamic) "auto" else "array",
ticktext = ticktext,
tickvals = tickvals,
categoryorder = "array",
categoryarray = ticktext,
nticks = nrow(rng),
ticks = if (is_blank(axisTicks)) "" else "outside",
tickcolor = toRGB(axisTicks$colour),
ticklen = unitConvert(theme$axis.ticks.length, "pixels", type),
tickwidth = unitConvert(axisTicks, "pixels", type),
showticklabels = !is_blank(axisText),
tickfont = text2font(axisText, type),
tickangle = - (axisText$angle %||% 0),
showline = !is_blank(axisLine),
linecolor = toRGB(axisLine$colour),
linewidth = unitConvert(axisLine, "pixels", type),
# TODO: always `showgrid=FALSE` and implement our own using traces
showgrid = !is_blank(panelGrid) && !"CoordSf" %in% class(p$coordinates),
domain = sort(as.numeric(doms[i, paste0(xy, c("start", "end"))])),
gridcolor = toRGB(panelGrid$colour),
gridwidth = unitConvert(panelGrid, "pixels", type),
zeroline = FALSE,
anchor = anchor,
# layout.axisid.title don't yet support alignment :(
title = list(
text = faced(axisTitleText, axisTitle$face),
font = text2font(axisTitle)
)
)
# set scaleanchor/scaleratio if these are fixed coordinates
# the logic here is similar to what p$coordinates$aspect() does,
# but the ratio is scaled to the data range by plotly.js
fixed_coords <- c("CoordSf", "CoordFixed", "CoordMap", "CoordQuickmap")
if (inherits(p$coordinates, fixed_coords)) {
axisObj$scaleanchor <- anchor
ratio <- p$coordinates$ratio %||% 1
axisObj$scaleratio <- if (xy == "y") ratio else 1 / ratio
if (inherits(p$coordinates, "CoordSf")) {
if (isTRUE(sf::st_is_longlat(rng$crs))) {
ratio <- cos(mean(rng$y_range) * pi/180)
}
# note how ratio is flipped in CoordSf$aspect() vs CoordFixed$aspect()
axisObj$scaleratio <- if (xy == "y") 1 / ratio else ratio
}
}
# TODO: seems like we _could_ support this with scaleanchors,
# but inverse transform by the panel ranges?
# also, note how aspect.ratio overwrites fixed coordinates:
# ggplot(mtcars, aes(wt, mpg)) + geom_point() + coord_fixed(0.5)
# ggplot(mtcars, aes(wt, mpg)) + geom_point() + coord_fixed(0.5) + theme(aspect.ratio = 1)
if (!is.null(theme$aspect.ratio)) {
warning(
"Aspect ratios aren't yet implemented, but you can manually set",
" a suitable height/width", call. = FALSE
)
}
# tickvals are currently on 0-1 scale, but we want them on data scale
axisObj$tickvals <- scales::rescale(
axisObj$tickvals, to = axisObj$range, from = c(0, 1)
)
# inverse transform date data based on tickvals/ticktext
invert_date <- function(x, scale) {
if (inherits(scale, "ScaleContinuousDatetime")) {
as.POSIXct(x, origin = "1970-01-01", tz = scale$timezone)
} else {
as.Date(x, origin = "1970-01-01", tz = scale$timezone)
}
}
if (isDateType) {
axisObj$range <- invert_date(axisObj$range, sc)
traces <- lapply(traces, function(tr) {
tr[[xy]] <- invert_date(tr[[xy]], sc)
# TODO: are there other similar cases we need to handle?
if (identical("bar", tr$type)) {
tr[["width"]] <- invert_date(tr[["width"]], sc)
}
tr
})
}
# inverse transform categorical data based on tickvals/ticktext
if (isDiscreteType) {
traces <- lapply(traces, function(tr) {
# map x/y trace data back to the 'closest' ticktext label
# http://r.789695.n4.nabble.com/check-for-nearest-value-in-a-vector-td4369339.html
tr[[xy]]<- vapply(tr[[xy]], function(val) {
with(axisObj, ticktext[[which.min(abs(tickvals - val))]])
}, character(1))
tr
})
if ("dodge" %in% sapply(layers, ggtype, "position")) gglayout$barmode <- "dodge"
}
# attach axis object to the layout
gglayout[[axisName]] <- axisObj
# do some stuff that should be done once for the entire plot
is_x <- xy == "x"
if (i == 1) {
# Split ticktext elements by "\n" to account for linebreaks
axisTickText <- strsplit(as.character(axisObj$ticktext), split = "\n", fixed = TRUE)
axisTickText <- longest_element(unlist(axisTickText))
side <- if (is_x) "b" else "l"
# account for axis ticks, ticks text, and titles in plot margins
# (apparently ggplot2 doesn't support axis.title/axis.text margins)
gglayout$margin[[side]] <- gglayout$margin[[side]] + axisObj$ticklen +
bbox(axisTickText, axisObj$tickangle, axisObj$tickfont$size)[[type]] +
bbox(axisTitleText, axisTitle$angle, unitConvert(axisTitle, "pixels", type))[[type]]
if (robust_nchar(axisTitleText) > 0) {
axisTextSize <- unitConvert(axisText, "npc", type)
axisTitleSize <- unitConvert(axisTitle, "npc", type)
}
# add space for exterior facet strips in `layout.margin`
if (has_facet(plot)) {
stripSize <- unitConvert(stripText, "pixels", type)
if (is_x) {
gglayout$margin$t <- gglayout$margin$t + stripSize
}
if (is_x && inherits(plot$facet, "FacetGrid")) {
gglayout$margin$r <- gglayout$margin$r + stripSize
}
# facets have multiple axis objects, but only one title for the plot,
# so we empty the titles and try to draw the title as an annotation
if (robust_nchar(axisTitleText) > 0) {
axisAnn <- make_label(
faced(axisTitleText, axisTitle$face),
el = axisTitle,
x = if (is_x) 0.5 else 0,
y = if (is_x) 0 else 0.5,
xanchor = if (is_x) "center" else "right",
yanchor = if (is_x) "top" else "center",
annotationType = "axis"
)
textMargin <- sum(axisText$margin[if (is_x) c(1, 3) else c(2, 4)])
class(textMargin) <- setdiff(class(textMargin), "margin")
titleMargin <- axisTitle$margin[if (is_x) 1 else 2]
class(titleMargin) <- setdiff(class(titleMargin), "margin")
offset <- bbox(axisTickText, axisText$angle, axisTextSize)[[type]] +
unitConvert(theme$axis.ticks.length, "npc", type) +
unitConvert(textMargin, "npc", type) +
unitConvert(titleMargin, "npc", type)
offset <- unitConvert(grid::unit(offset, "npc"), "pixels", type)
shift <- if (is_x) "yshift" else "xshift"
axisAnn[[1]][[shift]] <- -1 * offset
gglayout$annotations <- c(gglayout$annotations, axisAnn)
}
}
}
if (has_facet(plot)) gglayout[[axisName]]$title <- ""
} # end of axis loop
# theme(panel.border = ) -> plotly rect shape
xdom <- gglayout[[lay[, "xaxis"]]]$domain
ydom <- gglayout[[lay[, "yaxis"]]]$domain
border <- make_panel_border(xdom, ydom, theme)
gglayout$shapes <- c(gglayout$shapes, border)
# facet strips -> plotly annotations
if (has_facet(plot)) {
col_vars <- ifelse(inherits(plot$facet, "FacetWrap"), "facets", "cols")
col_txt <- paste(
plot$facet$params$labeller(
lay[names(plot$facet$params[[col_vars]])]
), collapse = br()
)
if (length(names(plot$facet$params[[col_vars]])) == 0) col_txt <- ""
if (is_blank(theme[["strip.text.x"]])) col_txt <- ""
if (inherits(plot$facet, "FacetGrid") && lay$ROW != 1) col_txt <- ""
if (robust_nchar(col_txt) > 0) {
col_lab <- make_label(
col_txt, x = mean(xdom), y = max(ydom),
el = theme[["strip.text.x"]] %||% theme[["strip.text"]],
xanchor = "center", yanchor = "bottom"
)
gglayout$annotations <- c(gglayout$annotations, col_lab)
strip <- make_strip_rect(xdom, ydom, theme, "top")
gglayout$shapes <- c(gglayout$shapes, strip)
}
# Only FacetGrid has no cols
if (inherits(plot$facet, "FacetGrid")) {
row_txt <- paste(
plot$facet$params$labeller(
lay[names(plot$facet$params$rows)]
),
collapse = br()
)
if (length(names(plot$facet$params$rows)) == 0) row_txt <- ""
if (is_blank(theme[["strip.text.y"]])) row_txt <- ""
if (lay$COL != nCols) row_txt <- ""
if (robust_nchar(row_txt) > 0) {
row_lab <- make_label(
row_txt, x = max(xdom), y = mean(ydom),
el = theme[["strip.text.y"]] %||% theme[["strip.text"]],
xanchor = "left", yanchor = "middle"
)
gglayout$annotations <- c(gglayout$annotations, row_lab)
strip <- make_strip_rect(xdom, ydom, theme, "right")
gglayout$shapes <- c(gglayout$shapes, strip)
}
}
}
} # end of panel loop
# ------------------------------------------------------------------------
# guide conversion
# Strategy: Obtain and translate the output of ggplot2:::guides_train().
# To do so, we borrow some of the body of ggplot2:::guides_build().
# ------------------------------------------------------------------------
# will there be a legend?
gglayout$showlegend <- sum(unlist(lapply(traces, "[[", "showlegend"))) >= 1
# legend styling
gglayout$legend <- list(
bgcolor = toRGB(theme$legend.background$fill),
bordercolor = toRGB(theme$legend.background$colour),
borderwidth = unitConvert(
theme$legend.background[[linewidth_or_size(theme$legend.background)]],
"pixels", "width"
),
font = text2font(theme$legend.text)
)
# if theme(legend.position = "none") is used, don't show a legend _or_ guide
if (npscales$n() == 0 || identical(theme$legend.position, "none")) {
gglayout$showlegend <- FALSE
} else {
# by default, guide boxes are vertically aligned
theme$legend.box <- theme$legend.box %||% "vertical"
# size of key (also used for bar in colorbar guide)
theme$legend.key.width <- theme$legend.key.width %||% theme$legend.key.size
theme$legend.key.height <- theme$legend.key.height %||% theme$legend.key.size
# legend direction must be vertical
theme$legend.direction <- theme$legend.direction %||% "vertical"
if (!identical(theme$legend.direction, "vertical")) {
warning(
"plotly.js does not (yet) support horizontal legend items \n",
"You can track progress here: \n",
"https://github.com/plotly/plotly.js/issues/53 \n",