only insert missing values to differentiate groups when it's relevant

Author: cpsievert

R/plotly_build.R

@@ -179,16 +179,13 @@ plotly_build.plotly <- function(p) {
       #    are combined into a single grouping variable, .plotlyGroupIndex
       builtData <- arrange_safe(builtData, ".plotlyTraceIndex")
       isComplete <- complete.cases(builtData[names(builtData) %in% c("x", "y", "z")])
-      # is grouping relevant for this geometry? (e.g., grouping doesn't effect a scatterplot)
-      hasGrp <- inherits(trace, paste0("plotly_", c("segment", "path", "line", "polygon"))) ||
-        (grepl("scatter", trace[["type"]]) && grepl("lines", trace[["mode"]]))
       # warn about missing values if groups aren't relevant for this trace type
-      if (any(!isComplete) && !hasGrp) {
+      if (any(!isComplete) && !has_group(trace)) {
         warning("Ignoring ", sum(!isComplete), " observations", call. = FALSE)
       }
       builtData[[".plotlyMissingIndex"]] <- cumsum(!isComplete)
       builtData <- builtData[isComplete, ]
-      if (length(grps) && hasGrp && isTRUE(trace[["connectgaps"]])) {
+      if (length(grps) && has_group(trace) && isTRUE(trace[["connectgaps"]])) {
         stop(
           "Can't use connectgaps=TRUE when data has group(s).", call. = FALSE
         )
@@ -229,7 +226,8 @@ plotly_build.plotly <- function(p) {
   traces <- lapply(traces, function(x) {
     d <- data.frame(x[names(x) %in% x$.plotlyVariableMapping], stringsAsFactors = FALSE)
     d <- group2NA(
-      d, ".plotlyGroupIndex", ordered = if (inherits(x, "plotly_line")) "x",
+      d, if (has_group(x)) ".plotlyGroupIndex", 
+      ordered = if (inherits(x, "plotly_line")) "x",
       retrace.first = inherits(x, "plotly_polygon")
     )
     for (i in x$.plotlyVariableMapping) {

R/utils.R

@@ -47,6 +47,12 @@ getLevels <- function(x) {
   if (is.factor(x)) levels(x) else sort(unique(x))
 }
 
+# is grouping relevant for this geometry? (e.g., grouping doesn't effect a scatterplot)
+has_group <- function(trace) {
+  inherits(trace, paste0("plotly_", c("segment", "path", "line", "polygon"))) ||
+    (grepl("scatter", trace[["type"]]) && grepl("lines", trace[["mode"]]))
+}
+
 # currently implemented non-positional scales in plot_ly()
 npscales <- function() {
   c("color", "symbol", "linetype", "size", "split")

tests/testthat/test-ggplot-heatmap.R

@@ -46,7 +46,7 @@ test_that("geom_tile() scale_fill_gradient2()", {
 })
 
 tidy_cor <- function(x) {
-  co <- as.data.frame(cor(x))
+  co <- as.data.frame(cor(x[vapply(x, is.numeric, logical(1))]))
   co$var1 <- row.names(co)
   tidyr::gather(co, var2, cor, -var1)
 }

tests/testthat/test-plotly-group.R

@@ -52,3 +52,16 @@ test_that("Missing values are preserved for lines within a color variable", {
   # connectgaps makes sense in this case
   l <- expect_traces(add_lines(p3, connectgaps = TRUE), 5, "NAs-within-color2")
 })
+
+m <- mtcars
+m$rowname <- rownames(mtcars)
+p <- m %>% 
+  dplyr::group_by_("rowname") %>%
+  plot_ly(x = ~wt, y = ~mpg) %>% 
+  add_markers()
+
+test_that("Groups are ignored if grouping is irrelevant for the geom", {
+  l <- expect_traces(p, 1, "no-NAs-for-irrelevant-group")
+  expect_length(l$data[[1]][["x"]], 32)
+  expect_length(l$data[[1]][["y"]], 32)
+})