replaced plyr occurences with data.table

tests/testthat/helper-plot-data.r

@@ -1,13 +1,16 @@
+library(data.table)
 # Transform the data as the coordinate system does
 cdata <- function(plot) {
   pieces <- ggplot_build(plot)
 
   lapply(pieces$data, function(d) {
-    plyr::ddply(d, "PANEL", function(panel_data) {
-      scales <- panel_scales(pieces$panel, panel_data$PANEL[1])
+    d <- as.data.table(d)  
+    d[, {
+      scales <- panel_scales(pieces$panel, .SD$PANEL[1])
       details <- plot$coordinates$train(scales)
-      plot$coordinates$transform(panel_data, details)
-    })
+      transformed_data <- plot$coordinates$transform(.SD, details)
+      transformed_data
+    }, by = .(PANEL)]
   })
 }
 

tests/testthat/test-compiler-animation.R

@@ -1,15 +1,19 @@
+library(data.table)
 acontext("animation")
 
-if(require(maps) && require(plyr)){
+if(require(maps)){
   data(UStornadoes, package = "animint2")
   stateOrder <- data.frame(state = unique(UStornadoes$state)[order(unique(UStornadoes$TornadoesSqMile), decreasing=T)], rank = 1:49) # order states by tornadoes per square mile
   UStornadoes$state <- factor(UStornadoes$state, levels=stateOrder$state, ordered=TRUE)
   UStornadoes$weight <- 1/UStornadoes$LandArea
   # useful for stat_bin, etc.
   USpolygons <- map_data("state")
-  USpolygons$state = state.abb[match(USpolygons$region, tolower(state.name))]
-  UStornadoCounts <-
-    ddply(UStornadoes, .(state, year), summarize, count=length(state))
+  USpolygons$state <- state.abb[match(USpolygons$region, tolower(state.name))]
+
+  # Convert to data.table
+  setDT(UStornadoes)
+  UStornadoCounts <- UStornadoes[, .(count = .N), by = .(state, year)]
+
   tornado.anim <- list(
     map=ggplot()+
       geom_polygon(aes(

tests/testthat/test-compiler-fortify.r

@@ -1,5 +1,7 @@
 context("Fortify")
 library(sp)
+library(ggplot2)
+library(dplyr)
 
 test_that("Spatial polygons have correct ordering", {
   make_square <- function(x = 0, y = 0, height = 1, width = 1){
@@ -32,6 +34,5 @@ test_that("Spatial polygons have correct ordering", {
   polys2_sp <- SpatialPolygons(polys2)
   fake_sp2 <- SpatialPolygonsDataFrame(polys2_sp, fake_data)
 
-  expect_equivalent(fortify(fake_sp), plyr::arrange(fortify(fake_sp2), id, order))
-
+  expect_equivalent(fortify(fake_sp), arrange(fortify(fake_sp2), id, order))
 })

tests/testthat/test-compiler-save-separate-chunks.R

@@ -1,5 +1,5 @@
 acontext("save separate chunks")
-library(plyr)
+library(data.table)
 
 data(FluView, package = "animint2")
 # use one season to test
@@ -85,15 +85,15 @@ test_that("save separate chunks for geom_polygon", {
 })
 
 ### test case 2
-USdots <-
-  ddply(FluView$USpolygons, .(region), summarise,
-        mean.lat = mean(lat), 
-        mean.long = mean(long))
-# add state flu to points.
-flu.points <- ldply(unique(state_flu$WEEKEND), function(we) {
+# Convert plyr::ddply to data.table
+USpolygons_dt <- as.data.table(FluView$USpolygons)
+USdots <- USpolygons_dt[, .(mean.lat = mean(lat), mean.long = mean(long)), by = region]
+
+# Convert plyr::ldply to data.table
+flu.points <- rbindlist(lapply(unique(state_flu$WEEKEND), function(we) {
   df <- subset(state_flu, WEEKEND == we)
   merge(USdots, df, by.x = "region", by.y = "state")
-})
+}))
 
 test_that("save separate chunks for geom_point without specifying group", {
   # the compiler will not break a geom into chunks if any of the resulting 

tests/testthat/test-renderer1-hjust-text-anchor.R

@@ -40,10 +40,15 @@ grad.desc <- function(
 dat <- grad.desc()
 contour <- dat$contour
 objective <- dat$objective
-objective <- plyr::ldply(objective$iteration, function(i) {
-  df <- subset(objective, iteration <= i)
+
+# Replaced plyr::ldply with data.table
+library(data.table) 
+objective_dt <- as.data.table(objective)
+objective <- objective_dt[, {
+  df <- .SD[iteration <= i]
   cbind(df, iteration2 = i)
-})
+}, by = .(iteration)]
+
 objective2 <- subset(objective, iteration == iteration2)
 
 grad.desc.viz <- function(hjust) {

tests/testthat/test-renderer1-interactivity.R

@@ -178,9 +178,12 @@ UStornadoes$state <- factor(UStornadoes$state, levels=stateOrder$state, ordered=
 UStornadoes$weight <- 1/UStornadoes$LandArea
 USpolygons <- map_data("state")
 USpolygons$state = state.abb[match(USpolygons$region, tolower(state.name))]
-library(plyr)
-UStornadoCounts <-
-  ddply(UStornadoes, .(state, year), summarize, count=length(state))
+
+# Replaced plyr::ddply with data.table
+library(data.table) 
+UStornadoes_dt <- as.data.table(UStornadoes)
+UStornadoCounts <- UStornadoes_dt[, .(count = .N), by = .(state, year)]
+
 seg.color <- "#55B1F7"
 tornado.lines <- list(
   map=ggplot()+