In cpsievert/pedestrians: Tools for exploring Melbourne's Pedestrian Data

This is a working example showing how we can perform linked highlighting between plotly and leaflet (see a video of it in action). Plotly itself has extensive support for linked highlighting where interaction types, colors, and persistent/transient selection can be specified via the highlight() function. See a growing set of examples here

DISCLAIMER: This is very experimental, and requires development versions of several packages. Press the "code" buttons to see how to install and create this example.

knitr::opts_chunk$set(message = FALSE, warning = FALSE)

# A standalone HTML version of launchApp()

# devtools::install_github("rstudio/leaflet@56eb3ecbb25ddc195c1cc6f530246dbb565f99ee")
library(leaflet)
# devtools::install_github("ropensci/plotly@dce5a288b2b7daddf3884b4f57dbfa4e02b9fab8")
library(plotly)
library(crosstalk)
library(htmltools)
library(dplyr)
library(tidyr)

data(pedestrians, package = "pedestrians")
data(sensors, package = "pedestrians")
data(cog, package = "pedestrians")

# put all the cognostics on a 0-1 scale
cogVars <- setdiff(names(cog), "ID") 
for (i in cogVars) {
  cog[[i]] <- scales::rescale(cog[[i]])
}
# attach the sensor description
cog <- dplyr::left_join(cog, sensors[c("ID", "Description")], "ID")

# random sample (needed for performance/speed)
n <- nrow(pedestrians)
pedSample <- pedestrians[sample(n, n * 0.01), ]

# Use the ID field as the shared key
pedestrians <- SharedData$new(pedestrians, key = ~ID, group = "melb")
pedSample <- SharedData$new(pedSample, key = ~ID, group = "melb")
sensors <- SharedData$new(sensors, key = ~ID, group = "melb")
cog <- SharedData$new(cog, key = ~ID, group = "melb")

# let leaflet know that selections should persist
options(persistent = TRUE)

# plot 1
p1 <- sensors %>%
  leaflet(height = 200) %>% 
  addTiles() %>% 
  fitBounds(
    ~min(Longitude), ~min(Latitude), ~max(Longitude), ~max(Latitude)
  ) %>%
  addCircleMarkers(
    ~Longitude, ~Latitude, layerId = ~ID, label = ~Description, color = "black"
  )

# plot 2
p <- cog %>%
  plot_ly(height = 200) %>% 
  gather_("variable", "value", cogVars) %>%
  ggplot(aes(variable, value, group = ID, text = Description)) + 
  geom_line() + geom_point(size = 0.01) + 
  theme(axis.text.x = element_text(angle = 45)) + 
  labs(x = NULL, y = NULL)

p2 <- p %>%
  ggplotly(tooltip = "Description", height = 200) %>%
  layout(dragmode = "select", margin = list(b = 70)) %>%
  highlight(dynamic = TRUE, persistent = TRUE)

# plot 3
p3 <- pedSample %>%
  plot_ly(x = ~Hour, y = ~Counts, 
          color = I("black"), alpha = 0.01, height = 200) %>% 
  toWebGL() %>%
  highlight(opacityDim = 1, persistent = TRUE)

# plot 4 (IQR ribbons)
tidyIQR <- function(data, groups = NULL) {
  if (is.SharedData(data)) data <- data$origData()
  for (i in groups) {
    data <- group_by_(data, i, add = TRUE)
  }
  summarise(
    data,
    min = min(Counts),
    q1 = quantile(Counts, 0.25),
    med = median(Counts),
    q3 = quantile(Counts, 0.75),
    max = max(Counts)
  )
}

byHour <- tidyIQR(pedestrians, "Hour")
byHourID <- tidyIQR(pedestrians, c("Hour", "ID"))
byHourID <- SharedData$new(byHourID, ~ID, "melb")

p4 <- plot_ly(byHour, x = ~Hour, color = I("black"), height = 200) %>%
  add_ribbons(ymin = ~q1, ymax = ~q3) %>%
  add_lines(y = ~med) %>%
  add_data(byHourID) %>%
  group_by(ID) %>%
  add_ribbons(ymin = ~q1, ymax = ~q3, color = I("red")) %>%
  add_lines(y = ~med, color = I("red")) %>%
  highlight(defaultValues = 1, opacityDim = 0, persistent = TRUE) %>%
  hide_legend()

browsable(tags$div(
  style = "display: flex; flex-wrap: wrap",
  tagList(
    tags$div(p1, style = "width: 50%; padding: 1em; border: solid;"),
    tags$div(p2, style = "width: 50%; padding: 1em; border: solid;"),
    tags$div(p3, style = "width: 50%; padding: 1em; border: solid;"),
    tags$div(p4, style = "width: 50%; padding: 1em; border: solid;")
  )
))