Create a Trelliscope Display

Create a Trelliscope Display

trelliscope(
  x,
  name,
  group = "common",
  panel_col = NULL,
  desc = "",
  md_desc = "",
  path,
  height = 500,
  width = 500,
  auto_cog = FALSE,
  state = NULL,
  nrow = 1,
  ncol = 1,
  jsonp = TRUE,
  split_sig = NULL,
  self_contained = FALSE,
  thumb = FALSE
)

Arguments

x	an object to create at trelliscope display for
name	name of the display
group	group that the display belongs to
panel_col	optional string specifying the column to use for panels (if there are multiple plot columns in x)
desc	optional text description of the display
md_desc	optional string of markdown that will be shown in the viewer for additional context about the display
path	the base directory of the trelliscope application
height	height in pixels of each panel
width	width in pixels of each panel
auto_cog	should auto cogs be computed (if possible)?
state	the initial state the display will open in
nrow	the number of rows of panels to display by default
ncol	the number of columns of panels to display by default
jsonp	should json for display object be jsonp (TRUE) or json (FALSE)?
split_sig	optional string that specifies the "signature" of the data splitting. If not specified, this is calculated as the md5 hash of the sorted unique facet variables. This is used to identify "related displays" - different displays that are based on the same faceting scheme. This parameter should only be specified manually if a display's faceting is mostly similar to another display's.
self_contained	should the Trelliscope display be a self-contained html document? (see note)
thumb	should a thumbnail be created?

Note

Note that self_contained is severely limiting and should only be used in cases where you would either like your display to show up in the RStudio viewer pane, in an interactive R Markdown Notebook, or in a self-contained R Markdown html document.

Examples

if (FALSE) {
library(dplyr)
library(tidyr)
library(purrr)
library(plotly)
library(ggplot2)

# tidyverse + plotly
d <- mpg %>%
  nest(data = !one_of(c("manufacturer", "class"))) %>%
  mutate(
    mean_city_mpg = map_dbl(data, ~ mean(.$cty)),
    panel = map_plot(data, function(x) {
      plot_ly(data = x, x = ~cty, y = ~hwy,
        type = "scatter", mode = "markers")
    })
  )

d %>% trelliscope(name = "city_vs_highway_mpg")

# set default layout
d %>% trelliscope(name = "city_vs_highway_mpg", nrow = 2, ncol = 3)

# set the output path for where files will be stored
my_displays <- tempfile()
d %>% trelliscope(name = "city_vs_highway_mpg", path = my_displays)

# multiple displays can be added to the same path and all will be available in the viewer
d %>% trelliscope(name = "city_vs_highway_mpg2", path = my_displays)

# ordering the data frame will set default sort order of the display
d %>%
  arrange(-mean_city_mpg) %>%
  trelliscope(name = "city_vs_highway_mpg")

# tidyverse + ggplot2
mpg %>%
  nest(data = !one_of(c("manufacturer", "class"))) %>%
  mutate(
    panel = map_plot(data, ~
      qplot(cty, hwy, data = .) + xlab("cty") + ylab("hwy") +
        xlim(7, 37) + ylim(9, 47) + theme_bw())) %>%
  trelliscope(name = "tidy_gg")

# computing additional cognostics
mpg_cog <- mpg %>%
  nest(data = !one_of(c("manufacturer", "class"))) %>%
  mutate(
    cogs = map_cog(data, ~ tibble(
      mean_city_mpg = mean(.$cty),
      mean_hwy_mpg = mean(.$hwy),
      most_common_drv = tail(names(table(.$drv)), 1)
    ))
  )

# computing additional cognostics explicitly using cog()
# so we can specify descriptions, etc.
mpg_cog2 <- mpg %>%
  nest(data = !one_of(c("manufacturer", "class"))) %>%
  mutate(
    cogs = map_cog(data, ~ tibble(
      mean_city_mpg = cog(mean(.$cty), desc = "Mean city mpg"),
      mean_hwy_mpg = cog(mean(.$hwy), desc = "Mean highway mpg"),
      most_common_drv = cog(tail(names(table(.$drv)), 1), desc = "Most common drive type")
    )),
    panel = map_plot(data, function(x) {
      plot_ly(data = x, x = ~cty, y = ~hwy,
        type = "scatter", mode = "markers") %>%
        layout(
          xaxis = list(range = c(9, 47)),
          yaxis = list(range = c(7, 37)))
    })
  )

mpg_cog2 %>%
  trelliscope(name = "city_vs_highway_mpg", nrow = 1, ncol = 2)
}