trelliscope: Create a Trelliscope Display

In trelliscopejs: Create Interactive Trelliscope Displays

Create a Trelliscope Display

Description

Create a Trelliscope Display

Usage

trelliscope(
  x,
  name,
  group = "common",
  panel_col = NULL,
  cond_cols = NULL,
  desc = "",
  md_desc = "",
  path,
  height = 500,
  width = 500,
  inputs = NULL,
  google_analytics_id = NULL,
  auto_cog = FALSE,
  state = NULL,
  views = NULL,
  nrow = 1,
  ncol = 1,
  jsonp = TRUE,
  split_sig = NULL,
  self_contained = FALSE,
  thumb = FALSE,
  require_token = FALSE,
  id = NULL,
  order = 1,
  disclaimer = FALSE,
  update_plots = TRUE
)

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)
cond_cols	optionsl vector of columns to specify as "conditioning" columns - combined they must be unique and not have any missing values
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
inputs	optional set of input specifications (using input_cogs) to allow user input for each panel
google_analytics_id	optional string specifying Google Analytics ID
auto_cog	should auto cogs be computed (if possible)?
state	the initial state the display will open in
views	an optional list of pre-specified views of the display (experimental)
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?
require_token	require a special token for all displays to be visible (experimental)
id	set a hard-coded ID for this app (do not set this if the display will be part of a larger web page)
order	an integer indicating the order that the display should appear in if using multiple displays
disclaimer	an optional string of html to include as a disclaimer for the set of displays
update_plots	should the plots be updated? This is to allow slight updates to the underlying display data without the need to re-render all of the plots. Use it carefully.

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

## Not run: 
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)

## End(Not run)

trelliscopejs documentation built on Aug. 8, 2025, 6:40 p.m.