Instantiate a trelliscope data frame

Usage

as_trelliscope_df(
  df,
  name = NULL,
  description = name,
  key_cols = NULL,
  tags = NULL,
  path = NULL,
  force_plot = FALSE,
  key_sig = NULL,
  order = 0,
  jsonp = TRUE
)

Arguments

df: A data frame that contains the metadata of the display as well as a column that indicate the panels to be displayed.
name: Name of the trelliscope display.
description: Description of the trelliscope display.
key_cols: Variable names in df that uniquely define a row of the data. If not supplied, an attempt will be made to infer them.
tags: Optional vector of tag names to identify the display in the case that there are many to search through.
path: Directory in which to place the trelliscope display when it is written using write_trelliscope().
force_plot: Should the panels be forced to be plotted, even if they have already been plotted and have not changed since the previous plotting?
key_sig: A string "signature" that represents the panels for this display. This should not be specified unless you know what you are doing.
order: If there will be multiple displays in the same directory, this can be used to specify the order in which they should be listed. The display with the lowest order will be shown on load.
jsonp: If true, app data files are written as "jsonp" format, otherwise "json" format. The "jsonp" format makes it possible to browse a trelliscope app without the need for a web server and is set to TRUE by default. Use "json" if you are deploying to a web server that doesn't work well with "jsonp".

Examples

# Use `as_trelliscope_df()` to convert panel metadata to a special
# trelliscope data frame
library(ggplot2)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

panel_dat <- (
  ggplot(gap, aes(year, life_exp)) +
    geom_point() +
    facet_panels(vars(country, continent))
  ) |>
    as_panels_df()

meta_dat <- gap |>
  group_by(country, continent) |>
  summarise(
    mean_life_exp = mean(life_exp),
    min_life_exp = min(life_exp),
    max_life_exp = max(life_exp),
    mean_gdp = mean(gdp_percap),
    .groups = "drop"
  )

joined_dat <- left_join(panel_dat, meta_dat) |>
  as_trelliscope_df(name = "life_expectancy", path = tempfile())
#> Joining with `by = join_by(country, continent)`

if (FALSE) {
view_trelliscope(joined_dat)
}

# You can also use `as_trelliscope_df()` on datasets that have links to
# images instead of conventional ggplot objects
if (FALSE) {
}