Create a trelliscopejs object populated by descriptors

While trelliscopejs is a very powerful visualization paradigm, it can be hard to munge the data into the appropriate format. This function automatically creates a trelliscopejs visualization populated with pivot tables created by rpivotTable.

trellis_descriptors(data, descriptors, diagnostics = NULL, path, name, group,
  size = 0.5, rank = FALSE, state = NULL, self_contained = FALSE,
  desc = "", md_desc = "", height = 500, width = 500, nrow = 1,
  ncol = 1, verbose = TRUE)

Arguments

data

A square, numeric dataframe, matrix or tibble.
descriptors

String name of the pivottable.js aggregator to prepopulate the pivot table.
path

The base directory of the trelliscope application.
name

A string name of the display.
group

Group that the display belongs to.
rank

The base directory of the trelliscope application.
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.
height

Height in pixels of each panel.
width

Width in pixels of each panel.
nrow

The number of rows of panels to display by default.
ncol

The number of columns of panels to display by default.
colors

The base directory of the trelliscope application.
name

Name of the display.

Value

A trellisopejs object populated by pivot tables.

Details

For the arguments aggregator and renderer see aggregatorName and rendererName arguments from the rpivotTable package. For all other arguments (except the data argument), see the identical arguments in the trelliscopejs package.

References

Muchmore, B., Muchmore P. and Alarcón-Riquelme ME. (2018). Optimal Distance Matrix Construction with PreciseDist and PreciseGraph.

Enzo Martoglio (2018). rpivotTable: Build Powerful Pivot Tables and Dynamically Slice & Dice your Data. R package version 0.3.0. https://CRAN.R-project.org/package=rpivotTable

Examples

library(PreciseDist)

test_matrix <- replicate(10, rnorm(100))

test_dists <- test_matrix %>%
    precise_dist(c("manhattan", "euclidean"))
#> [1] "Starting dists calculations at 2018-09-26 10:46:39"
#> [1] "Finished dists calculations at 2018-09-26 10:46:40"
#> [1] "Calculations took: 0.5 seconds"
#> [1] "Starting dist_funcs calculations at 2018-09-26 10:46:40"
#> [1] "Finished dist_funcs calculations at 2018-09-26 10:46:40"
#> [1] "Calculations took: 0 seconds"

test_fusion <- test_dists %>%
  precise_fusion(fusion = "fuse", verbose = TRUE)
#> [1] "Starting distance matrix fusion using fuse at 2018-09-26 10:46:40"
#> [1] "Finished distance matrix fusion at 2018-09-26 10:46:40"
#> [1] "Fusion took: 0 seconds"