Additional TrelliscopeJS features

Visualizing Big Data with Trelliscope in R

Ryan Hafen

Author, TrelliscopeJS

ggplot panel interactivity using Plotly

Simply add as_plotly = TRUE to facet_trelliscope().

For example:

gap_life <- select(gapminder, year, lifeExp, country, continent)
ggplot(gap_life, aes(year, lifeExp)) +
  geom_point() +
  facet_trelliscope(~ country + continent,
    name = "lifeExp_by_country",
    desc = "Life expectancy vs. year for 142 countries.",
    nrow = 2, ncol = 3,
    as_plotly = TRUE)
Visualizing Big Data with Trelliscope in R

ggplot panel interactivity using Plotly

Visualizing Big Data with Trelliscope in R

Context-based automatic cognostics

Simply add auto_cog = TRUE to facet_trelliscope().

For example:

ggplot(gap_life, aes(year, lifeExp)) +
  geom_point() +
  facet_trelliscope(~ country + continent,
    name = "lifeExp_by_country",
    desc = "Life expectancy vs. year for 142 countries.",
    nrow = 2, ncol = 3,
    auto_cog = TRUE)

See the help for autocogs::autocog for more information about available automatic cognostics.

Visualizing Big Data with Trelliscope in R

Context-based automatic cognostics

Visualizing Big Data with Trelliscope in R

Axis limits

Axis limit ranges can be controlled with the scales argument.

Three options:

  • "same" (default)
  • "sliced"
  • "free"
Visualizing Big Data with Trelliscope in R

scales = "same"

Each panel's limits are the same.

Enables "apples to apples" comparisons across panels.

Visualizing Big Data with Trelliscope in R

scales = "sliced"

Each panel's limits span the same range but don't necessarily start in the same place.

Useful for making comparisons of differences of scale.

Visualizing Big Data with Trelliscope in R

scales = "free"

Each panel's limits are based on the bounds of its own data.

Visualizing Big Data with Trelliscope in R

Let's practice!

Visualizing Big Data with Trelliscope in R

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