Adding your own cognostics

Visualizing Big Data with Trelliscope in R

Ryan Hafen

Author, TrelliscopeJS

New variables as cognostics

All variables in the data passed in to ggplot() are inspected for use as cognostics.

If the variable is numeric and varies within each panel group, a set of summary statistics is computed for each panel.
If the variable is constant within each panel group, a single cognostic with that value is computed for each panel.

Latest life expectancy as a cognostic

gap <- gapminder %>%
  group_by(country) %>%
  mutate(latestLifeExp = tail(lifeExp, 1))

gap

# A tibble: 1,704 x 7
# Groups:   country [142]
   country     continent  year lifeExp      pop gdpPercap latestLifeExp
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>         <dbl>
 1 Afghanistan Asia       1952    28.8  8425333       779          43.8
 2 Afghanistan Asia       1957    30.3  9240934       821          43.8
 3 Afghanistan Asia       1962    32.0 10267083       853          43.8
 4 Afghanistan Asia       1967    34.0 11537966       836          43.8
 5 Afghanistan Asia       1972    36.1 13079460       740          43.8
 6 Afghanistan Asia       1977    38.4 14880372       786          43.8
# ... with 1,698 more rows

Hyperlinks as cognostics

gap <- gapminder %>%
  group_by(country, continent) %>%
  mutate(wiki = paste0("https://en.wikipedia.org/wiki/", country))

Customizing custom cognostics

A function cog() can be wrapped around a variable to fine-tune how a cognostic is handled in Trelliscope.

With cog(), some of the most useful things you can specify include:

desc: a meaningful description for the cognostic
default_label: a boolean specifying whether the cognostic should be shown as a label by default or not

gap$wiki <- cog_href(gap$wiki, 
                     desc = "Link to wikipedia", 
                     default_label = TRUE)

Let's practice!

Visualizing Big Data with Trelliscope in R