3 Categories of data visualization

Dealing with complex datasets

Clarity in presentation

• Zooming allows the user to determine scale of presentation

• Hovering allows more information to be displayed ‘on-demand’

• Subsetting facilitates ease of interpretation

Who is your audience?

• Your advisor and colleagues?

• An external collaborator?

• The general public?

  • User archetypes

Iteration

A note about APIs

• API: Application Programming Interface

• A software intermediary that allows two applications to “communicate”

• Lots of R packages rely on APIs to access data on the web (e.g.,tidycensus)

• Facilitates reproducibility and powerful web applications built on R analyses

• May require “keys” and additional parsing (Mapbox and Google)

Interactive maps with mapview and tmap

• Easy extension of your existing

• Class Demo

Clarity in presentation (revisited)

Using plotly

• Syntax is similar to ggplot

• hoverinfo describes which elements you’d like to make interactive

• Other plot elements available (see ?plot_ly)

Using plotly

g <- txhousing %>% 
  # group by city
  group_by(city) %>%
  # initiate a plotly object with date on x and median on y
   plotly::plot_ly(x = ~date, y = ~median) %>%
  # add a line plot for all texan cities
   plotly::add_lines(name = "Texan Cities", hoverinfo = "none", 
            type = "scatter", mode = "lines", 
            line = list(color = 'rgba(192,192,192,0.4)')) %>%
  # plot separate lines for Dallas and Houston
   plotly::add_lines(name = ~city, 
            data = filter(txhousing, 
                          city %in% c("Dallas", "Houston")),
            hoverinfo = ~city,
            line = list(color = c("red", "blue")),
            color = ~city)