Applications of Interactive Exploration of Large Multi-Panel Faceted Displays Using Trelliscope

Hafen Consulting, LLC

Purdue University

@hafenstats

Ryan Hafen

http://bit.ly/hafenjsm17

Context: Exploratory analysis and statistical model building

Image source: Hadley Wickham

Small Multiples

A series of similar plots, usually each based on a different slice of data, arranged in a grid

"For a wide range of problems in data presentation, small multiples are the best design solution."

Edward Tufte (Envisioning Information)

This idea was formalized and popularized in S/S-PLUS and subsequently R with the trellis and lattice packages

Trelliscope: Interactive Small Multiple Display

• Small multiple displays are useful when visualizing data in detail
• But the number of panels in a display can be potentially very large, too large to view all at once

Trelliscope is a general solution that allows small multiple displays to come alive by providing the ability to interactively sort and filter the panels based on summary statistics, cognostics, that capture attributes of interest in the data being plotted

Application: Exploring Small Multiples

Gapminder

Suppose we want to understand mortality over time for each country

Observations: 1,704
Variables: 6
$ country   <fctr> Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afgh...
$ continent <fctr> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, As...
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 199...
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 4...
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372,...
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.113...

glimpse(gapminder)

https://www.gapminder.org/

qplot(year, lifeExp, data = gapminder, color = country, geom = "line")

Yikes! There are a lot of countries...

qplot(year, lifeExp, data = gapminder, color = continent,
  group = country, geom = "line")

Still too much going on...

qplot(year, lifeExp, data = gapminder, color = continent,
  group = country, geom = "line") +
    facet_wrap(~ continent, nrow = 1)

That helped a little...

p <- qplot(year, lifeExp, data = gapminder, color = continent,
  group = country, geom = "line") +
    facet_wrap(~ continent, nrow = 1)
plotly::ggplotly(p)

This helps but there is still too much overplotting...

(and hovering for additional info is too much work and we can only see more info one at a time)

qplot(year, lifeExp, data = gapminder) + theme_bw() +
  facet_wrap(~ country + continent)

qplot(year, lifeExp, data = gapminder) + theme_bw() +
  facet_trelliscope(~ country + continent, nrow = 2, ncol = 7, width = 300)

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Note: this and future plots in this presentation are interactive - feel free to explore!

qplot(year, lifeExp, data = gapminder) + theme_bw() +
  facet_trelliscope(~ country + continent,
    nrow = 2, ncol = 7, width = 300, as_plotly = TRUE)

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TrelliscopeJS

JavaScript Library

R Package

trelliscopejs-lib

trelliscopejs

• Built using React
• Pure JavaScript
• Interface agnostic

• htmlwidget interface to trelliscopejs-lib
• Evolved from CRAN "trelliscope" package (part of DeltaRho project)

Application:

Assessing Fits of Many Models

country_model <- function(df)
  lm(lifeExp ~ year, data = df)

by_country <- gapminder %>%
  group_by(country, continent) %>%
  nest() %>%
  mutate(
    model = map(data, country_model),
    resid_mad = map_dbl(model, function(x) mad(resid(x))))

by_country

Example adapted from "R for Data Science"

# A tibble: 142 × 5
       country continent              data    model resid_mad
        <fctr>    <fctr>            <list>   <list>     <dbl>
1  Afghanistan      Asia <tibble [12 × 4]> <S3: lm> 1.4058780
2      Albania    Europe <tibble [12 × 4]> <S3: lm> 2.2193278
3      Algeria    Africa <tibble [12 × 4]> <S3: lm> 0.7925897
4       Angola    Africa <tibble [12 × 4]> <S3: lm> 1.4903085
5    Argentina  Americas <tibble [12 × 4]> <S3: lm> 0.2376178
6    Australia   Oceania <tibble [12 × 4]> <S3: lm> 0.7934372
7      Austria    Europe <tibble [12 × 4]> <S3: lm> 0.3928605
8      Bahrain      Asia <tibble [12 × 4]> <S3: lm> 1.8201766
9   Bangladesh      Asia <tibble [12 × 4]> <S3: lm> 1.1947475
10     Belgium    Europe <tibble [12 × 4]> <S3: lm> 0.2353342
# ... with 132 more rows

Gapminder Example from "R for Data Science"

• One row per group
• Per-group data and models as "list-columns"

country_plot <- function(data, model) {
  figure(xlim = c(1948, 2011),
    ylim = c(10, 95), tools = NULL) %>%
    ly_points(year, lifeExp, data = data, hover = data) %>%
    ly_abline(model)
}

country_plot(by_country$data[[1]],
  by_country$model[[1]])

Plotting the Data and Model Fit for Each Group

We'll use the rbokeh package to make a plot function and apply it to the first row of our data

by_country <- by_country %>%
  mutate(plot = map2_plot(data, model, country_plot))

by_country

Example adapted from "R for Data Science"

# A tibble: 142 × 6
       country continent              data    model resid_mad         plot
        <fctr>    <fctr>            <list>   <list>     <dbl>       <list>
1  Afghanistan      Asia <tibble [12 × 4]> <S3: lm> 1.4058780 <S3: rbokeh>
2      Albania    Europe <tibble [12 × 4]> <S3: lm> 2.2193278 <S3: rbokeh>
3      Algeria    Africa <tibble [12 × 4]> <S3: lm> 0.7925897 <S3: rbokeh>
4       Angola    Africa <tibble [12 × 4]> <S3: lm> 1.4903085 <S3: rbokeh>
5    Argentina  Americas <tibble [12 × 4]> <S3: lm> 0.2376178 <S3: rbokeh>
6    Australia   Oceania <tibble [12 × 4]> <S3: lm> 0.7934372 <S3: rbokeh>
7      Austria    Europe <tibble [12 × 4]> <S3: lm> 0.3928605 <S3: rbokeh>
8      Bahrain      Asia <tibble [12 × 4]> <S3: lm> 1.8201766 <S3: rbokeh>
9   Bangladesh      Asia <tibble [12 × 4]> <S3: lm> 1.1947475 <S3: rbokeh>
10     Belgium    Europe <tibble [12 × 4]> <S3: lm> 0.2353342 <S3: rbokeh>
# ... with 132 more rows

Apply This Function to Every Row

A plot for each model

by_country %>%
  trelliscope(name = "by_country_lm", nrow = 2, ncol = 4)

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Application:

Images as Panels

(Database of Visualizations)

pokemon <- read_csv("http://bit.ly/plot_pokemon") %>%
  mutate_at(vars(matches("_id$")), as.character) %>%
  mutate(panel = img_panel(url_image))

pokemon

trelliscope(pokemon, name = "pokemon", nrow = 3, ncol = 6,
  state = list(labels = c("pokemon", "pokedex")))
​

data source

blog post

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read_csv("http://bit.ly/trs-mri") %>%
  mutate(img = img_panel(img)) %>%
  trelliscope("brain_MRI", nrow = 2, ncol = 5)

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Trelliscope Displays as Apps

library(shiny)
library(ggplot2)
library(gapminder)

server <- function(input, output) {
  output$countryPlot <- renderPlot({
    qplot(year, lifeExp,
      data = subset(gapminder,
        country == input$country)) +
      xlim(1948, 2011) + ylim(10, 95) +
      theme_bw()
  })
}

choices <- sort(unique(gapminder$country))

ui <- fluidPage(
  titlePanel("Gampinder Life Expectancy"),
  sidebarLayout(
    sidebarPanel(
      selectInput("country",
        label = "Select country: ",
        choices = choices,
        selected = "Afghanistan")
    ),
    mainPanel(
      plotOutput("countryPlot",
        height = "500px")
    )
  )
)

runApp(list(ui = ui, server = server))

Trelliscope Displays as Apps

If you have an app that has multiple inputs and produces a plot output, the idea is simply to enumerate all possible inputs as rows of a data frame and add the plot that corresponds to these parameters as column and plot it

Trelliscope displays are most useful as exploratory plots to guide the data scientist (because they can be created rapidly)

However, in many cases Trelliscope displays can be used as interactive applications for end-users, domain experts, etc. with the bonus that they are much easier to create than a custom app

Ongoing Work

• Trelliscope
  • Automatic cognostics: automatically compute useful cognostics based on the context of what is being plotted
  • Automatic determination of how "interesting" a given partitioning will be based on what is being plotted
  • When axes are "same", only show axes on plot margins instead of every panel (underway for ggplot2)
  • Deferred panels for very large displays
• trelliscopejs-lib
  • More visual filters for cognostics (dates, geographic, bivariate relationships, etc.)
  • Bookmarkable / sharable state
  • View multiple panels side-by-side
  • Support for receiving panels from other endpoints

For More Information

• Twitter: @hafenstats
• Blog: http://ryanhafen.com/blog
• Documentation: http://hafen.github.io/trelliscopejs
• Github: https://github.com/hafen/trelliscopejs

All examples in this talk are reproducible after installing and loading the following packages:

From ggplot2 Faceting to Trelliscope

Turning a ggplot2 faceted display into a Trelliscope display is as easy as changing:

to:

facet_wrap()

or:

facet_grid()

facet_trelliscope()

TrelliscopeJS in the Tidyverse

• Create a data frame with one row per group, typically using Tidyverse group_by() and nest() operations
• Add a column of plots
  • TrelliscopeJS provides purrr map functions map_plot(), map2_plot(), pmap_plot() that you can use to create these
  • You can use any graphics system to create the plot objects (ggplot2, htmlwidgets, lattice)
• Optionally add more columns to the data frame that will be used as cognostics - metrics with which you can interact with the panels
  • All atomic columns will be automatically used as cognostics
  • Map functions map_cog(), map2_cog(), pmap_cog() can be used for convenience to create columns of cognostics
• Simply pass the data frame in to trelliscope()

With plots as columns, TrelliscopeJS provides nearly effortless detailed, flexible, interactive visualization in the Tidyverse

library(visNetwork)

nnodes <- 100
nnedges <- 1000

nodes <- data.frame(
  id = 1:nnodes,
  label = 1:nnodes, value = rep(1, nnodes))
edges <- data.frame(
  from = sample(1:nnodes, nnedges, replace = T),
  to = sample(1:nnodes, nnedges, replace = T)) %>%
    group_by(from, to) %>%
    summarise(value = n())

network_plot <- function(id, hide_select = TRUE) {
  style <- ifelse(hide_select,
    "visibility: hidden; position: absolute", "")

  visNetwork(nodes, edges) %>%
    visIgraphLayout(layout = "layout_in_circle") %>%
    visNodes(fixed = TRUE, scaling =
      list(min = 20, max = 50,
        label = list(min = 35, max = 70,
          drawThreshold = 1, maxVisible = 100))) %>%
    visEdges(scaling = list(min = 5, max = 30)) %>%
    visOptions(highlightNearest = list(enabled = TRUE, degree = 0,
      hideColor = "rgba(200,200,200,0.2)"),
      nodesIdSelection = list(selected = as.character(id), style = style))
}

network_plot(1, hide_select = FALSE)

Network Vis with visNetwork htmlwidget

nodedat <- edges %>%
  group_by(from) %>%
  summarise(n_nodes = n(), tot_conns = sum(value)) %>%
  rename(id = from) %>%
  arrange(-n_nodes) %>%
  mutate(panel = map_plot(id, network_plot))

nodedat

# A tibble: 100 × 4
      id n_nodes tot_conns            panel
   <int>   <int>     <int>           <list>
1     58      17        19 <S3: visNetwork>
2     45      16        17 <S3: visNetwork>
3      9      15        18 <S3: visNetwork>
4     31      15        16 <S3: visNetwork>
5     14      14        15 <S3: visNetwork>
6     42      14        15 <S3: visNetwork>
7     90      14        14 <S3: visNetwork>
8     21      13        14 <S3: visNetwork>
9     37      13        14 <S3: visNetwork>
10    43      13        13 <S3: visNetwork>
# ... with 90 more rows

Trelliscope display with one panel per node

nodedat %>%
  arrange(-n_nodes) %>%
  trelliscope(name = "connections", nrow = 2, ncol = 4)

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