Applications of Interactive Exploration of Large Multi-Panel Faceted Displays Using Trelliscope
Ryan Hafen
http://bit.ly/hafenjsm17
Context: Exploratory analysis and statistical model building
Image source: Hadley Wickham
- Visualization is usually the driver of iteration
- It is particularly useful when working with a domain expert
- Interactive visualization often (not always) makes the process more effective
- The iteration in exploratory analysis necessitates rapid generation of many visualizations
- A lot of interactive visualization is very customized and time-consuming to create
- Not every plot is useful so we can't afford to waste a lot of time on any single visualization
- Just like using a high-level programming language for rapidly trying out ideas with data analysis, we need high-level ways to flexibly but quickly create interactive visualizations
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
Advantages of Small Multiple Displays
- Avoid overplotting
- Work with big or high dimensional data
-
It is often critical to the discovery of a new insight to be able to see multiple things at once
- Our brains are good at perceiving simple visual features like color or shape or size and they do it amazingly fast without any conscious effort
- We can tell immediately when a part of an image is different from the rest, without really having to focus on it
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)
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)
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)
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)
Application:
Images as Panels
(Database of Visualizations)
pokemon <- read_csv("http://bit.ly/plot_pokemon") %>% mutate_at(vars(matches("_id$")), as.character) %>% mutate(panel = img_panel(url_image)) pokemon
trelliscope(pokemon, name = "pokemon", nrow = 3, ncol = 6,
state = list(labels = c("pokemon", "pokedex")))
read_csv("http://bit.ly/trs-mri") %>% mutate(img = img_panel(img)) %>% trelliscope("brain_MRI", nrow = 2, ncol = 5)
Trelliscope Displays as Apps
library(shiny) library(ggplot2) library(gapminder) server <- function(input, output) { output$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
install.packages(c("tidyverse", "gapminder", "rbokeh", "visNetwork", "plotly")) devtools::install_github("hafen/trelliscopejs") library(tidyverse) library(gapminder) library(rbokeh) library(visNetwork) library(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
We create a one-row-per-node data frame with number of nodes connected to and total number of connections as cognostics and add a plot panel column
nodedat %>%
arrange(-n_nodes) %>%
trelliscope(name = "connections", nrow = 2, ncol = 4)
Applications of Interactive Exploration of Large Multi-Panel Faceted Displays Using Trelliscope
By Ryan Hafen
Applications of Interactive Exploration of Large Multi-Panel Faceted Displays Using Trelliscope
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