rbokeh
A Simple, Flexible, Declarative Framework for Interactive Graphics
Follow along live: http://bit.ly/rbokeh1v
View at your own pace: http://bit.ly/rbokeh1
What is rbokeh?
- An R interface to the Bokeh library
- Bokeh:
- Open source interactive visualization library
- Created by Continuum Analytics
- http://bokeh.pydata.org/
- rbokeh goals:
- General purpose web-based visualization system for ad-hoc statistical graphics
- R-like interface
- Simple transient interactivity for free or with very little effort (zoom / pan / tooltips / etc.)
- Put more complex interactivity within reach
bokeh
Interface
Python
rbokeh
R
Bokeh.jl
Julia
bokeh-scala
Scala
JSON spec
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}BokehJS
rbokeh Interface
-
Initialize a figure with figure() and add layers with pipes
-
Plots are highly customizable - for further control, other pipeable functions can augment aspects of a figure, including axes, themes, and interaction tools
-
Several features you are used to in ggplot2 are available, such as aesthetics, NSE, automatic range calculation if none specified, etc.
-
In addition to figure(), there is gmap() for superposition on a google map, grid_plot() for constructing a grid of figures
figure(data = iris) %>%
ly_points(Sepal.Length, Sepal.Width, color = Species)20 examples in 5 minutes
1: iris data ("hello world")
figure(data = iris) %>%
ly_points(Sepal.Length, Sepal.Width, color = Species
hover = list(Sepal.Length, Sepal.Width))2: custom hover and url on click
figure() %>%
ly_points(Sepal.Length, Sepal.Width, data = iris, color = Species,
hover = "The species of this point is '@Species'",
url = "https://www.google.com/search?q=@Species+iris")3: adding non-default tools (e.g. lasso select)
figure() %>%
ly_points(1:10) %>%
tool_lasso_select()
# or
tools <- c("pan", "wheel_zoom", "box_zoom", "resize", "lasso_select",
"reset", "save", "help")
figure(tools = tools) %>%
ly_points(1:10) %>%
See here for details and more examples
4: modifying axes
figure() %>%
ly_points(Date, Packages, Ecdat::CRANpackages) %>%
y_axis(log = TRUE)See here for details and more examples
5: stat layers: model fits
z <- lm(dist ~ speed, data = cars)
figure(width = 600, height = 600) %>%
ly_points(cars, hover = cars) %>%
ly_lines(lowess(cars), legend = "lowess") %>%
ly_abline(z, type = 2, legend = "lm")6: stat layers: histogram / density
figure(width = 600, height = 400) %>%
ly_hist(eruptions, data = faithful, breaks = 40, freq = FALSE) %>%
ly_density(eruptions, data = faithful)7: stat layers: hexbin
figure() %>%
ly_hexbin(rnorm(10000), rnorm(10000))8: stat layers: bar
figure(data = mpg, legend_location = "top_left", width = 600) %>%
ly_bar(class, color = drv) %>%
theme_axis("x", major_label_orientation = 45)9: categorical axes
# prepare data
elements <- subset(elements, !is.na(group))
elements$group <- as.character(elements$group)
elements$period <- as.character(elements$period)
# add colors for groups
metals <- c("alkali metal", "alkaline earth metal", "halogen",
"metal", "metalloid", "noble gas", "nonmetal", "transition metal")
colors <- c("#a6cee3", "#1f78b4", "#fdbf6f", "#b2df8a", "#33a02c",
"#bbbb88", "#baa2a6", "#e08e79")
elements$color <- colors[match(elements$metal, metals)]
elements$type <- elements$metal
# make coordinates for labels
elements$symx <- paste(elements$group, ":0.1", sep = "")
elements$numbery <- paste(elements$period, ":0.8", sep = "")
elements$massy <- paste(elements$period, ":0.15", sep = "")
elements$namey <- paste(elements$period, ":0.3", sep = "")
# create figure
p <- figure(title = "Periodic Table", tools = c("resize", "hover"),
ylim = as.character(c(7:1)), xlim = as.character(1:18),
xgrid = FALSE, ygrid = FALSE, xlab = "", ylab = "",
height = 445, width = 800) %>%
# plot rectangles
ly_crect(group, period, data = elements, 0.9, 0.9,
fill_color = color, line_color = color, fill_alpha = 0.6,
hover = list(name, atomic.number, type, atomic.mass,
electronic.configuration)) %>%
# add symbol text
ly_text(symx, period, text = symbol, data = elements,
font_style = "bold", font_size = "10pt",
align = "left", baseline = "middle") %>%
# add atomic number text
ly_text(symx, numbery, text = atomic.number, data = elements,
font_size = "6pt", align = "left", baseline = "middle") %>%
# add name text
ly_text(symx, namey, text = name, data = elements,
font_size = "4pt", align = "left", baseline = "middle") %>%
# add atomic mass text
ly_text(symx, massy, text = atomic.mass, data = elements,
font_size = "4pt", align = "left", baseline = "middle")
p10: embedding images
url <- "https://raw.githubusercontent.com/weinfz/nba_shot_value_charts/master/court.png"
figure(width = 600, height = 600 * 0.7963461, padding_factor = 0,
xlab = NULL, ylab = NULL) %>%
ly_image_url(x = -300, y = -20, image_url = url, w = 600, h = 440, anchor = "bottom_left") %>%
ly_points(rnorm(100, 0, 50), rnorm(100, 200, 50), color = "black")11: creating custom layer functions
ly_baseball <- function(x) {
base_x <- c(90 * cos(pi/4), 0, 90 * cos(3 * pi/4), 0)
base_y <- c(90 * cos(pi/4), sqrt(90^2 + 90^2), 90 * sin(pi/4), 0)
distarc_x <- lapply(c(2:4) * 100, function(a)
seq(a * cos(3 * pi/4), a * cos(pi/4), length = 200))
distarc_y <- lapply(distarc_x, function(x)
sqrt((x[1]/cos(3 * pi/4))^2 - x^2))
x %>%
## boundary
ly_segments(c(0, 0), c(0, 0), c(-300, 300), c(300, 300), alpha = 0.4) %>%
## bases
ly_crect(base_x, base_y, width = 10, height = 10,
angle = 45*pi/180, color = "black", alpha = 0.4) %>%
## infield/outfield boundary
ly_curve(60.5 + sqrt(95^2 - x^2),
from = base_x[3] - 26, to = base_x[1] + 26, alpha = 0.4) %>%
ly_multi_line(distarc_x, distarc_y, alpha = 0.4)
}
f <- tempfile()
download.file("https://gist.githubusercontent.com/hafen/77f25b556725b3d0066b/raw/10f0e811f09f2b9f0f9ccfb542e296dfac2761d4/doubles.csv", method="curl", f)
doubles <- read.csv(f)
figure(xgrid = FALSE, ygrid = FALSE, width = 630, height = 540,
xlab = "Horizontal distance from home plate (ft.)",
ylab = "Vertical distance from home plate (ft.)") %>%
ly_baseball() %>%
ly_hexbin(doubles, xbins = 50, shape = 0.77, alpha = 0.75, palette = "Spectral10")Simply take a figure as input and return a modified figure as output
12: plotting on maps
bike <- read.csv("https://gist.githubusercontent.com/hafen/3d534ee95b964ef753ab/raw/dbe9f0cbe29d17151d852e8cc1c3466f7a7f02e9/201512_nycbike_summ.csv", stringsAsFactors = FALSE)
cdist <- read.csv("https://gist.githubusercontent.com/hafen/a447521ff8b24ddefba5/raw/044e174d7b9e6a370fff429f9cda4d0903b4c0a6/communitydistricts.csv")
bike$diff <- bike$n_start - bike$n_end
bike$color = ifelse(bike$diff > 0, "#2CA02C", "#D62728")
gmap(lat = 40.73306, lng = -73.97351, zoom = 12,
width = 680, height = 600, map_style = gmap_style("blue_water")) %>%
ly_polygons(x, y, group = which, data = cdist,
fill_alpha = 0.1, line_width = 2, color = "orange") %>%
ly_points(lon, lat, data = bike,
hover = c(station, n_start, n_end, diff),
fill_alpha = 0.8, size = abs(diff), color = color, legend = FALSE)More tile-based map options to come
13: plotting lots of points
figure(webgl = TRUE) %>%
ly_points(rnorm(100000), rnorm(100000), alpha = 0.2)Not all glyphs supported by WebGL (but will soon!)
14: linked pan / zoom / brush
tools <- c("pan", "wheel_zoom", "box_zoom", "box_select", "reset")
nms <- expand.grid(names(iris)[1:4], rev(names(iris)[1:4]), stringsAsFactors = FALSE)
splom_list <- vector("list", 16)
for(ii in seq_len(nrow(nms))) {
splom_list[[ii]] <- figure(width = 130, height = 130, tools = tools,
xlab = nms$Var1[ii], ylab = nms$Var2[ii]) %>%
ly_points(nms$Var1[ii], nms$Var2[ii], data = iris,
color = Species, size = 5, legend = FALSE)
}
grid_plot(splom_list, ncol = 4, same_axes = TRUE, link_data = TRUE)15: embed in shiny application
server <- shinyServer(function(input, output, session) {
# Combine the selected variables into a new data frame
selectedData <- reactive({
iris[, c(input$xcol, input$ycol)]
})
clusters <- reactive({
kmeans(selectedData(), input$clusters)
})
output$plot1 <- renderRbokeh({
pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
"#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999")
cts <- data.frame(clusters()$centers)
figure(width = 500, height = 500) %>%
ly_points(selectedData(), color = pal[clusters()$cluster]) %>%
ly_points(cts, size = 40, glyph = 4, line_width = 4, color = "black")
})
})
ui <- shinyUI(pageWithSidebar(
headerPanel('Iris k-means clustering'),
sidebarPanel(
selectInput('xcol', 'X Variable', names(iris)),
selectInput('ycol', 'Y Variable', names(iris),
selected = names(iris)[[2]]),
numericInput('clusters', 'Cluster count', 3,
min = 1, max = 9)
),
mainPanel(
rbokehOutput('plot1')
)
))
shinyApp(ui, server)16: direct shiny interactivity
library("shiny")
library("rbokeh")
dat <- data.frame(x = rnorm(10), y = rnorm(10))
ui <- fluidPage(
rbokehOutput("rbokeh", width = 450, height = 450),
strong("x range change event:"),
textOutput("x_range_text"),
strong("y range change event:"),
textOutput("y_range_text"),
strong("hover event:"),
textOutput("hover_text"),
strong("triggered by tap/click:"),
htmlOutput("tap_text"),
strong("index of selected triggered by any selection:"),
textOutput("selection_text")
)
server <- function(input, output, session) {
output$rbokeh <- renderRbokeh({
figure() %>%
ly_points(x = x, y = y, data = dat,
hover = list(x, y), lname = "points") %>%
tool_hover(shiny_callback(id = "hover_info"), "points") %>%
tool_tap(shiny_callback(id = "tap_info"), "points") %>%
tool_box_select(shiny_callback(id = "selection_info"), "points") %>%
x_range(callback = shiny_callback(id = "x_range")) %>%
y_range(callback = shiny_callback(id = "y_range"))
})
output$x_range_text <- reactive({
xrng <- input$x_range
if(!is.null(xrng)) {
paste0("start: ", xrng$start, ", end: ", xrng$end)
} else {
"waiting for x-axis pan/zoom event (use pan/zoom to trigger)..."
}
})
output$y_range_text <- reactive({
yrng <- input$y_range
if(!is.null(yrng)) {
paste0("start: ", yrng$start,", end: ", yrng$end)
} else {
"waiting for y-axis pan/zoom event (use pan/zoom to trigger)..."
}
})
output$hover_text <- reactive({
hi <- input$hover_info
if(!is.null(hi)) {
paste0("index: ", hi$index[["1d"]]$indices, ", x: ",
hi$geom$sx, ", y:", hi$geom$sy)
} else {
"waiting for hover event (hover over plot or points on plot to trigger)..."
}
})
output$tap_text <- reactive({
ti <- input$tap_info
if(!is.null(ti)) {
paste("index:", paste(ti, collapse = ", "))
} else {
"waiting for tap/click event (click point(s) to trigger)..."
}
})
output$selection_text <- reactive({
si <- input$selection_info
if(!is.null(si)) {
paste("index:", paste(si, collapse = ", "))
} else {
"waiting for selection event (click point(s) or use box select tool to trigger)..."
}
})
}
shinyApp(ui, server)
17: custom JS callback 1
figure(title = "hover a blue point") %>%
ly_points(1:10, lname = "blue", lgroup = "g1", size = 20) %>%
ly_points(2:12, lname = "orange", lgroup = "g1") %>%
tool_hover(custom_callback(
code = "if(cb_data.index['1d'].indices.length > 0)
orange_data.get('data').x[cb_data.index['1d'].indices] += 0.1
orange_data.trigger('change')", "orange"), "blue")18: custom JS callback 2
dat <- data.frame(x = runif(500), y = runif(500))
p <- figure(title = "select points to adjust mean line",
tools = "lasso_select") %>%
ly_points(x, y, data = dat, lname = "points") %>%
ly_lines(x = c(0, 1), y = rep(mean(dat$y), 2),
line_width = 6, color = "orange", alpha = 0.75,
lname = "mean")
code <- "
var inds = cb_obj.get('selected')['1d'].indices;
var d = cb_obj.get('data');
var ym = 0;
if (inds.length == 0) { return; }
for (i = 0; i < inds.length; i++) {
ym += d['y'][inds[i]];
}
ym /= inds.length;
mean_data.get('data').y = [ym, ym];
cb_obj.trigger('change');
mean_data.trigger('change');
"
p %>% tool_lasso_select(
custom_callback(code, "mean"), "points")19: custom JS callback 3
range_callback <- custom_callback("
var rdiff = range.get('end') - range.get('start')
var zoomed_in = hexbin_glyph.get('_zoomed_in')
if(rdiff < 1.5) {
hexbin_glyph.get('_zoomed_in', true)
points_glyph.set('visible', true)
points_hov_glyph.set('visible', true)
hexbin_glyph.set('visible', false)
hexbin_hov_glyph.set('visible', false)
hexbin_glyph.trigger('change')
hexbin_hov_glyph.trigger('change')
points_glyph.trigger('change')
points_hov_glyph.trigger('change')
} else if(rdiff >= 1.5) {
hexbin_glyph.get('_zoomed_in', false)
points_glyph.set('visible', false)
points_hov_glyph.set('visible', false)
hexbin_glyph.set('visible', true)
hexbin_hov_glyph.set('visible', true)
hexbin_glyph.trigger('change')
hexbin_hov_glyph.trigger('change')
points_glyph.trigger('change')
points_hov_glyph.trigger('change')
}
", c("hexbin", "points"))
figure() %>%
ly_points(x, y, data = dat,
visible = FALSE, lname = "points") %>%
ly_hexbin(x, y, data = dat, hover = FALSE,
lname = "hexbin") %>%
x_range(callback = range_callback) %>%
y_range(callback = range_callback)20: embedded in trelliscope
panel <- function(x)
figure() %>% ly_points(time, medListPriceSqft, data = x)
hcog <- function(x) {
slp <- coef(lm(medListPriceSqft ~ time, data = x))[2]
list(
slope = cog(slp, desc = "list price slope"),
meanList = cogMean(x$medListPriceSqft),
meanSold = cogMean(x$medSoldPriceSqft)
)
}
housingData::housing %>%
qtrellis(by = c("county", "state"), panel, hcog, layout = c(2, 4))
Resources
- Documentation: http://hafen.github.io/rbokeh/
- Github: https://github.com/bokeh/rbokeh
- Bokeh examples
- Blog: http://ryanhafen.com/blog/
- @BokehPlots
- @hafenstats
install.packages("rbokeh")
Thanks
Some thoughts on interactivity in EDA
- Data analysis is iterative and visualization is usually the driver
- We must be able to quickly generate plots to explore, investigate ideas, validate models, etc.
- Any interactivity in our plots must be very easily specifiable
- This is not easy! (outside of simple but very useful transient interactions like pan / zoom / hover / click / select that we already get with things like rbokeh)
- Something to think about: in EDA, what do we really need in terms of interactivity?
- I actually don't think it's possible for an ad-hoc statistical visualization system to provide a flexible, rapidly specifiable interactivity scheme
- Unless you think in terms of Trelliscope (conditioning + cognostics)