Introduction to vegalite

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vegalite is an R htmlwidget interface to the Vega-Lite JavaScript visualization library.

What is “Vega” and why “-Lite”? Vega is _“a full declarative visualization grammar, suitable for expressive custom interactive visualization design and programmatic generation.“” Vega-Lite ”provides a higher-level grammar for visual analysis, comparable to ggplot or Tableau, that generates complete Vega specifications.“_ Vega-Lite compiles to Vega and is more compact and accessible than Vega. Both are just JSON data files with a particular schema that let you encode the data, encodings and aesthetics for statistical charts.

The following is a gallery of code & examples to help you get started with the package.

library(vegalite)

bar mark

dat <- jsonlite::fromJSON('[
      {"a": "A","b": 28}, {"a": "B","b": 55}, {"a": "C","b": 43},
      {"a": "D","b": 91}, {"a": "E","b": 81}, {"a": "F","b": 53},
      {"a": "G","b": 19}, {"a": "H","b": 87}, {"a": "I","b": 52}
    ]')

vegalite(viewport_height=250) %>%
  cell_size(400, 200) %>%
  add_data(dat) %>%
  encode_x("a", "ordinal") %>%
  encode_y("b", "quantitative") %>%
  mark_bar()

point mark

vegalite(viewport_width=400, viewport_height=400) %>%
  cell_size(400, 400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Miles_per_Gallon", "quantitative") %>%
  mark_point()

cirlce mark

vegalite(viewport_width=400, viewport_height=400) %>%
  cell_size(400, 400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Miles_per_Gallon", "quantitative") %>%
  mark_circle()

color and shape

vegalite(viewport_width=400, viewport_height=400) %>%
  cell_size(400, 400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Miles_per_Gallon", "quantitative") %>%
  encode_color("Origin", "nominal") %>%
  encode_shape("Origin", "nominal") %>%
  mark_point()

size

vegalite(viewport_width=400, viewport_height=400) %>%
  cell_size(400, 400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Miles_per_Gallon", "quantitative") %>%
  encode_size("Acceleration", "quantitative") %>%
  mark_point()

filtered line

vegalite(viewport_width=400, viewport_height=450) %>%
  cell_size(400, 450) %>%
  add_data("https://vega.github.io/vega-editor/app/data/stocks.csv") %>%
  add_filter("datum.symbol==='GOOG'") %>%
  encode_x("date", "temporal") %>%
  encode_y("price", "quantitative") %>%
  mark_line()

ticks

vegalite(viewport_height=200) %>%
  cell_size(400, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Cylinders", "ordinal") %>%
  mark_tick()

multi-series line

vegalite(viewport_height=500) %>%
  cell_size(400, 400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/stocks.csv") %>%
  encode_x("date", "temporal") %>%
  encode_y("price", "quantitative") %>%
  encode_color("symbol", "nominal") %>%
  mark_line()

facet col

vegalite(viewport_height=350) %>%
  add_data("https://vega.github.io/vega-editor/app/data/movies.json") %>%
  encode_x("Worldwide_Gross", "quantitative") %>%
  encode_y("US_DVD_Sales", "quantitative") %>%
  facet_col("MPAA_Rating", "ordinal") %>%
  mark_point()

facet row

vegalite(viewport_height=1400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/movies.json") %>%
  encode_x("Worldwide_Gross", "quantitative") %>%
  encode_y("US_DVD_Sales", "quantitative") %>%
  facet_row("MPAA_Rating", "ordinal") %>%
  mark_point()

facet both

vegalite(viewport_height=2900) %>%
  add_data("https://vega.github.io/vega-editor/app/data/movies.json") %>%
  encode_x("Worldwide_Gross", "quantitative") %>%
  encode_y("US_DVD_Sales", "quantitative") %>%
  facet_col("MPAA_Rating", "ordinal") %>%
  facet_row("Major_Genre", "ordinal") %>%
  mark_point()

log scale

dat <- jsonlite::fromJSON('[
      {"x": 0, "y": 1}, {"x": 1, "y": 10},
      {"x": 2, "y": 100}, {"x": 3, "y": 1000},
      {"x": 4, "y": 10000}, {"x": 5, "y": 100000},
      {"x": 6, "y": 1000000}, {"x": 7, "y": 10000000}
    ]')

vegalite(viewport_height=300) %>%
  add_data(dat) %>%
  encode_x("x", "quantitative") %>%
  encode_y("y", "quantitative") %>%
  mark_point() %>%
  scale_y_log()

aggregate bar chart

vegalite(viewport_width=500, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/population.json") %>%
  encode_x("people", "quantitative", aggregate="sum") %>%
  encode_y("age", "ordinal") %>%
  scale_y_ordinal(band_size=17) %>%
  add_filter("datum.year == 2000") %>%
  mark_bar()

binned scatterplot

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/movies.json") %>%
  encode_x("IMDB_Rating", "quantitative") %>%
  encode_y("Rotten_Tomatoes_Rating", "quantitative") %>%
  encode_size("*", "quantitative", aggregate="count") %>%
  bin_x(maxbins=10) %>%
  bin_y(maxbins=10) %>%
  mark_point()

slope graph

vegalite(viewport_width=400, viewport_height=600) %>%
  add_data("https://vega.github.io/vega-editor/app/data/barley.json") %>%
  encode_x("year", "ordinal") %>%
  encode_y("yield", "quantitative", aggregate="median") %>%
  encode_color("site", "nominal") %>%
  scale_x_ordinal(band_size=50, padding=0.5) %>%
  mark_line()

histogram

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/movies.json") %>%
  encode_x("IMDB_Rating", "quantitative") %>%
  encode_y("*", "quantitative", aggregate="count") %>%
  bin_x(maxbins=10) %>%
  mark_bar()

stacked bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/seattle-weather.csv") %>%
  encode_x("date", "temporal") %>%
  encode_y("*", "quantitative", aggregate="count") %>%
  encode_color("weather", "nominal") %>%
  scale_color_nominal(domain=c("sun","fog","drizzle","rain","snow"),
                      range=c("#e7ba52","#c7c7c7","#aec7e8","#1f77b4","#9467bd")) %>%
  timeunit_x("month") %>%
  mark_bar()

horizontal stacked bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/barley.json") %>%
  encode_x("yield", "quantitative", aggregate="sum") %>%
  encode_y("variety", "nominal") %>%
  encode_color("site", "nominal") %>%
  mark_bar()

stacked area chart

vegalite() %>%
  cell_size(300, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/unemployment-across-industries.json") %>%
  encode_x("date", "temporal") %>%
  encode_y("count", "quantitative", aggregate="sum") %>%
  encode_color("series", "nominal") %>%
  scale_color_nominal(range="category20b") %>%
  timeunit_x("yearmonth") %>%
  scale_x_time(nice="month") %>%
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  mark_area()

streamgraph!

vegalite() %>%
  cell_size(300, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/unemployment-across-industries.json") %>%
  encode_x("date", "temporal") %>%
  encode_y("count", "quantitative", aggregate="sum") %>%
  encode_color("series", "nominal") %>%
  scale_color_nominal(range="category20b") %>%
  timeunit_x("yearmonth") %>%
  scale_x_time(nice="month") %>%
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  mark_area(interpolate="basis", stack="center")

scatter text

vegalite() %>%
  cell_size(300, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Miles_per_Gallon", "quantitative") %>%
  encode_color("Origin", "nominal") %>%
  calculate("OriginInitial", "datum.Origin[0]") %>%
  encode_text("OriginInitial", "nominal") %>%
  mark_text()

area chart

vegalite() %>%
  cell_size(300, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/unemployment-across-industries.json") %>%
  encode_x("date", "temporal") %>%
  timeunit_x("yearmonth") %>%
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  encode_y("count", "quantitative", aggregate="sum") %>%
  mark_area()

grouped bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/population.json") %>%
  add_filter("datum.year == 2000") %>%
  calculate("gender", 'datum.sex == 2 ? "Female" : "Male"') %>%
  encode_x("gender", "nominal") %>%
  encode_y("people", "quantitative", aggregate="sum") %>%
  encode_color("gender", "nominal") %>%
  scale_x_ordinal(band_size=6) %>%
  scale_color_nominal(range=c("#EA98D2", "#659CCA")) %>%
  facet_col("age", "ordinal", padding=4) %>%
  axis_x(remove=TRUE) %>%
  axis_y(title="population", grid=FALSE) %>%
  axis_facet_col(orient="bottom", axisWidth=1, offset=-8) %>%
  facet_cell(stroke_width=0) %>%
  mark_bar()

normalized stacked bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/population.json") %>%
  calculate("gender", 'datum.sex == 2 ? "Female" : "Male"') %>%
  encode_x("age", "ordinal") %>%
  encode_y("people", "quantitative", aggregate="sum") %>%
  encode_color("gender", "nominal") %>%
  scale_x_ordinal(band_size=17) %>%
  scale_color_nominal(range=c("#EA98D2", "#659CCA")) %>%
  mark_bar(stack="normalize")

normalized stacked bar chart

vegalite() %>%
  cell_size(300, 300) %>%
  add_data("https://vega.github.io/vega-editor/app/data/unemployment-across-industries.json") %>%
  encode_x("date", "temporal") %>%
  encode_y("count", "quantitative", aggregate="sum") %>%
  encode_color("series", "nominal") %>%
  scale_x_time(nice="month") %>%
  scale_color_nominal(range="category20b") %>%
  axis_x(axisWidth=0, format="%Y", labelAngle=0) %>%
  axis_y(remove=TRUE) %>%
  timeunit_x("yearmonth") %>%
  mark_area(stack="normalize")

layered bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/population.json") %>%
  add_filter("datum.year == 2000") %>%
  calculate("gender", 'datum.sex == 2 ? "Female" : "Male"') %>%
  encode_x("age", "ordinal") %>%
  encode_y("people", "quantitative", aggregate="sum") %>%
  encode_color("gender", "nominal") %>%
  scale_x_ordinal(band_size=17) %>%
  scale_color_nominal(range=c("#e377c2","#1f77b4")) %>%
  axis_y(title="Population") %>%
  mark_bar(opacity=0.6, stack="none")

trellis bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/population.json") %>%
  add_filter("datum.year == 2000") %>%
  calculate("gender", 'datum.sex == 2 ? "Female" : "Male"') %>%
  encode_x("age", "ordinal") %>%
  encode_y("people", "quantitative", aggregate="sum") %>%
  encode_color("gender", "nominal") %>%
  facet_row("gender", "nominal") %>%
  scale_x_ordinal(band_size=17) %>%
  scale_color_nominal(range=c("#EA98D2","#659CCA")) %>%
  axis_y(title="Population") %>%
  mark_bar()

trellis stacked bar chart

vegalite(viewport_width=400, viewport_height=400) %>%
  add_data("https://vega.github.io/vega-editor/app/data/barley.json") %>%
  encode_x("yield", "quantitative", aggregate="sum") %>%
  encode_y("variety", "nominal") %>%
  encode_color("site", "nominal") %>%
  facet_col("year", "ordinal") %>%
  mark_bar()

trellis histograms

vegalite(viewport_height=700) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("*", "quantitative", aggregate="count") %>%
  encode_color("site", "nominal") %>%
  facet_row("Origin", "nominal") %>%
  bin_x(maxbins=15) %>%
  mark_bar()

becker’s barley trellis plot

vegalite(viewport_height=1200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/barley.json") %>%
  encode_x("yield", "quantitative", aggregate="mean") %>%
  encode_y("variety", "ordinal", sort=sort_def("yield", "mean")) %>%
  encode_color("year", "nominal") %>%
  facet_row("site", "ordinal") %>%
  scale_y_ordinal(band_size=12) %>%
  mark_point()

sorting line order

vegalite(viewport_width=300, viewport_height=300) %>%
  cell_size(300, 300) %>%
  add_data("https://vega.github.io/vega-editor/app/data/driving.json") %>%
  encode_x("miles", "quantitative") %>%
  encode_y("gas", "quantitative") %>%
  encode_path("year", "temporal") %>%
  scale_x_linear(zero=FALSE) %>%
  scale_y_linear(zero=FALSE) %>%
  mark_line()

sort layer scatterplot

vegalite(viewport_width=200, viewport_height=200) %>%
  cell_size(200, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative") %>%
  encode_y("Miles_per_Gallon", "quantitative") %>%
  encode_color("Origin", "nominal") %>%
  encode_order("Origin", "ordinal", sort="descending") %>%
  mark_point()

detail lines

vegalite(viewport_width=200, viewport_height=200) %>%
  cell_size(200, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/stocks.csv") %>%
  encode_x("date", "temporal") %>%
  encode_y("price", "quantitative") %>%
  encode_detail("symbol", "nominal") %>%
  mark_line()

detail points

vegalite() %>%
  cell_size(200, 200) %>%
  add_data("https://vega.github.io/vega-editor/app/data/cars.json") %>%
  encode_x("Horsepower", "quantitative", aggregate="mean") %>%
  encode_y("Displacement", "quantitative", aggregate="mean") %>%
  encode_detail("Origin", "nominal") %>%
  mark_point()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

Introduction to vegalite

Bob Rudis

2016-03-21

bar mark

point mark

cirlce mark

color and shape

size

filtered line

ticks

multi-series line

facet col

facet row

facet both

log scale

aggregate bar chart

binned scatterplot

slope graph

histogram

stacked bar chart

horizontal stacked bar chart

stacked area chart

streamgraph!

scatter text

area chart

grouped bar chart

normalized stacked bar chart

normalized stacked bar chart

layered bar chart

trellis bar chart

trellis stacked bar chart

trellis histograms

becker’s barley trellis plot

sorting line order

sort layer scatterplot

detail lines

detail points