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.
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) %>%
view_size(400, 200) %>%
add_data(dat) %>%
encode_x("a", "ordinal") %>%
encode_y("b", "quantitative") %>%
mark_bar()vegalite(viewport_width=400, viewport_height=400) %>%
view_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()vegalite(viewport_width=400, viewport_height=400) %>%
view_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()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_vl()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_vl(range_step=17) %>%
add_filter("datum.year == 2000") %>%
mark_bar()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()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_vl(range_step=50, padding=0.5) %>%
mark_line()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_vl(domain=c("sun","fog","drizzle","rain","snow"),
range=c("#e7ba52","#c7c7c7","#aec7e8","#1f77b4","#9467bd")) %>%
timeunit_x("month") %>%
mark_bar()vegalite() %>%
view_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_vl(scheme="category20b") %>%
timeunit_x("yearmonth") %>%
scale_x_time_vl(nice="month") %>%
axis_x(format="%Y", labelAngle=0) %>%
mark_area()vegalite() %>%
view_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", stack="center") %>%
encode_color("series", "nominal") %>%
scale_color_nominal_vl(scheme="category20b") %>%
timeunit_x("yearmonth") %>%
scale_x_time_vl(nice="month") %>%
axis_x(format="%Y", labelAngle=0) %>%
mark_area(interpolate="basis")vegalite() %>%
view_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()vegalite() %>%
view_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(format="%Y", labelAngle=0) %>%
encode_y("count", "quantitative", aggregate="sum") %>%
mark_area()vegalite(viewport_width=600, 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_vl(range_step = 8) %>%
scale_color_nominal_vl(range=c("#EA98D2", "#659CCA")) %>%
facet_col("age", "ordinal") %>%
axis_x(remove=TRUE) %>%
axis_y(title="population", grid=FALSE) %>%
view_config(stroke_width=0) %>%
mark_bar()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", stack="normalize") %>%
encode_color("gender", "nominal") %>%
scale_x_ordinal_vl(range_step=17) %>%
scale_color_nominal_vl(range=c("#EA98D2", "#659CCA")) %>%
mark_bar()vegalite() %>%
view_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", stack="normalize") %>%
encode_color("series", "nominal") %>%
scale_x_time_vl(nice="month") %>%
scale_color_nominal_vl(scheme="category20b") %>%
axis_x(format="%Y", labelAngle=0) %>%
axis_y(remove=TRUE) %>%
timeunit_x("yearmonth") %>%
mark_area()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", stack=NA) %>%
encode_color("gender", "nominal") %>%
scale_x_ordinal_vl(range_step=17) %>%
scale_color_nominal_vl(range=c("#e377c2","#1f77b4")) %>%
axis_y(title="Population") %>%
mark_bar(opacity=0.6)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_vl(range_step=17) %>%
scale_color_nominal_vl(range=c("#EA98D2","#659CCA")) %>%
axis_y(title="Population") %>%
mark_bar()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()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_vl(range_step=12) %>%
mark_point()vegalite(viewport_width=300, viewport_height=300) %>%
view_size(300, 300) %>%
add_data("https://vega.github.io/vega-editor/app/data/driving.json") %>%
encode_x("miles", "quantitative") %>%
encode_y("gas", "quantitative") %>%
encode_order("year", "temporal") %>%
scale_x_linear_vl(zero=FALSE) %>%
scale_y_linear_vl(zero=FALSE) %>%
mark_line()vegalite(viewport_width=200, viewport_height=200) %>%
view_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()