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Interactive Charts • altair

This document is adapted from the Interactive Charts section of the Altair Example Gallery.

Our first step is to set up our environment:

Altair example

The plot below uses an interval selection, which causes the chart to include an interactive brush (shown in grey). The brush selection parameterizes the red guideline, which visualizes the average value within the selected interval.

weather <- vega_data$seattle_weather()

glimpse(weather)

## Rows: 1,461
## Columns: 6
## $ date          <dttm> 2012-01-01, 2012-01-02, 2012-01-03, 2012-01-04, 2012-01…
## $ precipitation <dbl> 0.0, 10.9, 0.8, 20.3, 1.3, 2.5, 0.0, 0.0, 4.3, 1.0, 0.0,…
## $ temp_max      <dbl> 12.8, 10.6, 11.7, 12.2, 8.9, 4.4, 7.2, 10.0, 9.4, 6.1, 6…
## $ temp_min      <dbl> 5.0, 2.8, 7.2, 5.6, 2.8, 2.2, 2.8, 2.8, 5.0, 0.6, -1.1, …
## $ wind          <dbl> 4.7, 4.5, 2.3, 4.7, 6.1, 2.2, 2.3, 2.0, 3.4, 3.4, 5.1, 1…
## $ weather       <chr> "drizzle", "rain", "rain", "rain", "rain", "rain", "rain…

brush <- alt$selection(type = "interval", encodings = list("x"))

bars <- 
  alt$Chart()$
  mark_bar()$
  encode(
    x = alt$X("date:O", timeUnit="month"),
    y = "mean(precipitation):Q",
    opacity = alt$condition(
      brush, 
      alt$OpacityValue(1), 
      alt$OpacityValue(0.7)
    )
  )$
  properties(selection = brush)

line <- 
  alt$Chart()$
  mark_rule(color = "firebrick")$
  encode(
    y = "mean(precipitation):Q",
    size = alt$SizeValue(3)
  )$
  transform_filter(brush$ref())

chart <- alt$layer(bars, line, data = weather)

chart

Altair example

This example shows an interactive chart where selections in one portion of the chart affect what is shown in other panels. Click on the bar chart to see a detail of the distribution in the upper panel.

## Rows: 3,201
## Columns: 16
## $ Title                  <chr> "The Land Girls", "First Love, Last Rites", "I …
## $ US_Gross               <int> 146083, 10876, 203134, 373615, 1009819, 24551, …
## $ Worldwide_Gross        <dbl> 146083, 10876, 203134, 373615, 1087521, 2624551…
## $ US_DVD_Sales           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Production_Budget      <int> 8000000, 300000, 250000, 300000, 1000000, 16000…
## $ Release_Date           <chr> "Jun 12 1998", "Aug 07 1998", "Aug 28 1998", "S…
## $ MPAA_Rating            <chr> "R", "R", NA, NA, "R", NA, "R", "R", "R", NA, N…
## $ Running_Time_min       <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Distributor            <chr> "Gramercy", "Strand", "Lionsgate", "Fine Line",…
## $ Source                 <chr> NA, NA, NA, NA, "Original Screenplay", NA, NA, …
## $ Major_Genre            <chr> NA, "Drama", "Comedy", "Comedy", "Drama", NA, N…
## $ Creative_Type          <chr> NA, NA, NA, NA, "Contemporary Fiction", NA, NA,…
## $ Director               <chr> NA, NA, NA, NA, NA, NA, "Christopher Nolan", NA…
## $ Rotten_Tomatoes_Rating <int> NA, NA, NA, 13, 62, NA, NA, NA, 25, 86, 81, 84,…
## $ IMDB_Rating            <dbl> 6.1, 6.9, 6.8, NA, 3.4, NA, 7.7, 3.8, 5.8, 7.0,…
## $ IMDB_Votes             <int> 1071, 207, 865, NA, 165, NA, 15133, 353, 3275, …

pts <- alt$selection(type = "single", encodings = list("x"))

rect <- 
  alt$Chart(vega_data$movies$url)$
  mark_rect()$
  encode(
    x = alt$X("IMDB_Rating:Q", bin = TRUE),
    y = alt$Y("Rotten_Tomatoes_Rating:Q", bin=TRUE),
    color = alt$Color(
      "count()",
      scale = alt$Scale(scheme = "greenblue"),
      legend = alt$Legend(title = "Total Records")
    )
  )

circ <- 
  rect$
  mark_point()$
  encode(
    color = alt$ColorValue("grey"),
    size = alt$Size(
      "count()",
      legend = alt$Legend(title = "Records in Selection")
    )
  )$
  transform_filter(pts$ref())

bar <- 
  alt$Chart(vega_data$movies$url)$
  mark_bar()$
  encode(
    x = "Major_Genre:N",
    y = "count()",
    color = alt$condition(
      pts, 
      alt$ColorValue("steelblue"), 
      alt$ColorValue("grey"))
  )$
  properties(
    selection = pts,
    width = 550, 
    height = 200
  )

chart <- 
  ((rect + circ) & bar)$ 
  resolve_legend(color = "independent", size = "independent")

chart

Altair example

This example shows a multi-panel view of the same data, where you can interactively select a portion of the data in any of the panels to highlight that portion in any of the other panels.

Note: alt$repeat() must be translated to alt$\x60repeat\x60().

## Rows: 2,000
## Columns: 5
## $ date        <chr> "2001/01/14 21:55", "2001/03/26 20:15", "2001/03/05 14:55"…
## $ delay       <int> 0, -11, -3, 12, 2, 47, 3, -4, 4, 0, 18, -7, -10, 23, 7, -1…
## $ distance    <int> 480, 507, 714, 342, 373, 189, 872, 723, 318, 487, 239, 453…
## $ origin      <chr> "SAN", "PHX", "ELP", "SJC", "SMF", "DAL", "AUS", "GEG", "F…
## $ destination <chr> "SMF", "SLC", "LAX", "SNA", "LAX", "AUS", "PHX", "OAK", "J…

brush <- alt$selection_interval(encodings = list("x"))

# Define the base chart, with the common parts of the
# background and highlights
base <- 
  alt$Chart(vega_data$flights_2k$url)$
  mark_bar()$
  encode(
    x = alt$X(
      alt$`repeat`("column"), 
      type = "quantitative", 
      bin = alt$Bin(maxbins=20)
    ),
    y = "count()"
  )$
  properties(width = 160, height = 130)

# blue background with selection
background <- base$properties(selection = brush)

# yellow highlights on the transformed data
highlight <- 
  base$
  encode(
    color = alt$value("goldenrod")
  )$
  transform_filter(brush$ref())

# layer the two charts & repeat
chart <- 
  (background + highlight)$ 
  transform_calculate("time", "hours(datum.date)")$
  `repeat`(column = list("distance", "delay", "time"))

chart

Altair example

This example shows how to add a simple rectangular brush to a scatter plot. By clicking and dragging on the plot, you can highlight points within the range.

## Rows: 406
## Columns: 9
## $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 320", "pl…
## $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, NaN, NaN…
## $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8,…
## $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, 390, 133…
## $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, 190, 115…
## $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 4312, 4425,…
## $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5, 10.0, 8…
## $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-01, 1970…
## $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA…

brush <- alt$selection(type = "interval")

chart <- 
  alt$Chart(vega_data$cars())$
  mark_point()$
  encode(
    x = "Horsepower:Q",
    y = "Miles_per_Gallon:Q",
    color = alt$condition(brush, "Cylinders:O", alt$value("grey"))
  )$
  properties(selection = brush)

chart

Altair example

This example shows how to link a scatter plot and a histogram together such that clicking on a point in the scatter plot will isolate the distribution corresponding to that point, and vice versa.

source <- tibble(
  gender = c(rep("M", 1000), rep("F", 1000)),
  height = c(rnorm(1000, 69, 7), rnorm(1000, 64, 6)),
  weight = c(rnorm(1000, 195.8, 144), rnorm(1000, 167, 100)),
  age = c(rnorm(1000, 45, 8), rnorm(1000, 51, 6))
)

## Rows: 2,000
## Columns: 4
## $ gender <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M"…
## $ height <dbl> 67.32169, 76.02880, 70.84474, 74.81332, 63.78757, 79.18516, 65.…
## $ weight <dbl> 40.73732, -117.29040, -136.48887, 75.42213, 328.06836, 248.9139…
## $ age    <dbl> 50.12464, 62.52822, 37.64530, 46.06724, 63.17965, 39.90173, 46.…

selector <- alt$selection_single(empty = "all", fields = list("gender"))

color_scale <- 
  alt$Scale(
    domain = list("M", "F"),
    range = list('#1FC3AA', '#8624F5')
  )

base <- 
  alt$Chart(source)$
  properties(width = 250, height = 250)$
  add_selection(selector)

points <-
  base$mark_point(filled=TRUE, size=200)$
  encode(
    x = alt$X(
      "mean(height):Q",
      scale = alt$Scale(domain = list(0, 84))
    ),
    y = alt$Y(
      "mean(weight):Q",
      scale = alt$Scale(domain=list(0,250))),
      color = alt$condition(
        selector,
        "gender:N",
        alt$value("lightgray"),
        scale = color_scale
    )
  )$
  interactive()

hists <-
  base$
  mark_bar(opacity = 0.5, thickness = 100)$
  encode(
    x = alt$X(
      "age",
      bin = alt$Bin(step = 5), # step keeps bin size the same
      scale = alt$Scale(domain = list(0, 100))
    ),
    y = alt$Y(
      "count()",
      stack = NULL,
      scale = alt$Scale(domain = list(0, 350))
    ),
    color = alt$Color("gender:N", scale = color_scale)
  )$
  transform_filter(selector)

points | hists

Altair example

This multi-line chart uses an invisible Voronoi tessellation to handle mouseover to identify the nearest point and then highlight the line on which the point falls. It is adapted from the Vega-Lite example.

## Rows: 560
## Columns: 3
## $ symbol <chr> "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT", "MSFT",…
## $ date   <dttm> 2000-01-01, 2000-02-01, 2000-03-01, 2000-04-01, 2000-05-01, 20…
## $ price  <dbl> 39.81, 36.35, 43.22, 28.37, 25.45, 32.54, 28.40, 28.40, 24.53, …

highlight <- 
  alt$selection_single(
    on = "mouseover",
    fields = list("symbol"), 
    nearest = TRUE
  )

base <- 
  alt$Chart(vega_data$stocks())$
  encode(
    x = "date:T",
    y = "price:Q",
    color = "symbol:N"
  )

points <- 
  base$mark_circle()$
  encode(
    opacity = alt$value(0)
  )$
  properties(selection = highlight, width = 600)

lines <- 
  base$
  mark_line()$
  encode(
    size = alt$condition(highlight, alt$value(3), alt$value(1))
  )

chart <- (points + lines)

chart

Altair Example

This example shows how you can use selections and layers to create a multi-line tooltip that tracks the x position of the cursor.

To find the x-position of the cursor, we employ a little trick: we add some transparent points with only an x encoding (no y encoding) and tie a nearest selection to these, tied to the x field.

## Rows: 300
## Columns: 3
## $ category <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "…
## $ x        <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
## $ y        <dbl> 1.37, 0.81, 1.17, 1.80, 2.20, 2.09, 3.60, 3.51, 5.53, 5.47, 6…

# Create a selection that chooses the nearest point & selects based on x-value
nearest <- alt$selection(
  type = "single", 
  nearest = TRUE, 
  on = "mouseover",
  fields = list("x"), 
  empty = "none"
)

# The basic line
line <- 
  alt$Chart(data = data)$
  mark_line(interpolate = "basis")$
  encode(
    x = "x:Q",
    y = "y:Q",
    color = "category:N"
  )

# Transparent selectors across the chart. This is what tells us
# the x-value of the cursor
selectors <- 
  alt$Chart(data = data)$
  mark_point()$
  encode(
    x = "x:Q",
    opacity = alt$value(0)
  )$
  properties(selection = nearest)$
  copy()

# Draw points on the line, and highlight based on selection
points <-
  line$
  mark_point()$
  encode(
    opacity = alt$condition(nearest, alt$value(1), alt$value(0))
  )

# Draw text labels near the points, and highlight based on selection
text <- 
  line$
  mark_text(align = "left", dx = 5, dy = -5)$
  encode(
    text = alt$condition(nearest, "y:Q", alt$value(" "))
  )

# Draw a rule at the location of the selection
rules <- 
  alt$Chart(data = data)$
  mark_rule(color = "gray")$
  encode(
    x = "x:Q"
  )$
  transform_filter(nearest$ref())

# Put the five layers into a chart and bind the data
chart <-  
  (line + selectors + points + rules + text)$
  properties( width = 600, height = 300)

chart

Altair example

## Rows: 406
## Columns: 9
## $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 320", "pl…
## $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, NaN, NaN…
## $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8,…
## $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, 390, 133…
## $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, 190, 115…
## $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 4312, 4425,…
## $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5, 10.0, 8…
## $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-01, 1970…
## $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA…

source <- vega_data$cars()

brush = alt$selection(type = "interval", resolve = "global")

base = alt$Chart(source)$
  mark_point()$
  encode(
    y = "Miles_per_Gallon",
    color=alt$condition(brush, "Origin", alt$ColorValue("gray"))
  )$
  add_selection(brush)$
  properties(width = 250, height = 250)

base$encode(x = "Horsepower") | base$encode(x = "Acceleration")

Altair example

This example shows how multiple user inputs can be layered onto a chart. The four inputs have functionality as follows:

• Dropdown: Filters the movies by genre
• Radio Buttons: Highlights certain films by Worldwide Gross
• Mouse Drag and Scroll: Zooms the x and y scales to allow for panning.

## Rows: 3,201
## Columns: 16
## $ Title                  <chr> "The Land Girls", "First Love, Last Rites", "I …
## $ US_Gross               <int> 146083, 10876, 203134, 373615, 1009819, 24551, …
## $ Worldwide_Gross        <dbl> 146083, 10876, 203134, 373615, 1087521, 2624551…
## $ US_DVD_Sales           <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Production_Budget      <int> 8000000, 300000, 250000, 300000, 1000000, 16000…
## $ Release_Date           <chr> "Jun 12 1998", "Aug 07 1998", "Aug 28 1998", "S…
## $ MPAA_Rating            <chr> "R", "R", NA, NA, "R", NA, "R", "R", "R", NA, N…
## $ Running_Time_min       <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Distributor            <chr> "Gramercy", "Strand", "Lionsgate", "Fine Line",…
## $ Source                 <chr> NA, NA, NA, NA, "Original Screenplay", NA, NA, …
## $ Major_Genre            <chr> NA, "Drama", "Comedy", "Comedy", "Drama", NA, N…
## $ Creative_Type          <chr> NA, NA, NA, NA, "Contemporary Fiction", NA, NA,…
## $ Director               <chr> NA, NA, NA, NA, NA, NA, "Christopher Nolan", NA…
## $ Rotten_Tomatoes_Rating <int> NA, NA, NA, 13, 62, NA, NA, NA, 25, 86, 81, 84,…
## $ IMDB_Rating            <dbl> 6.1, 6.9, 6.8, NA, 3.4, NA, 7.7, 3.8, 5.8, 7.0,…
## $ IMDB_Votes             <int> 1071, 207, 865, NA, 165, NA, 15133, 353, 3275, …

movies <- 
  alt$UrlData(
    vega_data$movies$url,
    format = alt$DataFormat(parse = list(`Release Date` = "date"))
  )

ratings <- list("G", "NC-17", "PG", "PG-13", "R")
genres <- 
  list("Action", "Adventure", "Black Comedy", "Comedy", "Concert/Performance",
       "Documentary", "Drama", "Horror", "Musical", "Romantic Comedy", 
       "Thriller/Suspense", "Western")

base <-
  alt$Chart(movies, width = 200, height = 200)$
  mark_point(filled = TRUE)$
  transform_calculate(
    Rounded_IMDB_Rating = "floor(datum.IMDB_Rating)",
    Hundred_Million_Production = "datum.Production_Budget > 100000000.0 ? 100 : 10",
    Release_Year = "year(datum.Release_Date)"
  )$
  transform_filter(
    "datum.IMDB_Rating > 0"
  )$
  transform_filter(
    alt$FieldOneOfPredicate(field = "MPAA_Rating", oneOf = ratings)
  )$encode(
    x = alt$X(
      field = "Worldwide_Gross",
      type = "quantitative",
      scale = alt$Scale(domain = c(100000, 10**9), clamp = TRUE)
    ),
    y = alt$Y(field = "IMDB_Rating", type = "quantitative"),
    tooltip = "Title:N"
  )

# A slider filter
year_slider <- alt$binding_range(min = 1969, max = 2018, step = 1)
slider_selection <- 
  alt$selection_single(
    bind = year_slider, 
    fields = list("Release_Year"),
    name = "Release Year_"
  )

filter_year <- base$
  add_selection(slider_selection)$
  transform_filter(slider_selection)$
  properties(title = "Slider Filtering")

# A dropdown filter
genre_dropdown <- alt$binding_select(options = genres)
genre_select <- 
  alt$selection_single(
    fields = list("Major_Genre"), 
    bind = genre_dropdown,
    name = "Genre"
  )

filter_genres <- 
  base$
  add_selection(genre_select)$
  transform_filter(genre_select)$
  properties(title = "Dropdown Filtering")

#color changing marks
rating_radio <- alt$binding_radio(options = ratings)

rating_select <- 
  alt$selection_single(
    fields = list("MPAA_Rating"), 
    bind = rating_radio, 
    name = "Rating"
  )

rating_color_condition <- 
  alt$condition(
    rating_select,
    alt$Color("MPAA_Rating:N", legend = NULL),
    alt$value("lightgray")
  )

highlight_ratings <-
  base$
  add_selection(rating_select)$
  encode(
    color = rating_color_condition
  )$
  properties(title = "Radio Button Highlighting")

# Boolean selection for format changes
input_checkbox <- alt$binding_checkbox()
checkbox_selection <-
  alt$selection_single(bind = input_checkbox, name = "Big Budget Films")

size_checkbox_condition <-
  alt$condition(
    checkbox_selection,
    alt$SizeValue(25),
    alt$Size("Hundred_Million_Production:Q")
  )

budget_sizing <-
  base$
  add_selection(checkbox_selection)$
  encode(
    size = size_checkbox_condition)$
  properties(title = "Checkbox Formatting")

(filter_year | filter_genres) & (highlight_ratings | budget_sizing)

Altair example

This example shows how to link a scatter plot and a histogram together such that an interval selection in the histogram will plot the selected values in the scatter plot.

Note that both subplots need to know about the mbin field created by the transform_bin method. In order to achieve this, the data is not passed to the Chart() instances creating the subplots, but directly in the hconcat() function, which joins the two plots together.

## Rows: 100
## Columns: 3
## $ x <dbl> -0.0046207678, 0.7602421677, 0.0389909129, 0.7350721416, -0.14647262…
## $ y <dbl> 1.33491259, -0.86927176, 0.05548695, 0.04906691, -0.57835573, -0.998…
## $ m <dbl> 16.02914, 15.91477, 14.99754, 15.13601, 14.27985, 14.80188, 13.97079…

# interval selection in the scatter plot
pts <- alt$selection(type = "interval", encodings = list("x"))

# left panel: scatter plot
points <- alt$Chart()$
  mark_point(filled = TRUE, color = "black")$
  encode(
    x = "x",
    y = "y"
  )$
  transform_filter(pts)$
  properties(width = 300, height = 300)

# right panel: histogram
mag <- alt$Chart()$
  mark_bar()$
  encode(
    x = "mbin:N",
    y = "count()",
    color = alt$condition(pts, alt$value("black"), alt$value("lightgray"))
  )$
  properties(width = 300, height = 300)$
  add_selection(pts)

# build the chart:
alt$hconcat(points, mag, data = source)$
  transform_bin("mbin", field = "m", bin = alt$Bin(maxbins = 20))

Altair example

This example shows a selection that links two views of data: the left panel contains one point per object, and the right panel contains one line per object. Clicking on either the points or lines will select the corresponding objects in both views of the data.

The challenge lies in expressing such hierarchical data in a way that Altair can handle. We do this by merging the data into a “long form” dataframe, and aggregating identical metadata for the final plot.

n_objects <- 20
n_times <- 50

# Create one (x, y) pair of metadata per object
locations <- 
  tibble(
    id = seq(1, n_objects),
    x = rnorm(n_objects),
    y = rnorm(n_objects)
  )

# Create a 50-element time-series for each object
timeseries <-
  crossing(
    id = seq(1, n_objects),
    time = seq(1, n_times)
  ) %>%
  mutate(value = rnorm(n())) %>%
  group_by(id) %>%
  mutate(value = cumsum(value)) %>%
  ungroup()


# Merge the (x, y) metadata into the long-form view
data <- left_join(timeseries, locations, by = "id")

## Rows: 1,000
## Columns: 5
## $ id    <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ time  <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 1…
## $ value <dbl> 0.8029327, 0.2294558, -1.6986693, -1.0342785, -2.6368187, -3.991…
## $ x     <dbl> -0.2484829, -0.2484829, -0.2484829, -0.2484829, -0.2484829, -0.2…
## $ y     <dbl> 1.001113, 1.001113, 1.001113, 1.001113, 1.001113, 1.001113, 1.00…

selector <- alt$selection_single(empty = "all", fields = list("id"))

points <- 
  alt$Chart(data)$
  mark_point(filled = TRUE, size = 200)$
  encode(
    x = "mean(x)",
    y = "mean(y)",
    color = alt$condition(
      selector,
      "id:O",
      alt$value("lightgray"),
      legend = NULL
    )
  )$
  properties(selection = selector, width = 250, height = 250)

timeseries <- 
  alt$Chart(data)$
  mark_line()$
  encode(
    x = "time",
    y = alt$Y("value", scale = alt$Scale(domain = c(-15, 15))),
    color = alt$Color("id:O", legend = NULL)
  )$
  transform_filter(selector)$
  properties(selection = selector, width = 250, height = 250)

points | timeseries

Altair example

This chart shows an example of using an interval selection to filter the contents of an attached histogram, allowing the user to see the proportion of items in each category within the selection.

## Rows: 406
## Columns: 9
## $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 320", "pl…
## $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, NaN, NaN…
## $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8,…
## $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, 390, 133…
## $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, 190, 115…
## $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 4312, 4425,…
## $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5, 10.0, 8…
## $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-01, 1970…
## $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA…

cars <- vega_data$cars()

brush <- alt$selection(type="interval")

points <- 
  alt$Chart()$
  mark_point()$
  encode(
    x = "Horsepower:Q",
    y = "Miles_per_Gallon:Q",
    color = alt$condition(brush, "Origin:N", alt$value("lightgray"))
  )$
  properties(selection = brush)

bars <- 
  alt$Chart()$
  mark_bar()$
  encode(
    x = "count(Origin):Q",
    y = "Origin:N",
    color = "Origin:N"
  )$
  transform_filter(brush$ref())

chart <- alt$vconcat(points, bars, data = cars)

chart

Altair example

## Rows: 406
## Columns: 9
## $ Name             <chr> "chevrolet chevelle malibu", "buick skylark 320", "pl…
## $ Miles_per_Gallon <dbl> 18, 15, 18, 16, 17, 15, 14, 14, 14, 15, NaN, NaN, NaN…
## $ Cylinders        <dbl> 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8, 8, 8, 8,…
## $ Displacement     <dbl> 307, 350, 318, 304, 302, 429, 454, 440, 455, 390, 133…
## $ Horsepower       <dbl> 130, 165, 150, 150, 140, 198, 220, 215, 225, 190, 115…
## $ Weight_in_lbs    <dbl> 3504, 3693, 3436, 3433, 3449, 4341, 4354, 4312, 4425,…
## $ Acceleration     <dbl> 12.0, 11.5, 11.0, 12.0, 10.5, 10.0, 9.0, 8.5, 10.0, 8…
## $ Year             <dttm> 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-01, 1970…
## $ Origin           <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA…

chart <- 
  alt$Chart(vega_data$cars())$
  mark_circle()$
  encode(
    x = "Horsepower",
    y = "Miles_per_Gallon",
    color = "Origin"
  )$
  interactive()

chart

Altair example

#glimpse(vega_data$unemployment_across_industries())

source <- vega_data$unemployment_across_industries$url

base <-
  alt$Chart(source)$
  mark_area(color = "goldenrod", opacity = 0.3)$
  encode(
    x = "yearmonth(date):T",
    y = "sum(count):Q"
  )

brush <- alt$selection_interval(encodings = list("x"), empty = "all")

background <- base$add_selection(brush)

selected <- 
  base$
  transform_filter(brush)$
  mark_area(color = "goldenrod")

background + selected

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