vignettes/example-gallery-09-case-studies.Rmd
example-gallery-09-case-studies.RmdThis document is adapted from the Case Studies section of the Altair Example Gallery.
Our first step is to set up our environment:
library("altair")
library("tibble")
library("dplyr")
library("readr")
library("jsonlite")
library("stringr")
vega_data <- import_vega_data()
glimpse(vega_data$anscombe())
#> Rows: 44
#> Columns: 3
#> $ Series <chr> "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "I", "II", "I…
#> $ X <dbl> 10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5, 10, 8, 13, 9, 11, 14, 6, …
#> $ Y <dbl> 8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.81, 5.…
glimpse(vega_data$co2_concentration())
#> Rows: 713
#> Columns: 2
#> $ Date <chr> "1958-03-01", "1958-04-01", "1958-05-01", "1958-07-01", "1958-08-…
#> $ CO2 <dbl> 315.70, 317.46, 317.51, 315.86, 314.93, 313.21, 313.33, 314.67, 3…
source <- vega_data$co2_concentration$url
base <-
alt$Chart(source, title="Carbon Dioxide in the Atmosphere")$
transform_calculate(year = "year(datum.Date)")$
transform_calculate(decade = "floor(datum.year / 10)")$
transform_calculate(scaled_date = "(datum.year % 10) + (month(datum.Date)/12)")$
transform_window(
first_date = "first_value(scaled_date)",
last_date = "last_value(scaled_date)",
sort = list(alt$EncodingSortField(
field = "scaled_date", # field to use for the sort
order = "ascending" # order to sort in
)
),
groupby = list("decade"),
frame = list(NULL, NULL)
)$transform_calculate(
end = "datum.first_date === datum.scaled_date ? 'first' : datum.last_date === datum.scaled_date ? 'last' : null"
)$encode(
x = alt$X(
"scaled_date:Q",
axis = alt$Axis(title = "Year into Decade", tickCount = 11)
),
y=alt$Y(
"CO2:Q",
title = "CO2 concentration in ppm",
scale = alt$Scale(zero = FALSE)
)
)
line <-
base$
mark_line()$
encode(
color=alt$Color(
"decade:O",
scale = alt$Scale(scheme = "magma"),
legend = NULL
)
)
text <- base$encode(text = "year:N")
start_year <-
text$
transform_filter("datum.end == 'first'")$
mark_text(baseline = "top")
end_year <-
text$
transform_filter("datum.end == 'last'")$
mark_text(baseline="bottom")
chart <- (line + start_year + end_year)
chart <-
chart$
configure_text(align = "left", dx = 1, dy = 3)$
properties(width = 600, height = 375)
#chart$to_json(validate = FALSE) %>% as_vegaspec()
chart
glimpse(vega_data$barley())
#> Rows: 120
#> Columns: 4
#> $ yield <dbl> 27.00000, 48.86667, 27.43334, 39.93333, 32.96667, 28.96667, 43…
#> $ variety <chr> "Manchuria", "Manchuria", "Manchuria", "Manchuria", "Manchuria…
#> $ year <dbl> 1931, 1931, 1931, 1931, 1931, 1931, 1931, 1931, 1931, 1931, 19…
#> $ site <chr> "University Farm", "Waseca", "Morris", "Crookston", "Grand Rap…
chart <-
alt$Chart(vega_data$barley())$
mark_point()$
encode(
x = alt$X(
"yield",
title = "Barley Yield (bushels/acre)",
scale = alt$Scale(zero = FALSE),
axis = alt$Axis(grid = FALSE)
),
y = alt$Y(
"variety",
title = "",
sort = alt$EncodingSortField(
field = "yield",
op = "sum",
order = "descending"
),
axis = alt$Axis(grid = TRUE)
),
color = alt$Color("year:N", legend = alt$Legend(title = "Year")),
row=alt$Row(
"site:N",
title = "",
sort = alt$EncodingSortField(
field = "yield",
op = "sum",
order = "descending"
)
)
)$
properties(height = alt$Step(20))$
configure_view(stroke = "transparent")
chartThis chart shows cumulative donations to Wikipedia over the past 10 years. Inspired by this Reddit post but using lines instead of areas.
data <-
read_csv("https://frdata.wikimedia.org/donationdata-vs-day.csv") %>%
mutate(date = as.character(date))
#> Rows: 5397 Columns: 9
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (8): sum, refund_sum, donations, refunds, avg, max, ytdsum, ytdloss
#> date (1): date
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(data)
#> Rows: 5,397
#> Columns: 9
#> $ date <chr> "2007-12-31", "2008-11-03", "2008-11-04", "2008-11-05", "20…
#> $ sum <dbl> 250.00, 24.00, 2882.16, 97295.89, 97362.30, 75486.06, 65156…
#> $ refund_sum <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ donations <dbl> 1, 6, 73, 3715, 3537, 2823, 2334, 2415, 2142, 2046, 1863, 1…
#> $ refunds <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ avg <dbl> 250.00000, 4.00000, 39.48164, 26.19001, 27.52680, 26.73966,…
#> $ max <dbl> 250.00, 10.00, 375.00, 1287.00, 2500.00, 1000.00, 1000.00, …
#> $ ytdsum <dbl> 250.00, 24.00, 2906.16, 100202.05, 197564.35, 273050.41, 33…
#> $ ytdloss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
alt$data_transformers$disable_max_rows()
#> DataTransformerRegistry.enable('default')
chart <-
alt$Chart(data)$
mark_line()$
encode(
x = alt$X(
"date:T",
timeUnit = "monthdate",
axis = alt$Axis(format = "%B", title = "Month")
),
y = alt$Y(
"max(ytdsum):Q",
stack = NULL,
axis = alt$Axis(title = "Cumulative Donations")
),
color = alt$Color(
"date:O",
timeUnit = "year",
legend = alt$Legend(title = "Year")
),
order = alt$Order("data:O", timeUnit = "year")
)
chart
source <- fromJSON('[
{"year": "1875", "population": 1309},
{"year": "1890", "population": 1558},
{"year": "1910", "population": 4512},
{"year": "1925", "population": 8180},
{"year": "1933", "population": 15915},
{"year": "1939", "population": 24824},
{"year": "1946", "population": 28275},
{"year": "1950", "population": 29189},
{"year": "1964", "population": 29881},
{"year": "1971", "population": 26007},
{"year": "1981", "population": 24029},
{"year": "1985", "population": 23340},
{"year": "1989", "population": 22307},
{"year": "1990", "population": 22087},
{"year": "1991", "population": 22139},
{"year": "1992", "population": 22105},
{"year": "1993", "population": 22242},
{"year": "1994", "population": 22801},
{"year": "1995", "population": 24273},
{"year": "1996", "population": 25640},
{"year": "1997", "population": 27393},
{"year": "1998", "population": 29505},
{"year": "1999", "population": 32124},
{"year": "2000", "population": 33791},
{"year": "2001", "population": 35297},
{"year": "2002", "population": 36179},
{"year": "2003", "population": 36829},
{"year": "2004", "population": 37493},
{"year": "2005", "population": 38376},
{"year": "2006", "population": 39008},
{"year": "2007", "population": 39366},
{"year": "2008", "population": 39821},
{"year": "2009", "population": 40179},
{"year": "2010", "population": 40511},
{"year": "2011", "population": 40465},
{"year": "2012", "population": 40905},
{"year": "2013", "population": 41258},
{"year": "2014", "population": 41777}
]')
source2 <- fromJSON('[
{
"start": "1933",
"end": "1945",
"event": "Nazi Rule"
},
{
"start": "1948",
"end": "1989",
"event": "GDR (East Germany)"
}
]')
glimpse(source)
#> Rows: 38
#> Columns: 2
#> $ year <chr> "1875", "1890", "1910", "1925", "1933", "1939", "1946", "19…
#> $ population <int> 1309, 1558, 4512, 8180, 15915, 24824, 28275, 29189, 29881, …
glimpse(source2)
#> Rows: 2
#> Columns: 3
#> $ start <chr> "1933", "1948"
#> $ end <chr> "1945", "1989"
#> $ event <chr> "Nazi Rule", "GDR (East Germany)"
line <-
alt$Chart(source)$
mark_line(color="#333")$
encode(
x = alt$X("year:T", axis = alt$Axis(format = "%Y")),
y = "population"
)$
properties(
width = 500,
height = 300
)
point <- line$mark_point(color="#333")
rect <-
alt$Chart(source2)$
mark_rect()$
encode(
x = "start:T",
x2 = "end:T",
color = "event:N"
)
chart <- (rect + line + point)
chartThis example shows how to make a bubble plot showing the correlation between health and income for 187 countries in the world (modified from an example in Lisa Charlotte Rost’s blog post ‘One Chart, Twelve Charting Libraries’).
glimpse(vega_data$gapminder_health_income())
#> Rows: 187
#> Columns: 4
#> $ country <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "…
#> $ income <dbl> 1925, 10620, 13434, 46577, 7615, 21049, 17344, 7763, 44056,…
#> $ health <dbl> 57.63, 76.00, 76.50, 84.10, 61.00, 75.20, 76.20, 74.40, 81.…
#> $ population <dbl> 32526562, 2896679, 39666519, 70473, 25021974, 91818, 434167…This example is a fully developed stacked chart using the sample dataset of Iowa’s electricity sources.
glimpse(vega_data$iowa_electricity())
#> Rows: 51
#> Columns: 3
#> $ year <dttm> 2001-01-01, 2002-01-01, 2003-01-01, 2004-01-01, 2005-0…
#> $ source <chr> "Fossil Fuels", "Fossil Fuels", "Fossil Fuels", "Fossil…
#> $ net_generation <dbl> 35361, 35991, 36234, 36205, 36883, 37014, 41389, 42734,…
source = vega_data$iowa_electricity()
chart <-
alt$Chart(
source,
title="Iowa's renewable energy boom"
)$
mark_area()$
encode(
x=alt$X(
"year:T",
title = "Year"
),
y=alt$Y(
"net_generation:Q",
stack = "normalize",
title = "Share of net generation",
axis = alt$Axis(format = ".0%")
),
color=alt$Color(
"source:N",
legend = alt$Legend(title = "Electricity source")
)
)
chartIsotype Visualization shows the distribution of animals across UK and US. Inspired by Only An Ocean Between, 1943. Population Live Stock, p.13. This is adapted from a [Vega-Lite example])https://vega.github.io/editor/#/examples/vega-lite/isotype_bar_chart).
source <- fromJSON('[
{"country": "Great Britain", "animal": "cattle"},
{"country": "Great Britain", "animal": "cattle"},
{"country": "Great Britain", "animal": "cattle"},
{"country": "Great Britain", "animal": "pigs"},
{"country": "Great Britain", "animal": "pigs"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"}
]')
glimpse(source)
#> Rows: 37
#> Columns: 2
#> $ country <chr> "Great Britain", "Great Britain", "Great Britain", "Great Brit…
#> $ animal <chr> "cattle", "cattle", "cattle", "pigs", "pigs", "sheep", "sheep"…
domains = list('person', 'cattle', 'pigs', 'sheep')
shape_scale = alt$Scale(
domain=domains,
range=list(
'M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z',
'M4 -2c0 0 0.9 -0.7 1.1 -0.8c0.1 -0.1 -0.1 0.5 -0.3 0.7c-0.2 0.2 1.1 1.1 1.1 1.2c0 0.2 -0.2 0.8 -0.4 0.7c-0.1 0 -0.8 -0.3 -1.3 -0.2c-0.5 0.1 -1.3 1.6 -1.5 2c-0.3 0.4 -0.6 0.4 -0.6 0.4c0 0.1 0.3 1.7 0.4 1.8c0.1 0.1 -0.4 0.1 -0.5 0c0 0 -0.6 -1.9 -0.6 -1.9c-0.1 0 -0.3 -0.1 -0.3 -0.1c0 0.1 -0.5 1.4 -0.4 1.6c0.1 0.2 0.1 0.3 0.1 0.3c0 0 -0.4 0 -0.4 0c0 0 -0.2 -0.1 -0.1 -0.3c0 -0.2 0.3 -1.7 0.3 -1.7c0 0 -2.8 -0.9 -2.9 -0.8c-0.2 0.1 -0.4 0.6 -0.4 1c0 0.4 0.5 1.9 0.5 1.9l-0.5 0l-0.6 -2l0 -0.6c0 0 -1 0.8 -1 1c0 0.2 -0.2 1.3 -0.2 1.3c0 0 0.3 0.3 0.2 0.3c0 0 -0.5 0 -0.5 0c0 0 -0.2 -0.2 -0.1 -0.4c0 -0.1 0.2 -1.6 0.2 -1.6c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 0 -2.7 -0.2 -2.7c-0.1 0 -0.4 2 -0.4 2c0 0 0 0.2 -0.2 0.5c-0.1 0.4 -0.2 1.1 -0.2 1.1c0 0 -0.2 -0.1 -0.2 -0.2c0 -0.1 -0.1 -0.7 0 -0.7c0.1 -0.1 0.3 -0.8 0.4 -1.4c0 -0.6 0.2 -1.3 0.4 -1.5c0.1 -0.2 0.6 -0.4 0.6 -0.4z',
'M1.2 -2c0 0 0.7 0 1.2 0.5c0.5 0.5 0.4 0.6 0.5 0.6c0.1 0 0.7 0 0.8 0.1c0.1 0 0.2 0.2 0.2 0.2c0 0 -0.6 0.2 -0.6 0.3c0 0.1 0.4 0.9 0.6 0.9c0.1 0 0.6 0 0.6 0.1c0 0.1 0 0.7 -0.1 0.7c-0.1 0 -1.2 0.4 -1.5 0.5c-0.3 0.1 -1.1 0.5 -1.1 0.7c-0.1 0.2 0.4 1.2 0.4 1.2l-0.4 0c0 0 -0.4 -0.8 -0.4 -0.9c0 -0.1 -0.1 -0.3 -0.1 -0.3l-0.2 0l-0.5 1.3l-0.4 0c0 0 -0.1 -0.4 0 -0.6c0.1 -0.1 0.3 -0.6 0.3 -0.7c0 0 -0.8 0 -1.5 -0.1c-0.7 -0.1 -1.2 -0.3 -1.2 -0.2c0 0.1 -0.4 0.6 -0.5 0.6c0 0 0.3 0.9 0.3 0.9l-0.4 0c0 0 -0.4 -0.5 -0.4 -0.6c0 -0.1 -0.2 -0.6 -0.2 -0.5c0 0 -0.4 0.4 -0.6 0.4c-0.2 0.1 -0.4 0.1 -0.4 0.1c0 0 -0.1 0.6 -0.1 0.6l-0.5 0l0 -1c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 -0.7 -1.2 -0.6 -1.4c0.1 -0.1 0.1 -1.1 0.1 -1.1c0 0 -0.2 0.1 -0.2 0.1c0 0 0 0.9 0 1c0 0.1 -0.2 0.3 -0.3 0.3c-0.1 0 0 -0.5 0 -0.9c0 -0.4 0 -0.4 0.2 -0.6c0.2 -0.2 0.6 -0.3 0.8 -0.8c0.3 -0.5 1 -0.6 1 -0.6z',
'M-4.1 -0.5c0.2 0 0.2 0.2 0.5 0.2c0.3 0 0.3 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.4 -0.2c0.1 0 0.2 0.2 0.4 0.1c0.2 0 0.2 -0.2 0.4 -0.3c0.1 0 0.1 -0.1 0.4 0c0.3 0 0.3 -0.4 0.6 -0.4c0.3 0 0.6 -0.3 0.7 -0.2c0.1 0.1 1.4 1 1.3 1.4c-0.1 0.4 -0.3 0.3 -0.4 0.3c-0.1 0 -0.5 -0.4 -0.7 -0.2c-0.3 0.2 -0.1 0.4 -0.2 0.6c-0.1 0.1 -0.2 0.2 -0.3 0.4c0 0.2 0.1 0.3 0 0.5c-0.1 0.2 -0.3 0.2 -0.3 0.5c0 0.3 -0.2 0.3 -0.3 0.6c-0.1 0.2 0 0.3 -0.1 0.5c-0.1 0.2 -0.1 0.2 -0.2 0.3c-0.1 0.1 0.3 1.1 0.3 1.1l-0.3 0c0 0 -0.3 -0.9 -0.3 -1c0 -0.1 -0.1 -0.2 -0.3 -0.2c-0.2 0 -0.3 0.1 -0.4 0.4c0 0.3 -0.2 0.8 -0.2 0.8l-0.3 0l0.3 -1c0 0 0.1 -0.6 -0.2 -0.5c-0.3 0.1 -0.2 -0.1 -0.4 -0.1c-0.2 -0.1 -0.3 0.1 -0.4 0c-0.2 -0.1 -0.3 0.1 -0.5 0c-0.2 -0.1 -0.1 0 -0.3 0.3c-0.2 0.3 -0.4 0.3 -0.4 0.3l0.2 1.1l-0.3 0l-0.2 -1.1c0 0 -0.4 -0.6 -0.5 -0.4c-0.1 0.3 -0.1 0.4 -0.3 0.4c-0.1 -0.1 -0.2 1.1 -0.2 1.1l-0.3 0l0.2 -1.1c0 0 -0.3 -0.1 -0.3 -0.5c0 -0.3 0.1 -0.5 0.1 -0.7c0.1 -0.2 -0.1 -1 -0.2 -1.1c-0.1 -0.2 -0.2 -0.8 -0.2 -0.8c0 0 -0.1 -0.5 0.4 -0.8z'
))
color_scale <- alt$Scale(
domain=domains,
range=list('rgb(162,160,152)', 'rgb(194,81,64)', 'rgb(93,93,93)', 'rgb(91,131,149)')
)
chart <-
alt$Chart(source)$
mark_point(filled = TRUE, opacity = 1, size = 100)$
encode(
alt$X("x:O", axis = NULL),
alt$Y("animal:O", axis = NULL),
alt$Row("country:N", header = alt$Header(title = "")),
alt$Shape("animal:N", legend = NULL, scale = shape_scale),
alt$Color("animal:N", legend = NULL, scale = color_scale)
)$
transform_window(
x="rank()",
groupby = list("country", "animal")
)$
properties(width = 550, height = 140)
chartIsotype Visualization shows the distribution of animals across UK and US, using unicode emoji marks rather than custom SVG paths (see ). This is adapted from Vega-Lite example .
source <- fromJSON('[
{"country": "Great Britain", "animal": "cattle"},
{"country": "Great Britain", "animal": "cattle"},
{"country": "Great Britain", "animal": "cattle"},
{"country": "Great Britain", "animal": "pigs"},
{"country": "Great Britain", "animal": "pigs"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "Great Britain", "animal": "sheep"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "cattle"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "pigs"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"},
{"country": "United States", "animal": "sheep"}
]')
glimpse(source)
#> Rows: 37
#> Columns: 2
#> $ country <chr> "Great Britain", "Great Britain", "Great Britain", "Great Brit…
#> $ animal <chr> "cattle", "cattle", "cattle", "pigs", "pigs", "sheep", "sheep"…
chart <-
alt$Chart(source)$
mark_text(size = 45, baseline = "middle")$
encode(
x = alt$X("x:O", axis = NULL),
y = alt$Y("animal:O", axis = NULL),
text = alt$Text("emoji:N"),
row = alt$Row("country:N", header = alt$Header(title = ""))
)$
transform_calculate(
emoji = "{'cattle': '🐄', 'pigs': '🐖', 'sheep': '🐏'}[datum.animal]"
)$
transform_window(
x = "rank()",
groupby = list("country", "animal")
)$
properties(width=550, height=140)
chart
us <- vega_data$us_10m$url
airports <- vega_data$airports()
glimpse(airports)
#> Rows: 3,376
#> Columns: 7
#> $ iata <chr> "00M", "00R", "00V", "01G", "01J", "01M", "02A", "02C", "02G…
#> $ name <chr> "Thigpen", "Livingston Municipal", "Meadow Lake", "Perry-War…
#> $ city <chr> "Bay Springs", "Livingston", "Colorado Springs", "Perry", "H…
#> $ state <chr> "MS", "TX", "CO", "NY", "FL", "MS", "AL", "WI", "OH", "MO", …
#> $ country <chr> "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA", "USA…
#> $ latitude <dbl> 31.95376, 30.68586, 38.94575, 42.74135, 30.68801, 34.49167, …
#> $ longitude <dbl> -89.23450, -95.01793, -104.56989, -78.05208, -81.90594, -88.…
states <- alt$topo_feature(us, feature = "states")
# US states background
background <-
alt$Chart(states)$
mark_geoshape(
fill = "lightgray",
stroke = "white"
)$
properties(width = 500, height = 300)$
project("albersUsa")
# airport positions on background
points <-
alt$Chart(airports)$
mark_circle()$
encode(
longitude = "longitude:Q",
latitude = "latitude:Q",
size = alt$value(10),
color = alt$value("steelblue")
)
chart <- (background + points)
chartThis example shows the London tube lines against the background of the borough boundaries. It is based on the Vega-Lite example.
boroughs <- alt$topo_feature(vega_data$londonBoroughs$url, "boroughs")
tubelines <- alt$topo_feature(vega_data$londonTubeLines$url, "line")
glimpse(vega_data$londonCentroids())
#> Rows: 33
#> Columns: 3
#> $ name <chr> "Kingston upon Thames", "Croydon", "Bromley", "Hounslow", "Ealing…
#> $ cx <dbl> -0.28691466, -0.08716546, 0.05153659, -0.36715164, -0.33101932, 0…
#> $ cy <dbl> 51.38789, 51.35532, 51.37198, 51.46838, 51.52248, 51.56437, 51.54…
domain_line <-
list("Bakerloo", "Central", "Circle", "District", "DLR",
"Hammersmith & City", "Jubilee", "Metropolitan", "Northern",
"Piccadilly", "Victoria", "Waterloo & City" )
range_line <-
list("rgb(137,78,36)", "rgb(220,36,30)", "rgb(255,206,0)",
"rgb(1,114,41)", "rgb(0,175,173)", "rgb(215,153,175)",
"rgb(106,114,120)", "rgb(114,17,84)", "rgb(0,0,0)",
"rgb(0,24,168)", "rgb(0,160,226)", "rgb(106,187,170)")
background <-
alt$Chart(boroughs)$
mark_geoshape(stroke = "white", strokeWidth = 2)$
encode(
color = alt$value("#eee")
)$
properties(width = 700, height = 500)
labels <-
alt$Chart(vega_data$londonCentroids$url)$
mark_text()$
encode(
longitude = "cx:Q",
latitude = "cy:Q",
text = "bLabel:N",
size = alt$value(8),
opacity = alt$value(0.6)
)$
transform_calculate("bLabel", "indexof (datum.name,' ') > 0 ? substring(datum.name,0,indexof(datum.name, ' ')) : datum.name")
line_scale <-
alt$Scale(domain = domain_line, range = range_line)
lines <-
alt$Chart(tubelines)$
mark_geoshape(filled = FALSE, strokeWidth = 2)$
encode(
color = alt$Color(
"id:N",
legend = alt$Legend(
title = NULL,
orient = "bottom-right",
offset = 0
)
)
)
chart <- (background + labels + lines)
chart
glimpse(vega_data$disasters())
#> Rows: 803
#> Columns: 3
#> $ Entity <chr> "All natural disasters", "All natural disasters", "All natural …
#> $ Year <dbl> 1900, 1901, 1902, 1903, 1905, 1906, 1907, 1908, 1909, 1910, 191…
#> $ Deaths <dbl> 1267360, 200018, 46037, 6506, 22758, 42970, 1325641, 75033, 151…
chart <-
alt$Chart(vega_data$disasters$url)$
mark_circle(opacity = 0.8, stroke = "black", strokeWidth = 1)$
encode(
x = alt$X("Year:O", axis = alt$Axis(labelAngle = 0)),
y = alt$Y("Entity:N"),
size = alt$Size(
"Deaths:Q",
scale = alt$Scale(range = c(0, 5000)),
legend = alt$Legend(title = "Annual Global Deaths")
),
color = alt$Color("Entity:N", legend = NULL)
)$
properties(
width = 480,
height = 320
)$
transform_filter("datum.Entity != 'All natural disasters'")
chartIf you are building a chart using a local data frame, the default is that there has to be less than 5000 observations. If you build a chart using a URL (local or remote), there is no such default.
To modify this default:
alt$data_transformers$enable('default', max_rows=<your new max>)
glimpse(vega_data$zipcodes())
#> Rows: 42,049
#> Columns: 6
#> $ zip_code <chr> "00501", "00544", "00601", "00602", "00603", "00604", "00605…
#> $ latitude <dbl> 40.92233, 40.92233, 18.16527, 18.39310, 18.45591, 18.49352, …
#> $ longitude <dbl> -72.63708, -72.63708, -66.72258, -67.18095, -67.14578, -67.1…
#> $ city <chr> "Holtsville", "Holtsville", "Adjuntas", "Aguada", "Aguadilla…
#> $ state <chr> "NY", "NY", "PR", "PR", "PR", "PR", "PR", "PR", "PR", "PR", …
#> $ county <chr> "Suffolk", "Suffolk", "Adjuntas", "Aguada", "Aguadilla", "Ag…
chart <-
alt$Chart(vega_data$zipcodes$url)$
mark_circle(size = 3)$
encode(
longitude = "longitude:Q",
latitude = "latitude:Q",
color = "digit:N"
)$
properties(
projection = list(type = "albersUsa"),
width = 650,
height = 400
)$
transform_calculate("digit", "substring(datum.zip_code, 0, 1)")
chartThis example shows the 2010 daily high temperature (F) in Seattle, WA.
glimpse(vega_data$seattle_temps())
#> Rows: 8,759
#> Columns: 2
#> $ date <dttm> 2010-01-01 00:00:00, 2010-01-01 01:00:00, 2010-01-01 02:00:00, 2…
#> $ temp <dbl> 39.4, 39.2, 39.0, 38.9, 38.8, 38.7, 38.7, 38.6, 38.7, 39.2, 40.1,…
# Since the data is more than 5,000 rows we'll import it from a URL
source <- vega_data$seattle_temps$url
chart <-
alt$Chart(
source,
title = "2010 Daily High Temperature (F) in Seattle, WA"
)$
mark_rect()$
encode(
x = "date(date):O",
y = "month(date):O",
color = alt$Color("max(temp):Q", scale = alt$Scale(scheme = "inferno")),
tooltip = list(
alt$Tooltip("monthdate(date):T", title = "Date"),
alt$Tooltip("max(temp):Q", title = "Max Temp")
)
)$
properties(width = 550)
chartThis chart provides an interactive exploration of Seattle weather over the course of the year. It includes a one-axis brush selection to easily see the distribution of weather types in a particular date range. #### Data
glimpse(vega_data$seattle_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…
scale <- alt$Scale(
domain = list("sun", "fog", "drizzle", "rain", "snow"),
range = list("#e7ba52", "#a7a7a7", "#aec7e8", "#1f77b4", "#9467bd")
)
color <- alt$Color("weather:N", scale = scale)
# We create two selections:
# - a brush that is active on the top panel
# - a multi-click that is active on the bottom panel
brush <- alt$selection_interval(encodings = list("x"))
click <- alt$selection_multi(encodings = list("color"))
# Top panel is scatter plot of temperature vs time
points <-
alt$Chart(data = vega_data$seattle_weather())$
mark_point()$
encode(
x = alt$X(
"date:T",
timeUnit = "monthdate",
axis = alt$Axis(title = "Date")
),
alt$Y(
"temp_max:Q",
axis = alt$Axis(title = "Maximum Daily Temperature (C)"),
scale = alt$Scale(domain = list(-5, 40))
),
color = alt$condition(brush, color, alt$value("lightgray")),
size = alt$Size("precipitation:Q", scale = alt$Scale(range = list(5, 200)))
)$
properties(width = 600, height = 300, selection = brush)$
transform_filter(click)
# Bottom panel is a bar chart of weather type
bars <-
alt$Chart(data = vega_data$seattle_weather())$
mark_bar()$
encode(
x = "count(weather)",
y = "weather:N",
color = alt$condition(click, color, alt$value("lightgray"))
)$
transform_filter(brush)$
properties(width = 600, selection = click)
chart <-
(points & bars)$
properties(title = "Seattle Weather: 2012-2015")
chart
glimpse(vega_data$us_employment())
#> Rows: 120
#> Columns: 24
#> $ month <chr> "2006-01-01", "2006-02-01", "2006-0…
#> $ nonfarm <dbl> 135450, 135762, 136059, 136227, 136…
#> $ private <dbl> 113603, 113884, 114156, 114308, 114…
#> $ goods_producing <dbl> 22467, 22535, 22572, 22631, 22597, …
#> $ service_providing <dbl> 112983, 113227, 113487, 113596, 113…
#> $ private_service_providing <dbl> 91136, 91349, 91584, 91677, 91735, …
#> $ mining_and_logging <dbl> 656, 662, 669, 679, 681, 686, 690, …
#> $ construction <dbl> 7601, 7664, 7689, 7726, 7713, 7699,…
#> $ manufacturing <dbl> 14210, 14209, 14214, 14226, 14203, …
#> $ durable_goods <dbl> 8982, 8986, 9000, 9020, 9017, 9028,…
#> $ nondurable_goods <dbl> 5228, 5223, 5214, 5206, 5186, 5185,…
#> $ trade_transportation_utilties <dbl> 26162, 26196, 26239, 26230, 26223, …
#> $ wholesale_trade <dbl> 5840.4, 5854.8, 5873.3, 5886.9, 589…
#> $ retail_trade <dbl> 15351.5, 15361.3, 15388.0, 15348.5,…
#> $ transportation_and_warehousing <dbl> 4420.0, 4429.4, 4429.7, 4445.4, 445…
#> $ utilities <dbl> 549.8, 550.1, 547.5, 548.9, 548.3, …
#> $ information <dbl> 3052, 3052, 3055, 3046, 3039, 3036,…
#> $ financial_activities <dbl> 8307, 8332, 8348, 8369, 8376, 8362,…
#> $ professional_and_business_services <dbl> 17299, 17365, 17438, 17462, 17512, …
#> $ education_and_health_services <dbl> 17946, 17998, 18045, 18070, 18100, …
#> $ leisure_and_hospitality <dbl> 12945, 12980, 13034, 13074, 13052, …
#> $ other_services <dbl> 5425, 5426, 5425, 5426, 5433, 5432,…
#> $ government <dbl> 21847, 21878, 21903, 21919, 21926, …
#> $ nonfarm_change <dbl> 282, 312, 297, 168, 31, 79, 206, 17…
source <- vega_data$us_employment()
presidents = fromJSON('[
{
"start": "2006-01-01",
"end": "2009-01-19",
"president": "Bush"
},
{
"start": "2009-01-20",
"end": "2015-12-31",
"president": "Obama"
}
]')
bars <-
alt$Chart(
source,
title = "The U.S. employment crash during the Great Recession"
)$mark_bar()$
encode(
x = alt$X("month:T", title = ""),
y = alt$Y(
"nonfarm_change:Q",
title = "Change in non-farm employment (in thousands)"
),
color=alt$condition(
"datum.nonfarm_change > 0",
alt$value("steelblue"),
alt$value("orange")
)
)
rule <-
alt$Chart(presidents)$
mark_rule(color = "black",strokeWidth = 2)$
encode(
x = "end:T"
)$
transform_filter('datum.president == "Bush"')
text <-
alt$Chart(presidents)$
mark_text(
align = "left",
baseline = "middle",
dx = 7,
dy = -135,
size = 11
)$encode(
x = "start:T",
x2 = "end:T",
text = "president",
color = alt$value("#000000")
)
chart <- (bars + rule + text)$properties(width = 600)
chartThis example shows how to use the window and transformation filter to display the Top items of a long list of items in decreasing order. The sorting of the x-axis is needed for vega-lite and for the example does not do anything since we already have a unique value. Here we sort the top 10 highest ranking movies of IMDB.
glimpse(fromJSON(vega_data$movies$url))
#> 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, …
source <- vega_data$movies$url
# Top 10 movies by IMBD rating
chart <-
alt$Chart(source)$
mark_bar()$
encode(
x = alt$X(
"Title:N",
sort = alt$EncodingSortField(
field = "IMDB_Rating",
op = "mean",
order = "descending"
)
),
y = alt$Y("IMDB_Rating:Q"),
color = alt$Color("IMDB_Rating:Q")
)$
transform_window(
rank = "rank(IMDB_Rating)",
sort = list(alt$SortField("IMDB_Rating", order = "descending"))
)$
transform_filter("datum.rank < 10")
chartThis example shows how to use a window transform in order to display only the top K categories by number of entries. In this case, we rank the characters in the first paragraph of Dickens’ A Tale of Two Cities by number of occurrences.
txt = "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way - in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only."
ttxt <- txt %>%
str_remove_all(pattern = "[[:punct:]]") %>%
str_remove_all(' ') %>%
str_split(pattern = "")
source <- tibble(letters = ttxt[[1]])
glimpse(source)
#> Rows: 475
#> Columns: 1
#> $ letters <chr> "I", "t", "w", "a", "s", "t", "h", "e", "b", "e", "s", "t", "o…
chart <-
alt$Chart(source)$
transform_aggregate(count = "count()", groupby = list("letters"))$
transform_window(
rank = "rank(count)",
sort = list(alt$SortField("count", order = "descending"))
)$
transform_filter("datum.rank < 10")$
mark_bar()$
encode(
y = alt$Y(
"letters:N",
sort = alt$EncodingSortField(
field = "count",
op = "sum",
order = "descending"
)
),
x = "count:Q"
)
chart
glimpse(vega_data$us_state_capitals())
#> Rows: 50
#> Columns: 4
#> $ lon <dbl> -86.27912, -134.41974, -112.07384, -92.33112, -121.46893, -104.9…
#> $ lat <dbl> 32.36154, 58.30194, 33.44846, 34.73601, 38.55560, 39.73917, 41.7…
#> $ state <chr> "Alabama", "Alaska", "Arizona", "Arkansas", "California", "Color…
#> $ city <chr> "Montgomery", "Juneau", "Phoenix", "Little Rock", "Sacramento", …
states <- alt$topo_feature(vega_data$us_10m$url, "states")
capitals <- vega_data$us_state_capitals$url
# US states background
background <-
alt$Chart(states)$
mark_geoshape(fill = "lightgray",stroke = "white")$
properties(title = "US State Capitols", width = 700, height = 400)$
project("albersUsa")
# Points and text
hover <-
alt$selection(
type = "single",
on = "mouseover",
nearest = TRUE,
fields = list("lat", "lon")
)
base <-
alt$Chart(capitals)$
encode(
longitude = "lon:Q",
latitude = "lat:Q"
)
text <-
base$
mark_text(dy = -5, align = "right")$
encode(
alt$Text("city", type = "nominal"),
# switched from reference
opacity = alt$condition(hover, alt$value(1), alt$value(0))
)
points <-
base$
mark_point()$
encode(
color = alt$value("black"),
# switched from reference
size = alt$condition(hover, alt$value(100), alt$value(30))
)$
properties(selection = hover)
chart <- (background + points + text)
chartThis chart visualizes the age distribution of the US population over time. It uses a slider widget that is bound to the year to visualize the age distribution over time. #### Data
glimpse(vega_data$population())
#> Rows: 570
#> Columns: 4
#> $ year <dbl> 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 185…
#> $ age <dbl> 0, 0, 5, 5, 10, 10, 15, 15, 20, 20, 25, 25, 30, 30, 35, 35, 40,…
#> $ sex <dbl> 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, …
#> $ people <dbl> 1483789, 1450376, 1411067, 1359668, 1260099, 1216114, 1077133, …
pop <- vega_data$population$url
pink_blue <-
alt$Scale(
domain = list("Male", "Female"),
range = list("steelblue", "salmon")
)
slider <- alt$binding_range(min = 1900, max = 2000, step = 10)
year <- alt$selection_single(
name = "year",
fields = list("year"),
bind = slider
)
chart <-
alt$Chart(pop)$
mark_bar()$
encode(
x = alt$X("sex:N", axis = alt$Axis(title = NULL)),
y = alt$Y("people:Q", scale = alt$Scale(domain = c(0, 1.2e7))),
color = alt$Color("sex:N", scale = pink_blue),
column = "age:O"
)$
properties(width = 20, selection = year)$
transform_calculate("sex", "if(datum.sex == 1, 'Male', 'Female')")$
transform_filter(year$ref())
chartA population pyramid shows the distribution of age groups within a population. It uses a slider widget that is bound to the year to visualize the age distribution over time.
glimpse(vega_data$population())
#> Rows: 570
#> Columns: 4
#> $ year <dbl> 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 185…
#> $ age <dbl> 0, 0, 5, 5, 10, 10, 15, 15, 20, 20, 25, 25, 30, 30, 35, 35, 40,…
#> $ sex <dbl> 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, …
#> $ people <dbl> 1483789, 1450376, 1411067, 1359668, 1260099, 1216114, 1077133, …
source <- vega_data$population$url
slider <- alt$binding_range(min = 1850, max = 2000, step = 10)
select_year <-
alt$selection_single(
name = "year",
fields = list("year"),
bind = slider,
init = list(year = 2000)
)
base <-
alt$
Chart(source)$
add_selection(select_year)$
transform_filter(select_year)$
transform_calculate("gender", "if(datum.sex == 1, 'Male', 'Female')")$
properties(width=250)
color_scale <-
alt$Scale(
domain = list("Male", "Female"),
range = list("#1f77b4", "#e377c2")
)
left <-
base$
transform_filter("datum.gender == 'Female'")$
encode(
y = alt$Y("age:O", axis=NULL),
x = alt$X(
"sum(people):Q",
title = "population",
sort = alt$SortOrder("descending")
),
color = alt$Color("gender:N", scale = color_scale, legend = NULL)
)$
mark_bar()$
properties(title = "Female")
middle <-
base$
encode(
y = alt$Y("age:O", axis = NULL),
text = alt$Text("age:Q")
)$
mark_text()$
properties(width = 20)
right <-
base$
transform_filter("datum.gender == 'Male'")$
encode(
y = alt$Y("age:O", axis = NULL),
x = alt$X("sum(people):Q", title = "population"),
color = alt$Color("gender:N", scale = color_scale, legend = NULL)
)$
mark_bar()$
properties(title = "Male")
chart <- alt$concat(left, middle, right, spacing = 5)
chartThis chart visualizes the age distribution of the US population over time, using a wrapped faceting of the data by decade.
glimpse(vega_data$population())
#> Rows: 570
#> Columns: 4
#> $ year <dbl> 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 1850, 185…
#> $ age <dbl> 0, 0, 5, 5, 10, 10, 15, 15, 20, 20, 25, 25, 30, 30, 35, 35, 40,…
#> $ sex <dbl> 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, …
#> $ people <dbl> 1483789, 1450376, 1411067, 1359668, 1260099, 1216114, 1077133, …
source <- vega_data$population$url
chart <-
alt$Chart(source)$
mark_area()$
encode(
x = "age:O",
y = alt$Y(
"sum(people):Q",
title = "Population",
axis = alt$Axis(format = "~s")
),
facet = alt$Facet("year:O", columns = 5)
)$
properties(
title = "US Age Distribution By Year",
width = 90,
height = 80
)
chartA recreation of William Playfair’s classic chart visualizing the price of wheat, the wages of a mechanic, and the reigning British monarch.
This is a more polished version of the simpler chart in Bar Chart with Line on Dual Axis.
glimpse(vega_data$wheat())
#> Rows: 52
#> Columns: 3
#> $ year <dbl> 1565, 1570, 1575, 1580, 1585, 1590, 1595, 1600, 1605, 1610, 1615…
#> $ wheat <dbl> 41.0, 45.0, 42.0, 49.0, 41.5, 47.0, 64.0, 27.0, 33.0, 32.0, 33.0…
#> $ wages <dbl> 5.00, 5.05, 5.08, 5.12, 5.15, 5.25, 5.54, 5.61, 5.69, 5.78, 5.94…
base_wheat <-
alt$Chart(vega_data$wheat$url)$
transform_calculate(year_end = "+datum.year + 5")
base_monarchs <-
alt$Chart(vega_data$monarchs$url)$
transform_calculate(
offset = "((!datum.commonwealth && datum.index % 2) ? -1: 1) * 2 + 95",
off2 = "((!datum.commonwealth && datum.index % 2) ? -1: 1) + 95",
y = "95",
x = "+datum.start + (+datum.end - +datum.start)/2"
)
bars <-
base_wheat$
mark_bar(fill = "#aaa", stroke = "#999")$
encode(
x = alt$X("year:Q", axis = alt$Axis(format = "d", tickCount = 5)),
y = alt$Y("wheat:Q", axis = alt$Axis(zindex = 1)),
x2 = alt$X2("year_end")
)
area <-
base_wheat$
mark_area(color="#a4cedb", opacity = 0.7)$
encode(
x = alt$X("year:Q"),
y = alt$Y("wages:Q")
)
area_line_1 <- area$mark_line(color = "#000", opacity = 0.7)
area_line_2 <-area$mark_line(yOffset= -2, color = "#EE8182")
top_bars <-
base_monarchs$
mark_bar(stroke = "#000")$
encode(
x = alt$X("start:Q"),
x2 = alt$X2("end"),
y = alt$Y("y:Q"),
y2 = alt$Y2("offset"),
fill = alt$Fill(
"commonwealth:N",
legend = NULL,
scale = alt$Scale(range = list("black", "white"))
)
)
top_text <-
base_monarchs$
mark_text(yOffset = 14, fontSize = 9, fontStyle ="italic")$
encode(
x = alt$X("x:Q"),
y = alt$Y("off2:Q"),
text = alt$Text("name:N")
)
chart <-
(bars + area + area_line_1 + area_line_2 + top_bars + top_text)$
properties(width = 900, height = 400)$
configure_axis(
title = NULL,
gridColor = "white",
gridOpacity = 0.25,
domain = FALSE
)$
configure_view(stroke = "transparent")
chart