knitr::opts_chunk$set( collapse = TRUE, comment = "#>", screenshot.force = FALSE )
The objective of this vignette is to show how to quickly build data visualizations with the ApexCharts JavaScript library, as well as to give an overview of the different graphics available.
Data used are from ggplot2
package.
library(ggplot2) library(scales) library(apexcharter)
Simple bar charts can be created with:
data("mpg") apex(data = mpg, type = "column", mapping = aes(x = manufacturer))
Flipping coordinates can be done by using type = "bar"
:
apex(data = mpg, type = "bar", mapping = aes(x = manufacturer))
To create a dodge bar charts, use aesthetic fill
:
apex(data = mpg, type = "column", mapping = aes(x = manufacturer, fill = year))
For stacked bar charts, specify option stacked
in ax_chart
:
apex(data = mpg, type = "column", mapping = aes(x = manufacturer, fill = year)) %>% ax_chart(stacked = TRUE)
Simple line charts can be created with (works with character
, Date
or POSIXct
):
data("economics") apex(data = economics, type = "line", mapping = aes(x = date, y = uempmed))
To represent several lines, use a data.frame
in long format and the group
aesthetic:
data("economics_long") apex(data = economics_long, type = "line", mapping = aes(x = date, y = value01, group = variable)) %>% ax_yaxis(decimalsInFloat = 2) # number of decimals to keep
Create area charts with type = "area"
:
data("eco2mix", package = "apexcharter") apex(eco2mix, aes(datetime, production, fill = source), type = "area") %>% ax_chart(animations = list(enabled = FALSE), stacked = TRUE) %>% ax_stroke(width = 1) %>% ax_fill(opacity = 1, type = "solid") %>% ax_tooltip(x = list(format = "dd MMM, HH:mm")) %>% ax_yaxis(labels = list(formatter = format_num("~", suffix = "MW"))) %>% ax_colors_manual( list( "bioenergies" = "#156956", "fuel" = "#80549f", "coal" = "#a68832", "solar" = "#d66b0d", "gas" = "#f20809", "wind" = "#72cbb7", "hydraulic" = "#2672b0", "nuclear" = "#e4a701", "pumping" = "#0e4269" ) ) %>% ax_labs( title = "Electricity generation by sector in France", subtitle = "Data from \u00e9CO\u2082mix" )
You can create ribbon charts using ymin
and ymax
aesthetics :
data("temperatures", package = "apexcharter") apex( temperatures, aes(x = date, ymin = low, ymax = high), type = "rangeArea", serie_name = "Low/High (2018-2021)" ) %>% add_line(aes(date, `2023`)) %>% ax_chart(animations = list(enabled = FALSE)) %>% ax_yaxis(tickAmount = 7, labels = list(formatter = format_num("~", suffix = "°C"))) %>% ax_colors(c("#8485854D", "#FF0000")) %>% ax_stroke(width = c(1, 2)) %>% ax_fill(opacity = 1, type = "solid") %>% ax_labs( title = "Temperatures in 2023 with range from 2018 to 2021", subtitle = "Data from ENEDIS" )
Simple bar charts can be created with:
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg))
Color points according to a third variable:
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, fill = cyl))
And change point size using z
aesthetics:
apex(data = mtcars, type = "scatter", mapping = aes(x = wt, y = mpg, z = scales::rescale(qsec)))
Simple pie charts can be created with:
poll <- data.frame( answer = c("Yes", "No"), n = c(254, 238) ) apex(data = poll, type = "pie", mapping = aes(x = answer, y = n))
It's also possible to make donut chart:
apex(data = poll, type = "donut", mapping = aes(x = answer, y = n))
Simple radial charts can be created with (here we pass values directly in aes
, but you can use a data.frame
) :
apex(data = NULL, type = "radialBar", mapping = aes(x = "My value", y = 65))
Multi radial chart (more than one value):
fruits <- data.frame( name = c('Apples', 'Oranges', 'Bananas', 'Berries'), value = c(44, 55, 67, 83) ) apex(data = fruits, type = "radialBar", mapping = aes(x = name, y = value))
Simple radar charts can be created with:
mtcars$model <- rownames(mtcars) apex(data = head(mtcars), type = "radar", mapping = aes(x = model, y = qsec))
With a grouping variable:
# extremely complicated reshaping new_mtcars <- reshape( data = head(mtcars), idvar = "model", varying = list(c("drat", "wt")), times = c("drat", "wt"), direction = "long", v.names = "value", drop = c("mpg", "cyl", "hp", "dist", "qsec", "vs", "am", "gear", "carb") ) apex(data = new_mtcars, type = "radar", mapping = aes(x = model, y = value, group = time))
With some custom options for color mapping:
apex(mtcars, aes(rownames(mtcars), mpg), type = "polarArea") %>% ax_legend(show = FALSE) %>% ax_colors(col_numeric("Blues", domain = NULL)(mtcars$mpg)) %>% ax_fill(opacity = 1) %>% ax_stroke(width = 0) %>% ax_tooltip(fillSeriesColor = FALSE)
Create a heatmap with :
# create some data sales <- expand.grid(year = 2010:2020, month = month.name) sales$value <- sample(-10:30, nrow(sales), TRUE) apex( data = sales, type = "heatmap", mapping = aes(x = year, y = month, fill = value) ) %>% ax_dataLabels(enabled = FALSE) %>% ax_colors("#008FFB")
Create a treemap with:
data("mpg", package = "ggplot2") apex(mpg, aes(x = manufacturer), "treemap")
Create a candlestick chart with:
data("candles", package = "apexcharter") apex( candles, aes(x = datetime, open = open, close = close, low = low, high = high), type = "candlestick" )
Create boxplot (without outliers for now) with:
data("mpg", package = "ggplot2") apex(mpg, aes(hwy, class), "boxplot") %>% ax_plotOptions( boxPlot = boxplot_opts(color.upper = "#8BB0A6", color.lower = "#8BB0A6" ) ) %>% ax_stroke(colors = list("#2A5769")) %>% ax_grid( xaxis = list(lines = list(show = TRUE)), yaxis = list(lines = list(show = FALSE)) )
Create Dumbbell chart with:
data("life_expec", package = "apexcharter") apex(life_expec, aes(country, x = `1972`, xend = `2007`), type = "dumbbell") %>% ax_plotOptions( bar = bar_opts( dumbbellColors = list(list("#3d85c6", "#fb6003")) ) ) %>% ax_colors("#BABABA") %>% ax_labs( title = "Life expectancy : 1972 vs. 2007", subtitle = "Data from Gapminder dataset", x = "Life expectancy at birth, in years" )
Create a slope chart with:
data("life_expec_long", package = "apexcharter") apex( life_expec_long, mapping = aes(x = year, y = lifeExp, fill = country), type = "slope", height = "700px" ) %>% ax_chart(animations = list(enabled = FALSE)) %>% # aurora nord12 = #d08770 / aurora nord14 = #a3be8c -> darken colorspace::darken(, amount = 0.3) ax_colors(ifelse(unique(life_expec_long[, c("country", "type")])$type == "decreased", "#955945", "#6A8354")) %>% ax_labs( title = "Life expectancy : 1972 vs. 2007", subtitle = "Data from Gapminder dataset", x = "Life expectancy at birth, in years" ) %>% # ax_dataLabels(enabled = FALSE) %>% # show or note the labels + values ax_xaxis(position = "bottom")
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