Add Series to a SveltePlot Chart

This function adds additional series to an existing SveltePlot chart. It supports adding lines or points with customizable aesthetics such as color, size, and opacity. This is particularly useful for layering multiple data sets on a single plot for comparison or highlighting relationships.

Usage

sp_add_series(
  sp,
  data,
  mapping,
  type,
  alpha = 1,
  size = 2,
  colors = NULL,
  tooltip = TRUE,
  include_legend = TRUE,
  second_axis = FALSE
)

Arguments

sp

A SveltePlot htmlwidget object to which the series will be added. This is typically the output from a previous call to sp or sp_add_series.

data

A data frame containing the data to be added as a series to the chart.

mapping

A list of aesthetic mappings created by spaes. Each call to sp_add_series requires its own set of mappings to correctly display the data.

type

A character string specifying the type of series to add. Valid options are "points" for scatter plots, "line" for line charts, or "bands" for confidence intervals or other purposes.

alpha

A numeric value between 0 and 1 specifying the opacity of the series. Default is 1 (fully opaque).

size

A positive numeric value determining the size of the points or thickness of the line. Default is 2.

colors

A character vector of colors to use for the series. If NULL (the default), a default color scheme is applied.

tooltip

A logical value indicating whether tooltips should be shown on hover. Default is TRUE.

include_legend

A logical value indicating whether a legend entry should be added for the series. Default is TRUE.

second_axis

A logical value indicating if the series should be plotted on a secondary y-axis on the right side. Values will be scaled by default to the domain of the first y-axis.

Examples

library(SveltePlots)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(lubridate)
#> 
#> Attaching package: ‘lubridate’
#> The following objects are masked from ‘package:base’:
#> 
#>     date, intersect, setdiff, union

data("economics")
data("confidence_intervals")
data("purchases")

sp(
  data = economics,
  type = "line",
  mapping = spaes(x = date, y = unemploy),
  colors = "red"
) %>%
 sp_add_series(
  data = economics,
  mapping = spaes(x = date, y = pce),
  type = "line",
  colors = "green"
 ) %>%
 sp_add_series(
  data = economics,
  mapping = spaes(x = date, y = psavert),
  type = "line",
  colors = "blue"
)


data("gapminder")

gapminder <- gapminder %>%
  dplyr::mutate(
    country = as.character(country),
    year = lubridate::ymd(paste0(year, "-01-01"))
  )

sp <- SveltePlots::sp(
  data = gapminder %>%
    dplyr::group_by(year, continent) %>%
    dplyr::summarise(
      lifeExp = mean(lifeExp)
    ) %>%
    dplyr::ungroup(),
  mapping = spaes(x = year, y = lifeExp, group = continent),
  type = "line",
  combine_same_groups = FALSE
) %>%
  sp_add_series(
    data = gapminder %>%
      dplyr::filter(country == "Germany"),
    mapping = spaes(x = year, y = lifeExp, group = country),
    type = "line",
    colors = "gold"
  ) %>%
  sp_add_series(
    gapminder %>%
      dplyr::filter(country == "Chile"),
    mapping = spaes(x = year, y = lifeExp, group = country),
    type = "line",
    colors = "silver"
  ) %>%
  sp_add_series(
    gapminder %>%
      dplyr::filter(country == "Chile"),
    mapping = spaes(x = year, y = lifeExp, group = country),
    type = "points",
    size = 3,
    tooltip = FALSE
  )
#> `summarise()` has grouped output by 'year'. You can override using the
#> `.groups` argument.

sp(
  data = purchases,
  mapping = spaes(x = date, y = revenue_roll, group = age),
  type = "line",
  colors = c("red", "green", "blue"),
  combine_same_groups = FALSE
) %>%
  sp_add_series(
    data = purchases,
    mapping = spaes(x = date, y = revenue, group = age),
    type = "points",
    alpha = 0.4,
    tooltip = FALSE,
  ) %>%
  sp_add_series(
    data = purchases[purchases$revenue == max(purchases$revenue), ],
    mapping = spaes(x = date, y = revenue, group = age),
    type = "points",
    size = 5,
     tooltip = FALSE
  )