echarts4r (version 0.4.5)

e_heatmap: Heatmap

Description

Draw heatmap by coordinates.

Usage

e_heatmap(
  e,
  y,
  z,
  bind,
  name = NULL,
  coord_system = "cartesian2d",
  rm_x = TRUE,
  rm_y = TRUE,
  calendar = NULL,
  ...
)
e_heatmap_(
  e,
  y,
  z = NULL,
  bind = NULL,
  name = NULL,
  coord_system = "cartesian2d",
  rm_x = TRUE,
  rm_y = TRUE,
  calendar = NULL,
  ...
)

Arguments

e

An echarts4r object as returned by e_charts or a proxy as returned by echarts4rProxy.

y, z

Coordinates and values.

bind

Binding between datasets, namely for use of e_brush.

name

name of the serie.

coord_system

Coordinate system to plot against, takes cartesian2d, geo or calendar.

rm_x, rm_y

Whether to remove x and y axis, only applies if coord_system is not set to cartesian2d.

calendar

The index of the calendar to plot against.

...

Any other option to pass, check See Also section.

See Also

Additional arguments

Examples

v <- LETTERS[1:10]
matrix <- data.frame(
  x = sample(v, 300, replace = TRUE),
  y = sample(v, 300, replace = TRUE),
  z = rnorm(300, 10, 1),
  stringsAsFactors = FALSE
) |>
  dplyr::group_by(x, y) |>
  dplyr::summarise(z = sum(z)) |>
  dplyr::ungroup()

matrix |>
  e_charts(x) |>
  e_heatmap(y, z, itemStyle = list(emphasis = list(shadowBlur = 10))) |>
  e_visual_map(z)

# calendar
dates <- seq.Date(as.Date("2017-01-01"), as.Date("2018-12-31"), by = "day")
values <- rnorm(length(dates), 20, 6)

year <- data.frame(date = dates, values = values)

year |>
  e_charts(date) |>
  e_calendar(range = "2018") |>
  e_heatmap(values, coord_system = "calendar") |>
  e_visual_map(max = 30)

# calendar multiple years
year |>
  dplyr::mutate(year = format(date, "%Y")) |>
  group_by(year) |>
  e_charts(date) |>
  e_calendar(range = "2017", top = 40) |>
  e_calendar(range = "2018", top = 260) |>
  e_heatmap(values, coord_system = "calendar") |>
  e_visual_map(max = 30)

# map
quakes |>
  e_charts(long) |>
  e_geo(
    boundingCoords = list(
      c(190, -10),
      c(180, -40)
    )
  ) |>
  e_heatmap(
    lat,
    mag,
    coord_system = "geo",
    blurSize = 5,
    pointSize = 3
  ) |>
  e_visual_map(mag)

# timeline
library(dplyr)

axis <- LETTERS[1:10]
df <- expand.grid(axis, axis)

bind_rows(df, df) |>
  mutate(
    values = runif(n(), 1, 10),
    grp = c(
      rep("A", 100),
      rep("B", 100)
    )
  ) |>
  group_by(grp) |>
  e_charts(Var1, timeline = TRUE) |>
  e_heatmap(Var2, values) |>
  e_visual_map(values)