e_heatmap | R Documentation |
Draw heatmap by coordinates.
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,
...
)
e |
An |
y, z |
Coordinates and values. |
bind |
Binding between datasets, namely for use of |
name |
name of the serie. |
coord_system |
Coordinate system to plot against, takes
|
rm_x, rm_y |
Whether to remove x and y axis, only applies if |
calendar |
The index of the calendar to plot against. |
... |
Any other option to pass, check See Also section. |
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)
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