plotly (version 4.10.1)

add_trace: Add trace(s) to a plotly visualization

Description

Add trace(s) to a plotly visualization

Usage

add_trace(p, ..., data = NULL, inherit = TRUE)
add_markers(p, x = NULL, y = NULL, z = NULL, ..., data = NULL, inherit = TRUE)
add_text(
  p,
  x = NULL,
  y = NULL,
  z = NULL,
  text = NULL,
  ...,
  data = NULL,
  inherit = TRUE
)
add_paths(p, x = NULL, y = NULL, z = NULL, ..., data = NULL, inherit = TRUE)
add_lines(p, x = NULL, y = NULL, z = NULL, ..., data = NULL, inherit = TRUE)
add_segments(
  p,
  x = NULL,
  y = NULL,
  xend = NULL,
  yend = NULL,
  ...,
  data = NULL,
  inherit = TRUE
)
add_polygons(p, x = NULL, y = NULL, ..., data = NULL, inherit = TRUE)
add_sf(p, ..., x = ~x, y = ~y, data = NULL, inherit = TRUE)
add_table(p, ..., rownames = TRUE, data = NULL, inherit = TRUE)
add_ribbons(
  p,
  x = NULL,
  ymin = NULL,
  ymax = NULL,
  ...,
  data = NULL,
  inherit = TRUE
)
add_image(p, z = NULL, colormodel = NULL, ..., data = NULL, inherit = TRUE)
add_area(p, r = NULL, theta = NULL, t = NULL, ..., data = NULL, inherit = TRUE)
add_pie(p, values = NULL, labels = NULL, ..., data = NULL, inherit = TRUE)
add_bars(p, x = NULL, y = NULL, ..., data = NULL, inherit = TRUE)
add_histogram(p, x = NULL, y = NULL, ..., data = NULL, inherit = TRUE)
add_histogram2d(
  p,
  x = NULL,
  y = NULL,
  z = NULL,
  ...,
  data = NULL,
  inherit = TRUE
)
add_histogram2dcontour(
  p,
  x = NULL,
  y = NULL,
  z = NULL,
  ...,
  data = NULL,
  inherit = TRUE
)
add_heatmap(p, x = NULL, y = NULL, z = NULL, ..., data = NULL, inherit = TRUE)
add_contour(p, z = NULL, ..., data = NULL, inherit = TRUE)
add_boxplot(p, x = NULL, y = NULL, ..., data = NULL, inherit = TRUE)
add_surface(p, z = NULL, ..., data = NULL, inherit = TRUE)
add_mesh(p, x = NULL, y = NULL, z = NULL, ..., data = NULL, inherit = TRUE)
add_scattergeo(p, ...)
add_choropleth(p, z = NULL, ..., data = NULL, inherit = TRUE)

Arguments

p

a plotly object

...

Arguments (i.e., attributes) passed along to the trace type. See schema() for a list of acceptable attributes for a given trace type (by going to traces -> type -> attributes). Note that attributes provided at this level may override other arguments (e.g. plot_ly(x = 1:10, y = 1:10, color = I("red"), marker = list(color = "blue"))).

data

A data frame (optional) or crosstalk::SharedData object.

inherit

inherit attributes from plot_ly()?

x

the x variable.

y

the y variable.

z

a numeric matrix (unless add_image(), which wants a raster object, see as.raster()).

text

textual labels.

xend

"final" x position (in this context, x represents "start")

yend

"final" y position (in this context, y represents "start")

rownames

whether or not to display the rownames of data.

ymin

a variable used to define the lower boundary of a polygon.

ymax

a variable used to define the upper boundary of a polygon.

colormodel

Sets the colormodel for image traces if z is not a raster object. If z is a raster object (see as.raster()), the 'rgba' colormodel is always used.

r

Sets the radial coordinates.

theta

Sets the angular coordinates.

t

Deprecated. Use theta instead.

values

the value to associated with each slice of the pie.

labels

the labels (categories) corresponding to values.

Author

Carson Sievert

References

https://plotly-r.com/overview.html

https://plotly.com/r/

https://plotly.com/r/reference/

See Also

plot_ly()

Examples

# the `plot_ly()` function initiates an object, and if no trace type
# is specified, it sets a sensible default
p <- plot_ly(economics, x = ~date, y = ~uempmed)
p

# some `add_*()` functions are a specific case of a trace type
# for example, `add_markers()` is a scatter trace with mode of markers
add_markers(p)

# scatter trace with mode of text
add_text(p, text = "%")

# scatter trace with mode of lines 
add_paths(p)

# like `add_paths()`, but ensures points are connected according to `x`
add_lines(p)

# if you prefer to work with plotly.js more directly, can always
# use `add_trace()` and specify the type yourself
add_trace(p, type = "scatter", mode = "markers+lines")

# mappings provided to `plot_ly()` are "global", but can be overwritten
plot_ly(economics, x = ~date, y = ~uempmed, color = I("red"), showlegend = FALSE) %>% 
  add_lines() %>%
  add_markers(color = ~pop)

# a number of `add_*()` functions are special cases of the scatter trace
plot_ly(economics, x = ~date) %>% 
  add_ribbons(ymin = ~pce - 1e3, ymax = ~pce + 1e3)

# use `group_by()` (or `group2NA()`) to apply visual mapping
# once per group (e.g. one line per group)
txhousing %>% 
  group_by(city) %>% 
  plot_ly(x = ~date, y = ~median) %>%
  add_lines(color = I("black"))

if (FALSE) {
# use `add_sf()` or `add_polygons()` to create geo-spatial maps
# http://blog.cpsievert.me/2018/03/30/visualizing-geo-spatial-data-with-sf-and-plotly/
if (requireNamespace("sf", quietly = TRUE)) {
  nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)
  plot_ly() %>% add_sf(data = nc)
}

# univariate summary statistics
plot_ly(mtcars, x = ~factor(vs), y = ~mpg) %>% 
  add_boxplot()
plot_ly(mtcars, x = ~factor(vs), y = ~mpg) %>% 
  add_trace(type = "violin")
  
# `add_histogram()` does binning for you...
mtcars %>%
  plot_ly(x = ~factor(vs)) %>%
  add_histogram()
  
# ...but you can 'pre-compute' bar heights in R
mtcars %>%
  dplyr::count(vs) %>%
  plot_ly(x = ~vs, y = ~n) %>%
  add_bars()

# the 2d analogy of add_histogram() is add_histogram2d()/add_histogram2dcontour()
library(MASS)
(p <- plot_ly(geyser, x = ~waiting, y = ~duration))
add_histogram2d(p)
add_histogram2dcontour(p)

# the 2d analogy of add_bars() is add_heatmap()/add_contour()
# (i.e., bin counts must be pre-specified)
den <- kde2d(geyser$waiting, geyser$duration)
p <- plot_ly(x = den$x, y = den$y, z = den$z)
add_heatmap(p)
add_contour(p)

# `add_table()` makes it easy to map a data frame to the table trace type
plot_ly(economics) %>% 
  add_table()

# pie charts!
ds <- data.frame(labels = c("A", "B", "C"), values = c(10, 40, 60))
plot_ly(ds, labels = ~labels, values = ~values) %>%
  add_pie() %>%
  layout(title = "Basic Pie Chart using Plotly")
  
data(wind)
plot_ly(wind, r = ~r, theta = ~t) %>% 
  add_area(color = ~nms) %>%
  layout(
    polar = list(
      radialaxis = list(ticksuffix = "%"), 
      angularaxis = list(rotation = 90)
    )
  )

# ------------------------------------------------------------
# 3D chart types
# ------------------------------------------------------------
plot_ly(z = ~volcano) %>% 
  add_surface()
plot_ly(x = c(0, 0, 1), y = c(0, 1, 0), z = c(0, 0, 0)) %>% 
  add_mesh()
}