add_trace {plotly} | R Documentation

## 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_area(p, r = 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
`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.
`r` | For polar chart only. Sets the radial coordinates.
`t` | For polar chart only. Sets the radial coordinates.
`values` | the value to associated with each slice of the pie.
`labels` | the labels (categories) corresponding to `values`.

### Author(s)

Carson Sievert

### References

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

https://plot.ly/r

https://plot.ly/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"))

    ## Not run:
    # 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, t = ~t) %>%
      add_area(color = ~nms) %>%
      layout(radialaxis = list(ticksuffix = "%"), orientation = 270)

    # ------------------------------------------------------------
    # 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()

    ## End(Not run)

[Package plotly version 4.9.0 Index]