plot_ly {plotly} | R Documentation

## Initiate a plotly visualization

### Description

Transform data into a plotly visualization.

### Usage

    plot_ly(data = data.frame(), ..., type = NULL, color, colors = NULL,
      alpha = 1, symbol, symbols = NULL, size, sizes = c(10, 100), linetype,
      linetypes = NULL, split, frame, width = NULL, height = NULL,
      source = "A")

### Arguments

`data` | A data frame (optional) or crosstalk::SharedData object.
`...` | These arguments are documented at https://plot.ly/r/reference/ Note that acceptable arguments depend on the value of `type`.
`type` | A character string describing the type of trace.
`color` | A formula containing a name or expression. Values are scaled and mapped to color codes based on the value of `colors` and `alpha`. To avoid scaling, wrap with `I()`, and provide value(s) that can be converted to rgb color codes by `grDevices::col2rgb()`.
`colors` | Either a colorbrewer2.org palette name (e.g. "YlOrRd" or "Blues"), or a vector of colors to interpolate in hexadecimal "#RRGGBB" format, or a color interpolation function like `colorRamp()`.
`alpha` | A number between 0 and 1 specifying the alpha channel applied to color.
`symbol` | A formula containing a name or expression. Values are scaled and mapped to symbols based on the value of `symbols`. To avoid scaling, wrap with `I()`, and provide valid `pch()` values and/or valid plotly symbol(s) as a string
`symbols` | A character vector of symbol types. Either valid pch or plotly symbol codes may be supplied.
`size` | A formula containing a name or expression yielding a numeric vector. Values are scaled according to the range specified in `sizes`.
`sizes` | A numeric vector of length 2 used to scale sizes to pixels.
`linetype` | A formula containing a name or expression. Values are scaled and mapped to linetypes based on the value of `linetypes`. To avoid scaling, wrap with `I()`.
`linetypes` | A character vector of line types. Either valid par (lty) or plotly dash codes may be supplied.
`split` | A formula containing a name or expression. Similar to `group_by()`, but ensures at least one trace for each unique value. This replaces the functionality of the (now deprecated) `group` argument.
`frame` | A formula containing a name or expression. The resulting value is used to split data into frames, and then animated.
`width` | Width in pixels (optional, defaults to automatic sizing).
`height` | Height in pixels (optional, defaults to automatic sizing).
`source` | a character string of length 1. Match the value of this string with the source argument in `event_data()` to retrieve the event data corresponding to a specific plot (shiny apps can have multiple plots).

### Details

There are a number of "visual properties" that aren't included in the official Reference section (see below).

### Author(s)

Carson Sievert

### See Also

- For initializing a plotly-geo object: `plot_geo()`.

- For initializing a plotly-mapbox object: `plot_mapbox()`.

- For translating a ggplot2 object to a plotly object: `ggplotly()`.

- For modifying any plotly object: `layout()`, `add_trace()`, `style()`

### Examples

    ## Not run:

    # plot_ly() tries to create a sensible plot based on the information you
    # give it. If you don't provide a trace type, plot_ly() will infer one.
    plot_ly(economics, x = ~pop)
    plot_ly(economics, x = ~date, y = ~pop)
    # plot_ly() doesn't require data frame(s), which allows one to take
    # advantage of trace type(s) designed specifically for numeric matrices
    plot_ly(z = ~volcano)
    plot_ly(z = ~volcano, type = "surface")

    # plotly has a functional interface: every plotly function takes a plotly
    # object as it's first input argument and returns a modified plotly object
    add_lines(plot_ly(economics, x = ~date, y = ~unemploy/pop))

    # To make code more readable, plotly imports the pipe operator from magrittr
    economics %>% plot_ly(x = ~date, y = ~unemploy/pop) %>% add_lines()

    # Attributes defined via plot_ly() set 'global' attributes that
    # are carried onto subsequent traces, but those may be over-written
    plot_ly(economics, x = ~date, color = I("black")) %>%
     add_lines(y = ~uempmed) %>%
     add_lines(y = ~psavert, color = I("red"))

    # Attributes are documented in the figure reference -> https://plot.ly/r/reference
    # You might notice plot_ly() has named arguments that aren't in this figure
    # reference. These arguments make it easier to map abstract data values to
    # visual attributes.
    p <- plot_ly(iris, x = ~Sepal.Width, y = ~Sepal.Length)
    add_markers(p, color = ~Petal.Length, size = ~Petal.Length)
    add_markers(p, color = ~Species)
    add_markers(p, color = ~Species, colors = "Set1")
    add_markers(p, symbol = ~Species)
    add_paths(p, linetype = ~Species)

    ## End(Not run)

[Package plotly version 4.7.1 Index]