plotly (version 2.0.2)

plot_ly: Initiate a plotly visualization

Description

Transform data into a plotly visualization.

Usage

plot_ly(data = data.frame(), ..., type = "scatter", group, color, colors,
  symbol, symbols, size, width = NULL, height = NULL, inherit = TRUE,
  evaluate = FALSE)

Arguments

data

A data frame (optional).

...

These arguments are documented at https://plot.ly/r/reference/ Note that acceptable arguments depend on the value of type.

type

A charater string describing the type of trace.

group

Either a variable name or a vector to use for grouping. If used, a different trace will be created for each unique value.

color

Either a variable name or a vector to use for color mapping.

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().

symbol

Either a variable name or a (discrete) vector to use for symbol encoding.

symbols

A character vector of symbol types. Possible values: 'dot', 'cross', 'diamond', 'square', 'triangle-down', 'triangle-left', 'triangle-right', 'triangle-up'

size

A variable name or numeric vector to encode the size of markers.

width

Width in pixels (optional, defaults to automatic sizing).

height

Height in pixels (optional, defaults to automatic sizing).

inherit

logical. Should future traces inherit properties from this initial trace?

evaluate

logical. Evaluate arguments when this function is called?

Details

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

See Also

layout(), add_trace(), style()

Examples

data(economics, package = "ggplot2")
# basic time-series plot
p <- plot_ly(economics, x = date, y = uempmed, type = "scatter", 
  showlegend = FALSE)
# add a loess smoother
p2 <- add_trace(p, y = fitted(loess(uempmed ~ as.numeric(date))))
# add a title
p3 <- layout(p2, title = "Median duration of unemployment (in weeks)")
# change the font
layout(p3, font = list(family = "Courier New, monospace"))

# using the color argument
plot_ly(economics, x = date, y = unemploy / pop, color = pop, mode = "markers")
plot_ly(economics, x = date, y = unemploy / pop, color = pop, 
  colors = terrain.colors(5), mode = "markers")
  
# function to extract the decade of a given date
decade <- function(x) {
  factor(floor(as.numeric(format(x, "%Y")) / 10) * 10)
}
plot_ly(economics, x = unemploy / pop, color = decade(date), type = "box")

# plotly loves pipelines
economics %>%
 transform(rate = unemploy / pop) %>%
 plot_ly(x = date, y = rate) %>%
 loess(rate ~ as.numeric(date), data = .) %>%
 broom::augment() %>%
 add_trace(y = .fitted)

# sometimes, a data frame isn't fit for the use case...
# for 3D surface plots, a numeric matrix is more natural
plot_ly(z = volcano, type = "surface")