To create a plotly visualization, start with plot_ly().
library(plotly)
plot_ly(economics, x = date, y = unemploy / pop)A plotly visualization is composed of one (or more) trace(s), and every trace has a type (the default type is ‘scatter’). The arguments/properties that a trace will respect (documented here) depend on it’s type. A scatter trace respects mode, which can be any combination of “lines”, “markers”, “text” joined with a “+”:
library(plotly)
plot_ly(economics, x = date, y = unemploy / pop,
type = "scatter", mode = "markers+lines")You can manually add a trace to an existing plot with add_trace(). In that case, you’ll want to either name your traces, or hide the legend by setting showlegend = FALSE
m <- loess(unemploy / pop ~ as.numeric(date), data = economics)
p <- plot_ly(economics, x = date, y = unemploy / pop, name = "raw")
add_trace(p, x = date, y = fitted(m), name = "loess")plotly was designed with a pure, predictable, and pipeable interface in mind, so you can also use the %>% operator to create a visualization pipeline:
economics %>%
plot_ly(x = date, y = unemploy / pop) %>%
add_trace(x = date, y = fitted(m)) %>%
layout(showlegend = F)Furthermore, plot_ly(), add_trace(), and layout(), all accept a data frame as their first argument and output a data frame. As a result, we can inter-weave data manipulations and visual mappings in a single pipeline.
economics %>%
transform(rate = unemploy / pop) %>%
plot_ly(x = date, y = rate) %>%
subset(rate == max(rate)) %>%
layout(
showlegend = F,
annotations = list(x = date, y = rate, text = "Peak", showarrow = T)
)Although these functions output special “plotly data frames”, plot_ly() doesn’t require a data frame for input. This makes chart types that accept a z argument especially easy to use if you have a numeric matrix:
s <- plot_ly(z = volcano, type = "surface")If you have a free plotly account, plotly_POST() makes it easy to host/share any plotly figure (click on the image below to see the interactive version).
plotly_POST(s)The plotly function arguments used thus far are documented in the figure reference, but there are a handful of additional arguments not described in the reference that make common visualizations a bit easier. These arguments are very much inspired by the semantics of ggplot2’s qplot() in the sense that a scales are automatically applied these variables.
If a ordinal variable (aka a non-ordered factor variable) is assigned to color, then a qualitative color palette is used by default.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Species, mode = "markers")If you want to change the default palette, it’s recommended that you provide a http://colorbrewer2.org qualitative pallette name (e.g., “Set1” or “Accent”) to the colors argument.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Species, colors = "Set1", mode = "markers")In this case, the palette consists of 9 colors and the default behavior is to pick colors that are furthest apart (“#E41A1C”, “#FF7F00”, and “#999999”).
cols <- RColorBrewer::brewer.pal(9, "Set1")
scales::show_col(cols)
If you’d like more control over the mapping, you can provide a vector of colors (of appropriate length).
cols <- RColorBrewer::brewer.pal(nlevels(iris$Species), "Set1")
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Species, colors = cols, mode = "markers")If either a numeric or an ordered factor is mapped to color, plot_ly() applies a sequential color scale by default.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = as.ordered(Species), mode = "markers")In the case of continuous numeric variables, plot_ly() performs a linear mapping between the data and an interpolated color pallette.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Sepal.Length, mode = "markers")The colors argument takes arbitrary color codes of arbitrary length. Here is how we could use it to replicate the default mapping in ggplot2.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Sepal.Length, colors = c("#132B43", "#56B1F7"),
mode = "markers")To obtain a diverging color mapping, just provide a diverging palette to the colors argument.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Sepal.Length, colors = "PuOr", mode = "markers")To encode values using symbols, use the symbol argument.
plot_ly(iris, x = Petal.Length, y = Petal.Width,
symbol = Species, mode = "markers")To change the default symbols used, use the symbols argument. All the valid symbol types are listed here.
plot_ly(iris, x = Petal.Length, y = Petal.Width, mode = "markers",
symbol = Species, symbols = c("cross", "square", "triangle-down"))