Plotly is an R package for creating interactive web-based graphs via the open source JavaScript graphing library plotly.js. As of version 2.0 (November 17, 2015), Plotly graphs are rendered locally through the htmlwidgets framework.
To create a plotly visualization, start with plot_ly(). Your graph will appear in your web browser or in RStudio's viewer.
library(plotly)
p <- plot_ly(economics, x = date, y = unemploy / pop)
p
If you want to publish your graphs to the web, you can host your graphs in an online plotly account with plotly_POST. Learn more about hosting graphs on Plotly.
library(plotly)
p <- plot_ly(economics, x = date, y = unemploy / pop)
plotly_POST(p, filename="r-user-guide/publishing-example")
A plotly visualization is composed of one (or more) trace(s), and every trace has a type (the default type is 'scatter'). The arguments or properties that a trace will respect (documented here) depend on its type. A scatter trace respects mode, which can be any combination of "lines", "markers", "text" joined with a "+":
library(plotly)
p <- plot_ly(economics, x = date, y = unemploy / pop,
type = "scatter", mode = "markers+lines")
p
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")
p <- add_trace(p, y = fitted(m), name = "loess")
p
plotly was designed with a pure, predictable, and pipeable interface in mind, so you can also use the %>% operator to modify your plots:
p <- economics %>%
plot_ly(x = date, y = unemploy / pop) %>%
add_trace(y = fitted(m)) %>%
layout(showlegend = F)
p
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.
p <- 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)
)
p
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:
p <- plot_ly(z = volcano, type = "surface")
p
p <- plot_ly(z = volcano, type = "contour")
p
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.
p <- 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.
p <- 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").
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$Species), "Set1")
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Species, colors = cols, mode = "markers")
p
If either a numeric or an ordered factor is mapped to color, plot_ly() applies a sequential color scale by default.
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = as.ordered(Species), mode = "markers")
In the case of continuous numeric variables, plot_ly() performs a linear mapping between the data and an interpolated color pallette.
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Sepal.Length, mode = "markers")
The colors argument takes arbitrary color codes of arbitrary length. Here is how we could use it to replicate the default mapping in ggplot2.
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Sepal.Length, colors = c("#132B43", "#56B1F7"),
mode = "markers")
To obtain a diverging color mapping, just provide a diverging palette to the colors argument.
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
color = Sepal.Length, colors = "PuOr", mode = "markers")
To encode values using symbols, use the symbol argument.
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
symbol = Species, mode = "markers")
To change the default symbols used, use the symbols argument. All the valid symbol types are listed here.
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width, mode = "markers",
symbol = Species, symbols = c("cross", "square", "triangle-down"))
subplot()Using the group argument splits the data into different plotly "traces".
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width,
group = Species, mode = "markers")
Although we haven't specified a coloring scheme, plotly will employ one on it's own default scheme. The group argument is quite powerful when used in conjunction with subplot() in order to anchor traces onto different axes.
iris$id <- as.integer(iris$Species)
p <- plot_ly(iris, x = Petal.Length, y = Petal.Width, group = Species,
xaxis = paste0("x", id), mode = "markers")
p2 <- subplot(p)
p2
Since subplot() does not assume x/y axes are on a common scale, it does not impose any restrictions on the range by default. However, you can change this by pre-specifying the range of the axis objects via the layout() function.
p2 <- layout(
p,
xaxis = list(range = range(Petal.Length) + c(-0.1, 0.1)),
yaxis = list(range = range(Petal.Width) + c(-0.1, 0.1))
)
p2 <- subplot(p2)
p2
Part of the magic of subplot() is that it generates axis objects with appropriate anchor and domain properties. After generating a subplot, you can always reference these axis objects to customize each plot.
p2 <- layout(
subplot(p),
yaxis2 = list(title = ""),
yaxis3 = list(title = "")
)
p2
See here for another example of using the group argument to make small multiples (with maps!).