Plotly R Library 1.0

library(plotly)
set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]
plot_ly(d, x = carat, y = price, text = paste("Clarity: ", clarity),
        mode = "markers", color = carat, size = carat)

Plotly graphs are interactive. Click-drag to zoom, shift-click to pan, double-click to autoscale.

Know and love ggplot2? Try ggplotly

p <- ggplot(data = d, aes(x = carat, y = price)) +
  geom_point(aes(text = paste("Clarity:", clarity)), size = 4) +
  geom_smooth(aes(colour = cut, fill = cut)) + facet_wrap(~ cut)

(gg <- ggplotly(p))

Mix data manipulation and visualization verbs

Plotly objects are data frames with a class of plotly and an environment that tracks the mapping from data to visual properties.

str(p <- plot_ly(economics, x = date, y = uempmed))
## Classes 'plotly' and 'data.frame':   478 obs. of  6 variables:
##  $ date    : Date, format: "1967-06-30" "1967-07-31" ...
##  $ pce     : num  508 511 517 513 518 ...
##  $ pop     : int  198712 198911 199113 199311 199498 199657 199808 199920 200056 200208 ...
##  $ psavert : num  9.8 9.8 9 9.8 9.7 9.4 9 9.5 8.9 9.6 ...
##  $ uempmed : num  4.5 4.7 4.6 4.9 4.7 4.8 5.1 4.5 4.1 4.6 ...
##  $ unemploy: int  2944 2945 2958 3143 3066 3018 2878 3001 2877 2709 ...
##  - attr(*, "plotly_hash")= chr "7ff330ec8c566561765c62cbafed3e0f#2"

This allows us to mix data manipulation and visualization verbs in a pure(ly) functional, predictable and pipeable manner. Here, we take advantage of dplyr's filter() verb to label the highest peak in the time series:

p %>%
  add_trace(y = fitted(loess(uempmed ~ as.numeric(date)))) %>%
  layout(title = "Median duration of unemployment (in weeks)",
         showlegend = FALSE) %>%
  dplyr::filter(uempmed == max(uempmed)) %>%
  layout(annotations = list(x = date, y = uempmed, text = "Peak", showarrow = T))

3D WebGL and more

Although data frames can be thought of as the central object in this package, plotly visualizations don't actually require a data frame. This makes chart types that accept a z argument especially easy to use if you have a numeric matrix:

plot_ly(z = volcano, type = "surface")



Maps


Multiple Axes, Subplots, and Insets