Five Interactive R Visualizations With D3, ggplot2, & RStudio
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Plotly has a new R API and ggplot2 library for making beautiful graphs. The API lets you produce interactive D3.js graphs with R. This post has five examples. Head to our docs to get a key and you can start making, embedding, and sharing plots. The code below produces our first plot.
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)
Hover to see data, click and drag to zoom, or double-click to autoscale.
Interactive ggplot2 Plotting
Plotly’s ggplot2 converter turns ggplot2 plots into interactive, web-based plots. You can control the tooltip. For example, note the text.
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))
Plotly is free for online sharing. Licenses are available for on-premise deployments and Plotly Offline. Plotly Offline works inside RStudio:
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
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")
Interactive Maps
Plotly also supports interactive maps.
library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
"Fruits", total.fruits, "Veggies", total.veggies,
"<br>", "Wheat", wheat, "Corn", corn))
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
scope = 'usa',
projection = list(type = 'albers usa'),
showlakes = TRUE,
lakecolor = toRGB('white')
)
plot_ly(df, z = total.exports, text = hover, locations = code, type = 'choropleth',
locationmode = 'USA-states', color = total.exports, colors = 'Purples',
marker = list(line = l), colorbar = list(title = "Millions USD"),
filename="r-docs/usa-choropleth") %>%
layout(title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)', geo = g)
(Hover for breakdown)" width="450" />If you have sensitive data, need to collaborate with your team, and need interactive dashboards, contact us about Plotly on-premise. Plotly’s R API was developed by Carson Sievert and Chris Parmer. If you liked what you read, please consider sharing. Find us at [email protected] and @plotlygraphs.
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