Five Interactive R Visualizations With D3, ggplot2, & RStudio

August 24, 2015
By

(This article was first published on Modern Data » R, and kindly contributed to R-bloggers)

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.

 

price vs carat
Key features are: you can share plots with a private or public URL (e.g., https://plot.ly/1143/~RPlotBot/). Interactive graphs can be embedded in knitr docs, dashboards, websites, and Shiny Apps. Updating charts is easy. Users can collaborate from any device or browser in a web app or with Plotly’s APIs for Python and MATLAB.

 

 

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))

 

 

None, None, None, None, None, Fair, Good, Very Good, Premium, Ideal, Fair, Good, Very Good, Premium, Ideal

 

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))

 

Median duration of unemployment (in weeks)

 

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, '
'
, "Beef", beef, "Dairy", dairy, "
"
, "Fruits", total.fruits, "Veggies", total.veggies, "
"
, "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
(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.

To leave a comment for the author, please follow the link and comment on their blog: Modern Data » R.

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