DataVizR
Data Viz Versus Graphics
Data visualizations are interactive graphics designed for the Web.
- Unlike traditional static graphical displays, data visualizations are:
- Built for the Web
- Interactive
- Dynamic
Why Data Viz in R?
Majority of R tools for data viz are basically a set of functions to write javascript (js) output
Chances are, if you are looking at an interactive graphic on the Web, it's written in js using a library like d3
But...what if you don't know js? or you have to do a lot of analysis to produce the output for your chart?
Popular R Tools for Data Viz
ggvis
googleVis
plotly
rCharts
htmlwidgets
Overview
Demonstration
It is easiest to understand the differences between the visualization tools by seeing them in action
In what follows, I'll use R's built-in dataset state.x77 to conduct several charting exercises
data(state) state.x77 <- as.data.frame(state.x77) str(state.x77)
## 'data.frame': 50 obs. of 8 variables: ## $ Population: num 3615 365 2212 2110 21198 ... ## $ Income : num 3624 6315 4530 3378 5114 ... ## $ Illiteracy: num 2.1 1.5 1.8 1.9 1.1 0.7 1.1 0.9 1.3 2 ... ## $ Life Exp : num 69 69.3 70.5 70.7 71.7 ... ## $ Murder : num 15.1 11.3 7.8 10.1 10.3 6.8 3.1 6.2 10.7 13.9 ... ## $ HS Grad : num 41.3 66.7 58.1 39.9 62.6 63.9 56 54.6 52.6 40.6 ... ## $ Frost : num 20 152 15 65 20 166 139 103 11 60 ... ## $ Area : num 50708 566432 113417 51945 156361 ...
Task 1. Distribution of State Illiteracy
How can we make this histogram interactive?
# Static plot library(ggplot2) ggplot(state.x77, aes(x = Illiteracy)) + geom_histogram(binwidth = 0.5, fill = "#663399")
Demo - Histogram
Task 2. State Illiteracy Versus Income
How can we make this scatterplot interactive?
# Static plot ggplot(state.x77, aes(y = Illiteracy, x = Income)) + geom_point(size = 3, col = "#663399")
Demo - Scatterplot
Task 4. Add Trendlines
How can we add fitted lines to the scatterplot?
ggplot(state.x77, aes(y = Illiteracy, x = Income)) + geom_point(size = 3, col = "#663399") + geom_smooth(method = "loess", col = "#663399", fill = "#663399")
Demo - Trendlines
Task 4. Group Points
How can we group on a third variable?
colors <- colorRampPalette(c("#663399","#669999"))(4)
state.x77$region <- state.region
ggplot(state.x77, aes(y = Illiteracy, x = Income, colour = region)) +
geom_point(size = 3) +
scale_colour_manual("Region", values = colors)
Demo - Grouping Points
Task 5. Add State as a Tooltip
Tooltips are a way to avoid cluttered labels. How are they created in visualizations?
state.x77$state <- rownames(state.x77) ggplot(state.x77, aes(y = Illiteracy, x = Income)) + geom_point(size = 3, colour = colors[1]) + geom_text(aes(label = state))
Demo - Tooltip
Task 6. Adding Controls
Controls add interactivity that aren't possible with a static plot. How are controls added to visualizations?
ggplot(state.x77, aes(x = Illiteracy)) + geom_histogram(binwidth = 0.5, fill = "#663399")
Demo - Controls
Specialty Plots with htmlwidgets
Widgets are a way to add new types of HTML output to R markdown documents
This can be useful for enabling R to interact with javascript libraries like d3 or leaflet
Existing widgets (dygraphs, networkD3, d3heatmap) are just R packages
You can also create your own widgets with htmlwidgets
Some examples can be found at http://www.htmlwidgets.org/
Resources
In addition to package documentation...