Data Visualisation with R

These data visualisation modules provide an overview of packages for interactive graphs with R.

See here for the course materials.

Overview

For other R libraries, see leaflet, htmlwidgets, plotly and useful tutorials on data visualisation in R at flowingdata.com.

Many of the packages below require that you have Rtools installed in addition to base R. I also recommend using RStudio as a front end.

A. The rCharts package

The rCharts package allows you to create, customize and publish interactive javascript visualizations from R using a lattice style plotting interface.

To get started, open R and install and load the package as follows.

library(devtools)
install_github('ramnathv/rCharts')

Then you can start exploring the examples using pre-loaded datasets or your own data. For more examples, check out the rCharts gallery.

A.1. Population pyramid
library(rCharts)
source('https://raw.githubusercontent.com/walkerke/teaching-with-datavis/master/pyramids/rcharts_pyramids.R')

## Select population counts for Qatar in 2014
dat <- getAgeTable(country='QA', year=2014)
kable(head(dat))
Year Age total Male Female percent pctMale pctFemale sexratio ord
2 2014 0-4 101272 51253 50019 4.8 3.1 10.3 102.5 1
3 2014 5-9 88869 45308 43561 4.2 2.8 9.0 104.0 2
4 2014 10-14 74976 37916 37060 3.5 2.3 7.6 102.3 3
5 2014 15-19 87906 55349 32557 4.1 3.4 6.7 170.0 4
6 2014 20-24 196261 152929 43332 9.2 9.3 8.9 352.9 5
7 2014 25-29 322775 259540 63235 15.2 15.8 13.0 410.4 6 
n1 <- nPyramid('QA', 2014, colors = c('darkred', 'silver'))
n1$show('inline', include_assets = TRUE, cdn = TRUE)


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A.2. Data table
## Load data
SchoolsGroup <- read.csv("https://raw.github.com/patilv/rChartsTutorials/master/SchoolsGroup.csv")
kable(head(SchoolsGroup))
X Group.1 Group.2 Group.3 Group.4
1 Texas Cincinnati Kentucky Bowling Green
2 Ohio State Memphis Louisville William & Mary
3 Michigan Air Force South Carolina Montana
4 Alabama South Florida Minnesota North Dakota
5 Florida Boise State Washington South Alabama
6 Texas A&M New Mexico North Carolina Maine 
## Load styles
tab2 <- dTable(SchoolsGroup[,-1], sPaginationType = "full_numbers")
tab2$templates$script <- "http://timelyportfolio.github.io/rCharts_dataTable/chart_customsort.html" 

## Set column names
tab2$params$table$aoColumns =
  list(
    list(sType = "string_ignore_null", sTitle = "Group 1"),
    list(sType = "string_ignore_null", sTitle = "Group 2"),
    list(sType = "string_ignore_null", sTitle = "Group 3"),
    list(sType = "string_ignore_null", sTitle = "Group 4")
  )

## Produce data table
n1 <- tab2
n1$show('inline', include_assets = TRUE, cdn = TRUE)



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B. The googleVis package

The googleVis package provides an interface to Google Charts API, allowing users to create interactive charts based on R data frames.

B.1. Tree map
## Load the googleVis library
library(googleVis)

## Inspect data
kable(head(Regions))
Region Parent Val Fac
Global NA 10 2
America Global 2 4
Europe Global 99 11
Asia Global 10 8
France Europe 71 2
Sweden Europe 89 3 
## Plot
Tree <- gvisTreeMap(data = Regions,  
                    idvar = "Region", parentvar = "Parent", 
                    sizevar = "Val", colorvar = "Fac", 
                    options=list(fontSize=16))
plot(Tree)


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B.2. Sankey chart
datSK <- data.frame(From=c(rep("A",3), rep("B", 3)),
                    To=c(rep(c("X", "Y", "Z"),2)),
                    Weight=c(5,7,6,2,9,4))
kable(head(datSK))
From To Weight
A X 5
A Y 7
A Z 6
B X 2
B Y 9
B Z 4 
Sankey <- gvisSankey(data = datSK, from="From", to="To", weight="Weight",
                     options=list(
                       sankey="{link: {color: { fill: '#d799ae' } },
                            node: { color: { fill: '#a61d4c' },
                            label: { color: '#871b47' } }}"))
plot(Sankey)


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B.3. Calendar chart
kable(head(Cairo))
Date Temp
2558 2002-01-01 13.61111
2559 2002-01-02 15.16667
2560 2002-01-03 12.00000
2561 2002-01-04 12.00000
2562 2002-01-05 13.05556
2563 2002-01-06 10.05556 
Cal <- gvisCalendar(data = Cairo, 
                    datevar="Date", 
                    numvar="Temp",
                    options=list(
                      title="Daily temperature in Cairo",
                      height=320,
                      calendar="{yearLabel: { fontName: 'Times-Roman',
                               fontSize: 32, color: '#1A8763', bold: true},
                               cellSize: 10,
                               cellColor: { stroke: 'red', strokeOpacity: 0.2 },
                               focusedCellColor: {stroke:'red'}}")
)
plot(Cal)


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B.4. Pie chart
kable(head(CityPopularity))
City Popularity
New York 200
Boston 300
Miami 400
Chicago 500
Los Angeles 600
Houston 700 
Pie <- gvisPieChart(data = CityPopularity)
plot(Pie)


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B.5. Geo chart
kable(head(Exports))
Country Profit Online
Germany 3 TRUE
Brazil 4 FALSE
United States 5 TRUE
France 4 TRUE
Hungary 3 FALSE
India 2 TRUE 
Geo <- gvisGeoChart(data = Exports, locationvar="Country", 
                 colorvar="Profit",
                 options=list(projection="kavrayskiy-vii"))
plot(Geo)


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B.6. Motion chart
kable(head(Fruits))
Fruit Year Location Sales Expenses Profit Date
Apples 2008 West 98 78 20 2008-12-31
Apples 2009 West 111 79 32 2009-12-31
Apples 2010 West 89 76 13 2010-12-31
Oranges 2008 East 96 81 15 2008-12-31
Bananas 2008 East 85 76 9 2008-12-31
Oranges 2009 East 93 80 13 2009-12-31 
M <- gvisMotionChart(data = Fruits, idvar = 'Fruit', timevar = 'Year',
         options=list(width=400, height=350))
plot(M)


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