Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
In our R Academy course “Interactive Datavisualization with R” participants learned today how to build interactive graphics in R using the shiny and the googleVis package.
Rstudios shiny package is a great way to build an Application for interactive visualizations out of R.
For our example App we recreated Hans Rosslings (Gapminder) Visualization which puts a countries Life-Expectancy in relation with its Gross-Domestic-Product.
We used the data from datamarket.com via the rdatamarket Package. We combined data from Gapminder(Population), the World Bank (GDP) and from the United Nations (Life-Expectancy) with each other. Datamarket.com even offers an R-console snippet for importing the data directly into R (Under Export).
The following lines of code shows what you need to do. Have fun with R in the web.
# TODO: Add comment
#
# Author: msc
###############################################################################
library("rdatamarket")
library("shiny")
#getting Data from datamarket.com
#life_expectancy and gdp
dminit("ba22bf5bdf044aaf980cdbde3504248c")
life_expectancy <- dmlist("15r2!hrp")
gdp <- dmlist("15c9!hd1")
#population
dminit("ba22bf5bdf044aaf980cdbde3504248c")
population <- dmlist("1cfl!r3d")
#renaming the value variable
names(gdp)[3] <- "GDP"
names(life_expectancy)[3] <- "life_expectancy"
names(population)[3] <- "Population"
#merge to one dataframe
data <- merge(gdp,life_expectancy, by = c("Country","Year"))
data <- merge(data, population, by = c("Country","Year"))
#data is only until 2008 complete
data < - data[data$Year
# TODO: Add comment
#
# Author: msc
###############################################################################
if(!"googleVis"%in%installed.packages())install.packages("googleVis", repos = "http://ftp5.gwdg.de/pub/misc/cran/")
library("googleVis")
shinyServer(function(input, output) {
load("data.RData")
data$Population <- data$Population/1000
output$main_plot <- renderGvis({
if(input$xAchse == "log. GDB"){
data$GDP <- log(data$GDP)
}else{
data$GDP <- data$GDP
}
if(input$all == TRUE){
country_filter <- input$country_filter1
}
if(input$all == FALSE){
country_filter <- input$country_filter2
}
#it seems that gvisMotionChart needs different variables for idvar and colorvar
data$CountryCol <- data$Country
gvisMotionChart(data[data$Country %in% country_filter,],
idvar = "Country",
timevar = "Year",
xvar = "GDP",
yvar = "life_expectancy",
colorvar = "CountryCol",
sizevar = "Population",
options = list(showChartButtons = FALSE,
showSidePanel = FALSE,
showXScalePicker = FALSE,
showYScalePicker = FALSE)
)
})
output$map <- renderGvis({
if(input$all == TRUE){
country_filter <- input$country_filter1
}
if(input$all == FALSE){
country_filter <- input$country_filter2
}
year_filter <- input$year_filter
data$hover <- paste0("Life-Expectancy ",data$Country,": ",round(data$life_expectancy))
gvisGeoChart(data[data$Country %in% country_filter & data$Year == year_filter,], locationvar = "Country", colorvar = "GDP", hovervar = "hover")
})
})
# TODO: Add comment
#
# Author: msc
###############################################################################
library("shiny")
if(!"googleVis"%in%installed.packages())install.packages("googleVis", repos = "http://ftp5.gwdg.de/pub/misc/cran/")
library("googleVis")
load('data.RData')
#Buiding a HTML Table
shinyUI(pageWithSidebar(
headerPanel(tags$body(tags$img(src="eoda_logo.png", width = "400px", align ="right"),
h1("Life-Expectancy ~ GDP per capita"),
h2("(random Country preselection)"),
h5("sometimes changes doen't take effect immediatly in this case please refresh the whole page"))),
sidebarPanel(
selectInput('xAchse', 'X Axis', c('GDP', 'log. GDB')),
checkboxInput(inputId = 'all',label = 'all countries', value = TRUE),
conditionalPanel(condition = 'input.all == true',
checkboxGroupInput(inputId = 'country_filter1',
label = 'Countries', unique(data$Country),
selected = unique(data$Country))),
conditionalPanel(condition = 'input.all == false',
checkboxGroupInput('country_filter2',
label = 'Countries', choices = unique(data$Country)))
),
mainPanel(h3("MotionChart"),
h4("(circle-size = population in tsd)"),
htmlOutput(outputId = "main_plot"),
h3("World Map"),
h4("(colour range = GDP per capita/ additional Life-Expectancy per MouseOver Effect)"),
htmlOutput(outputId = "map"),
sliderInput(inputId = "year_filter",
label = "Year",
min = 1960,
max = 2008,
value = 2008,
format = "####")
)
))
In our R Academy we help you to become an expert in R – check it out!
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.