The great pivottabler
package enables pivot tables to be created with just a few lines of R.
The pivottabler
package aims to:
All calculations for the pivot tables take place inside R, enabling the use of a wide-range of R functions in the calculation logic, and are optimized with the use of packages dplyr & data.table
shinypivottabler
just adds a simple and useful Shiny module to build, visualize, customize and export custom pivot tables.
The rpivotTable
package is visualization library built around the Javascript pivottable library.
PivotTable.js
is a Javascript Pivot Table library with drag’n’drop functionality built on top of jQuery/jQueryUI and written in CoffeeScript (then compiled to JavaScript) by Nicolas Kruchten at Datacratic. It is available under an MIT license
rpivotTable
is really a great pivot table library with some really cool features such as :
But it’s a full Javascript tool, and so all the data are sended to the client, and all computations are done on the client. So it’s not a good idea to use rpivotTable
with a huge database.
No charts (yet…?) in shinypivottabler
but :
You can install:
require(shinypivottabler)
n <- 10000000
# create artificial dataset
data <- data.frame("gr1" = sample(c("A", "B", "C", "D"), size = n,
prob = rep(1, 4), replace = T),
"gr2" = sample(c("E", "F", "G", "H"), size = n,
prob = rep(1, 4), replace = T),
"gr3" = sample(c("I", "J", "K", "L"), size = n,
prob = rep(1, 4), replace = T),
"gr4" = sample(c("M", "N", "O", "P"), size = n,
prob = rep(1, 4), replace = T),
"value1" = rnorm(n),
"value2" = runif(n))
server = function(input, output, session) {
shiny::callModule(module = shinypivottabler,
id = "id",
data = data,
pivot_cols = c("gr1", "gr2", "gr3", "gr4"))
}
ui = shiny::fluidPage(
shinypivottablerUI(id = "id")
)
shiny::shinyApp(ui = ui, server = server)
# more info in documentation :
?shinypivottabler