CrossfilterCharts.jl is a Julia module which harnesses the power of DC.js to automagically generate linked data visualizations.
using RDatasets
iris = dataset("datasets", "iris");
CrossfilterCharts.jl is extremely easy to use. Here we are pulling a dataset using the RDatasets package in order to supply example datasets contained in DataFrame
s.
Basic use of CrossfilterCharts only requires importing the package and calling dc()
on the dataframe to generate a visualization:
using CrossfilterCharts
dc(iris)
Users have complete access to the DCOut
object, and can choose to structure the visualization themselves:
df = dataset("mlmRev", "Exam")
dcout = DCOut(df)
infer_dimensions!(dcout) # infer all dimensions
infer_groups!(dcout) # infer all groups
quick_add!(dcout, :School, piechart)
quick_add!(dcout, :NormExam, barchart)
quick_add!(dcout, :SchGend, rowchart)
quick_add!(dcout, :SchAvg, linechart)
quick_add!(dcout, :VR, piechart)
quick_add!(dcout, :Sex, piechart)
dcout.charts[end].typ[:innerRadius] = "50"
add_bubblechart!(dcout, :School, :NormExam, :SchAvg, :StandLRT)
add_datacountwidget!(dcout)
add_datatablewidget!(dcout)
dcout
School | NormExam | SchGend | SchAvg | VR | Intake | StandLRT | Sex | Type | Student |
---|
CrossfilterCharts will now not automatically infer charts if there are missing or NaN values.
msleep = dataset("ggplot2","msleep")
dc(msleep)