pivottabler

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The 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.

Pivot tables are rendered as htmlwidgets, Latex or plain text. The HTML/Latex/text can be exported for use outside of R.

Pivot tables can also be converted to a standard R matrix or data frame.

Installation

You can install:

install.packages("pivottabler")
devtools::install_github("cbailiss/pivottabler", build_vignettes = TRUE)

Example

pivottabler has many styling and formatting capabilities when rendering pivot tables in HTML / as htmlwidgets using pt$renderPivot(), however the most basic output is simply as plain text.

Plain Text Output

A simple example of creating a pivot table - summarising the types of trains run by different train companies:

library(pivottabler)
# arguments:  qpvt(dataFrame, rows, columns, calculations, ...)
qpvt(bhmtrains, "TOC", "TrainCategory", "n()") # TOC = Train Operating Company 
                     Express Passenger  Ordinary Passenger  Total  
Arriva Trains Wales               3079                 830   3909  
CrossCountry                     22865                  63  22928  
London Midland                   14487               33792  48279  
Virgin Trains                     8594                       8594  
Total                            49025               34685  83710  

pivottabler also offers a more verbose syntax that is more self-describing and offers additional options that aren’t available with the quick-pivot functions. The equivalent verbose commands to output the same pivot table as above are:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains) # bhmtrains is a data frame with columns TrainCategory, TOC, etc.
pt$addColumnDataGroups("TrainCategory") # e.g. Express Passenger
pt$addRowDataGroups("TOC") # TOC = Train Operating Company e.g. Arriva Trains Wales
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
pt

Multiple levels can be added to the pivot table row or column headings, e.g. looking at combinations of TOC and PowerType:

library(pivottabler)
qpvt(bhmtrains, c("TOC", "PowerType"), "TrainCategory", "n()")
library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$addRowDataGroups("PowerType") # D/EMU = Diesel/Electric Multiple Unit, HST=High Speed Train
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
pt
                            Express Passenger  Ordinary Passenger  Total  
Arriva Trains Wales  DMU                 3079                 830   3909  
                     Total               3079                 830   3909  
CrossCountry         DMU                22133                  63  22196  
                     HST                  732                        732  
                     Total              22865                  63  22928  
London Midland       DMU                 5638                5591  11229  
                     EMU                 8849               28201  37050  
                     Total              14487               33792  48279  
Virgin Trains        DMU                 2137                       2137  
                     EMU                 6457                       6457  
                     Total               8594                       8594  
Total                                   49025               34685  83710  

HTML Output

The HTML rendering of the same two pivot tables shown above (each constructed using both a quick-pivot function and verbose syntax) is:

library(pivottabler)
qhpvt(bhmtrains, "TOC", "TrainCategory", "n()") 
library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains) 
pt$addColumnDataGroups("TrainCategory")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$renderPivot()
http://cbailiss.me.uk/pivottablerreadmeimgs/example1.png
http://cbailiss.me.uk/pivottablerreadmeimgs/example1.png
library(pivottabler)
qhpvt(bhmtrains, c("TOC", "PowerType"), "TrainCategory", "n()")  
library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains) # bhmtrains is a data frame with columns TrainCategory, TOC, etc.
pt$addColumnDataGroups("TrainCategory") # e.g. Express Passenger
pt$addRowDataGroups("TOC") # TOC = Train Operating Company e.g. Arriva Trains Wales
pt$addRowDataGroups("PowerType") # D/EMU = Diesel/Electric Multiple Unit, HST=High Speed Train
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$renderPivot()
http://cbailiss.me.uk/pivottablerreadmeimgs/example2.png
http://cbailiss.me.uk/pivottablerreadmeimgs/example2.png

More Information

More complex pivot tables can also be created, e.g. with irregular layouts, using multiple data frames, using multiple calculations and/or custom R calculation functions. See the package vignettes for more details:

# to see a list of available package vignettes:
vignette(package="pivottabler")
# to open a specific vignette
vignette(topic="v01-introduction", package="pivottabler")

The vignettes can also be read on CRAN at: https://cran.r-project.org/package=pivottabler

More Examples

The following are a few of the example pivot tables constructed in the package vignettes (click to open full sized picture):

http://cbailiss.me.uk/pivottablerreadmeimgs/example3.png
http://cbailiss.me.uk/pivottablerreadmeimgs/example3.png