1. Finding and Formatting

Chris Bailiss

2019-08-05

In This Vignette

Finding and Formatting

This vignette explains how to find parts of a pivot table - either one or more data groups (i.e. row/column headings) or one or more cells in the body of the pivot table.

This is often useful to retrieve either a specific value/values, or to change the appearance of specific headings/cells - similar to the conditional formatting capabilities of many off-the-shelf tools.

This is a long vignette. Some readers may prefer to jump to the summary at the end for a more concise description of the functions and frequently used parameters.

Example Pivot Table

The following pivot table is used as the basis of the examples in the rest of this vignette:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$renderPivot()

Finding Headings

The findRowDataGroups() and findColumnDataGroups() functions are used to find data groups (i.e. row and/or column headings) that match specific criteria. The functions return a list of data group objects.

These functions can operate in two different ways, specified by the matchMode argument.

matchMode="simple" is used when matching only one variable, e.g. TrainCategory=“Express Passenger”.

matchMode="combinations" is used when matching for combinations of variables, e.g. TrainCategory=“Express Passenger” and PowerType=“DMU”, which would return the “DMU” data group underneath “Express Passenger” (but not the “DMU” data group underneath “Ordinary Passenger”). Examples of each follow below.

These functions also accept the following arguments:

Several examples follow below. In each of the examples the data groups that have been found are highlighted in yellow by specifying a different style.

The examples in this section use column data groups, but all are equally applicable to row data groups.

Examples: matchMode="simple"

“simple” match mode is the default. The following examples illustrate how the “simple” matching mode works.

variableNames

Find all of the data groups for the “TrainCategory” variable:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(variableNames="TrainCategory")
pt$setStyling(groups=groups, declarations=list("background-color"="#FFFF00"))
pt$renderPivot()

variableValues

Find all of the data groups for the “PowerType” variable with the values “DMU” and “HST”:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(variableValues=list("PowerType"=c("DMU", "HST")))
pt$setStyling(groups=groups, declarations=list("background-color"="#FFFF00"))
pt$renderPivot()

totals (exclude)

Exclude totals:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(variableNames="TrainCategory", totals="exclude")
pt$setStyling(groups=groups, declarations=list("background-color"="#FFFF00"))
pt$renderPivot()

totals (only)

Find only totals:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(variableNames="TrainCategory", totals="only")
pt$setStyling(groups=groups, declarations=list("background-color"="#FFFF00"))
pt$renderPivot()

includeDescendantGroups

Find all of the data groups for the “TrainCategory” variable with the value “Ordinary Passenger”, including the descendant data groups:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(
  variableValues=list("TrainCategory"="Ordinary Passenger"), 
  includeDescendantGroup=TRUE)
pt$setStyling(groups=groups, declarations=list("background-color"="#FFFF00"))
pt$renderPivot()

Selecting a grand total data group

To select the right-most/bottom total data group:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(
  variableValues=list("TrainCategory"="**"), 
  includeDescendantGroup=TRUE)
pt$setStyling(groups=groups, declarations=list("background-color"="#FFFF00"))
pt$renderPivot()

Examples: matchMode="combinations"

The following examples illustrate how the “combinations” matching mode works.

The key concept to understand here is that the filtering criteria (i.e. the variableName(s) and variableValues) set for a data group also apply to all descendant data groups. For example, in the example pivot table above, the “DMU” under “Express Passenger” effectively means WHERE ("TrainCategory"="Express Passenger") AND ("PowerType"="DMU").

variableNames

Find all of the data groups that have filter criteria specified for both the “TrainCategory” and “Power Type” variables:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(matchMode="combinations",
                                  variableNames=c("TrainCategory", "PowerType"))
pt$setStyling(groups=groups, declarations=list("background-color"="#00FFFF"))
pt$renderPivot()

In the example above, the first row of headings relates only to the “TrainCategory” variable. The second row of headings relates both to the “PowerType” variable and the “TrainCategory” variable.

variableValues

Find all of the data groups for the “PowerType” variable with the values “DMU” and “HST” for the “TrainCategory” of “Express Passenger”:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(matchMode="combinations",
  variableValues=list("TrainCategory"="Express Passenger", "PowerType"=c("DMU", "HST")))
pt$setStyling(groups=groups, declarations=list("background-color"="#00FFFF"))
pt$renderPivot()

In the above example, the highlighted “DMU” and “HST” data groups are subject to the “Express Passenger” filtering since they are underneath that data group.

The “combinations” match mode effectively AND’s the criteria together, i.e. the data groups must match both “TrainCategory”=“Express Passenger” AND “PowerType”=(“DMU” OR “HST”).

The “simple” match mode, by contrast, effectively OR’s the criteria together, i.e. the data groups must match either “TrainCategory”=“Express Passenger” OR “PowerType”=(“DMU” OR “HST”). Changing the match mode back to simple (but otherwise leaving the previous example unchanged):

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(
  variableValues=list("TrainCategory"="Express Passenger", "PowerType"=c("DMU", "HST")))
pt$setStyling(groups=groups, declarations=list("background-color"="#00FFFF"))
pt$renderPivot()

Another example - finding all of the “PowerType” groups under “Express Passenger”:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(matchMode="combinations", variableNames="PowerType",
  variableValues=list("TrainCategory"="Express Passenger"))
pt$setStyling(groups=groups, declarations=list("background-color"="#00FFFF"))
pt$renderPivot()

Selecting a specific sub-total

To select the sub-total data group under “Express Passenger”:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
groups <- pt$findColumnDataGroups(matchMode="combinations", 
  variableValues=list("TrainCategory"="Express Passenger", "PowerType"="**"))
pt$setStyling(groups=groups, declarations=list("background-color"="#00FFFF"))
pt$renderPivot()

Getting Cells By Row and/or Column Numbers

The getCells() function is used to retrieve one or more cells by row/column number in the body of the pivot table. The arguments can be specified in two different ways depending on the value of the specifyCellsAsList argument.

In versions v0.4.0 and earlier, the default value of the specifyCellsAsList argument was FALSE. The default value is now TRUE.

The getCells() function returns a list of cell objects.

Getting cells when specifyCellsAsList=TRUE

To get cells when specifyCellsAsList=TRUE:

Examples of the above are given below. The retrieved cells are highlighted in orange by specifying a different style.

Retrieving whole rows of cells when specifyCellsAsList=TRUE

Retrieving the first and third rows:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=TRUE, rowNumbers=c(1, 3))
pt$setStyling(cells=cells, declarations=list("background-color"="#FFCC66"))
pt$renderPivot()

Retrieving whole columns of cells when specifyCellsAsList=TRUE

Retrieving the second column:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=TRUE, columnNumbers=2)
pt$setStyling(cells=cells, declarations=list("background-color"="#FFCC66"))
pt$renderPivot()

Retrieving specific cells when specifyCellsAsList=TRUE

Retrieving the raw/formatted values of the cell in the third column on the second row:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=TRUE, cellCoordinates=list(c(2, 3)))
pt$setStyling(cells=cells, declarations=list("background-color"="#FFCC66"))
cat("The raw value of the cell is", cells[[1]]$rawValue, "and the formatted value is", cells[[1]]$formattedValue, ".")
## The raw value of the cell is 732 and the formatted value is 732 .
pt$renderPivot()

Retrieving multiple cells (2nd row-3rd column, 3rd row-4th column and 5th row-7th column):

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=TRUE, cellCoordinates=list(c(2, 3), c(3, 4), c(5, 7)))
pt$setStyling(cells=cells, declarations=list("background-color"="#FFCC66"))
pt$renderPivot()

Retrieving a mixture of rows, columns and cells when specifyCellsAsList=TRUE

Retrieving the 2nd row, 4th column and 5th row-7th column cell:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=TRUE, rowNumbers=2, columnNumbers=4, cellCoordinates=list(c(5, 7)))
pt$setStyling(cells=cells, declarations=list("background-color"="#FFCC66"))
pt$renderPivot()

Getting cells when specifyCellsAsList=FALSE

To get cells when specifyCellsAsList=FALSE:

Examples of the above are given below. The retrieved cells are highlighted in green by specifying a different style.

Retrieving whole rows of cells when specifyCellsAsList=FALSE

When retrieving just rows, the rowNumbers argument is specified the same irrespective of whether specifyCellsAsList is TRUE or FALSE.

Retrieving the first and third rows:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=FALSE, rowNumbers=c(1, 3))
pt$setStyling(cells=cells, declarations=list("background-color"="#00FF00"))
pt$renderPivot()

Retrieving whole columns of cells when specifyCellsAsList=FALSE

When retrieving just columns, the columnNumbers argument is specified the same irrespective of whether specifyCellsAsList is TRUE or FALSE.

Retrieving the second column:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=FALSE, columnNumbers=2)
pt$setStyling(cells=cells, declarations=list("background-color"="#00FF00"))
pt$renderPivot()

Retrieving specific cells when specifyCellsAsList=FALSE

When retrieving cells, the rowNumbers and columnNumbers arguments are specified differently depending on whether specifyCellsAsList is TRUE or FALSE.

Retrieving the raw/formatted values of the cell in the third column on the second row:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=FALSE, rowNumbers=2, columnNumbers=3)
pt$setStyling(cells=cells, declarations=list("background-color"="#00FF00"))
cat("The raw value of the cell is", cells[[1]]$rawValue, "and the formatted value is", cells[[1]]$formattedValue, ".")
## The raw value of the cell is 732 and the formatted value is 732 .
pt$renderPivot()

Retrieving multiple cells (2nd row-3rd column, 3rd row-4th column and 5th row-7th column):

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=FALSE, rowNumbers=c(2, 3, 5), columnNumbers=c(3, 4, 7))
pt$setStyling(cells=cells, declarations=list("background-color"="#00FF00"))
pt$renderPivot()

Retrieving a mixture of rows, columns and cells when specifyCellsAsList=FALSE

When retrieving cells, the rowNumbers and columnNumbers arguments are specified differently depending on whether specifyCellsAsList is TRUE or FALSE.

Retrieving the 2nd row, 4th column and 5th row-7th column cell:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$getCells(specifyCellsAsList=FALSE, rowNumbers=c(2, NA, 5), columnNumbers=c(NA, 4, 7))
pt$setStyling(cells=cells, declarations=list("background-color"="#00FF00"))
pt$renderPivot()

Finding Cells

The findCells() function is used to search for cells within the body of the pivot table matching one or more criteria. The function returns a list of cell objects. This function has the following parameters:

If multiple variable names and values are specified, then findCells() searches for cells that match all of the criteria - i.e. the equivalent of the combinations match method described above.

Several examples of the above are given below.

variableNames

Finding cells that reference the “PowerType” variable:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableNames="PowerType")
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

All of the cells above reference the “PowerType” variable. For the findCells() function, the variableNames argument is only really used when a pivot table is constructed that has a custom layout.

variableValues

Finding cells that reference the “DMU” and “HST” values for the “PowerType” variable:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableValues=list("PowerType"=c("DMU", "HST")))
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

Finding cells that reference the “DMU” and “HST” values for the “PowerType” variable and reference the “London Midland” value for the “TOC” variable:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableValues=list("PowerType"=c("DMU", "HST"), "TOC"="London Midland"))
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

totals

Finding only totals cells that reference the “PowerType” variable:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableNames="PowerType", totals="only")
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

In the example, probably more total cells have been matched than expected.

To explicitly match only the total columns for the “PowerType” variable, specify two asterixes as the variable value:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableValues=list("PowerType"="**"))
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

To explicitly match only the sub-total columns for the “PowerType” variable (i.e. excluding the far right TrainCategory total column), use the following:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableValues=list("TrainCategory"="!*", "PowerType"="**"))
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

To find the grand total cell:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(variableValues=list("TrainCategory"="**", "PowerType"="**", "TOC"="**"))
pt$setStyling(cells=cells, declarations=list("background-color"="#FF00FF"))
pt$renderPivot()

Conditional Formatting

The findCells() and getCells() functions can be used to help conditionally format a pivot table.

For example, to highlight in red those cells in the basic example pivot table that have a value between 30000 and 50000:

library(pivottabler)
pt <- PivotTable$new()
pt$addData(bhmtrains)
pt$addColumnDataGroups("TrainCategory")
pt$addColumnDataGroups("PowerType")
pt$addRowDataGroups("TOC")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()")
pt$evaluatePivot()
cells <- pt$findCells(minValue=30000, maxValue=50000, includeNull=FALSE, includeNA=FALSE)
pt$setStyling(cells=cells, declarations=list("background-color"="#FFC7CE", "color"="#9C0006"))
pt$renderPivot()

Another example: analysing the average arrival delay (in minutes) in the morning peak time, for the top 20 origin stations, broken down by the hour of the day (i.e. 5am, 6am, 7am, etc.), with the results formatted as follows:

The findCells() function is used to find the cells matching the criteria above. One of three custom styles (green, yellow or red) is then applied to the cells:

# calculate arrival delay information
library(dplyr)
library(lubridate)
library(pivottabler)

stations <- mutate(trainstations, CrsCodeChr=as.character(CrsCode))

topOrigins <- bhmtrains %>%
  mutate(OriginChr=as.character(Origin)) %>%
  filter(Origin != "BHM") %>%
  group_by(OriginChr) %>%
  summarise(TotalTrains = n()) %>%
  ungroup() %>%
  top_n(20, TotalTrains)

trains <- bhmtrains %>%
  mutate(OriginChr=as.character(Origin), DestinationChr=as.character(Destination)) %>%
  inner_join(topOrigins, by=c("OriginChr"="OriginChr")) %>%
  inner_join(stations, by=c("OriginChr"="CrsCodeChr")) %>%
  inner_join(stations, by=c("DestinationChr"="CrsCodeChr")) %>%
  select(TOC, TrainCategory, PowerType, Origin=StationName.x, 
         GbttArrival, ActualArrival, GbttDeparture, ActualDeparture) %>%
  mutate(GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
         GbttHourOfDay=hour(GbttDateTime),
         ArrivalDeltaMins=difftime(ActualArrival, GbttArrival, units="mins"),
         ArrivalDelayMins=ifelse(ArrivalDeltaMins<0, 0, ArrivalDeltaMins)) %>%
  filter(GbttHourOfDay %in% c(5, 6, 7, 8, 9, 10)) %>%
  select(TOC, TrainCategory, PowerType, Origin, GbttHourOfDay, ArrivalDelayMins)

# create the pivot table
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("GbttHourOfDay")
pt$addRowDataGroups("Origin")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()", visible=FALSE)
pt$defineCalculation(calculationName="TotalDelayMins", 
                     summariseExpression="sum(ArrivalDelayMins, na.rm=TRUE)", visible=FALSE)
pt$defineCalculation(calculationName="AvgDelayMins", type="calculation", 
                     basedOn=c("TotalDelayMins", "TotalTrains"),
                     calculationExpression="values$TotalDelayMins/values$TotalTrains",
                     format="%.1f")
pt$evaluatePivot()

# apply the green style for an average arrival delay of between 0 and 2 minutes
cells <- pt$findCells(minValue=0, maxValue=2, includeNull=FALSE, includeNA=FALSE)
pt$setStyling(cells=cells, declarations=list("background-color"="#C6EFCE", "color"="#006100"))
# apply the yellow style for an average arrival delay of between 2 and 4 minutes
cells <- pt$findCells(minValue=2, maxValue=4, includeNull=FALSE, includeNA=FALSE)
pt$setStyling(cells=cells, declarations=list("background-color"="#FFEB9C", "color"="#9C5700"))
# apply the red style for an average arrival delay of 4 minutes or greater
cells <- pt$findCells(minValue=4, includeNull=FALSE, includeNA=FALSE)
pt$setStyling(cells=cells, declarations=list("background-color"="#FFC7CE", "color"="#9C0006"))
pt$renderPivot()

It is also possible to iterate through the cells to use a continuous colour scale as opposed to three separate styles. In the example below (using exactly the same data and calculations as above), some helper functions are defined which calculate a colour in a continuous colour scale. The colour scale used is roughly that described above (i.e. green to yellow to red):

# calculate arrival delay information
library(dplyr)
library(lubridate)
library(pivottabler)

stations <- mutate(trainstations, CrsCodeChr=as.character(CrsCode))

topOrigins <- bhmtrains %>%
  mutate(OriginChr=as.character(Origin)) %>%
  filter(Origin != "BHM") %>%
  group_by(OriginChr) %>%
  summarise(TotalTrains = n()) %>%
  ungroup() %>%
  top_n(20, TotalTrains)

trains <- bhmtrains %>%
  mutate(OriginChr=as.character(Origin), DestinationChr=as.character(Destination)) %>%
  inner_join(topOrigins, by=c("OriginChr"="OriginChr")) %>%
  inner_join(stations, by=c("OriginChr"="CrsCodeChr")) %>%
  inner_join(stations, by=c("DestinationChr"="CrsCodeChr")) %>%
  select(TOC, TrainCategory, PowerType, Origin=StationName.x, 
         GbttArrival, ActualArrival, GbttDeparture, ActualDeparture) %>%
  mutate(GbttDateTime=if_else(is.na(GbttArrival), GbttDeparture, GbttArrival),
         GbttHourOfDay=hour(GbttDateTime),
         ArrivalDeltaMins=difftime(ActualArrival, GbttArrival, units="mins"),
         ArrivalDelayMins=ifelse(ArrivalDeltaMins<0, 0, ArrivalDeltaMins)) %>%
  filter(GbttHourOfDay %in% c(5, 6, 7, 8, 9, 10)) %>%
  select(TOC, TrainCategory, PowerType, Origin, GbttHourOfDay, ArrivalDelayMins)

# create the pivot table
library(pivottabler)
pt <- PivotTable$new()
pt$addData(trains)
pt$addColumnDataGroups("GbttHourOfDay")
pt$addRowDataGroups("Origin")
pt$defineCalculation(calculationName="TotalTrains", summariseExpression="n()", visible=FALSE)
pt$defineCalculation(calculationName="TotalDelayMins", 
                     summariseExpression="sum(ArrivalDelayMins, na.rm=TRUE)", visible=FALSE)
pt$defineCalculation(calculationName="AvgDelayMins", type="calculation", 
                     basedOn=c("TotalDelayMins", "TotalTrains"),
                     calculationExpression="values$TotalDelayMins/values$TotalTrains",
                     format="%.1f")
pt$evaluatePivot()

# colour scale helper functions
scaleNumber <- function(n1, n2, vMin, vMax, value) {
  if(n1==n2) return(n1)
  v <- value
  if(v < vMin) v <- vMin
  if(v > vMax) v <- vMax
  if(n1<n2) {
    return(n1+((v-vMin)/(vMax-vMin)*(n2-n1)))
  }
  else {
    return(n1-((v-vMin)/(vMax-vMin)*(n1-n2)))
  }
}
scale2Colours <- function(clr1, clr2, vMin, vMax, value) {
  r <- round(scaleNumber(clr1$r, clr2$r, vMin, vMax, value))
  g <- round(scaleNumber(clr1$g, clr2$g, vMin, vMax, value))
  b <- round(scaleNumber(clr1$b, clr2$b, vMin, vMax, value))
  return(paste0("#",format(as.hexmode(r), width=2), format(as.hexmode(g), width=2), format(as.hexmode(b), width=2)))
}
scale3Colours <- function(clr1, clr2, clr3, vMin, vMid, vMax, value) {
  if(value <= vMid) return(scale2Colours(clr1, clr2, vMin, vMid, value))
  else return(scale2Colours(clr2, clr3, vMid, vMax, value))
}
hexToClr <- function(hexclr) {
  clr <- list()
  clr$r <- strtoi(paste0("0x", substr(hexclr, 2, 3)))
  clr$g <- strtoi(paste0("0x", substr(hexclr, 4, 5)))
  clr$b <- strtoi(paste0("0x", substr(hexclr, 6, 7)))
  return(clr)
}

# colour constants
textClrGreen <- hexToClr("#006100")
textClrYellow <- hexToClr("#9C5700")
textClrRed <- hexToClr("#9C0006")
backClrGreen <- hexToClr("#C6EFCE")
backClrYellow <- hexToClr("#FFEB9C")
backClrRed <- hexToClr("#FFC7CE")

# specify some conditional formatting, calculating the appropriate text colour and back colour for each cell.
cells <- pt$findCells(includeNull=FALSE, includeNA=FALSE)
formatCell <- function(cell) {
  value <- cell$rawValue
  textClr <- scale3Colours(textClrGreen, textClrYellow, textClrRed, 0.5, 2, 4, value)
  backClr <- scale3Colours(backClrGreen, backClrYellow, backClrRed, 0.5, 2, 4, value)
  pt$setStyling(cells=cell, declarations=list("background-color"=backClr, "color"=textClr))
}
invisible(lapply(cells, formatCell))
pt$renderPivot()

Summary

Further Reading

The full set of vignettes is:

  1. Introduction
  2. Data Groups
  3. Calculations
  4. Outputs
  5. Latex Output
  6. Styling
  7. Finding and Formatting
  8. Cell Context
  9. Irregular Layout
  10. Performance
  11. Shiny
  12. Excel Export
  13. Appendix: Details
  14. Appendix: Calculations
  15. Appendix: Class Overview