PivotFilters: R6 class that defines a set of filter conditions.
In pivottabler: Create Pivot Tables
PivotFilters R Documentation
R6 class that defines a set of filter conditions.
Description
The 'PivotFilters' class allows multiple filter conditions relating to
different data frame columns to be combined, i.e. a 'PivotFilters' object
typically contains multiple PivotFilter objects.
Format
R6Class object.
Details
As well as acting as a container for multiple filter conditions, the
'PivotFilters' class also contains logic for combining filter.
The 'action' parameter in many of the methods controls how two filters
are combined.
Most common cases:
(1) When working out the rowColFilters for each pivot table cell, the
filters from the row and column leaf groups are combined using
'action="intersect"'.
(2) When combining the rowColFilters with calculation filters the
action could be any of (in order of most typical)
"intersect", "replace" or "union".
"intersect" would apply additional restrictions, e.g. see the
example in the Calculations vignette that has a measure for
weekend trains only.
"replace" would apply when doing things like percentage of row
total calculations - again, see example in the calculations vignette
"union" is probably much less likely (hard to envisage many
situations when that would be needed).
(3) In custom calculation functions, the action could be any of
"intersect", "replace" or "union".
NOTE: 'pivottabler' does not allow complex conditions to be built up,
such as ((A=X) or (B=Y)) and (C=2) since there is complex precedence
involved and conditions like this are not typical of pivot tables.
If they were really needed, a workaround would be to use a custom
calculation function and include this logic in that function.
See Appendix 2 vignette for many more complex calculation details.
Active bindings
countThe number of 'PivotFilter' objects in this 'PivotFilters' object.
filtersA list of 'PivotFilter' objects in this 'PivotFilters' object.
isALLIf TRUE, this 'PivotFilters' object matches all data.
isNONEIf TRUE, this 'PivotFilters' object matches no data.
filteredVariablesThe names of the variables that are filtered by this
'PivotFilters' object.
filteredValuesA list of the criteria values for each of the variables
filtered by this 'PivotFilters' object, where the list element names are the
variable names.
Methods
Public methods
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Method new()
Create a new 'PivotFilters' object, optionally adding a filter.
Usage
PivotFilters$new(
parentPivot = NULL,
variableName = NULL,
type = "ALL",
values = NULL
)
Arguments
parentPivotThe pivot table that this 'PivotFilters'
instance belongs to.
variableNameThe name of the column in the data frame that this filter
applies to. Specify 'NULL' to skip adding a filter.
typeMust be either "ALL", "VALUES" or "NONE". "VALUES" is the most
common type and means the data is filtered to a subset of values. "ALL" means
there is no filtering, i.e. all values match. "NONE" means there can be no
matching values/data.
valuesA single data value or a vector of multiple data values that
the filter will match on.
Returns
A new 'PivotFilters' object.
Method clearFilters()
Remove all filters from this 'PivotFilters' object.
Usage
PivotFilters$clearFilters()
Returns
No return value.
Method keepOnlyFiltersFor()
Remove the filters for all variables except those specified.
Usage
PivotFilters$keepOnlyFiltersFor(variableNames = NULL)
Arguments
variableNamesA character vector specifying the variable names
to retain the filter criteria for. Filter criteria for all other variables
will be cleared.
Returns
No return value.
Method removeFiltersFor()
Remove the filters for the specified variables.
Usage
PivotFilters$removeFiltersFor(variableNames = NULL)
Arguments
variableNamesA character vector specifying the variable names
for which the filter criteria will be cleared. Filter criteria for all
other variables will be retained.
Returns
No return value.
Method getFilter()
Find a filter with the specified variable name.
Usage
PivotFilters$getFilter(variableName = NULL)
Arguments
variableNameThe variable name to find a filter for.
Returns
A 'PivotFilter' object that filters on the specified variable.
Method isFilterMatch()
Tests whether this 'PivotFilters' object matches specified criteria.
Usage
PivotFilters$isFilterMatch(
matchMode = "simple",
variableNames = NULL,
variableValues = NULL
)
Arguments
matchModeEither "simple" (default) or "combinations".
"simple" is used when matching only one variable-value, multiple
variable-value combinations are effectively logical "OR", i.e.
any one single 'PivotFilter' match means the 'PivotFilters' object
is a match.
"combinations" is used when matching for combinations of variable
values, multiple variable-value combinations are effectively
logical "AND", i.e. there must be a matching 'PivotFilter' for
every variable name / variable values criteria specified.
See the "Finding and Formatting" vignette for graphical examples.
variableNamesThe variable name(s) to find a filter for. This
can be a vector containing more than one variable name.
variableValuesA list specifying the variable names and values
to find, e.g. 'variableValues=list("PowerType"=c("DMU", "HST"))'.
Returns
'TRUE' if this filters object matches the specified criteria,
'FALSE' otherwise.
Method setFilters()
Update the value of this 'PivotFilters' object with the filters from the
specified 'PivotFilters' object, either intersecting, replacing or unioning
the filter criteria.
Usage
PivotFilters$setFilters(filters = NULL, action = "replace")
Arguments
filtersA 'PivotFilters' object.
actionSpecifies how the criteria defined in 'filters' should be
combined with the existing filter criteria.
Must be one of "intersect", "replace" or "union".
Returns
No return value.
Method setFilter()
Update the value of this 'PivotFilters' object with the filters from the
specified 'PivotFilter' object, either intersecting, replacing or unioning
the filter criteria.
Usage
PivotFilters$setFilter(filter = NULL, action = "replace")
Arguments
filterA 'PivotFilter' object.
actionSpecifies how the criteria defined in 'filter' should be
combined with the existing filter criteria.
Must be one of "intersect", "replace" or "union".
Returns
No return value.
Method setFilterValues()
Update the value of this 'PivotFilters' object with additional filter criteria,
either intersecting, replacing or unioning the filter criteria.
Usage
PivotFilters$setFilterValues(
variableName = NULL,
type = "ALL",
values = NULL,
action = "replace"
)
Arguments
variableNameThe name of the column in the data frame that this criteria
applies to.
typeMust be either "ALL", "VALUES" or "NONE".
valuesA single data value or a vector of multiple data values that
comprise the additional filter criteria.
actionSpecifies how the criteria defined in 'filter' should be
combined with the existing filter criteria.
Must be one of "intersect", "replace" or "union".
Returns
No return value.
Method addFilter()
Add a new 'PivotFilter' object to the filter list in this 'PivotFilters'
object.
Usage
PivotFilters$addFilter(filter = NULL)
Arguments
filterA 'PivotFilter' object.
Returns
No return value.
Method getFilteredDataFrame()
Filters the specified data frame using the filters defined in this
'PivotFilters' object and returns the results as another data frame.
Usage
PivotFilters$getFilteredDataFrame(dataFrame = NULL)
Arguments
dataFrameA data frame to filter.
Returns
A data frame filtered according to the criteria in this 'PivotFilters' object.
Method getCopy()
Create a copy of this 'PivotFilters' object.
Usage
PivotFilters$getCopy()
Returns
A copy of this 'PivotFilters' object.
Method asList()
Return the contents of this object as a list for debugging.
Usage
PivotFilters$asList()
Returns
A list of various object properties.
Method asJSON()
Return the contents of this object as JSON for debugging.
Usage
PivotFilters$asJSON()
Returns
A JSON representation of various object properties.
Method asString()
Return a representation of this object as a character value.
Usage
PivotFilters$asString(includeVariableName = TRUE, seperator = ", ")
Arguments
includeVariableName'TRUE' (default) to include the variable name in
the string.
seperatorA character value used when concatenating
multiple filters.
Returns
A character summary of various object properties.
Method clone()
The objects of this class are cloneable with this method.
Usage
PivotFilters$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
pt <- PivotTable$new()
# ...
# PivotFilters constructor allows a filter to be defined
filters <- PivotFilters$new(pt, variableName="Year", values=2017)
# Create a new filter
filter <- PivotFilter$new(pt, variableName="Country", values="England")
# Combine the filters
filters$setFilter(filter)
# filters now contains criteria for both Year and Country
# Now add another filter, this time via an alternative method
filters$setFilterValues(variableName="Product", values="Cadbury Dairy Milk
Chocolate 100g")
# filters now contains criteria for Year, Country and Product
pivottabler documentation built on Oct. 1, 2023, 5:07 p.m.
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| PivotFilters | R Documentation |
R6 class that defines a set of filter conditions.
Description
The 'PivotFilters' class allows multiple filter conditions relating to
different data frame columns to be combined, i.e. a 'PivotFilters' object
typically contains multiple PivotFilter objects.
Format
R6Class object.
Details
As well as acting as a container for multiple filter conditions, the
'PivotFilters' class also contains logic for combining filter.
The 'action' parameter in many of the methods controls how two filters
are combined.
Most common cases:
(1) When working out the rowColFilters for each pivot table cell, the
filters from the row and column leaf groups are combined using
'action="intersect"'.
(2) When combining the rowColFilters with calculation filters the
action could be any of (in order of most typical)
"intersect", "replace" or "union".
"intersect" would apply additional restrictions, e.g. see the
example in the Calculations vignette that has a measure for
weekend trains only.
"replace" would apply when doing things like percentage of row
total calculations - again, see example in the calculations vignette
"union" is probably much less likely (hard to envisage many
situations when that would be needed).
(3) In custom calculation functions, the action could be any of
"intersect", "replace" or "union".
NOTE: 'pivottabler' does not allow complex conditions to be built up,
such as ((A=X) or (B=Y)) and (C=2) since there is complex precedence
involved and conditions like this are not typical of pivot tables.
If they were really needed, a workaround would be to use a custom
calculation function and include this logic in that function.
See Appendix 2 vignette for many more complex calculation details.
Active bindings
countThe number of 'PivotFilter' objects in this 'PivotFilters' object.
filtersA list of 'PivotFilter' objects in this 'PivotFilters' object.
isALLIf TRUE, this 'PivotFilters' object matches all data.
isNONEIf TRUE, this 'PivotFilters' object matches no data.
filteredVariablesThe names of the variables that are filtered by this 'PivotFilters' object.
filteredValuesA list of the criteria values for each of the variables filtered by this 'PivotFilters' object, where the list element names are the variable names.
Methods
Public methods
Method new()
Create a new 'PivotFilters' object, optionally adding a filter.
Usage
PivotFilters$new( parentPivot = NULL, variableName = NULL, type = "ALL", values = NULL )
Arguments
parentPivotThe pivot table that this 'PivotFilters' instance belongs to.
variableNameThe name of the column in the data frame that this filter applies to. Specify 'NULL' to skip adding a filter.
typeMust be either "ALL", "VALUES" or "NONE". "VALUES" is the most common type and means the data is filtered to a subset of values. "ALL" means there is no filtering, i.e. all values match. "NONE" means there can be no matching values/data.
valuesA single data value or a vector of multiple data values that the filter will match on.
Returns
A new 'PivotFilters' object.
Method clearFilters()
Remove all filters from this 'PivotFilters' object.
Usage
PivotFilters$clearFilters()
Returns
No return value.
Method keepOnlyFiltersFor()
Remove the filters for all variables except those specified.
Usage
PivotFilters$keepOnlyFiltersFor(variableNames = NULL)
Arguments
variableNamesA character vector specifying the variable names to retain the filter criteria for. Filter criteria for all other variables will be cleared.
Returns
No return value.
Method removeFiltersFor()
Remove the filters for the specified variables.
Usage
PivotFilters$removeFiltersFor(variableNames = NULL)
Arguments
variableNamesA character vector specifying the variable names for which the filter criteria will be cleared. Filter criteria for all other variables will be retained.
Returns
No return value.
Method getFilter()
Find a filter with the specified variable name.
Usage
PivotFilters$getFilter(variableName = NULL)
Arguments
variableNameThe variable name to find a filter for.
Returns
A 'PivotFilter' object that filters on the specified variable.
Method isFilterMatch()
Tests whether this 'PivotFilters' object matches specified criteria.
Usage
PivotFilters$isFilterMatch( matchMode = "simple", variableNames = NULL, variableValues = NULL )
Arguments
matchModeEither "simple" (default) or "combinations".
"simple" is used when matching only one variable-value, multiple variable-value combinations are effectively logical "OR", i.e. any one single 'PivotFilter' match means the 'PivotFilters' object is a match.
"combinations" is used when matching for combinations of variable values, multiple variable-value combinations are effectively logical "AND", i.e. there must be a matching 'PivotFilter' for every variable name / variable values criteria specified.
See the "Finding and Formatting" vignette for graphical examples.variableNamesThe variable name(s) to find a filter for. This can be a vector containing more than one variable name.
variableValuesA list specifying the variable names and values to find, e.g. 'variableValues=list("PowerType"=c("DMU", "HST"))'.
Returns
'TRUE' if this filters object matches the specified criteria, 'FALSE' otherwise.
Method setFilters()
Update the value of this 'PivotFilters' object with the filters from the specified 'PivotFilters' object, either intersecting, replacing or unioning the filter criteria.
Usage
PivotFilters$setFilters(filters = NULL, action = "replace")
Arguments
filtersA 'PivotFilters' object.
actionSpecifies how the criteria defined in 'filters' should be combined with the existing filter criteria. Must be one of "intersect", "replace" or "union".
Returns
No return value.
Method setFilter()
Update the value of this 'PivotFilters' object with the filters from the specified 'PivotFilter' object, either intersecting, replacing or unioning the filter criteria.
Usage
PivotFilters$setFilter(filter = NULL, action = "replace")
Arguments
filterA 'PivotFilter' object.
actionSpecifies how the criteria defined in 'filter' should be combined with the existing filter criteria. Must be one of "intersect", "replace" or "union".
Returns
No return value.
Method setFilterValues()
Update the value of this 'PivotFilters' object with additional filter criteria, either intersecting, replacing or unioning the filter criteria.
Usage
PivotFilters$setFilterValues( variableName = NULL, type = "ALL", values = NULL, action = "replace" )
Arguments
variableNameThe name of the column in the data frame that this criteria applies to.
typeMust be either "ALL", "VALUES" or "NONE".
valuesA single data value or a vector of multiple data values that comprise the additional filter criteria.
actionSpecifies how the criteria defined in 'filter' should be combined with the existing filter criteria. Must be one of "intersect", "replace" or "union".
Returns
No return value.
Method addFilter()
Add a new 'PivotFilter' object to the filter list in this 'PivotFilters' object.
Usage
PivotFilters$addFilter(filter = NULL)
Arguments
filterA 'PivotFilter' object.
Returns
No return value.
Method getFilteredDataFrame()
Filters the specified data frame using the filters defined in this 'PivotFilters' object and returns the results as another data frame.
Usage
PivotFilters$getFilteredDataFrame(dataFrame = NULL)
Arguments
dataFrameA data frame to filter.
Returns
A data frame filtered according to the criteria in this 'PivotFilters' object.
Method getCopy()
Create a copy of this 'PivotFilters' object.
Usage
PivotFilters$getCopy()
Returns
A copy of this 'PivotFilters' object.
Method asList()
Return the contents of this object as a list for debugging.
Usage
PivotFilters$asList()
Returns
A list of various object properties.
Method asJSON()
Return the contents of this object as JSON for debugging.
Usage
PivotFilters$asJSON()
Returns
A JSON representation of various object properties.
Method asString()
Return a representation of this object as a character value.
Usage
PivotFilters$asString(includeVariableName = TRUE, seperator = ", ")
Arguments
includeVariableName'TRUE' (default) to include the variable name in the string.
seperatorA character value used when concatenating multiple filters.
Returns
A character summary of various object properties.
Method clone()
The objects of this class are cloneable with this method.
Usage
PivotFilters$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
pt <- PivotTable$new()
# ...
# PivotFilters constructor allows a filter to be defined
filters <- PivotFilters$new(pt, variableName="Year", values=2017)
# Create a new filter
filter <- PivotFilter$new(pt, variableName="Country", values="England")
# Combine the filters
filters$setFilter(filter)
# filters now contains criteria for both Year and Country
# Now add another filter, this time via an alternative method
filters$setFilterValues(variableName="Product", values="Cadbury Dairy Milk
Chocolate 100g")
# filters now contains criteria for Year, Country and Product
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