mapMarkers.dt: Map Markers as data.table

In VEuPathDB/plot.data: Create `data.table`s of Ready to Plot Data

Map Markers as data.table

Description

This function returns a data.table of plot-ready data with one row per panel. Columns 'label' and 'value' contain the raw data for plotting. Column 'panel' specifies the facet panel the series data belongs to. There are two options to calculate y-values for plotting.
1) 'count' occurrences of values from data.table input
2) 'proportion' of occurrences of values from data.table input

Usage

mapMarkers.dt(
  data,
  variables,
  value = c("count", "proportion"),
  viewport = NULL,
  xValues = NULL,
  sampleSizes = c(TRUE, FALSE),
  completeCases = c(TRUE, FALSE),
  evilMode = c("noVariables", "allVariables", "strataVariables"),
  verbose = c(TRUE, FALSE)
)

Arguments

data	data.frame to make plot-ready data for
variables	veupathUtils::VariableMetadataList
value	String indicating how to calculate y-values ('count', 'proportion')
viewport	List of values indicating the visible range of data
xValues	veupathUtils::BinList providing overlay values of interest
sampleSizes	boolean indicating if sample sizes should be computed
completeCases	boolean indicating if complete cases should be computed
evilMode	String indicating how evil this plot is ('strataVariables', 'allVariables', 'noVariables')
verbose	boolean indicating if timed logging is desired

Value

data.table plot-ready data

Evil Mode

An 'evilMode' exists. It will do the following:
- when ‘strataVariables' it will return ’no data' as a regular value for strata vars but will discard such cases for the axes vars.
- when ‘allVariables' it will return ’no data' as a regular value for all variables.
- when 'noVariables' it will do the sensible thing and return complete cases only.
- not return statsTables
- allow smoothed means and agg values etc over axes values where we have no data for the strata vars
- return a total count of plotted incomplete cases
- represent missingness poorly, conflate the stories of completeness and missingness, mislead you and steal your soul

Geolocation Viewport Structure

This is a list of lists taking the form:
*latitude
**xMin = numeric
**xMax = numeric
*longitude
**left = numeric
**right = numeric

Examples

# Construct example data
df <- data.table('entity.xvar' = sample(c('a','b','c'), 100, replace=T),
                 'entity.facet' = sample(c('red','green','blue'), 100, replace=T))

# Create VariableMetadataList that specifies variable role in the plot and supplies variable metadata
variables <- veupathUtils::VariableMetadataList(
  veupathUtils::VariableMetadata(
    variableClass = veupathUtils::VariableClass(value = 'native'),
    variableSpec = veupathUtils::VariableSpec(variableId = 'xvar', entityId = 'entity'),
    plotReference = veupathUtils::PlotReference(value = 'xAxis'),
    dataType = veupathUtils::DataType(value = 'STRING'),
    dataShape = veupathUtils::DataShape(value = 'CATEGORICAL')
  ),
  veupathUtils::VariableMetadata(
    variableClass = veupathUtils::VariableClass(value = 'native'),
    variableSpec = veupathUtils::VariableSpec(variableId = 'facet', entityId = 'entity'),
    plotReference = veupathUtils::PlotReference(value = 'geo'),
    dataType = veupathUtils::DataType(value = 'STRING'),
    dataShape = veupathUtils::DataShape(value = 'CATEGORICAL')
  )
)

# Returns a data table with plot-ready data
dt <- mapMarkers.dt(df,map,value='count')

VEuPathDB/plot.data documentation built on Feb. 20, 2025, 6:33 p.m.