For each level, describe each numerical feature. Mean, sd, median, skewness (symmetry), kurtosis (flatness), pass normality?

Uses describeNumericalColumns() separately for each unique level in the column corresponding to column.name.containing.levels.within.feature. The results for each level are separated in the output by blank rows.

describeNumericalColumnsWithLevels(
  input.data,
  column.names.to.use,
  column.name.containing.levels.within.feature,
  file.name = NULL
)

Arguments

input.data	A dataframe.
column.names.to.use	Vector of strings of column names. Function will only describe these specified columns (features).
column.name.containing.levels.within.feature	Name of column that contains the levels that you want to separate by.
file.name	Name of the text file or absolute path of the file to write the output to. If the input is NULL, then no file is generated.

Value

A dataframe where the rows are the descriptions and columns are the features. Additionally, a text file will be created which also captured the function output.

The output for each level is separated by blank rows.

See also

Other Preprocessing functions: AddColBinnedToBinary(), AddColBinnedToQuartiles(), AddPCsToEnd(), ConvertDataToPercentiles(), CorAssoTestMultipleWithErrorHandling(), DownSampleDataframe(), GenerateElbowPlotPCA(), GeneratePC1andPC2PlotsWithAndWithoutOutliers(), Log2TargetDensityPlotComparison(), LookAtPCFeatureLoadings(), MultipleColumnsNormalCheckThenBoxCox(), NormalCheckThenBoxCoxTransform(), RanomlySelectOneRowForEach(), RecodeIdentifier(), RemoveColWithAllZeros(), RemoveRowsBasedOnCol(), RemoveSamplesWithInstability(), SplitIntoTrainTest(), StabilityTestingAcrossVisits(), SubsetDataByContinuousCol(), TwoSampleTTest(), ZScoreChallengeOutliers(), captureSessionInfo(), correlation.association.test(), describeNumericalColumns(), generate.descriptive.plots.save.pdf(), generate.descriptive.plots()