Checks multiple columns in a dataframe to see if each is normally distributed. If not, then box-cox transform

Checks multiple columns in a dataframe to see if each is normally distributed. If not, then box-cox transform

MultipleColumnsNormalCheckThenBoxCox(
  input.data,
  names.of.dependent.variables,
  alpha.for.shapiro,
  output.lambda.in.col.name = TRUE
)

Arguments

input.data	A dataframe.
names.of.dependent.variables	Vector of strings where each element is the name of a column to assess for normality and potentially transform.
alpha.for.shapiro	Numerical value from 0 to 1. Threshold for what is considered not normal. If p-value is less than this threshold, then the data is considered not normal.
output.lambda.in.col.name	Boolean indicating if the lambda used for boxcox should be included in the column name.

Value

A dataframe with the columns specified in names.of.dependent.variables.

See also

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