ExtractGroupSizeData function

ExtractGroupSizeData

ExtractGroupSizeData

This function constructs a table identifying the number of participants in each sequence group for a set of experiments each of which used a crossover design.

ExtractGroupSizeData( ExpDataWide, StudyID, ShortExperimentNames, Type, Groups = c("A", "B", "C", "D") )

Arguments

  • ExpDataWide: this is a list of tibbles each comprising data from one experiment in its wide format
  • StudyID: an identifier for the group of related experiments (i.e., a family).
  • ShortExperimentNames: a list of character strings identifying each experiment.
  • Type: A list identifying the type of crossover '2G' or '4G' for each experiment in the family
  • Groups: a list of the terms used to specify sequence groups in the experiments.

Returns

A tibble containing the number of participants in each sequence group in each experiment.

Examples

ExperimentNames <- c("EUBAS", "R1UCLM", "R2UCLM", "R3UCLM") ShortExperimentNames <- c("E1", "E2", "E3", "E4") Metrics <- c("Comprehension", "Modification") Type <- c("4G", "4G", "4G", "4G") Groups <- c("A", "B", "C", "D") StudyID <- "S2" Control <- "SC" # Obtain experimental data from a file and put in wide format dataset2 <- KitchenhamEtAl.CorrelationsAmongParticipants.Scanniello14TOSEM ReshapedData <- ExtractExperimentData(dataset2, ExperimentNames = ExperimentNames, idvar = "ParticipantID", timevar = "Period", ConvertToWide = TRUE ) ExtractGroupSizeData(ReshapedData, StudyID, ShortExperimentNames, Type, Groups = Groups) # A tibble: 16 x 4 # Study Exp Group n # <chr> <chr> <chr> <int> # 1 S2 Exp1 A 6 # 2 S2 Exp1 B 6 # 3 S2 Exp1 C 6 # 4 S2 Exp1 D 6 # 5 S2 Exp2 A 6 # 6 S2 Exp2 B 6 # 7 S2 Exp2 C 5 # 8 S2 Exp2 D 5 # 9 S2 Exp3 A 5 # 10 S2 Exp3 B 5 # 11 S2 Exp3 C 6 # 12 S2 Exp3 D 6 # 13 S2 Exp4 A 5 # 14 S2 Exp4 B 5 # 15 S2 Exp4 C 4 # 16 S2 Exp4 D 4

Author(s)

Barbara Kitchenham and Lech Madeyski