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 formatdataset2 <- 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