Automate Data Preparation using Functions from Package eatPrep

This function facilitates automated data preparation and wraps most functions from the eatPrep package.

Usage

automateDataPreparation(datList = NULL, inputList, path = NULL,
    readSpss, checkData,  mergeData, recodeData, recodeMnr = FALSE,
    aggregateData, scoreData, writeSpss, collapseMissings = FALSE,
    filedat = "mydata.txt", filesps = "readmydata.sps", breaks=NULL,
    nMbi = 2, rotation.id = NULL, suppressErr = FALSE, recodeErr = "mci",
    aggregatemissings = NULL, rename = TRUE, recodedData = TRUE,
    addLeadingZeros=FALSE, truncateSpaceChar = TRUE,
    newID = NULL, oldIDs = NULL, addMbd = TRUE, overwriteMbdSilently=TRUE,
    missing.rule = list(mvi = 0, mnr = 0, mci = NA, mbd = NA, mir = 0, mbi = 0),
    verbose=FALSE)

Arguments

datList: A list of data frames (see data(inputDat)). If NULL, readSPSS has to be TRUE. In this case, the function attempts to read SPSS .sav files.
inputList: A list of data frames containing neccessary information for data preparaton (see data(inputList) for details).
path: A character vector containing the path required by for writeSpss. Default is the current R working directory.
readSpss: Logical: If TRUE, the function readSpss will be called.
checkData: Logical: If TRUE, the function checkData will be called.
mergeData: Logical: If TRUE, the function mergeData will be called.
recodeData: Logical: If TRUE, the function recodeData will be called.
recodeMnr: Logical: If TRUE, the function mnrCoding will be called.
aggregateData: Logical: If TRUE, the function aggregateData will be called.
scoreData: Logical: If TRUE, the function scoreData will be called.
collapseMissings: Logical: If TRUE, the function collapseMissings will be called and a data frame with recoded missing values according to argument missing.rule will be returned.
writeSpss: Logical: If TRUE, the function writeSpss will be called.
filedat: a character string containing the name of the output data file for writeSpss.
filesps: a character string containing the name of the output syntax file for writeSpss.
breaks: Numeric vector passed on to function mnrCoding containing the number of blocks after which mbi shall be recoded to mnr, e.g., c(1,2) to specify breaks after the first and second block. numeric vector (argument used by ).
nMbi: Numeric vector of length 1 passed on to function mnrCoding containing the number of mbi-Codes required at the end of a block to code mnr. Needs to be > 0.
rotation.id: Character vector of length 1 passed on to function mnrCoding indicating the name of the rotation indicator (e.g. “booklet”) in the dataset.
suppressErr: Logical passed on to function aggregateData indicating whether aggregated cells with err should be recoded to another value..
recodeErr: Character vector of length 1 passed on to function aggregateData indicating to which err should be recoded. This argument is only evaluated when suppressErr = TRUE

missing.rule: A named list with definitions how to recode the different types of missings in the dataset. If writeSPSS = TRUE, missing values will be recoded to 0 or NA prior to writing the SPSS dataset. See collapseMissings for supported missing values.
aggregatemissings: A symmetrical n x n matrix or a data frame from inputList$aggrMiss passed on to function aggregateData with information on how missing values should be aggregated. If no matrix is given, the default will be used. See 'Details' in aggregateData.
rename: Logical passed on to function aggregateData indicating whether units with only one subunit should be renamed to their unit name? Default is FALSE.
recodedData: Logical passed on to function aggregateDataindicating whether colnames in dat are the subunit names (as in subunits$subunit) or recoded subunit names (as in subunits$subunitRecoded). Default is TRUE, meaning that colnames are recoded subitem names.
addLeadingZeros: logical. See readSpss.
truncateSpaceChar: logical. See readSpss.
newID: A character string containing the case IDs name in the final data frame. Default is ID or a character string specified in inputList$newID.
oldIDs: A vector of character strings containing the IDs names in the original SPSS datasets. Default is as specified in inputList$savFiles.
addMbd: Logical. Whether mbd should be added when merging, see mergeData. Also used in prep2GADS.
overwriteMbdSilently: Logical. Whether mbd will overwritten silently when other non-empty values are available when merging, see mergeData.
verbose: Logical: If TRUE, progress and additional information is printed.

Value

A data frame resulting from the final data preparation step.

Author

Karoline Sachse

Examples

data(inputList)
data(inputDat)
preparedData <- automateDataPreparation(inputList = inputList,
    datList = inputDat,  path = getwd(),
    readSpss = FALSE, checkData = TRUE,  mergeData = TRUE,
    recodeData = TRUE, recodeMnr = TRUE, breaks = c(1,2),
    aggregateData = TRUE, scoreData = TRUE,
    writeSpss = FALSE, verbose = TRUE)
#> Starting automateDataPreparation 2024-09-11 08:53:40.333721
#> 
#> Check data...
#> 
#> Checking dataset booklet1
#> Only valid codes in ID variable.
#> No duplicated entries in ID variable.
#> No duplicated variable names.
#> Found no variable information about variable(s) hisei. This/These variables will not be checked for missings and invalid codes.
#> Found no invalid codes.
#> 
#> Checking dataset booklet2
#> Only valid codes in ID variable.
#> No duplicated entries in ID variable.
#> No duplicated variable names.
#> Found no variable information about variable(s) hisei. This/These variables will not be checked for missings and invalid codes.
#> Found no invalid codes.
#> 
#> Checking dataset booklet3
#> Only valid codes in ID variable.
#> No duplicated entries in ID variable.
#> No duplicated variable names.
#> Found no variable information about variable(s) hisei. This/These variables will not be checked for missings and invalid codes.
#> Found no invalid codes.
#> 
#> Start merging.
#> Start merging of dataset 1.
#> Start merging of dataset 2.
#> Start merging of dataset 3.
#> Start adding mbd according to data pattern.
#> 
#> Start recoding.
#> 
#> Found no recode information for variable(s): 
#> ID, hisei. 
#> This/These variable(s) will not be recoded.
#> 
#> Variables... I01, I02, I03, I04, I05, I06, I07, I08, I09, I10, I11, I12a, I12b, I12c, I13, I14, I15, I16, I17, I18, I19, I20, I21, I22, I23, I24, I25, I26, I27, I28
#> ...have been recoded.
#> 
#> Start recoding Mbi to Mnr.
#> ...identifying items in data (reference is blocks$subunit)
#> Variables in data not recognized as items:
#> ID, booklet, hisei
#> If some of these excluded variables should have been identified as items (and thus be used for mnr coding) check 'blocks', 'subunits', 'dat'.
#> ...identifying items with no mbi-codes ('mbi'):
#> I04R, I08R
#> If you expect mbi-codes on these variables check your data and option 'mbiCode'
#> mnr statistics:
#>      mnr cells: 553
#>      unique cases with at least one mnr code: 89
#>      unique items with at least one mnr code: 16
#> unique cases ('ID') per booklet and booklet section (0s omitted):
#>    booklet booklet.section N.ID
#> 1 booklet1               2   11
#> 2 booklet1               3   28
#> 3 booklet2               1   28
#> 4 booklet2               2   11
#> 5 booklet2               3    1
#> 6 booklet3               3   31
#> 
#> start recoding (item-wise)
#> done
#> elapsed time: 0.0 secs
#> 
#> Start aggregating
#> Since inputList$aggrMiss exists, this will be used instead of default.
#> All aggregation rules will be defaulted to 'SUM', because no other type is currently supported.
#> Found 27 unit(s) with only one subunit in 'dat'. This/these subunit(s) will not be aggregated and renamed to their respective unit name(s).
#> 1 units were aggregated: I12.
#> 
#> Start scoring.
#> ✔ 1 unit was scored: `I12`.
#> 
#> No SPSS-File has been written.
#> 
#> Missings are UNcollapsed.
#> automateDataPreparation terminated successfully! 2024-09-11 08:53:40.582552