Nothing
R/NMcheckData.R
In NMdata: Preparation, Checking and Post-Processing Data for PK/PD Modeling
Defines functions
NMcheckData
Documented in
NMcheckData
##' Check data for Nonmem compatibility or check control stream for
##' data compatibility
##'
##' Check data in various ways for compatibility with Nonmem. Some
##' findings will be reported even if they will not make Nonmem fail
##' but because they are typical dataset issues.
##'
##' @param data The data to check. data.frame, data.table, tibble,
##' anything that can be converted to data.table.
##' @param file Alternatively to checking a data object, you can use
##' file to specify a control stream to check. This can either be
##' a (working or non-working) input control stream or an output
##' control stream. In this case, \code{NMdataCheck} checks column
##' names in data against control stream (see
##' \code{NMcheckColnames}), reads the data as Nonmem would do,
##' and do the same checks on the data as NMdataCheck would do
##' using the data argument. \code{col.flagn} is ignored in this
##' case - instead, ACCEPT/IGNORE statements in control stream are
##' applied. The file argument is useful for debugging a Nonmem
##' model.
##' @param covs columns that contain subject-level covariates. They
##' are expected to be non-missing, numeric and not varying within
##' subjects.
##' @param covs.occ A list specifying columns that contain
##' subject:occasion-level covariates. They are expected to be
##' non-missing, numeric and not varying within combinations of
##' subject and occasion. \code{covs.occ=list(PERIOD=c("FED"))}
##' means that \code{FED} is the covariate, while \code{PERIOD}
##' indicates the occasion.
##' @param cols.num Columns that are expected to be present, numeric
##' and non-NA. If a character vector is given, the columns are
##' expected to be used in all rows. If a column is only used for
##' a subset of rows, use a list and name the elements by
##' subsetting strings. See examples.
##' @param col.cmt The name(s) of the compartment column(s). These
##' will be checked to be positive integers for all rows. They are
##' also used in checks for row duplicates.
##' @param col.amt The name of the dose amount column.
##' @param col.id The name of the column that holds the subject
##' identifier. Default is "ID".
##' @param col.time The name of the column holding actual time.
##' @param col.dv The name of the column holding the dependent
##' variable. For now, only one column can be specified, and
##' \code{MDV} is assumed to match this column. Default is
##' \code{DV}.
##' @param col.mdv The name of the column holding the binary indicator
##' of the dependent variable missing. Default is \code{MDV}.
##' @param col.flagn Optionally, the name of the column holding
##' numeric exclusion flags. Default value is \code{FLAG} and can
##' be configured using \code{NMdataConf}. Even though \code{FLAG}
##' is the default value, no finding will be returned if the
##' column is missing unless explicitly defined as
##' \code{col.flagn="FLAG"}. This is because this way of using
##' exclusion flags is only one of many ways you could choose to
##' handle exclusions. Disable completely by using
##' \code{col.flagn=FALSE}.
##' @param col.row A column with a unique value for each row. Such a
##' column is recommended to use if possible. Default
##' (\code{"ROW"}) can be modified using \code{NMdataConf}.
##' @param col.usubjid Optional unique subject identifier. It is
##' recommended to keep a unique subject identifier (typically a
##' character string including an abbreviated study name and the
##' subject id) from the clinical datasets in the analysis set. If
##' you supply the name of the column holding this identifier,
##' NMcheckData will check that it is non-missing, that it is
##' unique within values of col.id (i.e. that the analysis subject
##' ID's are unique across actual subjects), and that col.id is
##' unique within the unique subject ID (a violation of the latter
##' is less likely).
##' @param cols.dup Additional column names to consider in search of
##' duplicate events. col.id, col.cmt, col.evid, and col.time are
##' always considered if found in data, and cols.dup is added to
##' this list if provided.
##' @param type.data "est" for estimation data (default), and "sim"
##' for simulation data. Differences are that \code{col.row} is
##' not expected for simulation data, and subjects will be checked
##' to have \code{EVID==0} rows for estimation data and
##' \code{EVID==2} rows for simulation data.
##' @param na.strings Strings to be accepted when trying to convert
##' characters to numerics. This will typically be a string that
##' represents missing values. Default is ".". Notice, actual NA,
##' i.e. not a string, is allowed independently of na.strings. See
##' \code{?NMisNumeric}.
##' @param return.summary If TRUE (not default), the table summary
##' that is printed if \code{quiet=FALSE} is returned as well. In
##' that case, a list is returned, and the findings are in an
##' element called findings.
##' @param quiet Keep quiet? Default is not to.
##' @param as.fun The default is to return data as a
##' \code{data.frame}. Pass a function (say
##' \code{tibble::as_tibble}) in as.fun to convert to something
##' else. If \code{data.tables} are wanted, use
##' \code{as.fun="data.table"}. The default can be configured
##' using \code{NMdataConf}.
##' @details The following checks are performed. The term "numeric"
##' does not refer to a numeric representation in R, but
##' compatibility with Nonmem. The character string "2" is in this
##' sense a valid numeric, "id2" is not. \itemize{
##'
##' \item Column
##' names must be unique and not contain special characters
##'
##' \item If an exclusion flag is used (for ACCEPT/IGNORE in Nonmem),
##' elements must be non-missing and integers. Notice, if an exclusion
##' flag is found, the rest of the checks are performed on rows
##' where that flag equals 0 (zero) only.
##'
##' \item If a unique row identifier is found, it has to be
##' non-missing, increasing integers.
##'
##' \item col.time (TIME),
##' EVID, col.id (ID), col.cmt (CMT), and col.mdv (MDV): If present, elements must be non-missing
##' and numeric.
##'
##' \item col.time (TIME) must be non-negative
##'
##' \item EVID must be in \{0,1,2,3,4\}.
##'
##' \item CMT must be positive integers. However, can be missing or zero for EVID==3.
##'
##' \item MDV must be the binary (1/0) representation of is.na(DV) for
##' dosing records (EVID==0).
##'
##' \item AMT must be 0 or NA for EVID 0 and 2
##'
##' \item AMT must be positive for EVID 1 and 4
##'
##' \item DV must be numeric
##'
##' \item DV must be missing for EVID in \{1,4\}.
##'
##' \item If found, RATE must be a numeric, equaling -2 or non-negative for dosing events.
##'
##' \item If found, SS must be a numeric, equaling 0 or 1 for dosing records.
##'
##' \item If found, \code{ADDL} must be a non-negative integer for dosing
##' records. II must be present.
##'
##' \item If found, II must be a non-negative integer for dosing
##' records. \code{ADDL} must be present.
##'
##' \item ID must be positive and values cannot be disjoint (all
##' records for each ID must be following each other. This is
##' technically not a requirement in Nonmem but most often an
##' error. Use a second ID column if you deliberately want to
##' soften this check)
##'
##' \item TIME cannot be decreasing within ID, unless EVID in \{3,4\}.
##'
##' \item all ID's must have doses (EVID in \{1,4\})
##'
##' \item all ID's must have observations (EVID==0)
##'
##' \item ID's should not have leading zeros since these will be lost
##' when Nonmem read, then write the data.
##'
##' \item If a unique row identifier is used, this must be
##' non-missing, increasing, integer
##'
##' \item Character values must not contain commas (they will mess up
##' writing/reading csv)
##'
##' \item Columns specified in covs argument must be non-missing,
##' numeric and not varying within subjects.
##'
##' \item Columns specified in covs.occ must be
##' non-missing, numeric and not varying within combinations of
##' subject and occasion.
##'
##' \item Columns specified in cols.num must be present, numeric
##' and non-NA.
##'
##' \item If a unique subject identifier column (col.usubjid) is
##' provided, col.id must be unique within values of col.usubjid and
##' vice versa.
##'
##' \item Events should not be duplicated. For all rows, the
##' combination of col.id, col.cmt ,col.evid, col.time plus the
##' optional columns specified in cols.dup must be unique. In other
##' words, if a subject (col.id) that has say observations (col.evid)
##' at the same time (col.time), this is considered a duplicate. The
##' exception is if there is a reset event (col.evid is 3 or 4) in
##' between the two rows. cols.dup can be used to add columns to this
##' analysis. This is useful for different assays run on the same
##' compartment (say a DVID column) or maybe stacked datasets. If
##' col.cmt is of length>1, this search is repeated for each cmt
##' column.
##'
##' }
##' @return A table with findings
##' @examples
##' \dontrun{
##' dat <- readRDS(system.file("examples/data/xgxr2.rds", package="NMdata"))
##' NMcheckData(dat)
##' dat[EVID==0,LLOQ:=3.5]
##' ## expecting LLOQ only for samples
##' NMcheckData(dat,cols.num=list(c("STUDY"),"EVID==0"=c("LLOQ")))
##' }
##' @import data.table
##' @export
NMcheckData <- function(data,file,covs,covs.occ,cols.num,col.id="ID",
col.time="TIME",col.dv="DV",col.mdv="MDV",
col.cmt="CMT",col.amt="AMT",col.flagn,col.row,
col.usubjid,cols.dup,type.data="est",
na.strings,return.summary=FALSE, quiet=FALSE,
as.fun){
#### Section start: Dummy variables, only not to get NOTE's in pacakge checks ####
. <- NULL
ADDL <- NULL
AMT <- NULL
CMT <- NULL
DV <- NULL
EVID <- NULL
ID.jump <- NULL
ID <- NULL
II <- NULL
MDVDV <- NULL
MDV <- NULL
N <- NULL
Nrep <- NULL
RATE <- NULL
ROW <- NULL
SS <- NULL
column <- NULL
check <- NULL
checkTimeInc <- NULL
cov <- NULL
newID <- NULL
isnewID <- NULL
level <- NULL
occ <- NULL
reset <- NULL
variable <- NULL
NUID <- NULL
NidByUID <- NULL
usubjNotUnique <- NULL
### Section end: Dummy variables, only not to get NOTE's in pacakge checks
#### Section start: Checks of arguments ####
col.evid <- "EVID"
if(missing(covs)) covs <- NULL
if(missing(covs.occ)) covs.occ <- NULL
if(missing(cols.num)) cols.num <- NULL
if(missing(na.strings)) na.strings <- "."
if(!is.character(type.data)||!type.data%in% c("est","sim")){
stop("type.data myst be either \"est\" or \"sim\".")
}
col.row.was.mising <- FALSE
if(missing(col.row)) {
col.row <- NULL
col.row.was.mising <- TRUE
}
if(! (type.data=="sim" && is.null(col.row))){
col.row <- NMdataDecideOption("col.row",col.row)
}
if(missing(as.fun)) as.fun <- NULL
as.fun <- NMdataDecideOption("as.fun",as.fun)
if(missing(col.flagn)) col.flagn <- NULL
col.flagn.orig <- col.flagn
col.flagn <- NMdataDecideOption("col.flagn",col.flagn)
if(missing(file)) file <- NULL
if(!is.null(covs) && !is.character(covs)) {
messageWrap("covs must be a character vector or a string.",fun.msg=stop)
}
if(!is.null(cols.num) && !is.list(cols.num) ){
cols.num <- list("TRUE"=cols.num)
}
if(missing(cols.dup)) cols.dup <- NULL
names.cols.num <- names(cols.num)
if(length(cols.num)>0){
if(length(names.cols.num)==0) names.cols.num <- rep("",length(cols.num))
names.cols.num[gsub(" +","",names.cols.num)==""] <- "TRUE"
names(cols.num) <- names.cols.num
}
if(!is.null(cols.num)) {
lapply(cols.num, function(x){if(!is.character(x)) {
messageWrap("cols.num must be a character vector or a string.",fun.msg=stop)
}})}
if(!is.null(covs.occ)){
if(!is.null(covs.occ)) {
if( !is.list(covs.occ) || is.data.frame(covs.occ)) {
messageWrap("covs.occ must be a list and not a data.frame.",fun.msg=stop)
}
if(is.null(names(covs.occ)) || ""%in%gsub(" ","",names(covs.occ))){
messageWrap("covs.occ must be a named list.",fun.msg=stop)
}
if(!(all(sapply(covs.occ,is.character)))){
messageWrap("covs.occ must be a named list of character vectors or strings.",fun.msg=stop)
}
}
}
if(!(is.character(col.dv)&&length(col.dv)==1)){
stop("col.dv must be a character and vector of length 1.")
}
if(!(is.character(col.mdv)&&length(col.mdv)==1)){
stop("col.mdv must be a character and vector of length 1.")
}
if(is.null(file)){
if(missing(data)||is.null(data)) {
message("No data supplied")
return(invisible(NULL))
}
if(!is.data.frame(data)) stop("Data must be inheriting from data.frame (meaning that more advanced classes like data.table and tibble are OK.")
data <- copy(as.data.table(data))
## for reference as data is edited
cnames.data.0 <- colnames(data)
if(!col.id %in%cnames.data.0) {stop("col.id must point to an existing column in data. If you don't have one, you can create a dummy column with a constant value.")}
}
### file mode
if(!is.null(file)){
if(!is.null(col.flagn.orig)){warning("col.flagn is not used when file is specified.")}
col.id <- "ID"
## use.rds <- FALSE
formats.read="csv"
file.mod <- NULL
res <- NMcheckDataFile(file=file,col.time=col.time,
col.row=col.row,col.id=col.id,
na.strings=na.strings
,formats.read=formats.read
,quiet=quiet,file.mod=file.mod,
as.fun=as.fun)
return(invisible(res))
}
### Section end: Checks of arguments
### row counter
c.row <- tmpcol(data,base="row",prefer.plain=TRUE)
data[,(c.row):=.I]
### save original col.id and col.row
if(!is.null(col.row)&&col.row%in%colnames(data)){
col.row.orig <- tmpcol(data,base="row.orig",prefer.plain=TRUE)
data[,(col.row.orig):=get(col.row)]
}
if(col.id%in%colnames(data)){
col.id.orig <- tmpcol(data,base="id.orig",prefer.plain=TRUE)
data[,(col.id.orig):=get(col.id)]
}
if(any(!cols.dup%in%colnames(data))){
warning("Not all columns specified in cols.dup found in data. Skipping those not found.")
cols.dup <- intersect(cols.dup,colnames(data))
}
NMasNumeric <- function(x,warn=F) {
if(warn){
as.numeric(as.character(x))
} else {
suppressWarnings(as.numeric(as.character(x)))
}
}
NMisMissing <- function(x) is.na(x) | (is.character(x) & x %in% na.strings)
## listEvents is for row-level findings
## @param col is the actual column to be used for the condition
## @param name The name of the check. Will end up in the check column in the resulting table.
## @param fun if fun does not return TRUE, we have a finding.
## @param colname is the column name reported to user.
## @param new.rows.only. For nested criteria. Say that CMT is not
## @param col.required Include a finding if column not found?
## numeric for row 10. Then don't report that it is not an integer
## too.
listEvents <- function(col,name,fun,colname=col,dat=data,events=NULL,invert=FALSE,new.rows.only=T,quiet=FALSE,col.required=TRUE,debug=F,args.fun=NULL){
if(debug) browser()
if(!col%in%colnames(dat)){
if(col.required){
if(is.null(events)||events[check=="Column not found"&column==colname&level=="column",.N]==0){
events <- rbind(events,
data.table(check="Column not found",column=colname,level="column")
,fill=TRUE) }
}
return(events)
}
if(invert){
rows <- dat[do.call(fun,append(list(get(col)),args.fun))==TRUE,get(c.row)]
} else {
rows <- dat[do.call(fun,append(list(get(col)),args.fun))==FALSE,get(c.row)]
}
if(length(rows)==0) {
res <- data.table(check=name,column=colname,row=NA,level="row")[0]
} else {
if(new.rows.only&&!is.null(events)){
rows <- setdiff(rows,events[column==colname,row])
}
if(length(rows)>0){
res <- data.table(check=name,column=colname,row=rows,level="row")
} else {
res <- NULL
}
}
rbind(events,res,fill=TRUE)
}
reportFindings <- function(findings,data,col.id,col.row,c.row,col.row.orig,col.id.orig,quiet,as.fun,return.summary){
### Add ID's to row-level findings
findings.row <- findings[level=="row"]
if(!is.null(col.id)&&nrow(findings.row)>0){
if(col.id%in%colnames(findings.row)) findings.row[,(col.id):=NULL]
findings.row <- mergeCheck(findings.row,data[,c(c.row,col.id.orig),with=F],by.x="row",by.y=c.row,all.x=T,fun.commoncols=stop,quiet=TRUE)
setnames(findings.row,col.id.orig,col.id)
findings <- rbind(findings[level!="row"],findings.row,fill=T)
}
if(nrow(findings)==0) {
if(!quiet) message("No findings. Great!")
summary.findings <- NULL
} else {
### use the row identifier for reporting
if(!is.null(col.row)&&col.row%in%colnames(data)){
findings <- mergeCheck(findings,
data[,c(c.row,col.row.orig),with=F],
by.x="row",by.y=c.row,all.x=T,
fun.commoncols=stop,quiet=TRUE)
setnames(findings,col.row.orig,col.row)
}
if(!col.id%in%colnames(findings)) findings[,(col.id):=NA_real_]
setcolorder(findings,c(c.row,col.id,"column","check"))
setorderv(findings,c(c.row,"column","check"))
summary.findings <- findings[,.(.N,Nid=uniqueN(get(col.id)[!is.na(get(col.id))])),by=.(column,check)]
if(!quiet) print(summary.findings,row.names=FALSE)
}
findings <- as.fun(findings)
res <- findings
if(return.summary){
res <- list(findings=findings,summary=summary.findings)
}
return(invisible(res))
}
findings <- data.table(check="is NA",column="TIME",row=0,level="row")[0]
####### Checking column names
cnames <- colnames(data)
### Duplicate column names
Ntab.cnames <- data.table(col=cnames)[,.N,by=.(col)]
dt.dups <- Ntab.cnames[N>1]
if(nrow(dt.dups)){
warning("Duplicate column names found. Please fix. Other checks may be affected.")
findings <- rbind(findings,
data.table(check="Non-unique column names",column=dt.dups[,col],row=NA_integer_,level="column"))
}
### special chars in col names
are.cols.num <- sapply(data,NMisNumeric,na.strings=na.strings)
cnames.num <- colnames(data)[are.cols.num]
cnames.spchars <- cnames.num[grep("[[:punct:]]",cnames.num)]
cnames.spchars <- setdiff(cnames.spchars,c("id.orig","row.orig"))
if(length(cnames.spchars)>0){
findings <- rbind(findings,
data.table(check="Special character in column name",column=cnames.spchars,row=NA_integer_,level="column"))
}
### check for na.strings in numeric columns encoded as character.
####### End checking column names
if(nrow(data)==0){
findings <- rbind(findings,
data.table(check="No rows in data",level="data"),
fill=T)
return(reportFindings(findings=findings,data=data,col.id=col.id,col.row=col.row,c.row=c.row,col.row.orig=col.row.orig,col.id.orig=col.id.orig,as.fun=as.fun,return.summary=return.summary,quiet=quiet))
}
### check unique row identifier
if(!is.null(col.row)){
findings <- listEvents(col.row,"Missing value",
fun=is.na,invert=TRUE,events=findings,debug=FALSE)
findings <- listEvents(col.row,"Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings),
events=findings,
new.rows.only=T)
## leading zeros if character?
if(col.row%in%colnames(data)&&data[,is.character(get(col.row))]){
findings <- listEvents(col.row,"Leading zero may corrupt merging",
fun=function(x)grepl("^0.+",x),invert=TRUE,
events=findings,debug=FALSE)
}
if(col.row%in%colnames(data)){
data[,(col.row):=NMasNumeric(get(col.row))]
}
## translate to numeric for rest of checks
findings <- listEvents(col.row,"Row identifier decreasing",
fun=function(x)!is.na(c(1,diff(x))) & c(1,diff(x))>0,
events=findings,debug=FALSE)
findings <- listEvents(col.row,"Duplicated",
fun=function(x)!is.na(x)&!duplicated(x),events=findings,
new.rows.only=T,debug=FALSE)
findings <- listEvents(col.row,
"Row identifier not an integer",
fun=function(x)x%%1==0,events=findings,
new.rows.only=T,debug=F)
}
### check flags for NA's before subsetting on FLAG
if(!is.null(col.flagn)){
findings <- listEvents(col.flagn,"Missing value",
fun=is.na,invert=TRUE,events=findings,
col.required=!is.null(col.flagn.orig),debug=FALSE)
## not sure if this should be NMisNumeric(...,each=T). I guess
## something is completely wrong with the column if elements
## are not numeric. But other columns are check with each=T.
findings <- listEvents(col.flagn,"Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings),
events=findings,
new.rows.only=T,
col.required=!is.null(col.flagn.orig))
findings <- listEvents(col.flagn,"col.flagn not an integer",
fun=function(x)as.numeric(x)%%1==0,events=findings,
new.rows.only=T,
col.required=!is.null(col.flagn.orig))
if(col.flagn%in%colnames(data)){
### Done checking flagn. For rest of checks, only consider data where col.flagn==0
data[,(col.flagn):=NMasNumeric(get(col.flagn))]
if(is.numeric(data[,get(col.flagn)])){
data <- data[get(col.flagn)==0]
}
}
}
if(nrow(data)==0){
findings <- rbind(findings,
data.table(check="Data empty after applying exclusion flags",level="data"),
fill=T)
return(reportFindings(findings=findings,data=data,col.id=col.id,col.row=col.row,c.row=c.row,col.row.orig=col.row.orig,col.id.orig=col.id.orig,as.fun=as.fun,return.summary=return.summary,quiet=quiet))
}
#### Section start: commas in character columns ####
cols.char <- colnames(data)[!are.cols.num]
newfinds <- rbindlist(
lapply(cols.char,listEvents,name="Comma in character string",fun=function(x)grepl(",",x),invert=TRUE,debug=FALSE)
)
findings <- rbind(findings,
newfinds
,fill=TRUE)
### Section end: commas in character columns
## a new row counter for internal use only. It matches the data we
## are checking but not the data supplied by user.
rowint <- tmpcol(data,base="ROWINT",prefer.plain=TRUE)
data[,(rowint):=.I]
######## Default numeric columns. Will be checked for presence, NA, non-numeric (col-level)
### Others that: If column present, must be numeric, and values must be non-NA. Remember eg DV, CMT and AMT can be NA.
cols.num.all <- c( col.time,"EVID",col.id,col.mdv,
covs,names(covs.occ),as.character(unlist(covs.occ))
)
if(!is.null(col.flagn.orig)) cols.num.all <- c(cols.num.all,col.flagn)
cols.num.all <- c(list("TRUE"=cols.num.all),
cols.num)
##### I believe this is covered altogether as part of cols.num.all.
## ## cols.num is a named list. Names are subsets.
if(!is.null(cols.num.all
)){
subsets.cols.num <- names(cols.num.all)
for(n in 1:length(cols.num.all)){
##
expr.sub <- subsets.cols.num[n]
rows.sub <- data[eval(parse(text=expr.sub)),get(rowint)]
for(col in cols.num.all[[n]]){
## will this ever be needed? cols.num is only what is
## supplied as arg, and those are already checked as
## part of cols.num.all.
findings <- listEvents(col,name="Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
new.rows.only=TRUE,events=findings,dat=data[eval(parse(text=expr.sub))])
findings <- listEvents(col,name="is NA",
fun=is.na,invert=TRUE,new.rows.only=T,debug=FALSE,events=findings,dat=data[get(rowint)%in%rows.sub])
## not expecting values if outside subset
### can a col.num ever be outside subset?
findings <- listEvents(col,name="NA expected",
fun=NMisMissing,
new.rows.only=FALSE,events=findings,dat=data[!get(rowint)%in%rows.sub])
}
}
}
###### checks on cols.num.all before converting to numeric
### if col.row or ID are characters, they must not have leading zeros.
## Check ID for leading zeros before converting to numeric
if(col.id%in%colnames(data)&&data[,is.character(get(col.id))]){
findings <- listEvents(col.id,"Leading zero may corrupt merging",
fun=function(x)grepl("^0.+",x),invert=TRUE,
events=findings,debug=FALSE)
}
cols.num.all <- intersect(
do.call(c,cols.num.all)
,cnames.data.0)
##### Done checking required columns for NMisNumeric. overwrite cols.num.all with NMasNumeric of cols.num.all.
data[,(cols.num.all):=lapply(.SD,NMasNumeric),.SDcols=cols.num.all]
### col.time must be positive
findings <- listEvents(col.time,"Negative time",fun=function(x)x>=0,events=findings,debug=F)
### EVID must be in c(0,1,2,3,4)
findings <- listEvents("EVID","EVID not in 0:4",function(x) x%in%c(0:4),events=findings)
### ID must be a positive integer
findings <- listEvents(col.id,paste(col.id,"not a positive integer"),
fun=function(x)x>0&x%%1==0,events=findings,
new.rows.only=T)
### MDV should perfectly reflect is.na(DV)
if(col.mdv%in%colnames(data)){
data[,MDVDV:=!is.na(get(col.mdv))&get(col.mdv)==as.numeric(is.na(get(col.dv)))]
findings <- listEvents("MDVDV","MDV does not match DV",colname=col.mdv,fun=function(x)x==TRUE,dat=data[get(col.evid)==0],events=findings)
}
### columns that are required for all rows done
#### all other required columns (NA elements OK). Run NMisNumeric for each element, then translate using NMasNumeric
cols.req <- c(col.cmt,col.dv,col.amt)
for(col in cols.req){
findings <- listEvents(col,name="Not numeric",fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
new.rows.only=T,events=findings)
}
cols.req.found <- intersect(cols.req,colnames(data))
if(length(cols.req.found)){
data[,(cols.req.found):=lapply(.SD,NMasNumeric),.SDcols=cols.req.found]
}
for(cmt in col.cmt){
### CMT must be a positive integer. It can be missing for CMT=3
findings <- listEvents(cmt,"Missing for EVID!=3",
fun=is.na,invert=TRUE,events=findings,
dat=data[EVID%in%c(1,2,4)])
## For EVID!=3, must be a positive integer
findings <- listEvents(cmt,"CMT not a positive integer",
fun=function(x)x>0&x%%1==0,events=findings,
dat=data[EVID%in%c(1,2,4)],
new.rows.only=T)
## For EVID==3, if CMT not missing, must be 0 or a positive integer
findings <- listEvents(cmt,"CMT not a positive integer",
fun=function(x)x>=0&x%%1==0,events=findings,
dat=data[EVID%in%c(3)&!is.na(CMT)]
)
}
###### DV
### DV must be present
### DV must be numeric for EVID==0
if(col.mdv%in%colnames(data)){
findings <- listEvents(col.dv,"DV not numeric",fun=is.na,events=findings,invert=TRUE,dat=data[EVID%in%c(0)&get(col.mdv)==0])
} else {
findings <- listEvents(col.dv,"DV not numeric",fun=is.na,events=findings,invert=TRUE,dat=data[EVID%in%c(0)])
}
### DV should be NA or 0 for dosing records
findings <- listEvents(col.dv,"DV not NA or 0 in dosing recs",fun=function(x)is.na(x)|as.numeric(x)==0,events=findings,dat=data[EVID%in%c(1,4)])
#### AMT
## must be numeric
findings <- listEvents(col.amt,name="Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
events=findings,
dat=data)
## positive for EVID 1 and 4
findings <- listEvents(col.amt,"Non-positive dose amounts",
fun=function(x)x>0,events=findings,
dat=data[EVID%in%c(1,4)])
## must be 0 or NA for EVID 0 and 2
findings <- listEvents(col.amt,"Non-zero dose for obs or sim record",
fun=function(x)is.na(x)|x==0,
events=findings,
dat=data[EVID%in%c(0,2)])
### optional default columns - RATE, SS, ADDL, II
cols.opt <- cc(RATE,SS,II,ADDL)
for(col in cols.opt){
findings <- listEvents(col,name="Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
events=findings,
col.required=FALSE,
dat=data)
}
cols.opt.found <- intersect(cols.opt,colnames(data))
if(length(cols.opt.found)>0){
data[,(cols.opt.found):=lapply(.SD,NMasNumeric),.SDcols=cols.opt.found]
}
## RATE -2 or positive for EVID%in%c(1,4)
findings <- listEvents("RATE","Must be -2 or non-negative",
fun=function(x)x==-2|x>=0,events=findings,
col.required=FALSE,
dat=data[EVID%in%c(1,4)])
## RATE 0 or missing for !EVID%in%c(1,4)
findings <- listEvents("RATE","Expecting missing or zero for non-dose events",
fun=function(x)is.na(x)|x==0,events=findings,
col.required=FALSE,
dat=data[!EVID%in%c(1,4)])
## SS 0 or 1
findings <- listEvents("SS","must be 0 or 1",
col.required=FALSE,
fun=function(x)x%in%c(0,1),events=findings
)
if("ADDL"%in%colnames(data)){
## Because ADDL is found, we require II. The label will be "II
## not found" or something loke that. The label below is not
## reported. We give a message because it is not clear why II
## is needed.
if(!quiet && !"II"%in%colnames(data)){
messageWrap("Column ADDL is found but not II.")
}
findings <- listEvents("II",name="(This label will not be used)",
fun=function(x)TRUE,
events=findings,
col.required=TRUE,
dat=data[EVID%in%c(1,4)])
findings <- listEvents("ADDL","Must be 0 or missing for non-dosing events",
fun=function(x)is.na(x)|x==0,
events=findings,
new.rows.only=T,
dat=data[!EVID%in%c(1,4)])
findings <- listEvents("ADDL","Must be a non-negative integer",
fun=function(x)x>=0&x%%1==0,events=findings,
dat=data[EVID%in%c(1,4)],
new.rows.only=T)
}
if("II"%in%colnames(data)){
## II only makes sense together with II
if(!quiet && !"ADDL"%in%colnames(data)){
messageWrap("Column II is found but not ADDL.")
}
findings <- listEvents("ADDL",name="(This label will not be used)",
fun=function(x)TRUE,
events=findings,
col.required=TRUE,
dat=data[EVID%in%c(1,4)])
## must be 0 or na for non-dosing events
findings <- listEvents("II","Must be 0 or missing for non-dosing events",
fun=function(x)is.na(x)|x==0,
events=findings,
new.rows.only=T,
dat=data[!EVID%in%c(1,4)])
findings <- listEvents("II","Must be a positive integer when ADDL is positive",
fun=function(x)x>0&x%%1==0,
events=findings,
new.rows.only=T,
dat=data[EVID%in%c(1,4)&!is.na(ADDL)&ADDL>0])
findings <- listEvents("II","Must not be positive when ADDL is missing or zero",
fun=function(x)is.na(x)|x==0,
events=findings,
new.rows.only=T,
dat=data[EVID%in%c(1,4)&(is.na(ADDL)|ADDL==0)])
}
######## End Default columns
## ID-level checks
### Warning if the same ID is in non-consequtive rows
data[,ID.jump:=c(0,diff(get(rowint))),by=col.id]
findings <- listEvents("ID.jump",colname=col.id,name="ID disjoint",fun=function(x) x<=1,events=findings,debug=FALSE)
### within ID, time must be increasing. Unless EVID%in% c(3,4) or events are jumped
data[,newID:=get(col.id)]
if(col.time%in%colnames(data)){
data[,isnewID:=get(col.id)!=shift(get(col.id),n=1)]
data[1,isnewID:=TRUE]
data[,reset:=FALSE]
if("EVID"%in%colnames(data)){
data[,reset:=EVID%in%c(3,4)]
}
data[,newID:=cumsum(as.numeric(isnewID)+as.numeric(reset))]
data[,checkTimeInc:=c(TRUE,diff(get(col.time))>=0),by=.(newID)]
findings <- listEvents(col="checkTimeInc",name="Time decreasing",fun=function(x) x==TRUE,
colname=col.time,events=findings)
}
### check for duplicate events
col.cmt.found <- intersect(col.cmt,colnames(data))
nruns <- length(col.cmt.found)
if(length(col.cmt.found)==0){
col.cmt.found <- NULL
nruns <- 1
}
nfindings <- findings[,.N]
for(count.cmt in 1:nruns){
data[,Nrep:=.N,by=intersect(c("newID",col.cmt.found[count.cmt],col.evid,col.time,cols.dup),colnames(data))]
findings <- listEvents(col="Nrep",name="Duplicated event",function(x) x<2,colname=paste(c(col.id,col.cmt.found[count.cmt], col.evid, col.time, cols.dup),collapse=", "),events=findings)
}
nfindings.new <- findings[,.N]
if(count.cmt>1 && nfindings.new>nfindings){
messageWrap("Due to multiple CMT columns, duplicate events may be reported with respect to each of these columns.",fun.msg=message)
}
if(col.evid%in%colnames(data)){
### subjects without doses
all.ids <- data[,unique(get(col.id.orig))]
tab.evid.id <- data[,.N,by=c(col.id.orig,col.evid)]
ids.no.doses <- setdiff(all.ids,tab.evid.id[EVID%in%c(1,4),get(col.id.orig)])
if(length(ids.no.doses)>0){
findings <- rbind(findings ,
data.table(check="Subject has no doses",column="EVID",ID=ids.no.doses,level="ID") ,
fill=TRUE)
}
### subjects without observations
ids.no.obs <- setdiff(all.ids,tab.evid.id[EVID%in%c(0),get(col.id.orig)])
ids.no.sim <- setdiff(all.ids,tab.evid.id[EVID%in%c(2),get(col.id.orig)])
if(type.data=="est"){
if(length(ids.no.obs)>0){
findings <- rbind(findings
,
data.table(check="Subject has no obs",column="EVID",ID=ids.no.obs,level="ID")
,fill=TRUE)
}
}
if(type.data=="sim"){
if(length(ids.no.sim)>0){
findings <- rbind(findings
,
data.table(check="Subject has no sim records",column="EVID",ID=ids.no.obs,level="ID")
,fill=TRUE)
}
}
}
##### Section start: Covariates ####
### Subject-level
list.findings.covs <- lapply(covs,function(cov){
ncomb.cov <- unique(data[,c(col.id,cov),with=FALSE])[,.N,by=c(col.id)]
non.covs <- ncomb.cov[N>1,c(col.id),with=FALSE]
non.covs[,`:=`(column=cov,check="Cov not unique within ID",level="ID")]
non.covs
})
if(length(list.findings.covs)>0){
findings <- rbind(findings,rbindlist(list.findings.covs),fill=TRUE)
}
### Occasion-level
## syntax: covs.occ=list(PERIOD=c("FED"))
all.occ.covs <- rbindlist(lapply(names(covs.occ),function(occ)data.table(occ=occ,cov=covs.occ[[occ]])))
all.occ.covs[,ROW:=.I]
if(nrow(all.occ.covs)>0){
findings.occ <- all.occ.covs[,{
obs.cov <- data[,c(col.id,occ,cov),with=F]
ncomb.cov <- unique(obs.cov)[,.N,by=c(col.id,occ)]
ncomb.cov <- ncomb.cov[N>1,c(col.id),with=FALSE]
ncomb.cov[,`:=`(column=paste(occ,cov,sep=", "),level="ID-occasion",check="Cov not unique within ID-occ")]
ncomb.cov
},by=.(ROW)]
## findings.occ <- rbindlist(list.findings.occ)
findings.occ[,ROW:=NULL]
if(length(findings.occ)>0){
findings <- rbind(findings,findings.occ,fill=TRUE)
}
}
### Section end: Covariates
#### Section start: usubjid ####
if(!missing(col.usubjid)&&!is.null(col.usubjid)){
findings <- listEvents(col=col.usubjid,name="Missing value",
fun=function(x){
!is.na(x) &
!(as.character(x)%in%unique(c("",na.strings)))
},
events=findings)
## check . All ID values must be represented exactly once. If not, report an ID-level finding.
## how many usubjids for each ID?
data[,NUID:=uniqueN(col.usubjid),by=c(col.id)]
## how many IDs for each usubjid?
data[,NidByUID:=uniqueN(get(col.id)),by=c(col.usubjid)]
data[,usubjNotUnique:=max(NidByUID)>1,by=c(col.id)]
if(data[,sum(NUID>1)]){
findings <- rbind(findings ,
data.table(check="Unique subject ID not unique within ID",column=col.usubjid,ID=data[NUID>1,get(col.id)],level="ID") ,
fill=TRUE)
}
if(data[,any(usubjNotUnique)]){
findings <- rbind(findings,
data.table(check="Subject ID not unique within unique subject IDs",column=col.id,ID=data[usubjNotUnique==TRUE,get(col.id)],level="ID"),
fill=TRUE)
}
}
### Section end: usubjid
return(reportFindings(findings=findings,data=data,col.id=col.id,c.row=c.row,col.row=col.row,col.row.orig=col.row.orig,col.id.orig=col.id.orig,return.summary=return.summary,as.fun=as.fun,quiet=quiet))
}
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Defines functions NMcheckData
Documented in NMcheckData
##' Check data for Nonmem compatibility or check control stream for
##' data compatibility
##'
##' Check data in various ways for compatibility with Nonmem. Some
##' findings will be reported even if they will not make Nonmem fail
##' but because they are typical dataset issues.
##'
##' @param data The data to check. data.frame, data.table, tibble,
##' anything that can be converted to data.table.
##' @param file Alternatively to checking a data object, you can use
##' file to specify a control stream to check. This can either be
##' a (working or non-working) input control stream or an output
##' control stream. In this case, \code{NMdataCheck} checks column
##' names in data against control stream (see
##' \code{NMcheckColnames}), reads the data as Nonmem would do,
##' and do the same checks on the data as NMdataCheck would do
##' using the data argument. \code{col.flagn} is ignored in this
##' case - instead, ACCEPT/IGNORE statements in control stream are
##' applied. The file argument is useful for debugging a Nonmem
##' model.
##' @param covs columns that contain subject-level covariates. They
##' are expected to be non-missing, numeric and not varying within
##' subjects.
##' @param covs.occ A list specifying columns that contain
##' subject:occasion-level covariates. They are expected to be
##' non-missing, numeric and not varying within combinations of
##' subject and occasion. \code{covs.occ=list(PERIOD=c("FED"))}
##' means that \code{FED} is the covariate, while \code{PERIOD}
##' indicates the occasion.
##' @param cols.num Columns that are expected to be present, numeric
##' and non-NA. If a character vector is given, the columns are
##' expected to be used in all rows. If a column is only used for
##' a subset of rows, use a list and name the elements by
##' subsetting strings. See examples.
##' @param col.cmt The name(s) of the compartment column(s). These
##' will be checked to be positive integers for all rows. They are
##' also used in checks for row duplicates.
##' @param col.amt The name of the dose amount column.
##' @param col.id The name of the column that holds the subject
##' identifier. Default is "ID".
##' @param col.time The name of the column holding actual time.
##' @param col.dv The name of the column holding the dependent
##' variable. For now, only one column can be specified, and
##' \code{MDV} is assumed to match this column. Default is
##' \code{DV}.
##' @param col.mdv The name of the column holding the binary indicator
##' of the dependent variable missing. Default is \code{MDV}.
##' @param col.flagn Optionally, the name of the column holding
##' numeric exclusion flags. Default value is \code{FLAG} and can
##' be configured using \code{NMdataConf}. Even though \code{FLAG}
##' is the default value, no finding will be returned if the
##' column is missing unless explicitly defined as
##' \code{col.flagn="FLAG"}. This is because this way of using
##' exclusion flags is only one of many ways you could choose to
##' handle exclusions. Disable completely by using
##' \code{col.flagn=FALSE}.
##' @param col.row A column with a unique value for each row. Such a
##' column is recommended to use if possible. Default
##' (\code{"ROW"}) can be modified using \code{NMdataConf}.
##' @param col.usubjid Optional unique subject identifier. It is
##' recommended to keep a unique subject identifier (typically a
##' character string including an abbreviated study name and the
##' subject id) from the clinical datasets in the analysis set. If
##' you supply the name of the column holding this identifier,
##' NMcheckData will check that it is non-missing, that it is
##' unique within values of col.id (i.e. that the analysis subject
##' ID's are unique across actual subjects), and that col.id is
##' unique within the unique subject ID (a violation of the latter
##' is less likely).
##' @param cols.dup Additional column names to consider in search of
##' duplicate events. col.id, col.cmt, col.evid, and col.time are
##' always considered if found in data, and cols.dup is added to
##' this list if provided.
##' @param type.data "est" for estimation data (default), and "sim"
##' for simulation data. Differences are that \code{col.row} is
##' not expected for simulation data, and subjects will be checked
##' to have \code{EVID==0} rows for estimation data and
##' \code{EVID==2} rows for simulation data.
##' @param na.strings Strings to be accepted when trying to convert
##' characters to numerics. This will typically be a string that
##' represents missing values. Default is ".". Notice, actual NA,
##' i.e. not a string, is allowed independently of na.strings. See
##' \code{?NMisNumeric}.
##' @param return.summary If TRUE (not default), the table summary
##' that is printed if \code{quiet=FALSE} is returned as well. In
##' that case, a list is returned, and the findings are in an
##' element called findings.
##' @param quiet Keep quiet? Default is not to.
##' @param as.fun The default is to return data as a
##' \code{data.frame}. Pass a function (say
##' \code{tibble::as_tibble}) in as.fun to convert to something
##' else. If \code{data.tables} are wanted, use
##' \code{as.fun="data.table"}. The default can be configured
##' using \code{NMdataConf}.
##' @details The following checks are performed. The term "numeric"
##' does not refer to a numeric representation in R, but
##' compatibility with Nonmem. The character string "2" is in this
##' sense a valid numeric, "id2" is not. \itemize{
##'
##' \item Column
##' names must be unique and not contain special characters
##'
##' \item If an exclusion flag is used (for ACCEPT/IGNORE in Nonmem),
##' elements must be non-missing and integers. Notice, if an exclusion
##' flag is found, the rest of the checks are performed on rows
##' where that flag equals 0 (zero) only.
##'
##' \item If a unique row identifier is found, it has to be
##' non-missing, increasing integers.
##'
##' \item col.time (TIME),
##' EVID, col.id (ID), col.cmt (CMT), and col.mdv (MDV): If present, elements must be non-missing
##' and numeric.
##'
##' \item col.time (TIME) must be non-negative
##'
##' \item EVID must be in \{0,1,2,3,4\}.
##'
##' \item CMT must be positive integers. However, can be missing or zero for EVID==3.
##'
##' \item MDV must be the binary (1/0) representation of is.na(DV) for
##' dosing records (EVID==0).
##'
##' \item AMT must be 0 or NA for EVID 0 and 2
##'
##' \item AMT must be positive for EVID 1 and 4
##'
##' \item DV must be numeric
##'
##' \item DV must be missing for EVID in \{1,4\}.
##'
##' \item If found, RATE must be a numeric, equaling -2 or non-negative for dosing events.
##'
##' \item If found, SS must be a numeric, equaling 0 or 1 for dosing records.
##'
##' \item If found, \code{ADDL} must be a non-negative integer for dosing
##' records. II must be present.
##'
##' \item If found, II must be a non-negative integer for dosing
##' records. \code{ADDL} must be present.
##'
##' \item ID must be positive and values cannot be disjoint (all
##' records for each ID must be following each other. This is
##' technically not a requirement in Nonmem but most often an
##' error. Use a second ID column if you deliberately want to
##' soften this check)
##'
##' \item TIME cannot be decreasing within ID, unless EVID in \{3,4\}.
##'
##' \item all ID's must have doses (EVID in \{1,4\})
##'
##' \item all ID's must have observations (EVID==0)
##'
##' \item ID's should not have leading zeros since these will be lost
##' when Nonmem read, then write the data.
##'
##' \item If a unique row identifier is used, this must be
##' non-missing, increasing, integer
##'
##' \item Character values must not contain commas (they will mess up
##' writing/reading csv)
##'
##' \item Columns specified in covs argument must be non-missing,
##' numeric and not varying within subjects.
##'
##' \item Columns specified in covs.occ must be
##' non-missing, numeric and not varying within combinations of
##' subject and occasion.
##'
##' \item Columns specified in cols.num must be present, numeric
##' and non-NA.
##'
##' \item If a unique subject identifier column (col.usubjid) is
##' provided, col.id must be unique within values of col.usubjid and
##' vice versa.
##'
##' \item Events should not be duplicated. For all rows, the
##' combination of col.id, col.cmt ,col.evid, col.time plus the
##' optional columns specified in cols.dup must be unique. In other
##' words, if a subject (col.id) that has say observations (col.evid)
##' at the same time (col.time), this is considered a duplicate. The
##' exception is if there is a reset event (col.evid is 3 or 4) in
##' between the two rows. cols.dup can be used to add columns to this
##' analysis. This is useful for different assays run on the same
##' compartment (say a DVID column) or maybe stacked datasets. If
##' col.cmt is of length>1, this search is repeated for each cmt
##' column.
##'
##' }
##' @return A table with findings
##' @examples
##' \dontrun{
##' dat <- readRDS(system.file("examples/data/xgxr2.rds", package="NMdata"))
##' NMcheckData(dat)
##' dat[EVID==0,LLOQ:=3.5]
##' ## expecting LLOQ only for samples
##' NMcheckData(dat,cols.num=list(c("STUDY"),"EVID==0"=c("LLOQ")))
##' }
##' @import data.table
##' @export
NMcheckData <- function(data,file,covs,covs.occ,cols.num,col.id="ID",
col.time="TIME",col.dv="DV",col.mdv="MDV",
col.cmt="CMT",col.amt="AMT",col.flagn,col.row,
col.usubjid,cols.dup,type.data="est",
na.strings,return.summary=FALSE, quiet=FALSE,
as.fun){
#### Section start: Dummy variables, only not to get NOTE's in pacakge checks ####
. <- NULL
ADDL <- NULL
AMT <- NULL
CMT <- NULL
DV <- NULL
EVID <- NULL
ID.jump <- NULL
ID <- NULL
II <- NULL
MDVDV <- NULL
MDV <- NULL
N <- NULL
Nrep <- NULL
RATE <- NULL
ROW <- NULL
SS <- NULL
column <- NULL
check <- NULL
checkTimeInc <- NULL
cov <- NULL
newID <- NULL
isnewID <- NULL
level <- NULL
occ <- NULL
reset <- NULL
variable <- NULL
NUID <- NULL
NidByUID <- NULL
usubjNotUnique <- NULL
### Section end: Dummy variables, only not to get NOTE's in pacakge checks
#### Section start: Checks of arguments ####
col.evid <- "EVID"
if(missing(covs)) covs <- NULL
if(missing(covs.occ)) covs.occ <- NULL
if(missing(cols.num)) cols.num <- NULL
if(missing(na.strings)) na.strings <- "."
if(!is.character(type.data)||!type.data%in% c("est","sim")){
stop("type.data myst be either \"est\" or \"sim\".")
}
col.row.was.mising <- FALSE
if(missing(col.row)) {
col.row <- NULL
col.row.was.mising <- TRUE
}
if(! (type.data=="sim" && is.null(col.row))){
col.row <- NMdataDecideOption("col.row",col.row)
}
if(missing(as.fun)) as.fun <- NULL
as.fun <- NMdataDecideOption("as.fun",as.fun)
if(missing(col.flagn)) col.flagn <- NULL
col.flagn.orig <- col.flagn
col.flagn <- NMdataDecideOption("col.flagn",col.flagn)
if(missing(file)) file <- NULL
if(!is.null(covs) && !is.character(covs)) {
messageWrap("covs must be a character vector or a string.",fun.msg=stop)
}
if(!is.null(cols.num) && !is.list(cols.num) ){
cols.num <- list("TRUE"=cols.num)
}
if(missing(cols.dup)) cols.dup <- NULL
names.cols.num <- names(cols.num)
if(length(cols.num)>0){
if(length(names.cols.num)==0) names.cols.num <- rep("",length(cols.num))
names.cols.num[gsub(" +","",names.cols.num)==""] <- "TRUE"
names(cols.num) <- names.cols.num
}
if(!is.null(cols.num)) {
lapply(cols.num, function(x){if(!is.character(x)) {
messageWrap("cols.num must be a character vector or a string.",fun.msg=stop)
}})}
if(!is.null(covs.occ)){
if(!is.null(covs.occ)) {
if( !is.list(covs.occ) || is.data.frame(covs.occ)) {
messageWrap("covs.occ must be a list and not a data.frame.",fun.msg=stop)
}
if(is.null(names(covs.occ)) || ""%in%gsub(" ","",names(covs.occ))){
messageWrap("covs.occ must be a named list.",fun.msg=stop)
}
if(!(all(sapply(covs.occ,is.character)))){
messageWrap("covs.occ must be a named list of character vectors or strings.",fun.msg=stop)
}
}
}
if(!(is.character(col.dv)&&length(col.dv)==1)){
stop("col.dv must be a character and vector of length 1.")
}
if(!(is.character(col.mdv)&&length(col.mdv)==1)){
stop("col.mdv must be a character and vector of length 1.")
}
if(is.null(file)){
if(missing(data)||is.null(data)) {
message("No data supplied")
return(invisible(NULL))
}
if(!is.data.frame(data)) stop("Data must be inheriting from data.frame (meaning that more advanced classes like data.table and tibble are OK.")
data <- copy(as.data.table(data))
## for reference as data is edited
cnames.data.0 <- colnames(data)
if(!col.id %in%cnames.data.0) {stop("col.id must point to an existing column in data. If you don't have one, you can create a dummy column with a constant value.")}
}
### file mode
if(!is.null(file)){
if(!is.null(col.flagn.orig)){warning("col.flagn is not used when file is specified.")}
col.id <- "ID"
## use.rds <- FALSE
formats.read="csv"
file.mod <- NULL
res <- NMcheckDataFile(file=file,col.time=col.time,
col.row=col.row,col.id=col.id,
na.strings=na.strings
,formats.read=formats.read
,quiet=quiet,file.mod=file.mod,
as.fun=as.fun)
return(invisible(res))
}
### Section end: Checks of arguments
### row counter
c.row <- tmpcol(data,base="row",prefer.plain=TRUE)
data[,(c.row):=.I]
### save original col.id and col.row
if(!is.null(col.row)&&col.row%in%colnames(data)){
col.row.orig <- tmpcol(data,base="row.orig",prefer.plain=TRUE)
data[,(col.row.orig):=get(col.row)]
}
if(col.id%in%colnames(data)){
col.id.orig <- tmpcol(data,base="id.orig",prefer.plain=TRUE)
data[,(col.id.orig):=get(col.id)]
}
if(any(!cols.dup%in%colnames(data))){
warning("Not all columns specified in cols.dup found in data. Skipping those not found.")
cols.dup <- intersect(cols.dup,colnames(data))
}
NMasNumeric <- function(x,warn=F) {
if(warn){
as.numeric(as.character(x))
} else {
suppressWarnings(as.numeric(as.character(x)))
}
}
NMisMissing <- function(x) is.na(x) | (is.character(x) & x %in% na.strings)
## listEvents is for row-level findings
## @param col is the actual column to be used for the condition
## @param name The name of the check. Will end up in the check column in the resulting table.
## @param fun if fun does not return TRUE, we have a finding.
## @param colname is the column name reported to user.
## @param new.rows.only. For nested criteria. Say that CMT is not
## @param col.required Include a finding if column not found?
## numeric for row 10. Then don't report that it is not an integer
## too.
listEvents <- function(col,name,fun,colname=col,dat=data,events=NULL,invert=FALSE,new.rows.only=T,quiet=FALSE,col.required=TRUE,debug=F,args.fun=NULL){
if(debug) browser()
if(!col%in%colnames(dat)){
if(col.required){
if(is.null(events)||events[check=="Column not found"&column==colname&level=="column",.N]==0){
events <- rbind(events,
data.table(check="Column not found",column=colname,level="column")
,fill=TRUE) }
}
return(events)
}
if(invert){
rows <- dat[do.call(fun,append(list(get(col)),args.fun))==TRUE,get(c.row)]
} else {
rows <- dat[do.call(fun,append(list(get(col)),args.fun))==FALSE,get(c.row)]
}
if(length(rows)==0) {
res <- data.table(check=name,column=colname,row=NA,level="row")[0]
} else {
if(new.rows.only&&!is.null(events)){
rows <- setdiff(rows,events[column==colname,row])
}
if(length(rows)>0){
res <- data.table(check=name,column=colname,row=rows,level="row")
} else {
res <- NULL
}
}
rbind(events,res,fill=TRUE)
}
reportFindings <- function(findings,data,col.id,col.row,c.row,col.row.orig,col.id.orig,quiet,as.fun,return.summary){
### Add ID's to row-level findings
findings.row <- findings[level=="row"]
if(!is.null(col.id)&&nrow(findings.row)>0){
if(col.id%in%colnames(findings.row)) findings.row[,(col.id):=NULL]
findings.row <- mergeCheck(findings.row,data[,c(c.row,col.id.orig),with=F],by.x="row",by.y=c.row,all.x=T,fun.commoncols=stop,quiet=TRUE)
setnames(findings.row,col.id.orig,col.id)
findings <- rbind(findings[level!="row"],findings.row,fill=T)
}
if(nrow(findings)==0) {
if(!quiet) message("No findings. Great!")
summary.findings <- NULL
} else {
### use the row identifier for reporting
if(!is.null(col.row)&&col.row%in%colnames(data)){
findings <- mergeCheck(findings,
data[,c(c.row,col.row.orig),with=F],
by.x="row",by.y=c.row,all.x=T,
fun.commoncols=stop,quiet=TRUE)
setnames(findings,col.row.orig,col.row)
}
if(!col.id%in%colnames(findings)) findings[,(col.id):=NA_real_]
setcolorder(findings,c(c.row,col.id,"column","check"))
setorderv(findings,c(c.row,"column","check"))
summary.findings <- findings[,.(.N,Nid=uniqueN(get(col.id)[!is.na(get(col.id))])),by=.(column,check)]
if(!quiet) print(summary.findings,row.names=FALSE)
}
findings <- as.fun(findings)
res <- findings
if(return.summary){
res <- list(findings=findings,summary=summary.findings)
}
return(invisible(res))
}
findings <- data.table(check="is NA",column="TIME",row=0,level="row")[0]
####### Checking column names
cnames <- colnames(data)
### Duplicate column names
Ntab.cnames <- data.table(col=cnames)[,.N,by=.(col)]
dt.dups <- Ntab.cnames[N>1]
if(nrow(dt.dups)){
warning("Duplicate column names found. Please fix. Other checks may be affected.")
findings <- rbind(findings,
data.table(check="Non-unique column names",column=dt.dups[,col],row=NA_integer_,level="column"))
}
### special chars in col names
are.cols.num <- sapply(data,NMisNumeric,na.strings=na.strings)
cnames.num <- colnames(data)[are.cols.num]
cnames.spchars <- cnames.num[grep("[[:punct:]]",cnames.num)]
cnames.spchars <- setdiff(cnames.spchars,c("id.orig","row.orig"))
if(length(cnames.spchars)>0){
findings <- rbind(findings,
data.table(check="Special character in column name",column=cnames.spchars,row=NA_integer_,level="column"))
}
### check for na.strings in numeric columns encoded as character.
####### End checking column names
if(nrow(data)==0){
findings <- rbind(findings,
data.table(check="No rows in data",level="data"),
fill=T)
return(reportFindings(findings=findings,data=data,col.id=col.id,col.row=col.row,c.row=c.row,col.row.orig=col.row.orig,col.id.orig=col.id.orig,as.fun=as.fun,return.summary=return.summary,quiet=quiet))
}
### check unique row identifier
if(!is.null(col.row)){
findings <- listEvents(col.row,"Missing value",
fun=is.na,invert=TRUE,events=findings,debug=FALSE)
findings <- listEvents(col.row,"Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings),
events=findings,
new.rows.only=T)
## leading zeros if character?
if(col.row%in%colnames(data)&&data[,is.character(get(col.row))]){
findings <- listEvents(col.row,"Leading zero may corrupt merging",
fun=function(x)grepl("^0.+",x),invert=TRUE,
events=findings,debug=FALSE)
}
if(col.row%in%colnames(data)){
data[,(col.row):=NMasNumeric(get(col.row))]
}
## translate to numeric for rest of checks
findings <- listEvents(col.row,"Row identifier decreasing",
fun=function(x)!is.na(c(1,diff(x))) & c(1,diff(x))>0,
events=findings,debug=FALSE)
findings <- listEvents(col.row,"Duplicated",
fun=function(x)!is.na(x)&!duplicated(x),events=findings,
new.rows.only=T,debug=FALSE)
findings <- listEvents(col.row,
"Row identifier not an integer",
fun=function(x)x%%1==0,events=findings,
new.rows.only=T,debug=F)
}
### check flags for NA's before subsetting on FLAG
if(!is.null(col.flagn)){
findings <- listEvents(col.flagn,"Missing value",
fun=is.na,invert=TRUE,events=findings,
col.required=!is.null(col.flagn.orig),debug=FALSE)
## not sure if this should be NMisNumeric(...,each=T). I guess
## something is completely wrong with the column if elements
## are not numeric. But other columns are check with each=T.
findings <- listEvents(col.flagn,"Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings),
events=findings,
new.rows.only=T,
col.required=!is.null(col.flagn.orig))
findings <- listEvents(col.flagn,"col.flagn not an integer",
fun=function(x)as.numeric(x)%%1==0,events=findings,
new.rows.only=T,
col.required=!is.null(col.flagn.orig))
if(col.flagn%in%colnames(data)){
### Done checking flagn. For rest of checks, only consider data where col.flagn==0
data[,(col.flagn):=NMasNumeric(get(col.flagn))]
if(is.numeric(data[,get(col.flagn)])){
data <- data[get(col.flagn)==0]
}
}
}
if(nrow(data)==0){
findings <- rbind(findings,
data.table(check="Data empty after applying exclusion flags",level="data"),
fill=T)
return(reportFindings(findings=findings,data=data,col.id=col.id,col.row=col.row,c.row=c.row,col.row.orig=col.row.orig,col.id.orig=col.id.orig,as.fun=as.fun,return.summary=return.summary,quiet=quiet))
}
#### Section start: commas in character columns ####
cols.char <- colnames(data)[!are.cols.num]
newfinds <- rbindlist(
lapply(cols.char,listEvents,name="Comma in character string",fun=function(x)grepl(",",x),invert=TRUE,debug=FALSE)
)
findings <- rbind(findings,
newfinds
,fill=TRUE)
### Section end: commas in character columns
## a new row counter for internal use only. It matches the data we
## are checking but not the data supplied by user.
rowint <- tmpcol(data,base="ROWINT",prefer.plain=TRUE)
data[,(rowint):=.I]
######## Default numeric columns. Will be checked for presence, NA, non-numeric (col-level)
### Others that: If column present, must be numeric, and values must be non-NA. Remember eg DV, CMT and AMT can be NA.
cols.num.all <- c( col.time,"EVID",col.id,col.mdv,
covs,names(covs.occ),as.character(unlist(covs.occ))
)
if(!is.null(col.flagn.orig)) cols.num.all <- c(cols.num.all,col.flagn)
cols.num.all <- c(list("TRUE"=cols.num.all),
cols.num)
##### I believe this is covered altogether as part of cols.num.all.
## ## cols.num is a named list. Names are subsets.
if(!is.null(cols.num.all
)){
subsets.cols.num <- names(cols.num.all)
for(n in 1:length(cols.num.all)){
##
expr.sub <- subsets.cols.num[n]
rows.sub <- data[eval(parse(text=expr.sub)),get(rowint)]
for(col in cols.num.all[[n]]){
## will this ever be needed? cols.num is only what is
## supplied as arg, and those are already checked as
## part of cols.num.all.
findings <- listEvents(col,name="Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
new.rows.only=TRUE,events=findings,dat=data[eval(parse(text=expr.sub))])
findings <- listEvents(col,name="is NA",
fun=is.na,invert=TRUE,new.rows.only=T,debug=FALSE,events=findings,dat=data[get(rowint)%in%rows.sub])
## not expecting values if outside subset
### can a col.num ever be outside subset?
findings <- listEvents(col,name="NA expected",
fun=NMisMissing,
new.rows.only=FALSE,events=findings,dat=data[!get(rowint)%in%rows.sub])
}
}
}
###### checks on cols.num.all before converting to numeric
### if col.row or ID are characters, they must not have leading zeros.
## Check ID for leading zeros before converting to numeric
if(col.id%in%colnames(data)&&data[,is.character(get(col.id))]){
findings <- listEvents(col.id,"Leading zero may corrupt merging",
fun=function(x)grepl("^0.+",x),invert=TRUE,
events=findings,debug=FALSE)
}
cols.num.all <- intersect(
do.call(c,cols.num.all)
,cnames.data.0)
##### Done checking required columns for NMisNumeric. overwrite cols.num.all with NMasNumeric of cols.num.all.
data[,(cols.num.all):=lapply(.SD,NMasNumeric),.SDcols=cols.num.all]
### col.time must be positive
findings <- listEvents(col.time,"Negative time",fun=function(x)x>=0,events=findings,debug=F)
### EVID must be in c(0,1,2,3,4)
findings <- listEvents("EVID","EVID not in 0:4",function(x) x%in%c(0:4),events=findings)
### ID must be a positive integer
findings <- listEvents(col.id,paste(col.id,"not a positive integer"),
fun=function(x)x>0&x%%1==0,events=findings,
new.rows.only=T)
### MDV should perfectly reflect is.na(DV)
if(col.mdv%in%colnames(data)){
data[,MDVDV:=!is.na(get(col.mdv))&get(col.mdv)==as.numeric(is.na(get(col.dv)))]
findings <- listEvents("MDVDV","MDV does not match DV",colname=col.mdv,fun=function(x)x==TRUE,dat=data[get(col.evid)==0],events=findings)
}
### columns that are required for all rows done
#### all other required columns (NA elements OK). Run NMisNumeric for each element, then translate using NMasNumeric
cols.req <- c(col.cmt,col.dv,col.amt)
for(col in cols.req){
findings <- listEvents(col,name="Not numeric",fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
new.rows.only=T,events=findings)
}
cols.req.found <- intersect(cols.req,colnames(data))
if(length(cols.req.found)){
data[,(cols.req.found):=lapply(.SD,NMasNumeric),.SDcols=cols.req.found]
}
for(cmt in col.cmt){
### CMT must be a positive integer. It can be missing for CMT=3
findings <- listEvents(cmt,"Missing for EVID!=3",
fun=is.na,invert=TRUE,events=findings,
dat=data[EVID%in%c(1,2,4)])
## For EVID!=3, must be a positive integer
findings <- listEvents(cmt,"CMT not a positive integer",
fun=function(x)x>0&x%%1==0,events=findings,
dat=data[EVID%in%c(1,2,4)],
new.rows.only=T)
## For EVID==3, if CMT not missing, must be 0 or a positive integer
findings <- listEvents(cmt,"CMT not a positive integer",
fun=function(x)x>=0&x%%1==0,events=findings,
dat=data[EVID%in%c(3)&!is.na(CMT)]
)
}
###### DV
### DV must be present
### DV must be numeric for EVID==0
if(col.mdv%in%colnames(data)){
findings <- listEvents(col.dv,"DV not numeric",fun=is.na,events=findings,invert=TRUE,dat=data[EVID%in%c(0)&get(col.mdv)==0])
} else {
findings <- listEvents(col.dv,"DV not numeric",fun=is.na,events=findings,invert=TRUE,dat=data[EVID%in%c(0)])
}
### DV should be NA or 0 for dosing records
findings <- listEvents(col.dv,"DV not NA or 0 in dosing recs",fun=function(x)is.na(x)|as.numeric(x)==0,events=findings,dat=data[EVID%in%c(1,4)])
#### AMT
## must be numeric
findings <- listEvents(col.amt,name="Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
events=findings,
dat=data)
## positive for EVID 1 and 4
findings <- listEvents(col.amt,"Non-positive dose amounts",
fun=function(x)x>0,events=findings,
dat=data[EVID%in%c(1,4)])
## must be 0 or NA for EVID 0 and 2
findings <- listEvents(col.amt,"Non-zero dose for obs or sim record",
fun=function(x)is.na(x)|x==0,
events=findings,
dat=data[EVID%in%c(0,2)])
### optional default columns - RATE, SS, ADDL, II
cols.opt <- cc(RATE,SS,II,ADDL)
for(col in cols.opt){
findings <- listEvents(col,name="Not numeric",
fun=function(x)NMisNumeric(x,na.strings=na.strings,each=TRUE),
events=findings,
col.required=FALSE,
dat=data)
}
cols.opt.found <- intersect(cols.opt,colnames(data))
if(length(cols.opt.found)>0){
data[,(cols.opt.found):=lapply(.SD,NMasNumeric),.SDcols=cols.opt.found]
}
## RATE -2 or positive for EVID%in%c(1,4)
findings <- listEvents("RATE","Must be -2 or non-negative",
fun=function(x)x==-2|x>=0,events=findings,
col.required=FALSE,
dat=data[EVID%in%c(1,4)])
## RATE 0 or missing for !EVID%in%c(1,4)
findings <- listEvents("RATE","Expecting missing or zero for non-dose events",
fun=function(x)is.na(x)|x==0,events=findings,
col.required=FALSE,
dat=data[!EVID%in%c(1,4)])
## SS 0 or 1
findings <- listEvents("SS","must be 0 or 1",
col.required=FALSE,
fun=function(x)x%in%c(0,1),events=findings
)
if("ADDL"%in%colnames(data)){
## Because ADDL is found, we require II. The label will be "II
## not found" or something loke that. The label below is not
## reported. We give a message because it is not clear why II
## is needed.
if(!quiet && !"II"%in%colnames(data)){
messageWrap("Column ADDL is found but not II.")
}
findings <- listEvents("II",name="(This label will not be used)",
fun=function(x)TRUE,
events=findings,
col.required=TRUE,
dat=data[EVID%in%c(1,4)])
findings <- listEvents("ADDL","Must be 0 or missing for non-dosing events",
fun=function(x)is.na(x)|x==0,
events=findings,
new.rows.only=T,
dat=data[!EVID%in%c(1,4)])
findings <- listEvents("ADDL","Must be a non-negative integer",
fun=function(x)x>=0&x%%1==0,events=findings,
dat=data[EVID%in%c(1,4)],
new.rows.only=T)
}
if("II"%in%colnames(data)){
## II only makes sense together with II
if(!quiet && !"ADDL"%in%colnames(data)){
messageWrap("Column II is found but not ADDL.")
}
findings <- listEvents("ADDL",name="(This label will not be used)",
fun=function(x)TRUE,
events=findings,
col.required=TRUE,
dat=data[EVID%in%c(1,4)])
## must be 0 or na for non-dosing events
findings <- listEvents("II","Must be 0 or missing for non-dosing events",
fun=function(x)is.na(x)|x==0,
events=findings,
new.rows.only=T,
dat=data[!EVID%in%c(1,4)])
findings <- listEvents("II","Must be a positive integer when ADDL is positive",
fun=function(x)x>0&x%%1==0,
events=findings,
new.rows.only=T,
dat=data[EVID%in%c(1,4)&!is.na(ADDL)&ADDL>0])
findings <- listEvents("II","Must not be positive when ADDL is missing or zero",
fun=function(x)is.na(x)|x==0,
events=findings,
new.rows.only=T,
dat=data[EVID%in%c(1,4)&(is.na(ADDL)|ADDL==0)])
}
######## End Default columns
## ID-level checks
### Warning if the same ID is in non-consequtive rows
data[,ID.jump:=c(0,diff(get(rowint))),by=col.id]
findings <- listEvents("ID.jump",colname=col.id,name="ID disjoint",fun=function(x) x<=1,events=findings,debug=FALSE)
### within ID, time must be increasing. Unless EVID%in% c(3,4) or events are jumped
data[,newID:=get(col.id)]
if(col.time%in%colnames(data)){
data[,isnewID:=get(col.id)!=shift(get(col.id),n=1)]
data[1,isnewID:=TRUE]
data[,reset:=FALSE]
if("EVID"%in%colnames(data)){
data[,reset:=EVID%in%c(3,4)]
}
data[,newID:=cumsum(as.numeric(isnewID)+as.numeric(reset))]
data[,checkTimeInc:=c(TRUE,diff(get(col.time))>=0),by=.(newID)]
findings <- listEvents(col="checkTimeInc",name="Time decreasing",fun=function(x) x==TRUE,
colname=col.time,events=findings)
}
### check for duplicate events
col.cmt.found <- intersect(col.cmt,colnames(data))
nruns <- length(col.cmt.found)
if(length(col.cmt.found)==0){
col.cmt.found <- NULL
nruns <- 1
}
nfindings <- findings[,.N]
for(count.cmt in 1:nruns){
data[,Nrep:=.N,by=intersect(c("newID",col.cmt.found[count.cmt],col.evid,col.time,cols.dup),colnames(data))]
findings <- listEvents(col="Nrep",name="Duplicated event",function(x) x<2,colname=paste(c(col.id,col.cmt.found[count.cmt], col.evid, col.time, cols.dup),collapse=", "),events=findings)
}
nfindings.new <- findings[,.N]
if(count.cmt>1 && nfindings.new>nfindings){
messageWrap("Due to multiple CMT columns, duplicate events may be reported with respect to each of these columns.",fun.msg=message)
}
if(col.evid%in%colnames(data)){
### subjects without doses
all.ids <- data[,unique(get(col.id.orig))]
tab.evid.id <- data[,.N,by=c(col.id.orig,col.evid)]
ids.no.doses <- setdiff(all.ids,tab.evid.id[EVID%in%c(1,4),get(col.id.orig)])
if(length(ids.no.doses)>0){
findings <- rbind(findings ,
data.table(check="Subject has no doses",column="EVID",ID=ids.no.doses,level="ID") ,
fill=TRUE)
}
### subjects without observations
ids.no.obs <- setdiff(all.ids,tab.evid.id[EVID%in%c(0),get(col.id.orig)])
ids.no.sim <- setdiff(all.ids,tab.evid.id[EVID%in%c(2),get(col.id.orig)])
if(type.data=="est"){
if(length(ids.no.obs)>0){
findings <- rbind(findings
,
data.table(check="Subject has no obs",column="EVID",ID=ids.no.obs,level="ID")
,fill=TRUE)
}
}
if(type.data=="sim"){
if(length(ids.no.sim)>0){
findings <- rbind(findings
,
data.table(check="Subject has no sim records",column="EVID",ID=ids.no.obs,level="ID")
,fill=TRUE)
}
}
}
##### Section start: Covariates ####
### Subject-level
list.findings.covs <- lapply(covs,function(cov){
ncomb.cov <- unique(data[,c(col.id,cov),with=FALSE])[,.N,by=c(col.id)]
non.covs <- ncomb.cov[N>1,c(col.id),with=FALSE]
non.covs[,`:=`(column=cov,check="Cov not unique within ID",level="ID")]
non.covs
})
if(length(list.findings.covs)>0){
findings <- rbind(findings,rbindlist(list.findings.covs),fill=TRUE)
}
### Occasion-level
## syntax: covs.occ=list(PERIOD=c("FED"))
all.occ.covs <- rbindlist(lapply(names(covs.occ),function(occ)data.table(occ=occ,cov=covs.occ[[occ]])))
all.occ.covs[,ROW:=.I]
if(nrow(all.occ.covs)>0){
findings.occ <- all.occ.covs[,{
obs.cov <- data[,c(col.id,occ,cov),with=F]
ncomb.cov <- unique(obs.cov)[,.N,by=c(col.id,occ)]
ncomb.cov <- ncomb.cov[N>1,c(col.id),with=FALSE]
ncomb.cov[,`:=`(column=paste(occ,cov,sep=", "),level="ID-occasion",check="Cov not unique within ID-occ")]
ncomb.cov
},by=.(ROW)]
## findings.occ <- rbindlist(list.findings.occ)
findings.occ[,ROW:=NULL]
if(length(findings.occ)>0){
findings <- rbind(findings,findings.occ,fill=TRUE)
}
}
### Section end: Covariates
#### Section start: usubjid ####
if(!missing(col.usubjid)&&!is.null(col.usubjid)){
findings <- listEvents(col=col.usubjid,name="Missing value",
fun=function(x){
!is.na(x) &
!(as.character(x)%in%unique(c("",na.strings)))
},
events=findings)
## check . All ID values must be represented exactly once. If not, report an ID-level finding.
## how many usubjids for each ID?
data[,NUID:=uniqueN(col.usubjid),by=c(col.id)]
## how many IDs for each usubjid?
data[,NidByUID:=uniqueN(get(col.id)),by=c(col.usubjid)]
data[,usubjNotUnique:=max(NidByUID)>1,by=c(col.id)]
if(data[,sum(NUID>1)]){
findings <- rbind(findings ,
data.table(check="Unique subject ID not unique within ID",column=col.usubjid,ID=data[NUID>1,get(col.id)],level="ID") ,
fill=TRUE)
}
if(data[,any(usubjNotUnique)]){
findings <- rbind(findings,
data.table(check="Subject ID not unique within unique subject IDs",column=col.id,ID=data[usubjNotUnique==TRUE,get(col.id)],level="ID"),
fill=TRUE)
}
}
### Section end: usubjid
return(reportFindings(findings=findings,data=data,col.id=col.id,c.row=c.row,col.row=col.row,col.row.orig=col.row.orig,col.id.orig=col.id.orig,return.summary=return.summary,as.fun=as.fun,quiet=quiet))
}
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