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ds.dataFrame.R
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ds.dataFrame.R
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#' @title Generates a data frame object in the server-side
#' @description Creates a data frame from its elemental components:
#' pre-existing data frames, single variables or matrices.
#' @details It creates a data frame by combining
#' pre-existing data frames, matrices or variables.
#'
#' The length of all component variables and the number of rows
#' of the data frames or matrices must be the same. The output
#' data frame will have the same number of rows.
#'
#' Server functions called: \code{classDS}, \code{colnamesDS}, \code{dataFrameDS}
#'
#' @param x a character string that provides the name of the objects
#' to be combined.
#' @param row.names NULL, integer or character string that provides the
#' row names of the output data frame.
#' @param check.rows logical. If TRUE then the rows are checked for consistency of
#' length and names. Default is FALSE.
#' @param check.names logical. If TRUE the column names
#' in the data frame are checked to ensure that is unique. Default is TRUE.
#' @param stringsAsFactors logical. If true the character vectors are
#' converted to factors. Default TRUE.
#' @param completeCases logical. If TRUE rows with one or more
#' missing values will be deleted from the output data frame.
#' Default is FALSE.
#' @param DataSHIELD.checks logical. Default FALSE. If TRUE undertakes all DataSHIELD checks
#' (time-consuming) which are:\cr
#' 1. the input object(s) is(are) defined in all the studies\cr
#' 2. the input object(s) is(are) of the same legal class in all the studies\cr
#' 3. if there are any duplicated column names in the input objects in each study\cr
#' 4. the number of rows of the data frames or matrices and the length of all component variables
#' are the same
#' @param newobj a character string that provides the name for the output data frame
#' that is stored on the data servers. Default \code{dataframe.newobj}.
#' @param datasources a list of \code{\link{DSConnection-class}} objects obtained after login.
#' If the \code{datasources} argument is not specified
#' the default set of connections will be used: see \code{\link{datashield.connections_default}}.
#' @param notify.of.progress specifies if console output should be produced to indicate
#' progress. Default is FALSE.
#' @return \code{ds.dataFrame} returns the object specified by the \code{newobj} argument
#' which is written to the serverside. Also, two validity messages are returned to the
#' client-side indicating the name of the \code{newobj} that has been created in each data source
#' and if it is in a valid form.
#' @examples
#'
#' \dontrun{
#'
#' ## Version 6, for version 5 see the Wiki
#' # Connecting to the Opal servers
#'
#' require('DSI')
#' require('DSOpal')
#' require('dsBaseClient')
#'
#' builder <- DSI::newDSLoginBuilder()
#' builder$append(server = "study1",
#' url = "http://192.168.56.100:8080/",
#' user = "administrator", password = "datashield_test&",
#' table = "CNSIM.CNSIM1", driver = "OpalDriver")
#' builder$append(server = "study2",
#' url = "http://192.168.56.100:8080/",
#' user = "administrator", password = "datashield_test&",
#' table = "CNSIM.CNSIM2", driver = "OpalDriver")
#' builder$append(server = "study3",
#' url = "http://192.168.56.100:8080/",
#' user = "administrator", password = "datashield_test&",
#' table = "CNSIM.CNSIM3", driver = "OpalDriver")
#'
#' logindata <- builder$build()
#'
#' # Log onto the remote Opal training servers
#' connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D")
#'
#' # Create a new data frame
#' ds.dataFrame(x = c("D$LAB_TSC","D$GENDER","D$PM_BMI_CATEGORICAL"),
#' row.names = NULL,
#' check.rows = FALSE,
#' check.names = TRUE,
#' stringsAsFactors = TRUE, #character variables are converted to a factor
#' completeCases = TRUE, #only rows with not missing values are selected
#' DataSHIELD.checks = FALSE,
#' newobj = "df1",
#' datasources = connections[1], #only the first Opal server is used ("study1")
#' notify.of.progress = FALSE)
#'
#'
#' # Clear the Datashield R sessions and logout
#' datashield.logout(connections)
#' }
#' @author DataSHIELD Development Team
#' @export
ds.dataFrame <- function(x=NULL, row.names=NULL, check.rows=FALSE, check.names=TRUE, stringsAsFactors=TRUE, completeCases=FALSE, DataSHIELD.checks=FALSE, newobj=NULL, datasources=NULL, notify.of.progress=FALSE){
# look for DS connections
if(is.null(datasources)){
datasources <- datashield.connections_find()
}
if(is.null(x)){
stop("Please provide the name of the list that holds the input vectors!", call.=FALSE)
}
# create a name by default if user did not provide a name for the new variable
if(is.null(newobj)){
newobj <- "dataframe.newobj"
}
# the input variable might be given as column table (i.e. D$vector)
# or just as a vector not attached to a table (i.e. vector)
# we have to make sure the function deals with each case
xnames <- extract(x)
varnames <- xnames$elements
obj2lookfor <- xnames$holders
if(DataSHIELD.checks){
# check if the input object(s) is(are) defined in all the studies
for(i in 1:length(varnames)){
if(is.na(obj2lookfor[i])){
defined <- isDefined(datasources, varnames[i])
}else{
defined <- isDefined(datasources, obj2lookfor[i])
}
}
# call the internal function that checks the input object(s) is(are) of the same legal class in all studies.
for(i in 1:length(x)){
typ <- checkClass(datasources, x[i])
if(!('data.frame' %in% typ) & !('matrix' %in% typ) & !('factor' %in% typ) & !('character' %in% typ) & !('numeric' %in% typ) & !('integer' %in% typ) & !('logical' %in% typ)){
stop("Only objects of type 'data.frame', 'matrix', 'numeric', 'integer', 'character', 'factor' and 'logical' are allowed.", call.=FALSE)
}
}
# check that there are no duplicated column names in the input components
for(j in 1:length(datasources)){
colNames <- list()
for(i in 1:length(x)){
typ <- checkClass(datasources, x[i])
if(typ %in% c('data.frame', 'matrix')){
colNames[[i]] <- ds.colnames(x=x[i], datasources=datasources[j])
}
if(typ %in% c('factor', 'character', 'numeric', 'integer', 'logical')){
colNames[[i]] <- as.character(x[i])
}
colNames <- unlist(colNames)
if(anyDuplicated(colNames) != 0){
cat("\n Warning: Some column names in study", j, "are duplicated and a suffix '.k' will be added to the kth replicate \n")
}
}
}
# check that the number of rows is the same in all componets to be cbind
for(j in 1:length(datasources)){
nrows <- list()
for(i in 1:length(x)){
typ <- checkClass(datasources, x[i])
if(typ %in% c('data.frame', 'matrix')){
nrows[[i]] <- ds.dim(x=x[i], type='split', datasources=datasources[j])[[1]][1]
}
if(typ %in% c('factor', 'character', 'numeric', 'integer', 'logical')){
nrows[[i]] <- ds.length(x[i], type='split', datasources=datasources[j])[[1]]
}
}
nrows <- unlist(nrows)
if(any(nrows != nrows[1])){
stop("The number of rows is not the same in all of the input components", call.=FALSE)
}
}
}
# check newobj not actively declared as null
if(is.null(newobj)){
newobj <- "df_new"
}
# CREATE THE VECTOR OF COLUMN NAMES
colname.list <- list()
for (std in 1:length(datasources)){
colname.vector <- NULL
class.vector <- NULL
for(j in 1:length(x)){
testclass.var <- x[j]
calltext1 <- call('classDS', testclass.var)
next.class <- DSI::datashield.aggregate(datasources[std], calltext1)
class.vector <- c(class.vector, next.class[[1]])
if (notify.of.progress){
cat("\n",j," of ", length(x), " elements to combine in step 1 of 2 in study ", std, "\n")
}
}
for(j in 1:length(x)){
test.df <- x[j]
if(class.vector[j]!="data.frame" && class.vector[j]!="matrix"){
colname.vector <- c(colname.vector, test.df)
if (notify.of.progress){
cat("\n",j," of ", length(x), " elements to combine in step 2 of 2 in study ", std, "\n")
}
}else{
calltext2 <- call('colnamesDS', test.df)
df.names <- DSI::datashield.aggregate(datasources[std], calltext2)
colname.vector <- c(colname.vector, df.names[[1]])
if (notify.of.progress){
cat("\n", j," of ", length(x), " elements to combine in step 2 of 2 in study ", std, "\n")
}
}
}
colname.list[[std]] <- colname.vector
}
if (notify.of.progress){
cat("\nBoth steps in all studies completed\n")
}
# prepare vectors for transmission
x.names.transmit <- paste(x, collapse=",")
colnames.transmit <- list()
for (std in 1:length(datasources)){
colnames.transmit[[std]] <- paste(colname.list[[std]], collapse=",")
}
if(!is.null(row.names)){
row.names.transmit <- paste(row.names, collapse=",")
}
###############################
# call the server side function
#The serverside function dataFrameDS calls dsBase::dataFrameDS in dsBase repository
for(std in 1:length(datasources)){
if(is.null(row.names)){
cally <- call("dataFrameDS", x.names.transmit, NULL, check.rows, check.names,
colnames.transmit[[std]], stringsAsFactors, completeCases)
}else{
cally <- call("dataFrameDS", x.names.transmit, row.names.transmit, check.rows, check.names,
colnames.transmit[[std]], stringsAsFactors, completeCases)
}
DSI::datashield.assign(datasources[std], newobj, cally)
}
#############################################################################################################
#DataSHIELD CLIENTSIDE MODULE: CHECK KEY DATA OBJECTS SUCCESSFULLY CREATED #
#
#SET APPROPRIATE PARAMETERS FOR THIS PARTICULAR FUNCTION #
test.obj.name<-newobj #
#
#TRACER #
#return(test.obj.name) #
#} #
#
#
# CALL SEVERSIDE FUNCTION #
calltext <- call("testObjExistsDS", test.obj.name) #
#
object.info<-DSI::datashield.aggregate(datasources, calltext) #
#
# CHECK IN EACH SOURCE WHETHER OBJECT NAME EXISTS #
# AND WHETHER OBJECT PHYSICALLY EXISTS WITH A NON-NULL CLASS #
num.datasources<-length(object.info) #
#
#
obj.name.exists.in.all.sources<-TRUE #
obj.non.null.in.all.sources<-TRUE #
#
for(j in 1:num.datasources){ #
if(!object.info[[j]]$test.obj.exists){ #
obj.name.exists.in.all.sources<-FALSE #
} #
if(is.null(object.info[[j]]$test.obj.class) || object.info[[j]]$test.obj.class=="ABSENT"){ #
obj.non.null.in.all.sources<-FALSE #
} #
} #
#
if(obj.name.exists.in.all.sources && obj.non.null.in.all.sources){ #
#
return.message<- #
paste0("A data object <", test.obj.name, "> has been created in all specified data sources") #
#
#
}else{ #
#
return.message.1<- #
paste0("Error: A valid data object <", test.obj.name, "> does NOT exist in ALL specified data sources") #
#
return.message.2<- #
paste0("It is either ABSENT and/or has no valid content/class,see return.info above") #
#
return.message.3<- #
paste0("Please use ds.ls() to identify where missing") #
#
#
return.message<-list(return.message.1,return.message.2,return.message.3) #
#
} #
#
calltext <- call("messageDS", test.obj.name) #
studyside.message<-DSI::datashield.aggregate(datasources, calltext) #
#
no.errors<-TRUE #
for(nd in 1:num.datasources){ #
if(studyside.message[[nd]]!="ALL OK: there are no studysideMessage(s) on this datasource"){ #
no.errors<-FALSE #
} #
} #
#
#
if(no.errors){ #
validity.check<-paste0("<",test.obj.name, "> appears valid in all sources") #
return(list(is.object.created=return.message,validity.check=validity.check)) #
} #
#
if(!no.errors){ #
validity.check<-paste0("<",test.obj.name,"> invalid in at least one source. See studyside.messages:") #
return(list(is.object.created=return.message,validity.check=validity.check, #
studyside.messages=studyside.message)) #
} #
#
#END OF CHECK OBJECT CREATED CORECTLY MODULE #
#############################################################################################################
}
#ds.dataFrame