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| #' Delete different events occurring at the same time | ||
| #' | ||
| #' Fast algorithm to get rid of transitions to different states occurring at the same exact time | ||
| #' when dealing with augmented data as computed by \code{augment}. | ||
| #' | ||
| #' @param data An augmented \code{data.table} or \code{data.frame} object where each row | ||
| #' represents a transition. If \code{data} is a \code{data.frame}, then \code{quake} internally | ||
| #' casts it to a \code{data.table}. | ||
| #' @param data_key A keying variable which \code{quake} uses to define a key for \code{data}. | ||
| #' This represents the subject ID (See \code{\link[data.table]{setkey}}). | ||
| #' @param pattern ID status at the end of the study as passed to \code{augment} (See | ||
| #' \code{\link[msmtools]{augment}}). | ||
| #' @param target The target variable to check duplicates. By default it is set to 'augmented_int'. | ||
| #' @param check_NA If \code{TRUE}, then arguments \code{data_key}, \code{pattern}, | ||
| #' and \code{target} are looked up for any missing data and if the function finds | ||
| #' any, it stops with error. Default is \code{FALSE}. | ||
| #' @param verbose If \code{FALSE}, all information produced by \code{print}, \code{cat} and | ||
| #' \code{message} are suppressed. All is done internally so that no global | ||
| #' options are changed. \code{verbose} can be set to \code{FALSE} on all common OS | ||
| #' (see also \code{\link[base]{sink}} and \code{\link[base]{options}}). Default is \code{TRUE}. | ||
| #' | ||
| #' @details blablabla some details to write down | ||
| #' @seealso \code{\link[msmtools]{augment}} | ||
| #' | ||
| #' @examples | ||
| #' data( hosp ) | ||
| #' hosp_aug = augment( data = hosp, data_key = subj, n_events = adm_number, pattern = label_3, | ||
| #' t_start = dateIN, t_end = dateOUT, t_cens = dateCENS ) | ||
| #' hosp_aug_clean = quake( data = hosp_aug, data_key = subj, pattern = label_3 ) | ||
| #' | ||
| #' @author Francesco Grossetti \email{francesco.grossetti@@polimi.it}. | ||
| #' @import data.table | ||
| #' @export | ||
|
|
||
| quake = function( data, data_key, pattern, target, check_NA = FALSE, verbose = TRUE ) { | ||
|
|
||
| tic = proc.time() | ||
| index = NULL | ||
|
|
||
| if ( missing( data ) ) { | ||
| stop( 'a dataset of class data.table or data.frame must be provided' ) | ||
| } | ||
| if ( !inherits( data, "data.table" ) && !inherits( data, "data.frame" ) ) { | ||
| stop( "a dataset of class data.table or data.frame must be provided" ) | ||
| } | ||
| if ( missing( data_key ) ) { | ||
| stop( 'a variable of keying must be provided' ) | ||
| } | ||
| if ( missing( pattern ) ) { | ||
| stop( "a pattern must be provided" ) | ||
| } | ||
| if ( inherits( data, 'data.frame' ) ) { | ||
| setDT( data ) | ||
| } | ||
| if ( verbose == TRUE ) { | ||
| cat( '-------------------------------------\n' ) | ||
| cat( '# # # # setting everything up # # # #\n' ) | ||
| cat( '-------------------------------------\n' ) | ||
| } | ||
|
|
||
| setkey( data, NULL ) | ||
| cols = as.character( substitute( list( data_key ) )[ -1L ] ) | ||
| if ( !length( cols ) ) { | ||
| cols = colnames( data ) | ||
| } | ||
| setkeyv( data, cols ) | ||
| pattern = as.character( substitute( list( pattern ) )[ -1L ] ) | ||
| if ( missing( target ) ) { | ||
| target = 'augmented_int' | ||
| } else { | ||
| target = as.character( substitute( list( target ) )[ -1L ] ) | ||
| } | ||
|
|
||
| if ( check_NA == TRUE ) { | ||
| if ( verbose == TRUE ) { | ||
| message( 'checking for any missing values in function arguments' ) | ||
| } | ||
| checks = c( cols, pattern, target ) | ||
| test = apply( data[ , checks, with = FALSE ], 2, function( x ) any( sum( is.na( x ) ) > 0 ) ) | ||
| if ( any ( test ) ) { | ||
| cat( '---\n' ) | ||
| if ( verbose == TRUE ) { | ||
| message( 'detected missing values in the following variables:' ) | ||
| } | ||
| invisible( sapply( names( test[ test == TRUE ] ), function( x ) cat( x, '\n' ) ) ) | ||
| stop( 'Please, fix the issues and relaunch shiver()' ) | ||
| } else { | ||
| cat( 'Ok, no missing values detected\n') | ||
| cat( '---\n' ) | ||
| } | ||
| } | ||
|
|
||
| data[ , index := sequence( .N ) ] | ||
| n_patients = uniqueN( eval( substitute( data$cols ) ) ) | ||
| values = sort( eval( substitute( unique( data$pattern ) ) ) ) | ||
| if ( length( values ) < 2 ) { | ||
| stop( 'unit identification label must be an integer, a factor or a character | ||
| with at least 2 elements' ) | ||
| } | ||
|
|
||
| alive = data[ get( pattern ) == values[ 1 ] ] | ||
| alive.last = alive[ alive[ , .I[ .N ], by = eval( cols ) ]$V1 ] | ||
| setkey( alive.last, index ) | ||
| setkey( alive, index ) | ||
| alive.no.last = alive[ !alive.last ] | ||
|
|
||
| if ( verbose == TRUE ) { | ||
| message( 'checking ', substitute( pattern ), ' and defining patterns' ) | ||
| } | ||
| if ( length( values ) == 2 ) { | ||
| cat( 'detected only 2 values\n' ) | ||
| cat( '---\n' ) | ||
| dead = data[ get( pattern ) == values[ 2 ] ] | ||
| } else if ( length( values ) == 3 ) { | ||
| cat( 'Ok, detected 3 values\n' ) | ||
| dead = data[ get( pattern ) != values[ 1 ] ] | ||
| cat( '---\n' ) | ||
| } | ||
|
|
||
| l = list( alive.no.last, dead ) | ||
| data.no.last.event = rbindlist( l ) | ||
| row.duplicated = duplicated( data.no.last.event, by = c( eval( cols ), eval( target ) ) ) | ||
| duplicated = data.no.last.event[ row.duplicated == TRUE ] | ||
| n_duplicated = uniqueN( eval( substitute( duplicated$cols ) ) ) | ||
| setkeyv( duplicated, cols ) | ||
|
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||
| if ( n_duplicated == 0 ) { | ||
| message( 'Hurray! No duplicated occurrences have been found in ', substitute( data ), | ||
| ' according to variable ', substitute( target ) ) | ||
| } else { | ||
| message( 'Spotted ', n_duplicated, | ||
| ' patients with at least a duplicated occurrence according to variable ', | ||
| substitute( target ) ) | ||
| data.clean = data[ !duplicated ] | ||
| n_patients.to.keep = uniqueN( eval( substitute( data.clean$cols ) ) ) | ||
| cat( n_patients.to.keep, ' patients have been reained corresponding to ', | ||
| round( 100 * ( n_patients.to.keep / n_patients ), 2 ), '%\n', sep = '' ) | ||
| cat( 'Duplicated patients have been sucessfully removed\n' ) | ||
| } | ||
|
|
||
| data[ , index := NULL ] | ||
| if ( n_duplicated > 0 ) { | ||
| data.clean[ , index := NULL ] | ||
| } | ||
| toc = proc.time() | ||
| time = toc - tic | ||
| cat( '---------------------------\n' ) | ||
| cat( 'quake() took:', time[ 3 ], 'sec. \n', sep = ' ' ) | ||
| cat( '---------------------------\n' ) | ||
|
|
||
| if ( n_duplicated == 0 ) { | ||
| return( invisible( data ) ) | ||
| } else { | ||
| return( invisible( data.clean ) ) | ||
| } | ||
| } |
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