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#' fold: A Self-Describing Dataset Format and Interface
#'
#' The fold package defines a compact, table-based, tool-neutral data format
#' designed to accommodate embedded metadata. Not surprisingly, it also implements
#' an interface for this format in R.
#'
#' The goal is to store metadata along with data. We do this by transforming
#' tabular data into a folded format -- still a table, but with a META column
#' that associates attributes (metadata) with the data items they describe.
#'
#' This all works much better when the data is clean: that is, there are a set
#' of grouping columns, the interaction of which makes each record unique. The
#' fold package can guess a lot of things, but you need to specify the groups
#' -- most general first.
#'
#' Here we supply a quick-start micro-vignette. See also \code{\link{fold.data.frame}}.
#'
#' @examples
#' library(magrittr)
#' library(wrangle)
#' library(dplyr)
#'
#' data(events)
#' x <- events
#'
#' # Step 0. Rename columns to remove semantic (non-syntactic) underscores.
#'
#' # Step 1. De-interlace the data. Limit to a subset so that each column means only one thing.
#'
#' x %<>% filter(CMT == 2) %>% select(-EVID,-CMT,-AMT)
#'
#' # Step 2. Describe the groups (unique key). Order is important. (Start with most general.)
#'
#' x %<>% group_by(USUBJID, TIME)
#' x %>% status
#'
#' # Step 3. Supply metadata as values or factors. Hardcode or merge from source.
#'
#' x %<>% mutate(
#' ID_LABEL = 'subject identifier',
#' C_LABEL = 'comment flag',
#' USUBJID_LABEL = 'universal subject identifier',
#' TIME_LABEL = 'time',
#' DV_LABEL = 'parent drug',
#' BLQ_LABEL = 'below limit of quantitation',
#' LLOQ_LABEL = 'lower limit of quantitation',
#' TAD_LABEL = 'time since most recent dose',
#' SEX_LABEL = 'sex',
#' WT_LABEL = 'weight',
#' PRED_LABEL = 'population prediction'
#' )
#'
#' x %<>% mutate(
#' C_GUIDE = factor(paste(C), exclude = NULL,
#' levels = c('NA','C'),
#' labels = c('not commented','commented')),
#' TIME_GUIDE = 'h',
#' DV_GUIDE = 'ng/mL',
#' BLQ_GUIDE = factor(BLQ,
#' levels = 0:1,
#' labels = c('not quantifiable','quantifiable')),
#' LLOQ_GUIDE = 'ng/mL',
#' TAD_GUIDE = 'h',
#' SEX_GUIDE = factor(SEX,
#' levels = 0:1,
#' labels = c('female','male')),
#' WT_GUIDE = 'kg',
#' PRED_GUIDE = 'ng/mL'
#' )
#'
#' # Step 4. Fold and unfold your data.
#'
#' x %>% fold
#' x %>% fold %>% unfold
#' x %>% fold %>% unfold %>% fold
#' x %>% fold %>% unfold(PRED,TIME,WT)
#'
#' data(eventsf)
#' stopifnot(identical(x %>% fold, eventsf) )
#' @docType package
#' @name foldpkg
#' @aliases fold-package
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