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guess <- function(x) {
if ("value" %chin% names(x))
return("value")
if ("(all)" %chin% names(x))
return("(all)")
var <- names(x)[ncol(x)]
message("Using '", var, "' as value column. Use 'value.var' to override")
return(var)
}
dcast <- function(data, formula, fun.aggregate = NULL, ..., margins = NULL,
subset = NULL, fill = NULL, value.var = guess(data)) {
if (is.data.table(data))
UseMethod("dcast", data)
else {
# reshape2::dcast is not generic so we have to call it explicitly. See comments at the top of fmelt.R too.
# nocov start
ns = tryCatch(getNamespace("reshape2"), error=function(e)
stop("The dcast generic in data.table has been passed a ",class(data)[1L]," (not a data.table) but the reshape2 package is not installed to process this type. Please either install reshape2 and try again, or pass a data.table to dcast instead."))
ns$dcast(data, formula, fun.aggregate = fun.aggregate, ..., margins = margins,
subset = subset, fill = fill, value.var = value.var)
# nocov end
}
}
check_formula <- function(formula, varnames, valnames) {
if (is.character(formula)) formula = as.formula(formula)
if (!inherits(formula, "formula") || length(formula) != 3L)
stop("Invalid formula. Cast formula should be of the form LHS ~ RHS, for e.g., a + b ~ c.") # nocov; couldn't find a way to construct a test formula with length!=3L
vars = all.vars(formula)
vars = vars[!vars %chin% c(".", "...")]
allvars = c(vars, valnames)
if (any(allvars %chin% varnames[duplicated(varnames)]))
stop('data.table to cast must have unique column names')
ans = deparse_formula(as.list(formula)[-1L], varnames, allvars)
}
deparse_formula <- function(expr, varnames, allvars) {
lvars = lapply(expr, function(this) {
if (is.call(this)) {
if (this[[1L]] == quote(`+`))
unlist(deparse_formula(as.list(this)[-1L], varnames, allvars))
else this
} else if (is.name(this)) {
if (this == quote(`...`)) {
subvars = setdiff(varnames, allvars)
lapply(subvars, as.name)
} else this
}
})
lvars = lapply(lvars, function(x) if (length(x) && !is.list(x)) list(x) else x)
}
value_vars <- function(value.var, varnames) {
if (is.character(value.var))
value.var = list(value.var)
value.var = lapply(value.var, unique)
valnames = unique(unlist(value.var))
iswrong = which(!valnames %chin% varnames)
if (length(iswrong))
stop("value.var values [", paste(value.var[iswrong], collapse=", "), "] are not found in 'data'.")
value.var
}
aggregate_funs <- function(funs, vals, sep="_", ...) {
if (is.call(funs) && funs[[1L]] == "eval")
funs = eval(funs[[2L]], parent.frame(2L), parent.frame(2L))
if (is.call(funs) && as.character(funs[[1L]]) %chin% c("c", "list"))
funs = lapply(as.list(funs)[-1L], function(x) {
if (is.call(x) && as.character(x[[1L]]) %chin% c("c", "list")) as.list(x)[-1L] else x
})
else funs = list(funs)
if (length(funs) != length(vals)) {
if (length(vals) == 1L)
vals = replicate(length(funs), vals)
else stop("When 'fun.aggregate' and 'value.var' are both lists, 'value.var' must be either of length =1 or =length(fun.aggregate).")
}
only_one_fun = length(unlist(funs)) == 1L
dots = list(...)
construct_funs <- function(fun, val) {
if (!is.list(fun)) fun = list(fun)
ans = vector("list", length(fun)*length(val))
nms = vector("character", length(ans))
k = 1L
for (i in fun) {
for (j in val) {
expr = list(i, as.name(j))
if (length(dots))
expr = c(expr, dots)
ans[[k]] = as.call(expr)
# changed order of arguments here, #1153
nms[k] = if (only_one_fun) j else
paste(j, all.names(i, max.names=1L, functions=TRUE), sep=sep)
k = k+1L;
}
}
setattr(ans, 'names', nms)
}
ans = mapply(construct_funs, funs, vals, SIMPLIFY=FALSE)
as.call(c(quote(list), unlist(ans)))
}
dcast.data.table <- function(data, formula, fun.aggregate = NULL, sep = "_", ..., margins = NULL, subset = NULL, fill = NULL, drop = TRUE, value.var = guess(data), verbose = getOption("datatable.verbose")) {
if (!is.data.table(data)) stop("'data' must be a data.table.")
drop = as.logical(rep(drop, length.out=2L))
if (anyNA(drop)) stop("'drop' must be logical TRUE/FALSE")
lvals = value_vars(value.var, names(data))
valnames = unique(unlist(lvals))
lvars = check_formula(formula, names(data), valnames)
lvars = lapply(lvars, function(x) if (!length(x)) quote(`.`) else x)
# tired of lapply and the way it handles environments!
allcols = c(unlist(lvars), lapply(valnames, as.name))
dat = vector("list", length(allcols))
for (i in seq_along(allcols)) {
x = allcols[[i]]
dat[[i]] = if (identical(x, quote(`.`))) rep(".", nrow(data))
else eval(x, data, parent.frame())
if (is.function(dat[[i]]))
stop("Column [", deparse(x), "] not found or of unknown type.")
}
setattr(lvars, 'names', c("lhs", "rhs"))
# Have to take care of duplicate names, and provide names for expression columns properly.
varnames = make.unique(vapply_1c(unlist(lvars), all.vars, max.names=1L), sep=sep)
dupidx = which(valnames %chin% varnames)
if (length(dupidx)) {
dups = valnames[dupidx]
valnames = tail(make.unique(c(varnames, valnames)), -length(varnames))
lvals = lapply(lvals, function(x) { x[x %chin% dups] = valnames[dupidx]; x })
}
lhsnames = head(varnames, length(lvars$lhs))
rhsnames = tail(varnames, -length(lvars$lhs))
setattr(dat, 'names', c(varnames, valnames))
setDT(dat)
if (any(vapply_1b(as.list(dat)[varnames], is.list))) {
stop("Columns specified in formula can not be of type list")
}
m <- as.list(match.call()[-1L])
subset <- m[["subset"]][[2L]]
if (!is.null(subset)) {
if (is.name(subset)) subset = as.call(list(quote(`(`), subset))
idx = which(eval(subset, data, parent.frame())) # any advantage thro' secondary keys?
dat = .Call(CsubsetDT, dat, idx, seq_along(dat))
}
if (!nrow(dat) || !ncol(dat)) stop("Can not cast an empty data.table")
fun.call = m[["fun.aggregate"]]
fill.default = NULL
if (is.null(fun.call)) {
oo = forderv(dat, by=varnames, retGrp=TRUE)
if (attr(oo, 'maxgrpn') > 1L) {
message("Aggregate function missing, defaulting to 'length'")
fun.call = quote(length)
}
}
if (!is.null(fun.call)) {
fun.call = aggregate_funs(fun.call, lvals, sep, ...)
errmsg = "Aggregating function(s) should take vector inputs and return a single value (length=1). However, function(s) returns length!=1. This value will have to be used to fill any missing combinations, and therefore must be length=1. Either override by setting the 'fill' argument explicitly or modify your function to handle this case appropriately."
if (is.null(fill)) {
fill.default <- suppressWarnings(dat[0L][, eval(fun.call)])
# tryCatch(fill.default <- dat[0L][, eval(fun.call)], error = function(x) stop(errmsg, call.=FALSE))
if (nrow(fill.default) != 1L) stop(errmsg, call.=FALSE)
}
if (!any(valnames %chin% varnames)) {
dat = dat[, eval(fun.call), by=c(varnames)]
} else {
dat = dat[, { .SD; eval(fun.call) }, by=c(varnames), .SDcols = valnames]
}
}
order_ <- function(x) {
o = forderv(x, retGrp=TRUE, sort=TRUE)
idx = attr(o, 'starts')
if (!length(o)) o = seq_along(x)
o[idx] # subsetVector retains attributes, using R's subset for now
}
cj_uniq <- function(DT) {
do.call("CJ", lapply(DT, function(x)
if (is.factor(x)) {
xint = seq_along(levels(x))
setattr(xint, 'levels', levels(x))
setattr(xint, 'class', class(x))
} else .Call(CsubsetVector, x, order_(x))
))}
valnames = setdiff(names(dat), varnames)
# 'dat' != 'data'? then setkey to speed things up (slightly), else ad-hoc (for now). Still very fast!
if (!is.null(fun.call) || !is.null(subset))
setkeyv(dat, varnames)
if (length(rhsnames)) {
lhs = shallow(dat, lhsnames); rhs = shallow(dat, rhsnames); val = shallow(dat, valnames)
# handle drop=TRUE/FALSE - Update: Logic moved to R, AND faster than previous version. Take that... old me :-).
if (all(drop)) {
map = setDT(lapply(list(lhsnames, rhsnames), function(cols) frankv(dat, cols=cols, ties.method="dense")))
maporder = lapply(map, order_)
mapunique = lapply(seq_along(map), function(i) .Call(CsubsetVector, map[[i]], maporder[[i]]))
lhs = .Call(CsubsetDT, lhs, maporder[[1L]], seq_along(lhs))
rhs = .Call(CsubsetDT, rhs, maporder[[2L]], seq_along(rhs))
} else {
lhs_ = if (!drop[1L]) cj_uniq(lhs) else setkey(unique(lhs, by=names(lhs)))
rhs_ = if (!drop[2L]) cj_uniq(rhs) else setkey(unique(rhs, by=names(rhs)))
map = vector("list", 2L)
.Call(Csetlistelt, map, 1L, lhs_[lhs, which=TRUE])
.Call(Csetlistelt, map, 2L, rhs_[rhs, which=TRUE])
setDT(map)
mapunique = vector("list", 2L)
.Call(Csetlistelt, mapunique, 1L, seq_len(nrow(lhs_)))
.Call(Csetlistelt, mapunique, 2L, seq_len(nrow(rhs_)))
lhs = lhs_; rhs = rhs_
}
maplen = vapply_1i(mapunique, length)
idx = do.call("CJ", mapunique)[map, I := .I][["I"]] # TO DO: move this to C and avoid materialising the Cross Join.
ans = .Call(Cfcast, lhs, val, maplen[[1L]], maplen[[2L]], idx, fill, fill.default, is.null(fun.call))
allcols = do.call("paste", c(rhs, sep=sep))
if (length(valnames) > 1L)
allcols = do.call("paste", if (identical(".", allcols)) list(valnames, sep=sep)
else c(CJ(valnames, allcols, sorted=FALSE), sep=sep))
# removed 'setcolorder()' here, #1153
setattr(ans, 'names', c(lhsnames, allcols))
setDT(ans); setattr(ans, 'sorted', lhsnames)
} else {
# formula is of the form x + y ~ . (rare case)
if (drop) {
if (is.null(subset) && is.null(fun.call)) {
dat = copy(dat) # can't be avoided
setkeyv(dat, lhsnames)
}
ans = dat
} else {
lhs = shallow(dat, lhsnames)
val = shallow(dat, valnames)
lhs_ = cj_uniq(lhs)
idx = lhs_[lhs, I := .I][["I"]]
lhs_[, I := NULL]
ans = .Call(Cfcast, lhs_, val, nrow(lhs_), 1L, idx, fill, fill.default, is.null(fun.call))
setDT(ans); setattr(ans, 'sorted', lhsnames)
setnames(ans, c(lhsnames, valnames))
}
if (length(valnames) == 1L)
setnames(ans, valnames, value.var)
}
return (ans)
}
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