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#' Listing BigML Datasets
#' @export
#' @family dataset methods
#' @references \url{https://bigml.com/developers/datasets}
#' @param flatten A logical value indicating whether to flatten the response
#' into a dataframe.
#' @param datasets_only A logical value indicating whether to only return
#' the data frame of field information (only valid if \code{flatten} is
#' \code{TRUE}).
#' @template dots
#' @template dataset_list_return
#' @template author
listDatasets <-
function (flatten = TRUE, datasets_only = TRUE, ...)
{
message("Retrieving the dataset list...")
response = .basic_api(.DATASET_URL)$list(...)
if (flatten) {
response$datasets = ldply(response$objects, function(x) {
x$fields = NULL
x$field_types = NULL
x$field_new_fields = NULL
x$status$field_errors = NULL
x$status$row_format_errors = NULL
x$output_fields = NULL
x$tags = NULL
print (unlist(x, recursive = F))
as.data.frame(unlist(x, recursive = F), stringsAsFactors=FALSE)
})
response$fields = ldply(response$objects, function(y) {
ldply(y$fields, function(z) {
z = as.list(z)
z$resource = y$resource
as.data.frame(z, stringsAsFactors=FALSE)
})
})
response$field_errors = ldply(response$objects, function(y) {
ldply(y$status$field_errors, function(z) {
z$resource = y$resource
z$example = z$sample[[1]][[1]]
z$count = z$sample[[1]][[2]]
z$sample = NULL
as.data.frame(z, stringsAsFactors=FALSE)
})
})
response$row_format_errors = ldply(response$objects,
function(y) {
ldply(y$status$row_format_errors, function(z) {
z$resource = y$resource
x$status$field_errors = NULL
x$status$row_format_errors = NULL
z$example = z$sample[[1]][[1]]
z$count = z$sample[[1]][[2]]
z$sample = NULL
as.data.frame(z, stringsAsFactors=FALSE)
})
})
response$objects = NULL
if (datasets_only) {
response = response$datasets
}
}
return(response)
}
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