From 4a5ccadd687b539bdc56f8a4e8a7d419980ed652 Mon Sep 17 00:00:00 2001 From: Nicholas Masel Date: Mon, 16 Sep 2024 17:24:38 -0400 Subject: [PATCH] write updates --- R/variable_metadata.R | 179 +++++++++++++++++++++++ R/write_dataset_json.R | 46 +++++- data/iris_items.rda | Bin 334 -> 350 bytes dm.json | 1 + ta.json | 1 + test.json | 19 +++ tests/testthat/test-read_dataset_json.R | 22 ++- tests/testthat/test-write_dataset_json.R | 132 ++++++++++------- tests/testthat/testdata/adsl.json | 2 +- tests/testthat/testdata/ae.json | 2 +- tests/testthat/testdata/invalid_dm.json | 1 + 11 files changed, 338 insertions(+), 67 deletions(-) create mode 100644 R/variable_metadata.R create mode 100644 dm.json create mode 100644 ta.json create mode 100644 test.json create mode 100644 tests/testthat/testdata/invalid_dm.json diff --git a/R/variable_metadata.R b/R/variable_metadata.R new file mode 100644 index 0000000..185f80f --- /dev/null +++ b/R/variable_metadata.R @@ -0,0 +1,179 @@ +#' Generate an individual element that fills the itemGroupData field +#' +#' @param items Variable metadata +#' @param .data Dataframe to be written to Dataset JSON file +#' +#' @return dataset_metadata object +#' @export +#' @examples +#' dataset_meta <- dataset_metadata( +#' items = iris_items +#' ) +variable_metadata <- function(items, .data) { + + # Check items before moving any further + validate_variable_items(items) + + # Attach in the variable metadata + if (!("ITEMGROUPDATASEQ" %in% items$itemOID)) { + igds_row <- data.frame( + itemOID = "ITEMGROUPDATASEQ", + name = "ITEMGROUPDATASEQ", + label = "Record Identifier", + dataType = "integer" + ) + + # Match up columns and fill + igds_row[setdiff(names(items), names(igds_row))] <- NA + items[setdiff(names(igds_row), names(items))] <- NA + + items <- rbind(igds_row, items) + } + + items_converted <- df_to_list_rows(items) + + # Create the container with proper elements + # x <- list( + # # list( + # # "records" = NULL, + # # "name" = name, + # # "label" = label, + # "columns" = items_converted + # # , + # # "itemData" = NULL + # # ) + # ) + + # Set the Object ID + # names(x) <- item_id + + structure( + items_converted, + class = c('variable_metadata', 'list') + ) + + # Set data if it's provided + # if (!missing(.data)) { + # set_item_data(x, .data) + # } + + # x +} + +#' #' Apply dataframe to itemData attribute +#' #' +#' #' This function will set the itemData attribute within a datasetjson or +#' #' dataset_metadata object. It additionally sets the records parameter with the +#' #' proper number of rows in .data. +#' #' +#' #' @param x Object to set itemData +#' #' @param .data Dataframe to be written to Dataset JSON file +#' #' @param ... Additional params +#' #' +#' #' @return Input object with itemData applied +#' #' @noRd +#' set_item_data <- function(x, .data, ...) { +#' stopifnot_dataset_metadata(x) +#' +#' if (!inherits(.data, "data.frame")) { +#' stop(".data must be a data.frame", call.=FALSE) +#' } +#' +#' records <- nrow(.data) +#' # Derive ITEMGROUPDATASEQ and insert it up front in the dataframe +#' item_data <- cbind(ITEMGROUPDATASEQ = 1:records, .data) +#' +#' # Convert data and date times +#' item_data <- convert_to_sas_datenum(item_data) +#' item_data <- convert_to_sas_datetimenum(item_data) +#' +#' # Insert into object in proper spots +#' x[[1]][['records']] <- records +#' x[[1]][['itemData']] <- unname(item_data) +#' x +#' } + +#' Verify that the item metadata supplied is the appropriate format +#' +#' This function does the following checks and consolidates to a single error message: +#' - Columns missing that must be present +#' - Columns present that are not permissible +#' - Columns with NAs that must be fully populated +#' - Columns columns that should be character or integer but aren't +#' - Within the type column, if the values are within the permissible list per +#' the schema +#' @param items +#' +#' @return Error Check +#' @noRd +validate_variable_items <- function(items) { + required_cols <- c("itemOID", "name", "label", "dataType") + all_cols <- c("itemOID", "name", "label", "dataType", "targetDataType", + "displayFormat", "length", "keySequence") + + # Check for missing or extraneous columns + missing_cols <- setdiff(required_cols, names(items)) + err_missing_cols <- sprintf("Column `%s` is missing and must be present", missing_cols) + additional_cols <- setdiff(names(items), all_cols) + err_additional_cols <- sprintf("Column `%s` is not a permissible column", additional_cols) + + # Check for for NAs in required columns + any_nas <- vapply(items[intersect(required_cols, names(items))], function(X) any(is.na(X)), FUN.VALUE = TRUE) + has_nas <- names(any_nas)[any_nas] + err_nas <- sprintf("Column `%s` must not have NA values", has_nas) + + # Check columns that should be character + char_cols <- intersect(c("itemOID", "name", "label", "dataType", "targetDataType", "displayFormat"), names(items)) + are_char_cols <- vapply(items[char_cols], is.character, FUN.VALUE=TRUE) + not_char_cols <- names(are_char_cols)[!are_char_cols] + err_char_cols <- sprintf("Column `%s` must be of type character", not_char_cols) + + # Check columns that should be integers + int_cols <- intersect(c("length", "keySequence"), names(items)) + are_int_cols <- vapply(items[int_cols], is.integer, FUN.VALUE=TRUE) + not_int_cols <- names(are_int_cols)[!are_int_cols] + err_int_cols <- sprintf("Column `%s` must be of type integer", not_int_cols) + + # Check that dataType values are within the permissible list + err_type_vars <- character() + if ('dataType' %in% names(items)) { + bad_types <- !(items$dataType %in% c("string", "integer", "float", "double", "decimal", "boolean", + "datetime", "date", "time", "URI")) + bad_type_vars <- items$name[bad_types] + bad_type_vals <- items$dataType[bad_types] + err_type_vars <- sprintf( + paste("Variable %s has an invalid dataType value of %s.", + "Must be one of string, integer, float, double, decimal, boolean, datetime, date, time, URI"), + bad_type_vars, bad_type_vals + ) + } + + + all_errs <- c(err_missing_cols, err_additional_cols, err_nas, err_char_cols, err_int_cols, err_type_vars) + + if (length(all_errs) > 0) { + msg_prep <- paste0("\n\t", all_errs) + err_msg <- paste0(c("Error: Issues found in items data:", msg_prep)) + stop(err_msg, call.=FALSE) + } +} + +#' Convert an dataframe into a named list of rows without NAs +#' +#' The variable attributes are stored as named lists within the output +#' JSON file, so to write them out the dataframe needs to be a named +#' list of rows +#' +#' @param x A data.frame +#' +#' @return List of named lists with single elements +#' @noRd +df_to_list_rows <- function(x) { + # Split the dataframe rows into individual rows + rows <- unname(split(x, seq(nrow(x)))) + # Convert each row into a named list while removing NAs + lapply(rows, function(X) { + y <- as.list(X) + y[!is.na(y)] + }) +} diff --git a/R/write_dataset_json.R b/R/write_dataset_json.R index 099bba0..9976c5a 100644 --- a/R/write_dataset_json.R +++ b/R/write_dataset_json.R @@ -3,6 +3,8 @@ #' @param x datasetjson object #' @param file File path to save Dataset JSON file #' @param pretty If TRUE, write with readable formatting +#' @param items Variable metadata +#' @param type JSON or NDJSON, if NDJSON variable names are left on rows for streaming #' #' @return NULL when file written to disk, otherwise character string #' @export @@ -10,19 +12,51 @@ #' @examples #' # Write to character object #' ds_json <- dataset_json(iris, "IG.IRIS", "IRIS", "Iris", iris_items) -#' js <- write_dataset_json(ds_json) +#' js <- write_dataset_json(ds_json, iris_items) #' #' # Write to disk #' \dontrun{ #' write_dataset_json(ds_json, "path/to/file.json") #' } -write_dataset_json <- function(x, file, pretty=FALSE) { +write_dataset_json <- function(x, file, pretty=FALSE, items, type="JSON") { stopifnot_datasetjson(x) # Populate the creation datetime - x[['creationDateTime']] <- get_datetime() + attr(x, 'datasetJSONCreationDateTime') <- get_datetime() - x <- remove_nulls(x) + # Store number of records + attr(x, 'records') <- nrow(x) + + # Pull attributes into a list and order + temp <- attributes(x)[c( + "datasetJSONCreationDateTime", + "datasetJSONVersion", + "fileOID", + "dbLastModifiedDateTime", + "originator", + "sourceSystem", + "studyOID", + "metaDataVersionOID", + "metaDataRef", + "itemGroupOID", + "isReferenceData", + "records", + "name", + "label") + ] + + # add ITEMGROUPDATASEQ to data + records <- nrow(x) + x <- cbind(ITEMGROUPDATASEQ = 1:records, x) + + # add variable metadata and data + temp$columns <- variable_metadata(items) + temp$rows <- x + + # Leave the names for NSJSON, remove for JSON + if (type == "JSON") { + names(temp$rows) <- NULL + } if (!missing(file)) { # Make sure the output path exists @@ -40,14 +74,14 @@ write_dataset_json <- function(x, file, pretty=FALSE) { if (!missing(file)) { # Write file to disk yyjsonr::write_json_file( - x, + temp, filename = file, opts = json_opts ) } else { # Print to console yyjsonr::write_json_str( - x, + temp, opts = json_opts ) } diff --git a/data/iris_items.rda b/data/iris_items.rda index c817b7bcade4b4c19ef7e04458e7bc780fc49fa3..d5b350f36279e80f175ebba728c67ef9f3d6a0c4 100644 GIT binary patch literal 350 zcmV-k0ipgvT4*^jL0KkKS;nK)uK)oaf589$cmO~F5C8=v+`zx@{-8hr012=GTT2)+ zP>rHwG|_;7(?9{B+8}A9)iDN!AOHY1pa2>P5=1BJo~BI!ki;<$MxmyI6VrOMMFCX` z_6#@XOACS^LB}Ik_>efP-0XIMnFTDm zHFc<%b}$w6fN3N{2nLii;slVj%g#bdu2Xw8p@yU|`2U@B^XRtCNUpZ@PQ<6Dytzzh zG_w{{Ru{Sy5`|(3+n(O3kx$S;L_|cDWtOc2C5|Ic-wo|Fzq{m%1@)u^E#dp<7|<-5 zl<7*=4begb@l1r`AlwWksL@(u*EDm0Qwy@&=nItlfH7Pyvmz5f3R4V&P@vMtdFb7y w%k7{VF9zU5P}3EgD5UQ32AgB#wxVP17?cd 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