dq_report2: Generate a full DQ report, v2
In dataquieR: Data Quality in Epidemiological Research

dq_report2

R Documentation

Generate a full DQ report, v2

Description

Generate a full DQ report, v2

Usage

dq_report2(
  study_data,
  meta_data = "item_level",
  label_col = LABEL,
  meta_data_segment = "segment_level",
  meta_data_dataframe = "dataframe_level",
  meta_data_cross_item = "cross-item_level",
  meta_data_v2,
  ...,
  dimensions = c("Completeness", "Consistency"),
  cores = list(mode = "socket", logging = FALSE, cpus = util_detect_cores(),
    load.balancing = TRUE),
  specific_args = list(),
  advanced_options = list(),
  author = prep_get_user_name(),
  title = "Data quality report",
  subtitle = as.character(Sys.Date()),
  user_info = NULL,
  debug_parallel = FALSE,
  resp_vars = character(0),
  filter_indicator_functions = character(0),
  filter_result_slots = c("^Summary", "^Segment", "^DataTypePlotList",
    "^ReportSummaryTable", "^Dataframe", "^Result", "^VariableGroup"),
  mode = c("default", "futures", "queue", "parallel"),
  mode_args = list(),
  notes_from_wrapper = list()
)

Arguments

`study_data`	data.frame the data frame that contains the measurements
`meta_data`	data.frame the data frame that contains metadata attributes of study data
`label_col`	variable attribute the name of the column in the metadata with labels of variables
`meta_data_segment`	data.frame – optional: Segment level metadata
`meta_data_dataframe`	data.frame – optional: Data frame level metadata
`meta_data_cross_item`	data.frame – optional: Cross-item level metadata
`meta_data_v2`	character path to workbook like metadata file, see `prep_load_workbook_like_file` for details. ALL LOADED DATAFRAMES WILL BE PURGED, using `prep_purge_data_frame_cache`, if you specify `meta_data_v2`.
`...`	arguments to be passed to all called indicator functions if applicable.
`dimensions`	dimensions Vector of dimensions to address in the report. Allowed values in the vector are Completeness, Consistency, and Accuracy. The generated report will only cover the listed data quality dimensions. Accuracy is computational expensive, so this dimension is not enabled by default. Completeness should be included, if Consistency is included, and Consistency should be included, if Accuracy is included to avoid misleading detections of e.g. missing codes as outliers, please refer to the data quality concept for more details. Integrity is always included.
`cores`	integer number of cpu cores to use or a named list with arguments for parallelMap::parallelStart or NULL, if parallel has already been started by the caller. Can also be a cluster.
`specific_args`	list named list of arguments specifically for one of the called functions, the of the list elements correspond to the indicator functions whose calls should be modified. The elements are lists of arguments.
`advanced_options`	list options to set during report computation, see `options()`
`author`	character author for the report documents.
`title`	character optional argument to specify the title for the data quality report
`subtitle`	character optional argument to specify a subtitle for the data quality report
`user_info`	list additional info stored with the report, e.g., comments, title, ...
`debug_parallel`	logical print blocks currently evaluated in parallel
`resp_vars`	variable list the name of the measurement variables for the report. If missing, all variables will be used. Only item level indicator functions are filtered, so far.
`filter_indicator_functions`	character regular expressions, only if an indicator function's name matches one of these, it'll be used for the report. If of length zero, no filtering is performed.
`filter_result_slots`	character regular expressions, only if an indicator function's result's name matches one of these, it'll be used for the report. If of length zero, no filtering is performed.
`mode`	character work mode for parallel execution. default is "default", the values mean: - default: use `queue` except `cores` has been set explicitly - futures: use the `future` package - queue: use a queue as described in the examples from the `callr` package by Csárdi and Chang and start sub-processes as workers that evaluate the queue. - parallel: use the cluster from `cores` to evaluate all calls of indicator functions using the classic R `parallel` back-ends
`mode_args`	list of arguments for the selected `mode`. As of writing this manual, only for the mode `queue` the argument `step` is supported, which gives the number of function calls that are run by one worker at a time. the default is 15, which gives on most of the tested systems a good balance between synchronization overhead and idling workers.
`notes_from_wrapper`	list a list containing notes about changed labels by `dq_report_by` (otherwise NULL)

Details

See dq_report_by for a way to generate stratified or splitted reports easily.

Value

a dataquieR_resultset2 that can be printed creating a HTML-report.

Examples

## Not run: 
prep_load_workbook_like_file("inst/extdata/meta_data_v2.xlsx")
meta_data <- prep_get_data_frame("item_level")
meta_data_cross <- prep_get_data_frame("cross-item_level")
x <- dq_report2("study_data", dimensions = NULL, label_col = "LABEL")
xx <- pbapply::pblapply(x, util_eval_to_dataquieR_result, env = environment())
xx <- pbapply::pblapply(tail(x), util_eval_to_dataquieR_result, env = environment())
xx <- parallel
cat(vapply(x, deparse1, FUN.VALUE = character(1)), sep = "\n", file = "all_calls.txt")
rstudioapi::navigateToFile("all_calls.txt")
eval(x$`acc_multivariate_outlier.Blood pressure checks`)

## End(Not run)

dataquieR documentation built on May 29, 2024, 7:18 a.m.