dataquieR (version 2.1.0)

dq_report2: 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()
)

Value

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

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.

See Also

• as.data.frame.dataquieR_resultset

• as.list.dataquieR_resultset

• print.dataquieR_resultset

• summary.dataquieR_resultset

• dq_report_by

Examples

if (FALSE) {
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`)
}