con_detection_limits: Detects variable values exceeding detection limits

Description

Inadmissible numerical values can be of type integer or float. This implementation requires the definition of intervals in the metadata to examine the admissibility of numerical study data.

This helps identify inadmissible measurements according to hard limits (for multiple variables).

Usage

con_detection_limits(
  resp_vars = NULL,
  label_col,
  study_data,
  meta_data,
  limits = c("DETECTION_LIMITS", "HARD_LIMITS", "SOFT_LIMITS"),
  flip_mode = "flip"
)

Value

a list with:

FlaggedStudyData data.frame related to the study data by a 1:1 relationship, i.e. for each observation is checked whether the value is below or above the limits.
SummaryTable data.frame summarizes limit deviations for each variable.
SummaryPlotList list of ggplots The plots for each variable are either a histogram (continuous) or a barplot (discrete).
ModifiedStudyData data.frame If the function identifies limit deviations, the respective values are removed in ModifiedStudyData.
ReportSummaryTable: heatmap-like data frame about limit violations

Arguments

resp_vars: variable list the name of the measurement variables
label_col: variable attribute the name of the column in the metadata with labels of variables
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
limits: enum HARD_LIMITS | SOFT_LIMITS | DETECTION_LIMITS. what limits from metadata to check for
flip_mode: enum default | flip | noflip | auto. Should the plot be in default orientation, flipped, not flipped or auto-flipped. Not all options are always supported. In general, this con be controlled by setting the roptions(dataquieR.flip_mode = ...). If called from dq_report, you can also pass flip_mode to all function calls or set them specifically using specific_args.

Details

Algorithm of this implementation:

Remove missing codes from the study data (if defined in the metadata)
Interpretation of variable specific intervals as supplied in the metadata.
Identification of measurements outside defined limits. Therefore two output data frames are generated:
- on the level of observation to flag each deviation, and
- a summary table for each variable.
A list of plots is generated for each variable examined for limit deviations. The histogram-like plots indicate respective limits as well as deviations.
Values exceeding limits are removed in a data frame of modified study data