pro_applicability_matrix: Check applicability of DQ functions on study data

In dataquieR: Data Quality in Epidemiological Research

Check applicability of DQ functions on study data

Description

Checks applicability of DQ functions based on study data and metadata characteristics

Usage

pro_applicability_matrix(
  study_data,
  meta_data,
  split_segments = FALSE,
  label_col,
  max_vars_per_plot = 20,
  meta_data_segment,
  meta_data_dataframe,
  flip_mode = "noflip"
)

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
split_segments	logical return one matrix per study segment
label_col	variable attribute the name of the column in the metadata with labels of variables
max_vars_per_plot	integer from=0. The maximum number of variables per single plot.
meta_data_segment	data.frame – optional: Segment level metadata
meta_data_dataframe	data.frame – optional: Data frame level metadata
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

This is a preparatory support function that compares study data with associated metadata. A prerequisite of this function is that the no. of columns in the study data complies with the no. of rows in the metadata.

For each existing R-implementation, the function searches for necessary static metadata and returns a heatmap like matrix indicating the applicability of each data quality implementation.

In addition, the data type defined in the metadata is compared with the observed data type in the study data.

Value

a list with:

• SummaryTable: data frame about the applicability of each indicator function (each function in a column). its integer values can be one of the following four categories: 0. Non-matching datatype + Incomplete metadata, 1. Non-matching datatype + complete metadata, 2. Matching datatype + Incomplete metadata, 3. Matching datatype + complete metadata, 4. Not applicable according to data type

• ApplicabilityPlot: ggplot2 heatmap plot, graphical representation of SummaryTable

• ApplicabilityPlotList: list of plots per (maybe artificial) segment

• ReportSummaryTable: data frame underlying ApplicabilityPlot

Examples

## Not run: 
load(system.file("extdata/meta_data.RData", package = "dataquieR"), envir =
  environment())
load(system.file("extdata/study_data.RData", package = "dataquieR"), envir =
  environment())
appmatrix <- pro_applicability_matrix(study_data = study_data,
                                      meta_data = meta_data,
                                      label_col = LABEL)

## End(Not run)

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