pro_applicability_matrix: Function to check applicability of DQ functions on study data

Description Usage Arguments Details Value Examples

View source: R/pro_applicability_matrix.R

Description

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

Usage

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pro_applicability_matrix(
  study_data,
  meta_data,
  split_segments = FALSE,
  label_col,
  max_vars_per_plot = 20
)

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.

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:

Examples

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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)

dataquieR documentation built on Feb. 26, 2021, 5:08 p.m.