des_scatterplot_matrix: Compute Pairwise Correlations

In dataquieR: Data Quality in Epidemiological Research

Compute Pairwise Correlations

Description

works on variable groups (cross-item_level), which are expected to show a Pearson correlation

Usage

des_scatterplot_matrix(
  label_col,
  study_data,
  item_level = "item_level",
  meta_data_cross_item = "cross-item_level",
  meta_data = item_level,
  meta_data_v2,
  cross_item_level,
  `cross-item_level`
)

Arguments

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
item_level	data.frame the data frame that contains metadata attributes of study data
meta_data_cross_item	meta_data_cross
meta_data	data.frame old name for item_level
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.
cross_item_level	data.frame alias for meta_data_cross_item
`cross-item_level`	data.frame alias for meta_data_cross_item

Details

Descriptor # TODO: This can be an indicator

Value

a list with the slots:

• SummaryPlotList: for each variable group a ggplot2::ggplot object with pairwise correlation plots

• SummaryData: table with columns VARIABLE_LIST, cors, max_cor, min_cor

• SummaryTable: like SummaryData, but machine readable and with stable column names.

Examples

## Not run: 
devtools::load_all()
prep_load_workbook_like_file("meta_data_v2")
des_scatterplot_matrix("study_data")

## End(Not run)

dataquieR documentation built on Jan. 8, 2026, 5:08 p.m.