des_scatterplot_matrix function

Compute Pairwise Correlations

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

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

Returns

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.

Details

Descriptor # TODO: This can be an indicator

Examples

## Not run:

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