The data frame storage works as a cache area where data frames are temporary stored.

Several functions can add one or multiple data frames to the dataquieR data frame storage area:

  • prep_load_workbook_like_file(), add to the storage area all the spreadsheets of an Excel workbook files (i.e., multiple data frames);

  • prep_load_folder_with_metadata(), add to the storage area all the metadata files and the study data files that are inside one folder;

  • prep_add_data_frames(), add one data frame to the storage area.

In addition, whenever prep_get_data_frame() fetches a single data frame this is also added to the data frame storage.

The following functions can be used to see or modify the content of this storage area:

  • prep_list_dataframes(), to obtain the content of this storage area, that is not visible in the Global Environment;

  • prep_get_data_frame(), to get a data frame from the dataquieR data frame storage area and have it available in the Global Environment;

  • prep_purge_data_frame_cache(), to delete everything from this storage area;

  • prep_remove_from_cache(), to delete only one specific data frame from this storage area.

When a table is available in the dataquieR storage area, it can be directly used in a dataquieR function by its name.

Example:

library (dataquieR)
# Import the dataquier metadata example for the synthetic data 
prep_load_workbook_like_file("meta_data_v2") 

# Look at the content of the dataquieR data frame storage area
prep_list_dataframes() 
##  [1] "columns definition"                  
##  [2] "cross-item_level"                    
##  [3] "dataframe_level"                     
##  [4] "expected_id"                         
##  [5] "item_level"                          
##  [6] "meta_data_v2.xlsx|columns definition"
##  [7] "meta_data_v2.xlsx|cross-item_level"  
##  [8] "meta_data_v2.xlsx|dataframe_level"   
##  [9] "meta_data_v2.xlsx|expected_id"       
## [10] "meta_data_v2.xlsx|item_level"        
## [11] "meta_data_v2.xlsx|missing_table"     
## [12] "meta_data_v2.xlsx|segment_level"     
## [13] "meta_data_v2|columns definition"     
## [14] "meta_data_v2|cross-item_level"       
## [15] "meta_data_v2|dataframe_level"        
## [16] "meta_data_v2|expected_id"            
## [17] "meta_data_v2|item_level"             
## [18] "meta_data_v2|missing_table"          
## [19] "meta_data_v2|segment_level"          
## [20] "missing_table"                       
## [21] "segment_level"

Now you can just use “item_level” in the meta_data argument to check for data type mismatches.

datatype_mismatch <- int_datatype_matrix(study_data = "study_data", 
                                         meta_data = "item_level")

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