EDCimport (version 0.5.2)

edc_data_warn: Standardized warning system

Description

When checking your data, filter your dataset to get only problematic rows.
Then, use either:

• edc_data_warn() to generate a standardized warning that can be forwarded to the datamanager

• edc_data_warn() to abort the script if the problem is too serious

Database issues should be traced in a separate file, each with an identifying row number, and the file should be shared with the data-manager.
Use edc_data_warnings() to generate the table for such a file.

Usage

edc_data_warn(
  df,
  message,
  ...,
  issue_n = "xx",
  max_subjid = 5,
  csv_path = FALSE,
  col_subjid = get_subjid_cols()
)
edc_data_stop(df, message, ..., issue_n, max_subjid, csv_path, col_subjid)
edc_data_warnings()

Value

df invisibly

Arguments

df

the filtered dataframe

message

the message. Can use cli formats. df can be accessed using the .data special keyword (see example)

...

unused

issue_n

identifying row number

max_subjid

max number of subject ID to show in the message

csv_path

a path to save df in a csv file that can be shared with the DM for more details.

col_subjid

column name for subject ID. Set to NULL to ignore.

Examples

library(dplyr)
tm = edc_example()
load_list(tm)
db0 %>% 
  filter(age>70) %>% 
  edc_data_warn("Age should not be >70", issue_n=1)

db0 %>% 
  filter(age<25) %>% 
  edc_data_warn("Age should not be <25", issue_n=2)

db1 %>% 
  filter(n()>1, .by=SUBJID) %>% 
  edc_data_warn("There are duplicated patients in `db1` ({nrow(.data)} rows)", issue_n=3)

db0 %>% 
  filter(age<25) %>% 
  edc_data_warn("Age should not be <25", issue_n=NULL)
  
edc_data_warnings()

if (FALSE) {
db0 %>% 
  filter(age<25) %>% 
  edc_data_warn("Age should not be <25", csv_path="check/check_age_25.csv")
  
db0 %>% 
  filter(age<25) %>% 
  edc_data_stop("Age should *never* be <25")
}