Standardized warning system — edc_data

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_stop() to abort the script if the problem is too serious.

Each time edc_data_warn is used, the warning is saved internally so that a summary of all your warnings can be retreived using edc_data_warnings.
The result can be saved into an Excel file using save_edc_data_warnings().

Usage

edc_data_warn(
  df,
  message,
  ...,
  issue_n = "xx",
  max_subjid = 5,
  csv_path = FALSE,
  envir = parent.frame(),
  col_subjid = get_subjid_cols()
)

edc_data_stop(df, message, ..., issue_n, max_subjid, csv_path, envir, col_subjid)

edc_data_warnings()

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.
envir: the environment to evaluate message in.
col_subjid: column name for subject ID. Set to NULL to ignore.

Value

df invisibly

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
db = edc_example()
#> Warning: Option "edc_lookup" has been overwritten.
load_database(db)
enrol %>% 
  filter(age>70) %>% 
  edc_data_warn("Age should not be >70", issue_n=1)
#> Warning: Datasets from this lookup are not available in the global environment.
#> ℹ Did you forget to use `EDCimport::load_database(db)` to load the tables?
#> This warning is displayed once per session.
#> Warning: Issue #01: Age should not be >70 (2 patients: #9 and #12)

enrol %>% 
  filter(age<25) %>% 
  edc_data_warn("Age should not be <25", issue_n=2)
#> Warning: Issue #02: Age should not be <25 (1 patient: #18)

data1 %>% 
  filter(n()>1, .by=subjid) %>% 
  edc_data_warn("There are duplicated patients in `data1` ({nrow(.data)} rows)", issue_n=3)
#> Warning: Issue #03: There are duplicated patients in `data1` (100 rows) (50 patients:
#> #1, #2, #3, #4, #5, …)

enrol %>% 
  filter(age<25) %>% 
  edc_data_warn("Age should not be <25", issue_n=NULL)
#> Warning: Age should not be <25 (1 patient: #18)
  
edc_data_warnings()
#> # A tibble: 3 × 4
#>   issue_n message                                             subjid    data    
#>   <chr>   <chr>                                               <list>    <list>  
#> 1 01      Age should not be >70                               <chr [2]> <tibble>
#> 2 02      Age should not be <25                               <chr [1]> <tibble>
#> 3 03      There are duplicated patients in `data1` (100 rows) <chr>     <tibble>

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