This function search for date columns in every tables and returns the latest date for each patient with the variable it comes from. Useful in survival analysis to get the right censoring time.
Usage
lastnews_table(
except = NULL,
with_ties = FALSE,
show_delta = FALSE,
numeric_id = TRUE,
prefer = NULL,
regex = FALSE,
warn_if_future = TRUE
)Arguments
- except
the datasets/columns that should not be searched. Example: a scheduled visit for which the patient may have died before attending should not be considered.
- with_ties
in case of tie, whether to return the first
origin(FALSE) or all the origins that share this tie (TRUE).- show_delta
whether to compute the difference between the last
preferdate and the actual last date- numeric_id
set to FALSE if the patient ID column is not numeric
- prefer
preferred origins in the event of a tie. Usually the followup table.
- regex
whether to consider
exceptandpreferas regex.- warn_if_future
whether to show a warning about dates that are after the extraction date. Can also be a csv file path to save the warning as csv (see
csv_pathargument in edc_data_warn).
Examples
db = edc_example()
#> Warning: Option "edc_lookup" has been overwritten.
load_database(db)
lastnews_table()
#> # A tibble: 50 × 5
#> subjid last_date origin_data origin_col origin_label
#> <dbl> <date> <chr> <chr> <chr>
#> 1 1 2010-08-01 data2 date4 Date at visit 4
#> 2 2 2010-07-31 data2 date4 Date at visit 4
#> 3 3 2010-07-21 data2 date5 Date at visit 5
#> 4 4 2010-07-23 data3 date10 Date at visit 10
#> 5 5 2010-07-14 data3 date10 Date at visit 10
#> 6 6 2010-07-20 data3 date10 Date at visit 10
#> 7 7 2010-07-28 data3 date9 Date at visit 9
#> 8 8 2010-07-19 data3 date9 Date at visit 9
#> 9 9 2010-08-10 data3 date9 Date at visit 9
#> 10 10 2010-07-30 data3 date10 Date at visit 10
#> # ℹ 40 more rows
lastnews_table(except="data3")
#> # A tibble: 50 × 5
#> subjid last_date origin_data origin_col origin_label
#> <dbl> <date> <chr> <chr> <chr>
#> 1 1 2010-08-01 data2 date4 Date at visit 4
#> 2 2 2010-07-31 data2 date4 Date at visit 4
#> 3 3 2010-07-21 data2 date5 Date at visit 5
#> 4 4 2010-06-19 data2 date6 Date at visit 6
#> 5 5 2010-06-14 data2 date5 Date at visit 5
#> 6 6 2010-06-11 data2 date6 Date at visit 6
#> 7 7 2010-06-16 data2 date6 Date at visit 6
#> 8 8 2010-06-21 data2 date6 Date at visit 6
#> 9 9 2010-05-30 data2 date6 Date at visit 6
#> 10 10 2010-06-11 data2 date6 Date at visit 6
#> # ℹ 40 more rows
lastnews_table(except="data3$date9")
#> # A tibble: 50 × 5
#> subjid last_date origin_data origin_col origin_label
#> <dbl> <date> <chr> <chr> <chr>
#> 1 1 2010-08-01 data2 date4 Date at visit 4
#> 2 2 2010-07-31 data2 date4 Date at visit 4
#> 3 3 2010-07-21 data2 date5 Date at visit 5
#> 4 4 2010-07-23 data3 date10 Date at visit 10
#> 5 5 2010-07-14 data3 date10 Date at visit 10
#> 6 6 2010-07-20 data3 date10 Date at visit 10
#> 7 7 2010-07-11 data3 date10 Date at visit 10
#> 8 8 2010-07-12 data3 date10 Date at visit 10
#> 9 9 2010-07-16 data3 date8 Date at visit 8
#> 10 10 2010-07-30 data3 date10 Date at visit 10
#> # ℹ 40 more rows
lastnews_table(prefer="date10", show_delta=TRUE)
#> # A tibble: 50 × 8
#> subjid last_date origin_data origin_col origin_label preferred_last_date
#> <dbl> <date> <chr> <chr> <chr> <date>
#> 1 1 2010-08-01 data3 date10 Date at visit 10 2010-08-01
#> 2 2 2010-07-31 data3 date10 Date at visit 10 2010-07-31
#> 3 3 2010-07-21 data3 date10 Date at visit 10 2010-07-21
#> 4 4 2010-07-23 data3 date10 Date at visit 10 2010-07-23
#> 5 5 2010-07-14 data3 date10 Date at visit 10 2010-07-14
#> 6 6 2010-07-20 data3 date10 Date at visit 10 2010-07-20
#> 7 7 2010-07-28 data3 date9 Date at visit 9 2010-07-11
#> 8 8 2010-07-19 data3 date9 Date at visit 9 2010-07-12
#> 9 9 2010-08-10 data3 date9 Date at visit 9 2010-07-09
#> 10 10 2010-07-30 data3 date10 Date at visit 10 2010-07-30
#> # ℹ 40 more rows
#> # ℹ 2 more variables: preferred_origin <chr>, delta <drtn>
lastnews_table() %>%
dplyr::count(origin = glue::glue("{origin_data}${origin_col}"),
sort=TRUE)
#> # A tibble: 5 × 2
#> origin n
#> <glue> <int>
#> 1 data3$date10 36
#> 2 data3$date9 10
#> 3 data2$date4 2
#> 4 data2$date5 1
#> 5 data3$date8 1
csv_file = tempfile(fileext=".csv")
lastnews_table(prefer="date9", warn_if_future=csv_file)
#> # A tibble: 50 × 5
#> subjid last_date origin_data origin_col origin_label
#> <dbl> <date> <chr> <chr> <chr>
#> 1 1 2010-08-01 data2 date4 Date at visit 4
#> 2 2 2010-07-31 data2 date4 Date at visit 4
#> 3 3 2010-07-21 data2 date5 Date at visit 5
#> 4 4 2010-07-23 data3 date10 Date at visit 10
#> 5 5 2010-07-14 data3 date10 Date at visit 10
#> 6 6 2010-07-20 data3 date10 Date at visit 10
#> 7 7 2010-07-28 data3 date9 Date at visit 9
#> 8 8 2010-07-19 data3 date9 Date at visit 9
#> 9 9 2010-08-10 data3 date9 Date at visit 9
#> 10 10 2010-07-30 data3 date10 Date at visit 10
#> # ℹ 40 more rows