# REDCapCAST • REDCapCAST

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# REDCapCAST

Source: `vignettes/REDCapCAST.Rmd`
`REDCapCAST.Rmd`

    library(REDCapCAST)

This vignette covers the basics to get you started with the two basic features of REDCapCAST:

- Casting REDCap metadata to create a new REDCap database or extend an existing with a new instrument

- Reading REDCap data in a convenient and focused way, by only getting the data you need, while preserving as much metadata as possible.

## Casting meta data

The easiest way is to use the `shiny_cast()`. You can access a hosted version here or launch it locally like this:

    shiny_cast()

## Reading data from REDCap

To get you started, the easiest way possible, you can use the `easy_redcap()` function (example below).

You will need an API-key for your REDCap server, the uri/URL/address for the API connection (usually the address used for accessing your institutions REDCap server, with an appended “/api/”).

This function includes a few convenience features to ease your further work.

If your project uses repeating instruments possible as a longitudinal project, you can choose to widen the data. If not, the result will be a list of each instrument you have chosen to extract data from. Make sure to specify only the fields or instruments you need, and avoid to save any of the data locally, but always source from REDCap to avoid possibly insecure local storage of sensitive data.

    easy_redcap(
      uri = "YOUR URI",
      project.name = "MY_PROJECT",
      widen.data = TRUE,
      fields = c("record_id", "OTHER FIELDS")
    )

## Splitting the dataset

The `easy_redcap()` function does a few things under the hood. Below are a few examples to show how the nicely formatted output is achieved.

A sample dataset and Data Dictionary/metadata is provided for this demonstration:

    redcapcast_data |> gt::gt()

record_id | redcap_event_name | redcap_repeat_instrument | redcap_repeat_instance | cpr | inclusion | inclusion_time | dob | age | age_integer | sex | cohabitation | hypertension | diabetes | region | baseline_data_start_complete | mrs_assessed | mrs_date | mrs_score | mrs_complete | con_mrs | con_calc | consensus_complete | event_datetime | event_age | event_type | new_event_complete
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
1 | inclusion | NA | NA | 1203401OB4 | 2023-03-13 | 12:38:49 | 1940-03-12 | 83.00239 | 83 | female | Yes | No | Yes | East | Incomplete | Yes | 2023-03-13 | 1 | Incomplete | NA | NA | NA | NA | NA | NA | NA
2 | inclusion | NA | NA | 0102342303 | 2023-03-01 | 10:38:57 | 1934-02-01 | 89.07780 | 89 | male | Yes | No | No | South | Incomplete | Yes | 2023-03-07 | 1 | Incomplete | NA | NA | NA | NA | NA | NA | NA
2 | follow1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Yes | 2023-03-09 | 3 | Incomplete | NA | NA | Incomplete | NA | NA | NA | NA
2 | follow1 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:49:42 | NA | TIA | Incomplete
3 | inclusion | NA | NA | 2301569823 | 2022-03-08 | 12:01:07 | 1956-01-23 | 66.12319 | 66 | male | No | Yes | Yes | North | Incomplete | NA | NA | NA | Incomplete | NA | NA | NA | NA | NA | NA | NA
3 | follow1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Yes | 2022-08-16 | 2 | Incomplete | NA | NA | Incomplete | NA | NA | NA | NA
3 | follow2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Yes | 2023-03-13 | 1 | Incomplete | NA | NA | Incomplete | NA | NA | NA | NA
3 | follow1 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:49:58 | NA | AIS | Incomplete
3 | follow1 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:01 | NA | ICH | Incomplete
3 | follow2 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:05 | NA | ICH | Incomplete
3 | follow2 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:07 | NA | TIA | Incomplete
3 | follow2 | New Event (?) | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:09 | NA | AIS | Incomplete
4 | inclusion | NA | NA | 0204051342 | 2023-03-14 | 20:39:19 | 1905-04-02 | 117.94903 | 117 | female | NA | NA | NA | NA | Incomplete | NA | NA | NA | Incomplete | NA | NA | NA | NA | NA | NA | NA
4 | follow1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Incomplete | NA | NA | Incomplete | NA | NA | NA | NA
4 | follow2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Incomplete | NA | NA | Incomplete | NA | NA | NA | NA
4 | follow1 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2001-04-11 08:39:05 | 96 | TIA | Complete
4 | follow1 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2010-04-11 08:39:25 | 105 | TIA | Complete
4 | follow2 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:19 | 118 | AIS | Complete
4 | follow2 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:22 | 118 | ICH | Incomplete
4 | follow2 | New Event (?) | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-01-18 12:50:24 | 118 | Unknown | Complete
5 | inclusion | NA | NA | 0201976043 | 2023-03-23 | 08:50:31 | 1897-01-02 | 126.21751 | 126 | male | No | Yes | Yes | East | Complete | NA | NA | NA | Incomplete | NA | NA | NA | NA | NA | NA | NA
5 | follow1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Incomplete | NA | NA | Incomplete | NA | NA | NA | NA
5 | follow1 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-04-11 09:00:33 | 127 | AIS | Complete
5 | follow1 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2024-04-11 09:00:41 | 127 | ICH | Complete
6 | inclusion | NA | NA | 1202320122 | 2024-01-25 | 08:49:28 | 1932-02-12 | 91.95261 | 91 | female | No | Yes | No | East | Complete | NA | NA | NA | Incomplete | NA | NA | NA | NA | NA | NA | NA

    redcapcast_meta |> gt::gt()

field_name | form_name | section_header | field_type | field_label | select_choices_or_calculations | field_note | text_validation_type_or_show_slider_number | text_validation_min | text_validation_max | identifier | branching_logic | required_field | custom_alignment | question_number | matrix_group_name | matrix_ranking | field_annotation
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
record_id | baseline_data_start | NA | text | ID | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
cpr | baseline_data_start | NA | text | CPR (Danish civil registration number) | NA | ddmmyyxxxx | NA | NA | NA | y | NA | y | NA | NA | NA | NA | NA
inclusion | baseline_data_start | NA | text | Inclusion date | NA | NA | date_ymd | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
inclusion_time | baseline_data_start | NA | text | Inclusion time | NA | NA | time_hh_mm_ss | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA

dob

baseline_data_start

NA

text

Date of birth (From CPR)

NA

NA

date_ymd

NA

NA

NA

NA

NA

NA

NA

NA

NA

@CALCTEXT(if([cpr]!="", concat(if(mid([cpr], 7, 1)>=0 and mid([cpr], 7, 1)<=3,19, if(mid([cpr], 5, 2)>=0 and mid([cpr], 5, 2)<=36 and mid([cpr], 7, 1)>=4 and mid([cpr], 7, 1)<=4,20, if(mid([cpr], 5, 2)>=37 and mid([cpr], 5, 2)<=99 and mid([cpr], 7, 1)>=4 and mid([cpr], 7, 1)<=4,19, if(mid([cpr], 5, 2)>=0 and mid([cpr], 5, 2)<=57 and mid([cpr], 7, 1)>=5 and mid([cpr], 7, 1)<=5,20, if(mid([cpr], 5, 2)>=58 and mid([cpr], 5, 2)<=99 and mid([cpr], 7, 1)>=5 and mid([cpr], 7, 1)<=5,18, if(mid([cpr], 5, 2)>=0 and mid([cpr], 5, 2)<=57 and mid([cpr], 7, 1)>=6 and mid([cpr], 7, 1)<=6,20, if(mid([cpr], 5, 2)>=58 and mid([cpr], 5, 2)<=99 and mid([cpr], 7, 1)>=6 and mid([cpr], 7, 1)<=6,18, if(mid([cpr], 5, 2)>=0 and mid([cpr], 5, 2)<=57 and mid([cpr], 7, 1)>=7 and mid([cpr], 7, 1)<=7,20, if(mid([cpr], 5, 2)>=58 and mid([cpr], 5, 2)<=99 and mid([cpr], 7, 1)>=7 and mid([cpr], 7, 1)<=7,18, if(mid([cpr], 5, 2)>=0 and mid([cpr], 5, 2)<=57 and mid([cpr], 7, 1)>=8 and mid([cpr], 7, 1)<=8,20, if(mid([cpr], 5, 2)>=58 and mid([cpr], 5, 2)<=99 and mid([cpr], 7, 1)>=8 and mid([cpr], 7, 1)<=8,18, if(mid([cpr], 5, 2)>=0 and mid([cpr], 5, 2)<=36 and mid([cpr], 7, 1)>=9 and mid([cpr], 7, 1)<=9,20, if(mid([cpr], 5, 2)>=37 and mid([cpr], 5, 2)<=99 and mid([cpr], 7, 1)>=9 and mid([cpr], 7, 1)<=9,19,17))))))))))))), mid([cpr], 5, 2), "-",mid([cpr],3, 2), "-", left([cpr], 2) ), ""))

age | baseline_data_start | NA | calc | Age Note: Apparently, the build in datediff() function does not handle counting whole years. This results in wrongly counting age higher around the date of birth. | if([cpr]!="" and [inclusion]!="", datediff([dob], [inclusion], 'y'), "") | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA

age_integer

baseline_data_start

NA

calc

Age integer Note: as opposed to the build in datediff() this handles counting years as integers very well. Calculate decimal years in statistical programming software. In R you can use with(ds, stRoke::age_calc(dob, inclusion)).

if([cpr]!="", left([inclusion], 4)-left([dob], 4) - if(mid([dob], 6, 2) < mid([inclusion], 6, 2) or (mid([dob], 6, 2) = mid([inclusion], 6, 2) and mid([dob], 9, 2) <= mid([inclusion], 9, 2)), 0, 1),"")

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

sex | baseline_data_start | NA | text | Legal sex | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | @CALCTEXT(if([cpr]!="",if((right([cpr],1)=1 or right([cpr],1)=3 or right([cpr],1)=5 or right([cpr],1)=7 or right([cpr],1)=9),"male","female"),""))
cohabitation | baseline_data_start | History and social | radio | Cohabitation | 1, Yes | 2, No | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
hypertension | baseline_data_start | NA | radio | Hypertension | 1, Yes | 2, No | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
diabetes | baseline_data_start | NA | radio | Diabetes | 1, Yes | 2, No | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
region | baseline_data_start | Area | dropdown | Region | 1, North | 2, East | 3, South | 4, West | NA | autocomplete | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
mrs_assessed | mrs | NA | radio | Assesed | 1, Yes | 2, No | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
mrs_date | mrs | NA | text | Assessment date | NA | NA | date_dmy | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
mrs_score | mrs | NA | radio | mRS score | 0, 0 | 1, 1 | 2, 2 | 3, 3 | 4, 4 | 5, 5 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
con_event_note | consensus | NA | descriptive | [follow1_arm_1][event_type][1] : [follow1_arm_1][event_type][2] | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA
con_mrs | consensus | NA | text | Same event type | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | @IF('[follow1_arm_1][event_type][1]'='[follow1_arm_1][event_type][2]',@DEFAULT='pass',@DEFAULT="fail")
con_calc | consensus | NA | text | calc | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | @CALCTEXT(if([follow1_arm_1][event_type][1]=[follow1_arm_1][event_type][2],[follow1_arm_1][event_type][1],"fail"))
event_datetime | new_event | NA | text | Time of event | NA | NA | datetime_seconds_ymd | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA

event_age

new_event

NA

calc

Age at event

if([event_datetime]!="", left([event_datetime], 4)-left([inclusion_arm_1][dob], 4) - if(mid([inclusion_arm_1][dob], 6, 2) < mid([event_datetime], 6, 2) or (mid([inclusion_arm_1][dob], 6, 2) = mid([event_datetime], 6, 2) and mid([inclusion_arm_1][dob], 9, 2) <= mid([event_datetime], 9, 2)), 0, 1),"")

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

event_type | new_event | NA | radio | Neurovascular event | 1, TIA | 2, AIS | 3, ICH | 4, SAH | 99, Unknown | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA

To save the metadata as labels in the dataset, we can save field labels and the choices from radio buttons and dropdown features:

    labelled_data <-
      apply_field_label(
        data = redcapcast_data,
        meta = redcapcast_meta
      ) |>
      apply_factor_labels(meta = redcapcast_meta)

The `REDCap_split` function splits the data set into a list of data.frames.

    list <-
      REDCap_split(
        records = labelled_data,
        metadata = redcapcast_meta,
        forms = "all"
      ) |>
      # Next steps cleans up and removes generic columns
      sanitize_split()
    str(list)
    #> List of 3
    #>  $ baseline_data_start: tibble [6 × 14] (S3: tbl_df/tbl/data.frame)
    #>   ..$ record_id                   : num [1:6] 1 2 3 4 5 6
    #>   .. ..- attr(*, "label")= chr "ID"
    #>   ..$ redcap_event_name           : chr [1:6] "inclusion" "inclusion" "inclusion" "inclusion" ...
    #>   ..$ cpr                         : chr [1:6] "1203401OB4" "0102342303" "2301569823" "0204051342" ...
    #>   .. ..- attr(*, "label")= chr "CPR (Danish civil registration number)"
    #>   ..$ inclusion                   : Date[1:6], format: "2023-03-13" "2023-03-01" ...
    #>   ..$ inclusion_time              : 'hms' num [1:6] 12:38:49 10:38:57 12:01:07 20:39:19 ...
    #>   .. ..- attr(*, "units")= chr "secs"
    #>   ..$ dob                         : Date[1:6], format: "1940-03-12" "1934-02-01" ...
    #>   ..$ age                         : num [1:6] 83 89.1 66.1 117.9 126.2 ...
    #>   .. ..- attr(*, "label")= chr "Age\r\nNote: Apparently, the build in datediff() function does not handle counting whole years. This results in"| __truncated__
    #>   ..$ age_integer                 : num [1:6] 83 89 66 117 126 91
    #>   .. ..- attr(*, "label")= chr "Age integer\r\nNote: as opposed to the build in datediff() this handles counting years as integers very well. C"| __truncated__
    #>   ..$ sex                         : chr [1:6] "female" "male" "male" "female" ...
    #>   .. ..- attr(*, "label")= chr "Legal sex"
    #>   ..$ cohabitation                : 'labelled' chr [1:6] "Yes" "Yes" "No" NA ...
    #>   .. ..- attr(*, "label")= chr "Cohabitation"
    #>   .. ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>   ..$ hypertension                : 'labelled' chr [1:6] "No" "No" "Yes" NA ...
    #>   .. ..- attr(*, "label")= chr "Hypertension"
    #>   .. ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>   ..$ diabetes                    : 'labelled' chr [1:6] "Yes" "No" "Yes" NA ...
    #>   .. ..- attr(*, "label")= chr "Diabetes"
    #>   .. ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>   ..$ region                      : 'labelled' chr [1:6] "East" "South" "North" NA ...
    #>   .. ..- attr(*, "label")= chr "Region"
    #>   .. ..- attr(*, "labels")= Named chr [1:4] "1" "2" "3" "4"
    #>   .. .. ..- attr(*, "names")= chr [1:4] "North" "East" "South" "West"
    #>   ..$ baseline_data_start_complete: chr [1:6] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
    #>  $ mrs                : tibble [5 × 6] (S3: tbl_df/tbl/data.frame)
    #>   ..$ record_id        : num [1:5] 1 2 2 3 3
    #>   .. ..- attr(*, "label")= chr "ID"
    #>   ..$ redcap_event_name: chr [1:5] "inclusion" "inclusion" "follow1" "follow1" ...
    #>   ..$ mrs_assessed     : 'labelled' chr [1:5] "Yes" "Yes" "Yes" "Yes" ...
    #>   .. ..- attr(*, "label")= chr "Assesed"
    #>   .. ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>   ..$ mrs_date         : Date[1:5], format: "2023-03-13" "2023-03-07" ...
    #>   ..$ mrs_score        : 'labelled' num [1:5] 1 1 3 2 1
    #>   .. ..- attr(*, "label")= chr "mRS score"
    #>   .. ..- attr(*, "labels")= Named chr [1:6] "0" "1" "2" "3" ...
    #>   .. .. ..- attr(*, "names")= chr [1:6] "0" "1" "2" "3" ...
    #>   ..$ mrs_complete     : chr [1:5] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
    #>  $ new_event          : tibble [13 × 8] (S3: tbl_df/tbl/data.frame)
    #>   ..$ record_id               : num [1:13] 2 3 3 3 3 3 4 4 4 4 ...
    #>   .. ..- attr(*, "label")= chr "ID"
    #>   ..$ redcap_event_name       : chr [1:13] "follow1" "follow1" "follow1" "follow2" ...
    #>   ..$ redcap_repeat_instrument: chr [1:13] "new_event" "new_event" "new_event" "new_event" ...
    #>   ..$ redcap_repeat_instance  : num [1:13] 1 1 2 1 2 3 1 2 1 2 ...
    #>   ..$ event_datetime          : POSIXct[1:13], format: "2024-01-18 12:49:42" "2024-01-18 12:49:58" ...
    #>   ..$ event_age               : num [1:13] NA NA NA NA NA NA 96 105 118 118 ...
    #>   .. ..- attr(*, "label")= chr "Age at event"
    #>   ..$ event_type              : 'labelled' chr [1:13] "TIA" "AIS" "ICH" "ICH" ...
    #>   .. ..- attr(*, "label")= chr "Neurovascular event"
    #>   .. ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>   ..$ new_event_complete      : chr [1:13] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...

The `easy_redcap()` will then (optionally) continue to widen the data, by transforming the list of data.frames to a single data.frame with one row for each subject/record_id (wide data format):

    wide_data <- redcap_wider(list,
      event.glue = "{.value}____{redcap_event_name}",
      inst.glue = "{.value}____{redcap_repeat_instance}"
    )
    #> Joining with `by = join_by(record_id)`
    #> Joining with `by = join_by(record_id)`
    wide_data |> str()
    #> tibble [6 × 49] (S3: tbl_df/tbl/data.frame)
    #>  $ record_id                         : num [1:6] 1 2 3 4 5 6
    #>   ..- attr(*, "label")= chr "ID"
    #>  $ cpr                               : chr [1:6] "1203401OB4" "0102342303" "2301569823" "0204051342" ...
    #>   ..- attr(*, "label")= chr "CPR (Danish civil registration number)"
    #>  $ inclusion                         : Date[1:6], format: "2023-03-13" "2023-03-01" ...
    #>  $ inclusion_time                    : 'hms' num [1:6] 12:38:49 10:38:57 12:01:07 20:39:19 ...
    #>   ..- attr(*, "units")= chr "secs"
    #>  $ dob                               : Date[1:6], format: "1940-03-12" "1934-02-01" ...
    #>  $ age                               : num [1:6] 83 89.1 66.1 117.9 126.2 ...
    #>   ..- attr(*, "label")= chr "Age\r\nNote: Apparently, the build in datediff() function does not handle counting whole years. This results in"| __truncated__
    #>  $ age_integer                       : num [1:6] 83 89 66 117 126 91
    #>   ..- attr(*, "label")= chr "Age integer\r\nNote: as opposed to the build in datediff() this handles counting years as integers very well. C"| __truncated__
    #>  $ sex                               : chr [1:6] "female" "male" "male" "female" ...
    #>   ..- attr(*, "label")= chr "Legal sex"
    #>  $ cohabitation                      : 'labelled' chr [1:6] "Yes" "Yes" "No" NA ...
    #>   ..- attr(*, "label")= chr "Cohabitation"
    #>   ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>  $ hypertension                      : 'labelled' chr [1:6] "No" "No" "Yes" NA ...
    #>   ..- attr(*, "label")= chr "Hypertension"
    #>   ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>  $ diabetes                          : 'labelled' chr [1:6] "Yes" "No" "Yes" NA ...
    #>   ..- attr(*, "label")= chr "Diabetes"
    #>   ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>  $ region                            : 'labelled' chr [1:6] "East" "South" "North" NA ...
    #>   ..- attr(*, "label")= chr "Region"
    #>   ..- attr(*, "labels")= Named chr [1:4] "1" "2" "3" "4"
    #>   .. ..- attr(*, "names")= chr [1:4] "North" "East" "South" "West"
    #>  $ baseline_data_start_complete      : chr [1:6] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
    #>  $ mrs_assessed____inclusion         : 'labelled' chr [1:6] "Yes" "Yes" NA NA ...
    #>   ..- attr(*, "label")= chr "Assesed"
    #>   ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>  $ mrs_assessed____follow1           : 'labelled' chr [1:6] NA "Yes" "Yes" NA ...
    #>   ..- attr(*, "label")= chr "Assesed"
    #>   ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>  $ mrs_assessed____follow2           : 'labelled' chr [1:6] NA NA "Yes" NA ...
    #>   ..- attr(*, "label")= chr "Assesed"
    #>   ..- attr(*, "labels")= Named chr [1:2] "1" "2"
    #>   .. ..- attr(*, "names")= chr [1:2] "Yes" "No"
    #>  $ mrs_date____inclusion             : Date[1:6], format: "2023-03-13" "2023-03-07" ...
    #>  $ mrs_date____follow1               : Date[1:6], format: NA "2023-03-09" ...
    #>  $ mrs_date____follow2               : Date[1:6], format: NA NA ...
    #>  $ mrs_score____inclusion            : 'labelled' num [1:6] 1 1 NA NA NA NA
    #>   ..- attr(*, "label")= chr "mRS score"
    #>   ..- attr(*, "labels")= Named chr [1:6] "0" "1" "2" "3" ...
    #>   .. ..- attr(*, "names")= chr [1:6] "0" "1" "2" "3" ...
    #>  $ mrs_score____follow1              : 'labelled' num [1:6] NA 3 2 NA NA NA
    #>   ..- attr(*, "label")= chr "mRS score"
    #>   ..- attr(*, "labels")= Named chr [1:6] "0" "1" "2" "3" ...
    #>   .. ..- attr(*, "names")= chr [1:6] "0" "1" "2" "3" ...
    #>  $ mrs_score____follow2              : 'labelled' num [1:6] NA NA 1 NA NA NA
    #>   ..- attr(*, "label")= chr "mRS score"
    #>   ..- attr(*, "labels")= Named chr [1:6] "0" "1" "2" "3" ...
    #>   .. ..- attr(*, "names")= chr [1:6] "0" "1" "2" "3" ...
    #>  $ mrs_complete____inclusion         : chr [1:6] "Incomplete" "Incomplete" NA NA ...
    #>  $ mrs_complete____follow1           : chr [1:6] NA "Incomplete" "Incomplete" NA ...
    #>  $ mrs_complete____follow2           : chr [1:6] NA NA "Incomplete" NA ...
    #>  $ event_datetime____1____follow1    : POSIXct[1:6], format: NA "2024-01-18 12:49:42" ...
    #>  $ event_datetime____1____follow2    : POSIXct[1:6], format: NA NA ...
    #>  $ event_age____1____follow1         : num [1:6] NA NA NA 96 127 NA
    #>   ..- attr(*, "label")= chr "Age at event"
    #>  $ event_age____1____follow2         : num [1:6] NA NA NA 118 NA NA
    #>   ..- attr(*, "label")= chr "Age at event"
    #>  $ event_type____1____follow1        : 'labelled' chr [1:6] NA "TIA" "AIS" "TIA" ...
    #>   ..- attr(*, "label")= chr "Neurovascular event"
    #>   ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>  $ event_type____1____follow2        : 'labelled' chr [1:6] NA NA "ICH" "AIS" ...
    #>   ..- attr(*, "label")= chr "Neurovascular event"
    #>   ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>  $ new_event_complete____1____follow1: chr [1:6] NA "Incomplete" "Incomplete" "Complete" ...
    #>  $ new_event_complete____1____follow2: chr [1:6] NA NA "Incomplete" "Complete" ...
    #>  $ event_datetime____2____follow1    : POSIXct[1:6], format: NA NA ...
    #>  $ event_datetime____2____follow2    : POSIXct[1:6], format: NA NA ...
    #>  $ event_datetime____3____follow1    : POSIXct[1:6], format: NA NA ...
    #>  $ event_datetime____3____follow2    : POSIXct[1:6], format: NA NA ...
    #>  $ event_age____2____follow1         : num [1:6] NA NA NA 105 127 NA
    #>   ..- attr(*, "label")= chr "Age at event"
    #>  $ event_age____2____follow2         : num [1:6] NA NA NA 118 NA NA
    #>   ..- attr(*, "label")= chr "Age at event"
    #>  $ event_age____3____follow1         : num [1:6] NA NA NA NA NA NA
    #>   ..- attr(*, "label")= chr "Age at event"
    #>  $ event_age____3____follow2         : num [1:6] NA NA NA 118 NA NA
    #>   ..- attr(*, "label")= chr "Age at event"
    #>  $ event_type____2____follow1        : 'labelled' chr [1:6] NA NA "ICH" "TIA" ...
    #>   ..- attr(*, "label")= chr "Neurovascular event"
    #>   ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>  $ event_type____2____follow2        : 'labelled' chr [1:6] NA NA "TIA" "ICH" ...
    #>   ..- attr(*, "label")= chr "Neurovascular event"
    #>   ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>  $ event_type____3____follow1        : 'labelled' chr [1:6] NA NA NA NA ...
    #>   ..- attr(*, "label")= chr "Neurovascular event"
    #>   ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>  $ event_type____3____follow2        : 'labelled' chr [1:6] NA NA "AIS" "Unknown" ...
    #>   ..- attr(*, "label")= chr "Neurovascular event"
    #>   ..- attr(*, "labels")= Named chr [1:5] "1" "2" "3" "4" ...
    #>   .. ..- attr(*, "names")= chr [1:5] "TIA" "AIS" "ICH" "SAH" ...
    #>  $ new_event_complete____2____follow1: chr [1:6] NA NA "Incomplete" "Complete" ...
    #>  $ new_event_complete____2____follow2: chr [1:6] NA NA "Incomplete" "Incomplete" ...
    #>  $ new_event_complete____3____follow1: chr [1:6] NA NA NA NA ...
    #>  $ new_event_complete____3____follow2: chr [1:6] NA NA "Incomplete" "Complete" ...

Transfer suffixes to labels:

    wide_data_suffixes <- wide_data |> suffix2label()

## Creating a nice table

    wide_data_suffixes |>
      as_factor()|>
      dplyr::select(sex, hypertension, diabetes,mrs_score____follow2) |>
      gtsummary::tbl_summary(type = gtsummary::all_dichotomous() ~ "categorical")

Characteristic | N = 61
--- | ---
Legal sex |
female | 3 (50%)
male | 3 (50%)
Hypertension |
Yes | 3 (60%)
No | 2 (40%)
Unknown | 1
Diabetes |
Yes | 3 (60%)
No | 2 (40%)
Unknown | 1
mRS score (follow2) |
0 | 0 (0%)
1 | 1 (100%)
2 | 0 (0%)
3 | 0 (0%)
4 | 0 (0%)
5 | 0 (0%)
Unknown | 5
1 n (%)