This vignette covers the included functions and basic functionality.
A dataset and a meta data file are provided with the package for demonstration of the functions.
Splitting the dataset
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 |
list <-
REDCap_split(
records = redcapcast_data,
metadata = redcapcast_meta,
forms = "all"
) |>
sanitize_split()
str(list)
#> List of 4
#> $ baseline_data_start: tibble [6 × 14] (S3: tbl_df/tbl/data.frame)
#> ..$ record_id : num [1:6] 1 2 3 4 5 6
#> ..$ redcap_event_name : chr [1:6] "inclusion" "inclusion" "inclusion" "inclusion" ...
#> ..$ cpr : chr [1:6] "1203401OB4" "0102342303" "2301569823" "0204051342" ...
#> ..$ 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 ...
#> ..$ age_integer : num [1:6] 83 89 66 117 126 91
#> ..$ sex : chr [1:6] "female" "male" "male" "female" ...
#> ..$ cohabitation : chr [1:6] "Yes" "Yes" "No" NA ...
#> ..$ hypertension : chr [1:6] "No" "No" "Yes" NA ...
#> ..$ diabetes : chr [1:6] "Yes" "No" "Yes" NA ...
#> ..$ region : chr [1:6] "East" "South" "North" NA ...
#> ..$ baseline_data_start_complete: chr [1:6] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
#> ..- attr(*, "problems")=<externalptr>
#> $ mrs : tibble [5 × 6] (S3: tbl_df/tbl/data.frame)
#> ..$ record_id : num [1:5] 1 2 2 3 3
#> ..$ redcap_event_name: chr [1:5] "inclusion" "inclusion" "follow1" "follow1" ...
#> ..$ mrs_assessed : chr [1:5] "Yes" "Yes" "Yes" "Yes" ...
#> ..$ mrs_date : Date[1:5], format: "2023-03-13" "2023-03-07" ...
#> ..$ mrs_score : num [1:5] 1 1 3 2 1
#> ..$ mrs_complete : chr [1:5] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
#> ..- attr(*, "problems")=<externalptr>
#> $ consensus : tibble [0 × 5] (S3: tbl_df/tbl/data.frame)
#> ..$ record_id : num(0)
#> ..$ redcap_event_name : chr(0)
#> ..$ con_mrs : logi(0)
#> ..$ con_calc : logi(0)
#> ..$ consensus_complete: chr(0)
#> ..- attr(*, "problems")=<externalptr>
#> $ 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 ...
#> ..$ 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 ...
#> ..$ event_type : chr [1:13] "TIA" "AIS" "ICH" "ICH" ...
#> ..$ new_event_complete : chr [1:13] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
#> ..- attr(*, "problems")=<externalptr>Reading data from REDCap
This function wraps all the above demonstrated function to get the
dataset, the metadata, apply the REDCap_splitfunction and
then a bit of cleaning. It just cuts outs all the steps for an easier
approach.
The function works very similar to the
REDCapR::redcap_read() in allowing to specify fields,
events and forms for export instead of exporting the whole database and
filtering afterwards. I believe this is a better and safer, focused
approach.
# read_redcap_tables(uri = "YOUR URI", token = "YOUR TOKEN")Pivotting to wider format
redcap_wider(list) |> str()
#> Joining with `by = join_by(record_id)`
#> Joining with `by = join_by(record_id)`
#> Joining with `by = join_by(record_id)`
#> 'data.frame': 6 obs. of 52 variables:
#> $ record_id : num 1 2 3 4 5 6
#> $ cpr : chr "1203401OB4" "0102342303" "2301569823" "0204051342" ...
#> $ inclusion : Date, format: "2023-03-13" "2023-03-01" ...
#> $ inclusion_time : 'hms' num 12:38:49 10:38:57 12:01:07 20:39:19 ...
#> ..- attr(*, "units")= chr "secs"
#> $ dob : Date, format: "1940-03-12" "1934-02-01" ...
#> $ age : num 83 89.1 66.1 117.9 126.2 ...
#> $ age_integer : num 83 89 66 117 126 91
#> $ sex : chr "female" "male" "male" "female" ...
#> $ cohabitation : chr "Yes" "Yes" "No" NA ...
#> $ hypertension : chr "No" "No" "Yes" NA ...
#> $ diabetes : chr "Yes" "No" "Yes" NA ...
#> $ region : chr "East" "South" "North" NA ...
#> $ baseline_data_start_complete: chr "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
#> $ mrs_assessed_inclusion : chr "Yes" "Yes" NA NA ...
#> $ mrs_assessed_follow1 : chr NA "Yes" "Yes" NA ...
#> $ mrs_assessed_follow2 : chr NA NA "Yes" NA ...
#> $ mrs_date_inclusion : Date, format: "2023-03-13" "2023-03-07" ...
#> $ mrs_date_follow1 : Date, format: NA "2023-03-09" ...
#> $ mrs_date_follow2 : Date, format: NA NA ...
#> $ mrs_score_inclusion : num 1 1 NA NA NA NA
#> $ mrs_score_follow1 : num NA 3 2 NA NA NA
#> $ mrs_score_follow2 : num NA NA 1 NA NA NA
#> $ mrs_complete_inclusion : chr "Incomplete" "Incomplete" NA NA ...
#> $ mrs_complete_follow1 : chr NA "Incomplete" "Incomplete" NA ...
#> $ mrs_complete_follow2 : chr NA NA "Incomplete" NA ...
#> $ con_mrs : logi NA NA NA NA NA NA
#> $ con_calc : logi NA NA NA NA NA NA
#> $ consensus_complete : chr NA NA NA NA ...
#> $ event_datetime_1_follow1 : POSIXct, format: NA "2024-01-18 12:49:42" ...
#> $ event_datetime_1_follow2 : POSIXct, format: NA NA ...
#> $ event_age_1_follow1 : num NA NA NA 96 127 NA
#> $ event_age_1_follow2 : num NA NA NA 118 NA NA
#> $ event_type_1_follow1 : chr NA "TIA" "AIS" "TIA" ...
#> $ event_type_1_follow2 : chr NA NA "ICH" "AIS" ...
#> $ new_event_complete_1_follow1: chr NA "Incomplete" "Incomplete" "Complete" ...
#> $ new_event_complete_1_follow2: chr NA NA "Incomplete" "Complete" ...
#> $ event_datetime_2_follow1 : POSIXct, format: NA NA ...
#> $ event_datetime_2_follow2 : POSIXct, format: NA NA ...
#> $ event_datetime_3_follow1 : POSIXct, format: NA NA ...
#> $ event_datetime_3_follow2 : POSIXct, format: NA NA ...
#> $ event_age_2_follow1 : num NA NA NA 105 127 NA
#> $ event_age_2_follow2 : num NA NA NA 118 NA NA
#> $ event_age_3_follow1 : num NA NA NA NA NA NA
#> $ event_age_3_follow2 : num NA NA NA 118 NA NA
#> $ event_type_2_follow1 : chr NA NA "ICH" "TIA" ...
#> $ event_type_2_follow2 : chr NA NA "TIA" "ICH" ...
#> $ event_type_3_follow1 : chr NA NA NA NA ...
#> $ event_type_3_follow2 : chr NA NA "AIS" "Unknown" ...
#> $ new_event_complete_2_follow1: chr NA NA "Incomplete" "Complete" ...
#> $ new_event_complete_2_follow2: chr NA NA "Incomplete" "Incomplete" ...
#> $ new_event_complete_3_follow1: chr NA NA NA NA ...
#> $ new_event_complete_3_follow2: chr NA NA "Incomplete" "Complete" ...