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 | dob | age | age_integer | sex | cohabitation | hypertension | diabetes | region | baseline_data_start_complete | mrs_assessed | mrs_date | mrs_score | mrs_complete | event_date | event_type | new_event_complete |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | inclusion | NA | NA | 1203401OB4 | 2023-03-13 | 1940-03-12 | 83.00239 | 83 | female | Yes | No | Yes | East | Incomplete | Yes | 2023-03-13 | 1 | Incomplete | NA | NA | NA |
| 2 | inclusion | NA | NA | 0102342303 | 2023-03-01 | 1934-02-01 | 89.07780 | 89 | male | Yes | No | No | South | Incomplete | Yes | 2023-03-07 | 1 | Incomplete | NA | NA | NA |
| 2 | follow1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Yes | 2023-03-09 | 3 | Incomplete | NA | NA | NA |
| 2 | follow1 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-03-13 | TIA | Incomplete |
| 3 | inclusion | NA | NA | 2301569823 | 2022-03-08 | 1956-01-23 | 66.12319 | 66 | male | No | Yes | Yes | North | Incomplete | NA | NA | NA | Incomplete | NA | NA | NA |
| 3 | follow1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Yes | 2022-08-16 | 2 | Incomplete | NA | NA | NA |
| 3 | follow2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Yes | 2023-03-13 | 1 | Incomplete | NA | NA | NA |
| 3 | follow1 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2022-11-08 | AIS | Incomplete |
| 3 | follow1 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2022-12-14 | ICH | Incomplete |
| 3 | follow2 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-02-06 | ICH | Incomplete |
| 3 | follow2 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-03-13 | TIA | Incomplete |
| 3 | follow2 | New Event (?) | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-02-28 | AIS | Incomplete |
| 4 | inclusion | NA | NA | 0204051342 | 2023-03-14 | 1905-04-02 | 117.94903 | 117 | female | NA | NA | NA | NA | Incomplete | NA | NA | NA | Incomplete | NA | NA | NA |
| 4 | follow2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Incomplete | NA | NA | NA |
| 4 | follow2 | New Event (?) | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-01-02 | AIS | Complete |
| 4 | follow2 | New Event (?) | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-02-06 | ICH | Incomplete |
| 4 | follow2 | New Event (?) | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2023-03-05 | Unknown | Complete |
| 5 | inclusion | NA | NA | 0201976043 | 2023-03-23 | 1897-01-02 | 126.21751 | 126 | male | No | Yes | Yes | East | Complete | NA | NA | NA | Incomplete | 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 |
| 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 |
| event_date | new_event | NA | text | Date of event | NA | NA | date_dmy | 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 = "repeating")
str(list)
#> List of 2
#> $ :'data.frame': 9 obs. of 17 variables:
#> ..$ record_id : num [1:9] 1 2 2 3 3 3 4 4 5
#> ..$ redcap_event_name : chr [1:9] "inclusion" "inclusion" "follow1" "inclusion" ...
#> ..$ cpr : chr [1:9] "1203401OB4" "0102342303" NA "2301569823" ...
#> ..$ inclusion : Date[1:9], format: "2023-03-13" "2023-03-01" ...
#> ..$ dob : Date[1:9], format: "1940-03-12" "1934-02-01" ...
#> ..$ age : num [1:9] 83 89.1 NA 66.1 NA ...
#> ..$ age_integer : num [1:9] 83 89 NA 66 NA NA 117 NA 126
#> ..$ sex : chr [1:9] "female" "male" NA "male" ...
#> ..$ cohabitation : chr [1:9] "Yes" "Yes" NA "No" ...
#> ..$ hypertension : chr [1:9] "No" "No" NA "Yes" ...
#> ..$ diabetes : chr [1:9] "Yes" "No" NA "Yes" ...
#> ..$ region : chr [1:9] "East" "South" NA "North" ...
#> ..$ baseline_data_start_complete: chr [1:9] "Incomplete" "Incomplete" NA "Incomplete" ...
#> ..$ mrs_assessed : chr [1:9] "Yes" "Yes" "Yes" NA ...
#> ..$ mrs_date : Date[1:9], format: "2023-03-13" "2023-03-07" ...
#> ..$ mrs_score : num [1:9] 1 1 3 NA 2 1 NA NA NA
#> ..$ mrs_complete : chr [1:9] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
#> $ new_event:'data.frame': 9 obs. of 7 variables:
#> ..$ record_id : num [1:9] 2 3 3 3 3 3 4 4 4
#> ..$ redcap_event_name : chr [1:9] "follow1" "follow1" "follow1" "follow2" ...
#> ..$ redcap_repeat_instrument: chr [1:9] "new_event" "new_event" "new_event" "new_event" ...
#> ..$ redcap_repeat_instance : num [1:9] 1 1 2 1 2 3 1 2 3
#> ..$ event_date : Date[1:9], format: "2023-03-13" "2022-11-08" ...
#> ..$ event_type : chr [1:9] "TIA" "AIS" "ICH" "ICH" ...
#> ..$ new_event_complete : chr [1:9] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
list <-
REDCap_split(records = redcapcast_data,
metadata = redcapcast_meta,
forms = "all")
str(list)
#> List of 3
#> $ baseline_data_start:'data.frame': 9 obs. of 13 variables:
#> ..$ record_id : num [1:9] 1 2 2 3 3 3 4 4 5
#> ..$ redcap_event_name : chr [1:9] "inclusion" "inclusion" "follow1" "inclusion" ...
#> ..$ cpr : chr [1:9] "1203401OB4" "0102342303" NA "2301569823" ...
#> ..$ inclusion : Date[1:9], format: "2023-03-13" "2023-03-01" ...
#> ..$ dob : Date[1:9], format: "1940-03-12" "1934-02-01" ...
#> ..$ age : num [1:9] 83 89.1 NA 66.1 NA ...
#> ..$ age_integer : num [1:9] 83 89 NA 66 NA NA 117 NA 126
#> ..$ sex : chr [1:9] "female" "male" NA "male" ...
#> ..$ cohabitation : chr [1:9] "Yes" "Yes" NA "No" ...
#> ..$ hypertension : chr [1:9] "No" "No" NA "Yes" ...
#> ..$ diabetes : chr [1:9] "Yes" "No" NA "Yes" ...
#> ..$ region : chr [1:9] "East" "South" NA "North" ...
#> ..$ baseline_data_start_complete: chr [1:9] "Incomplete" "Incomplete" NA "Incomplete" ...
#> $ mrs :'data.frame': 9 obs. of 6 variables:
#> ..$ record_id : num [1:9] 1 2 2 3 3 3 4 4 5
#> ..$ redcap_event_name: chr [1:9] "inclusion" "inclusion" "follow1" "inclusion" ...
#> ..$ mrs_assessed : chr [1:9] "Yes" "Yes" "Yes" NA ...
#> ..$ mrs_date : Date[1:9], format: "2023-03-13" "2023-03-07" ...
#> ..$ mrs_score : num [1:9] 1 1 3 NA 2 1 NA NA NA
#> ..$ mrs_complete : chr [1:9] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...
#> $ new_event :'data.frame': 9 obs. of 7 variables:
#> ..$ record_id : num [1:9] 2 3 3 3 3 3 4 4 4
#> ..$ redcap_event_name : chr [1:9] "follow1" "follow1" "follow1" "follow2" ...
#> ..$ redcap_repeat_instrument: chr [1:9] "new_event" "new_event" "new_event" "new_event" ...
#> ..$ redcap_repeat_instance : num [1:9] 1 1 2 1 2 3 1 2 3
#> ..$ event_date : Date[1:9], format: "2023-03-13" "2022-11-08" ...
#> ..$ event_type : chr [1:9] "TIA" "AIS" "ICH" "ICH" ...
#> ..$ new_event_complete : chr [1:9] "Incomplete" "Incomplete" "Incomplete" "Incomplete" ...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")