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Minor additions to include cpr and forloeabid for new entries

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This commit is contained in:
AG Damsbo 2022-08-17 13:26:19 +02:00
parent ea0f8f6e1e
commit cd00ade74d
1 changed files with 39 additions and 39 deletions
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78
conversion_upload.R
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@ -63,8 +63,40 @@ for (i in 1:length(r_lup)){
cnames<-c(cnames,colnames(r_lup[[i]]))
}
## -----------------------------------------------------------------------------
## Fix missing record_id's for upload
## -----------------------------------------------------------------------------
## REDCap pull with minimum data
token=names(suppressWarnings(read.csv("/Users/au301842/nottreated_redcap_token.csv",colClasses = "character")))
uri="https://redcap.au.dk/api/"
library(REDCapR)
redcap <- redcap_read_oneshot(
redcap_uri = uri,
token = token,
# forms = "ID",
fields = c("record_id","forloebid")
)$data|>
filter(record_id %in% 1:204) # Selecting only the first 204
## Adding and creating record_id's
ids_r<-full_join(ids,redcap)
n_na<-length(ids_r$record_id[is.na(ids_r$record_id)])
n_id<-max(ids_r$record_id,na.rm=T)
# filter(!is.na(akut_ind))%>%
ids_r$record_id[is.na(ids_r$record_id)]<-(n_id+1):(n_id+n_na) # Simple math
for (i in 1:length(r_lup)){
r_lup[[i]]<-left_join(r_lup[[i]],ids_r)
}
## -----------------------------------------------------------------------------
## Appending suffix for duplicated colnames from different sheets
## -----------------------------------------------------------------------------
dup_names<-unique(cnames[duplicated(cnames)])[-1] # omit the first, "forloebid"
for (i in 1:length(r_lup)){
@ -82,32 +114,14 @@ for (i in 1:length(r_lup)){
}
## -----------------------------------------------------------------------------
## Fix missing record_id's for upload
## Creating ID instrument data - fidly!
## -----------------------------------------------------------------------------
## REDCap pull with minimum data
token=names(suppressWarnings(read.csv("/Users/au301842/nottreated_redcap_token.csv",colClasses = "character")))
uri="https://redcap.au.dk/api/"
id_data<-read_excel("/Volumes/Data/toorisky/2022-02-08_ddsc_dataexport.xlsx", sheet = "Patienter")|>
select(c("cpr","forloebid"))|>
filter(forloebid %in% r_lup$Patienter$forloebid)|>
full_join(ids_r)
library(REDCapR)
redcap <- redcap_read_oneshot(
redcap_uri = uri,
token = token,
fields = c("record_id","forloebid")
)$data
## Adding and creating record_id's
ids_r<-full_join(ids,redcap)
n_na<-length(ids_r$record_id[is.na(ids_r$record_id)])
n_id<-max(ids_r$record_id,na.rm=T)
# filter(!is.na(akut_ind))%>%
ids_r$record_id[is.na(ids_r$record_id)]<-(n_id+1):(n_id+n_na) # Simple math
for (i in 1:length(r_lup)){
r_lup[[i]]<-left_join(r_lup[[i]],ids_r)
}
## -----------------------------------------------------------------------------
## Data dictionary
@ -198,22 +212,8 @@ for (i in 1:length(inst_names)){
exp<-r_lup|>
purrr::reduce(full_join)|>
select(-matches("forloebid"))|>
select(record_id,everything()) # To arrange "record_id" as first column
select(record_id,everything())|> # To arrange "record_id" as first column
full_join(id_data) # Adding cpr and forloebid to the newly added
write.csv(exp,"ddsc_dataset.csv",row.names = FALSE,na="")
## -----------------------------------------------------------------------------
## Dataset export of remaing
## -----------------------------------------------------------------------------
## REDCap faces troubles importing too large data sets. Apparently.
ups <- redcap_read_oneshot(
redcap_uri = uri,
token = token,
fields = "record_id"
)$data
write.csv(exp|>
filter(!record_id %in% ups$record_id),"ddsc_dataset_remain.csv",row.names = FALSE,na="")