Complete workflow. Comments are lacking a bit
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AG Damsbo
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897d391add
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fd920d2027
@ -3,7 +3,8 @@
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## -----------------------------------------------------------------------------
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##
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## This script is to create a datadictionary for REDCap, to upload data.
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##
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## The data frame consits of severral excel sheets, nad the database already contains data
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## Instead of a complete datadictionary, individual sheets will be converted to instrument files.
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##
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## -----------------------------------------------------------------------------
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@ -20,22 +21,65 @@ library(dplyr)
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setwd("/Volumes/Data/toorisky/")
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dta<-read_dta("Alle apo Aarhus 2018.dta") %>% # Defined dataset
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filter(VaskDiag==1) %>%
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mutate(treatment=factor(case_when(trombolyse!=2&trombektomi!=2 ~ 0,
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trombolyse==2|trombektomi==2 ~ 1)),
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sex.n=factor(ifelse(as.integer(substr(cpr, start = 10, stop = 10)) %%2 == 0,
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"female", "male"))) %>%
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left_join(.,read_excel("2022-02-08_ddsc_dataexport.xlsx", sheet = "Patienter"),
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by=c("ForloebID"="forloebid"))%>%
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left_join(.,read_excel("2022-02-08_ddsc_dataexport.xlsx", sheet = "3 mdr. opf."),
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by=c("ForloebID"="ForloebID"))%>%
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mutate(cpr=cpr.x,
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ID=ID.x)%>%
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dplyr::select(-starts_with("cpr."))%>%
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dplyr::select(-starts_with("ID."))
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dput(excel_sheets("2022-02-08_ddsc_dataexport.xlsx")) # Getting sheet names
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colnames(dta)<-tolower(colnames(dta))
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shts_sel<-c("Patienter", # Selecting relevant sheets
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"Basis-skema",
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"Trombolyse",
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"Trombektomi",
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"3 mdr. opf.")
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inst_names<-c("Patienter", # Renaming to avoid trailing numbers and spaces, as per REDCap
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"Basisskema",
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"Trombolyse",
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"Trombektomi",
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"Followup")
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ids<-read_dta("Alle apo Aarhus 2018.dta") |> # Defined dataset
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filter(VaskDiag==1) |>
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select("ForloebID") |>
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data.frame()
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colnames(ids)<-tolower(colnames(ids))
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## Importing xls sheets and naming list from sheet names
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r_lup<-list()
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for (i in 1:length(shts_sel)){
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xcl<-read_excel("2022-02-08_ddsc_dataexport.xlsx", sheet = shts_sel[i])|>
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dplyr::select(-starts_with("cpr"))|>
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dplyr::select(-starts_with("ID"))|>
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data.frame()
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colnames(xcl)<-tolower(colnames(xcl))
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r_lup[[i]]<-xcl|>
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filter(forloebid %in% ids$forloebid)
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names(r_lup)[i]<-inst_names[i]
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}
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cnames<-c()
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for (i in 1:length(r_lup)){
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cnames<-c(cnames,colnames(r_lup[[i]]))
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}
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## Appending suffix for duplicated colnames from different sheets
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dup_names<-unique(cnames[duplicated(cnames)])[-1] # omit the first, "forloebid"
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for (i in 1:length(r_lup)){
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# Getting sheetname bit
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sht_name<-gsub('[ .]', '', names(r_lup)[i])|>
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substr(1,6)|>
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tolower()|>
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(\(x) paste0("_",x))() # Lambda function in lack of better
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# Pasting
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sht_dup<-paste0(colnames(r_lup[[i]])[colnames(r_lup[[i]]) %in% dup_names],sht_name)
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# Renaming
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colnames(r_lup[[i]])[colnames(r_lup[[i]]) %in% dup_names] <- sht_dup
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}
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## -----------------------------------------------------------------------------
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## Fix missing record_id's for upload
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@ -53,14 +97,17 @@ redcap <- redcap_read_oneshot(
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fields = c("record_id","forloebid")
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)$data
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## Adding and creating record_id's
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ids_r<-full_join(ids,redcap)
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## Joining and adding record_id's
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dta<-full_join(dta,redcap)
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n_na<-length(dta$record_id[is.na(dta$record_id)])
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n_id<-max(dta$record_id,na.rm=T)
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n_na<-length(ids_r$record_id[is.na(ids_r$record_id)])
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n_id<-max(ids_r$record_id,na.rm=T)
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# filter(!is.na(akut_ind))%>%
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dta$record_id[is.na(dta$record_id)]<-(n_id+1):(n_id+n_na) # Simple math
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ids_r$record_id[is.na(ids_r$record_id)]<-(n_id+1):(n_id+n_na) # Simple math
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for (i in 1:length(r_lup)){
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r_lup[[i]]<-left_join(r_lup[[i]],ids_r)
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}
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## -----------------------------------------------------------------------------
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## Data dictionary
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@ -68,32 +115,105 @@ dta$record_id[is.na(dta$record_id)]<-(n_id+1):(n_id+n_na) # Simple math
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setwd("/Users/au301842/nottreated/")
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icname<-colnames(read.csv("examlpe instrument.csv"))
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dd<-data.frame(matrix(ncol = length(icname),
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nrow=ncol(dta))) ## Data frame to collect all
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icname<-colnames(read.csv("example instrument.csv"))
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dd<-data.frame(matrix(ncol = length(icname))) ## Data frame to collect all
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colnames(dd)<-icname
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## Instrument for DataDictionary
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## Format dataset for REDCap upload
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for (i in 1:length(r_lup)){
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dd_i<-data.frame(matrix(ncol = length(icname),nrow = ncol("[["(r_lup,i)))) ## Data frame to collect all
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colnames(dd_i)<-icname ## for easier reading
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## Variable names
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dd[1]<-colnames(dta)
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dd_i[1]<-colnames("[["(r_lup,i))
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## Form Name
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dd[2]<-"ddsc"
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dd_i[2]<-names(r_lup)[i]
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## Field Type
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# dd_i[4]<-ifelse(sapply(r_lup[[i]], class)=="factor","radio","text")
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dd[4]<-"text"
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dd_i[4]<-"text"
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dd[5]<-colnames(dta)
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## Field Label
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## Using original attributes as field labels
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fl<-lapply(r_lup[[i]], attr, "label")
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for (j in 1:length(fl)){
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fl[[j]]<-ifelse(is.null(fl[[j]]),
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names(fl)[[j]],
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fl[[j]])
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## If no attributes, variable name is used as "placeholder"
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}
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dd_i[5]<-unlist(fl)
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## Merge all
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dd<-rbind(dd,dd_i)
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}
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dd_exp<-dd %>%
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filter(!Variable...Field.Name %in% c("forloebid","record_id")) # These fields already exists in the database, and would cause conflicts
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## -----------------------------------------------------------------------------
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## Instrument file
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## Instrument files
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## -----------------------------------------------------------------------------
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write.csv(dd,"ddsc_instrument.csv",row.names = FALSE,na="")
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# Loop for doing it all - nice!
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# Splitting individual instruments from Data Dictionary file
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for (i in 1:length(inst_names)){
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if (!file.exists("instruments/")){ # Creating "instrument" subdirectory if it does not already exist
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dir.create("instruments/", showWarnings = TRUE, recursive = FALSE, mode = "0777")}
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new_path<-paste0("instruments/",inst_names[i]) # Path for instrument folder
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dir.create(new_path, showWarnings = FALSE, recursive = FALSE, mode = "0777") # Creating the folder path
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file.copy("OriginID.txt", # Copying origin file as per REDCap
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new_path)
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dd_exp|>
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filter(Form.Name==inst_names[i])|> # Filtering instrument entries
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write.csv(paste0(new_path,"/instrument.csv"),row.names = FALSE,na="") # Exporting to folder
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setwd(paste0("/Users/au301842/nottreated/",new_path)) # Setting wd in subfolder for zipping
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zip(zipfile = inst_names[i], # Naming the zipfile according to instrument
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files = dir(full.names = TRUE))
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setwd("/Users/au301842/nottreated/")
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zip_file<-list.files(paste0("instruments/",inst_names[i]),
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pattern = ".zip",
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full.names = TRUE)
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file.copy(zip_file, # Copy and remove zip-file for easy upload
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"instruments/")
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file.remove(zip_file)
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}
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## -----------------------------------------------------------------------------
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## Dataset export
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## -----------------------------------------------------------------------------
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write.csv(dta,"ddsc_dataset.csv",row.names = FALSE)
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exp<-r_lup|>
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purrr::reduce(full_join)|>
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select(-matches("forloebid"))|>
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select(record_id,everything()) # To arrange "record_id" as first column
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write.csv(exp,"ddsc_dataset.csv",row.names = FALSE,na="")
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## -----------------------------------------------------------------------------
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## Dataset export of remaing
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## -----------------------------------------------------------------------------
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## REDCap faces troubles importing too large data sets. Apparently.
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ups <- redcap_read_oneshot(
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redcap_uri = uri,
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token = token,
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fields = "record_id"
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)$data
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write.csv(exp|>
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filter(!record_id %in% ups$record_id),"ddsc_dataset_remain.csv",row.names = FALSE,na="")
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