moved vignettes out

.Rbuildignore

@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^\.github$

.Rprofile

@@ -0,0 +1,10 @@
+options(
+  renv.settings.snapshot.type = "explicit",
+  renv.config.auto.snapshot = TRUE,
+  renv.config.pak.enabled = TRUE
+)
+source("renv/activate.R")
+
+if (interactive()) {
+  suppressMessages(require(usethis))
+}

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: stRoke
 Title: Clinical Stroke Research
-Version: 23.9.1
+Version: 24.2.1
 Authors@R: 
     person("Andreas Gammelgaard", "Damsbo", , "agdamsbo@clin.au.dk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-7559-1154"))
@@ -17,7 +17,7 @@ BugReports: https://github.com/agdamsbo/stRoke/issues
 License: GPL-3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 LazyData: true
 Suggests:
     covr,
@@ -25,13 +25,18 @@ Suggests:
     knitr,
     rmarkdown,
     spelling,
-    testthat (>= 3.0.0)
-Language: en-US
+    usethis,
+    testthat,
+    git2r,
+    pak,
+    here,
+    rhub
 Config/testthat/edition: 3
 Imports: 
     calendar,
     dplyr,
     ggplot2,
+    glue,
     grDevices,
     gtsummary,
     lubridate,
@@ -39,7 +44,10 @@ Imports:
     rankinPlot,
     stats,
     tidyr,
-    utils
+    utils,
+    tibble,
+    tidyselect
 Depends: 
     R (>= 2.10)
 VignetteBuilder: knitr
+Language: en-US

NAMESPACE

@@ -11,10 +11,12 @@ export(cpr_check)
 export(cpr_dob)
 export(cpr_female)
 export(ds2dd)
+export(ds2ical)
 export(files_filter)
 export(generic_stroke)
 export(index_plot)
 export(label_select)
+export(mfi_domains)
 export(n_chunks)
 export(pase_calc)
 export(quantile_cut)

NEWS.md

@@ -1,3 +1,15 @@
+# stRoke 24.2.1
+
+### Functions:
+
+* NEW: `ds2ical()` converts data set to ical format with easy glue string for summary and description. Export .ics file with `calendar::ic_write()`.
+
+* UPDATE: `pase_calc()` updated for uniform column naming in output as well as streamlining the function a bit. 
+
+* UPDATE: `add_padding()` updated to include option to add leading and/or tailing string with `lead` or `tail`.
+
+* NEW: `mfi_calc()` calculates domain scores from the MFI questionnaire. Takes data frame of 20 ordered as the questionnaire. Default is to reverse questions with reverse scoring.
+
 # stRoke 23.9.1
 
 ### Functions:

R/add_padding.R

@@ -4,13 +4,15 @@
 #' @param length final string length
 #' @param after if padding should be added after as opposed to default before
 #' @param pad padding string of length 1
+#' @param lead leading string for all. Number or character vector. Cycled.
+#' @param tail tailing string for all. Number or character vector. Cycled.
 #'
 #' @return vector or character strings of same length.
 #' @export
 #'
 #' @examples
-#' add_padding(sample(1:200,5))
-add_padding <- function(d,length=NULL,after=FALSE,pad="0"){
+#' add_padding(sample(1:200,5),tail="AA",lead=c(2,3,"e"))
+add_padding <- function(d,length=NULL,after=FALSE,pad="0",lead=NULL,tail=NULL){
   if (!is.vector(d)) {
     stop("Please supply vector")
   }
@@ -31,8 +33,11 @@ add_padding <- function(d,length=NULL,after=FALSE,pad="0"){
     paste(rep(pad,i),collapse="")}))
   
   if (after) {
-    paste0(d,ps)
+    out <- paste0(d,ps)
   } else {
-    paste0(ps,d)
+    out <- paste0(ps,d)
   }
+  
+  paste0(lead,out,tail)
+
 }

R/ds2ical.R

@@ -0,0 +1,65 @@
+#' Convert data set to ical file
+#'
+#' @param data data set
+#' @param start dplyr style event start datetime column name. Data or datetime
+#' object.
+#' @param end dplyr style event end datetime column name. Data or datetime
+#' object.
+#' @param location dplyr style event location column name
+#' @param summary.glue.string character string to pass to glue::glue() for event
+#' name (summary). Can take any column from data set.
+#' @param description.glue.string character string to pass to glue::glue() for
+#' event description. Can take any column from data set.
+#'
+#' @return tibble of class "ical"
+#' @export
+#'
+#' @examples
+#' df <- dplyr::tibble(
+#'   start = c(Sys.time(), Sys.time() + lubridate::days(2)),
+#'   id = c("1", 3), assessor = "A", location = "111", note = c(NA, "OBS")
+#' ) |>
+#'   dplyr::mutate(end = start + lubridate::hours(2))
+#' df |> ds2ical()
+#' df |> ds2ical(summary.glue.string = "ID {id} [{assessor}] {note}")
+#' # Export .ics file: (not run)
+#' ical <- df |> ds2ical(start, end, location,
+#' description.glue.string = "{note}")
+#' # ical |> calendar::ic_write(file=here::here("calendar.ics"))
+ds2ical <- function(data,
+                    start = start,
+                    end = end,
+                    location = NULL,
+                    summary.glue.string = "ID {id} [{assessor}]",
+                    description.glue.string = NULL) {
+  ds <- data |>
+    dplyr::mutate(
+      SUMMARY = glue::glue(summary.glue.string, .na = ""),
+      DTSTART = {{ start }},
+      DTEND = {{ end }},
+      LOCATION = {{ location }}
+    )
+  
+  if (!is.null(description.glue.string)) {
+    ds <- dplyr::mutate(ds,
+                        DESCRIPTION = glue::glue(
+                          description.glue.string,
+                          .na = ""
+                        )
+    )
+  }
+  
+  ds |>
+    dplyr::select(tidyselect::any_of(c("SUMMARY",
+                                       "DTSTART",
+                                       "DTEND",
+                                       "LOCATION",
+                                       "DESCRIPTION"))) |>
+    (\(x){
+      x |>
+        dplyr::mutate(UID = replicate(nrow(x), calendar::ic_guid()))
+    })() |>
+    dplyr::filter(!is.na(DTSTART)) |>
+    # dplyr::filter(dplyr::if_any(DTSTART, Negate(is.na))) |> 
+    calendar::ical()
+}

R/mfi_calc.R

@@ -0,0 +1,79 @@
+#' Title
+#'
+#' @param d data frame or tibble
+#' @param var numeric vector of indices of columns to reverse
+#'
+#' @return data.frame or tibble depending on input
+#'
+#' @examples
+#' # rep_len(sample(1:5),length.out = 100) |> matrix(ncol=10) |> multi_rev(2:4)
+multi_rev <- function(d, var){
+  # Forcing and coercing to numeric
+  dm <- d |> as.matrix() |> 
+    as.numeric()|> 
+    matrix(ncol=ncol(d)) |> 
+    data.frame()
+  
+  # Reversing everything (fast enough not to subset)
+  dr <- range(dm,na.rm=TRUE) |> sum()-dm
+  
+  # Inserting reversed scores in correct places
+  for (i in var){
+    dm[i] <- dr[i]
+  }
+  
+  if (tibble::is_tibble(d)){
+    tibble::tibble(dm)
+  } else {
+      dm
+    }
+
+}
+
+#' MFI domain score calculator
+#'
+#' @param ds data set of MFI scores, 20 columns
+#' @param reverse.vars variables/columns to reverse
+#' @param reverse reverse scoring
+#'
+#' @return tibble of domain scores
+#' @export
+#'
+#' @examples
+#' mfi_mess <- data.frame(matrix(
+#' sample(c(" 1. ", "2. -A", "3.", " 4  ", "5.", NA),200,replace=TRUE),ncol=20))
+#' mfi_mess |> mfi_domains()
+mfi_domains <-
+  function(ds,
+           reverse = TRUE,
+           reverse.vars = c(2, 5, 9, 10, 13, 14, 16, 17, 18, 19)) {
+    
+  if(ncol(ds)!=20){
+    stop("The supplied dataset should only contain the 20 MFI subscores")}
+  
+  # Subscore indexes
+  indexes <- list(
+    data.frame(grp="gen", ndx=c(1, 5, 12, 16)),
+    data.frame(grp="phy", ndx=c(2, 8, 14, 20)),
+    data.frame(grp="act", ndx=c(3, 6, 10, 17)),
+    data.frame(grp="mot", ndx=c(4, 9, 15, 18)),
+    data.frame(grp="men", ndx=c(7, 11, 13, 19))
+  ) |> dplyr::bind_rows() |> dplyr::arrange(ndx)
+  
+  # Removes padding and converts to numeric
+  ds_n <- ds |> dplyr::mutate_if(is.factor, as.character) |> 
+    dplyr::mutate(dplyr::across(tidyselect::everything(),
+                  # Removes everything but the leading alphanumeric character
+                  # Data should be cleaned accordingly
+                  ~str_extract(d=.,pattern="[[:alnum:]]")))
+  
+  # Assumes reverse scores are not correctly reversed
+  if (reverse){ds_n <- ds_n |> multi_rev(var=reverse.vars)}
+  
+  # Domain wise summations
+  split.default(ds_n, factor(indexes$grp)) |> 
+    lapply(function(x){
+      apply(x, MARGIN = 1, sum, na.ignore=FALSE)
+    }) |> dplyr::bind_cols()
+  
+  }

R/pase_calc.R

@@ -3,6 +3,7 @@
 #' Calculates PASE score from raw questionnaire data.
 #' @param ds data set
 #' @param adjust_work flag to set whether to include 10b type 1. 
+#' @param consider.missing character vector of values considered missing.
 #' Default is TRUE.
 #'
 #' @return data.frame
@@ -27,10 +28,16 @@
 #' 
 #' @examples
 #' summary(pase_calc(stRoke::pase)[,13])
+#' str(pase_calc(stRoke::pase))
 #' 
-pase_calc <- function(ds, adjust_work = FALSE) {
+pase_calc <- function(ds, 
+                      adjust_work = FALSE, 
+                      consider.missing = c("Not available")) {
   
-  if (ncol(ds) != 21) stop("supplied data set has to contain exactly 21 columns")
+  if (ncol(ds) != 21) {
+    stop("supplied data set has to contain exactly 21 columns.
+         Formatting should follow the stRoke::pase data set.")
+    }
   
   pase <- ds
   
@@ -41,10 +48,20 @@ pase_calc <- function(ds, adjust_work = FALSE) {
   pase <- do.call(data.frame, lapply(pase, as.character))
   
   ## Missings and incompletes
+  # Cosidered missing if all data is missing
   missings <- apply(apply(ds, 2, is.na), 1, all)
+  
+  # Considered incomplete if any entry in main answers is missing
+  mains <- grep("([0-9]{2}|(09[a-d]))$",colnames(pase))
+  
+  if (length(mains)!=13){
+    stop("The supplied dataset does not contain expected variable names.
+         Please run str(stRoke::pase) and format your data accordingly.")
+    }
+  
   incompletes <-
-    apply(sapply(ds[, c(1, 3, 5, 7, 9, 11, 13:20)], function(x) {
-      x == "Not available" | is.na(x)
+    apply(sapply(ds[, mains], function(x) {
+      x %in% consider.missing | is.na(x)
     }), 1, any)
   
   names(pase) <- c(
@@ -78,7 +95,7 @@ pase_calc <- function(ds, adjust_work = FALSE) {
   
   ## PASE 2-6
   
-  pase_weigths <- list(
+  pase_weights <- list(
     "1" = c(
       "1" = 0.11,
       "2" = 0.32,
@@ -104,37 +121,45 @@ pase_calc <- function(ds, adjust_work = FALSE) {
   
   pase_score_26 <- lapply(seq_along(pase_list[2:6]), function(x) {
     df <- pase_list[2:6][[x]]
-    score <- c()
+    # score <- c()
     
     ## =====================
     ## Checking labelling
-    if (!all(range(suppressWarnings(as.numeric(substr(
-      df[, 1], 1, 1
-    ))), na.rm = TRUE) == c(0, 3))) {
-      stop("Labelling of 02-06 should start with a number ranging 1-4")
+    if (!all(stRoke::str_extract(df[, 1], "^[0-3]") |> 
+             as.numeric() |> 
+             range(na.rm = TRUE) == c(0, 3))) {
+      stop("Labelling of 02-06 should start with a number ranging 0-3")
     }
+    
+    if (!all(stRoke::str_extract(df[, 2], "^[1-4]") |> 
+             as.numeric() |> 
+             range(na.rm = TRUE) == c(1, 4))) {
+      stop("Labelling of 02-06 subscores should start with a number ranging 1-4")
+    }
+    
     ## =====================
     
-    for (i in seq_len(nrow(df))) {
-      # Setting categories from numbers
-      n1 <- suppressWarnings(as.numeric(substr(df[, 1][i], 1, 1)))
+    ## Extracting the first string element in main entry
+    n1 <- stRoke::str_extract(df[, 1],"^[0-3]") |> as.numeric()
+    ## Extracting the first string element in subentry
+    n2 <- stRoke::str_extract(df[, 2],"^[1-4]") |> as.numeric()
+    
+    score <- c()
+    for (i in seq_along(n1)) {
       
-      # Using if statement to calculate row wise
-      if (n1 %in% c(1:3)) {
-        # Second category
-        n2 <- suppressWarnings(as.numeric(substr(df[, 2][i], 1, 1)))
-        score[i] <- pase_weigths[[n1]][n2] * pase_multip_26[x] 
-        
-      } else if (n1 %in% 0) {
-        score[i] <- 0
+      ind1 <- match(n1[i],seq_along(pase_weights))
+      
+      if (is.na(ind1)){
+        score[i] <- n1[i]
       } else {
-        score[i] <- NA
+        score[i] <- pase_weights[[ind1]][n2[i]] * pase_multip_26[x] 
       }
+      
     }
     score
   })
   
-  names(pase_score_26) <- paste0("score_", names(pase_list[2:6]))
+  names(pase_score_26) <- paste0("pase_score_", names(pase_list[2:6]))
   
   ## PASE 7-9d
   pase_multip_79 <- c(25, 25, 30, 36, 20, 35)
@@ -147,7 +172,7 @@ pase_calc <- function(ds, adjust_work = FALSE) {
     ) * pase_multip_79))
   
   names(pase_score_79) <-
-    paste0("score_", sub("pase", "", names(pase_score_79)))
+    paste0("pase_score_", sub("pase","",names(pase_score_79)))
   
   ## PASE 10
   ## Completely ignores if 10b is not completed
@@ -157,15 +182,15 @@ pase_calc <- function(ds, adjust_work = FALSE) {
     # Only includes work time if 10b is != 1
     pase_score_10[substr(pase_list[[10]][[3]],1,1) == "1"] <- 0
     # Consequently consider "Not available" in 10b as incomplete
-    incompletes[ds[,21] == "Not available" & !incompletes & !is.na(incompletes)] <- TRUE
+    incompletes[ds[,21] %in% consider.missing & !incompletes & !is.na(incompletes)] <- TRUE
   }
   
   pase_score <- cbind(pase_score_26, pase_score_79, pase_score_10)
   
   data.frame(
     pase_score,
-    score_sum = rowSums(pase_score, na.rm = TRUE),
-    score_missings = missings,
-    score_incompletes = incompletes
+    pase_score_sum = rowSums(pase_score, na.rm = TRUE),
+    pase_score_missings = missings,
+    pase_score_incompletes = incompletes
   )
 }

inst/WORDLIST

@@ -1,15 +1,14 @@
 Andreas
-CMD
 Changelog
 Codecov
 DDMMYY
 DOI
-DataDictionary
 Gammelgaard
 Github
 Kraglund
 Labelling
 METACRAN
+MFI
 NA's
 OLR
 ORCID
@@ -43,11 +42,13 @@ colouring
 cpr
 cprs
 daDoctoR
+datetime
 ddmmyy
 ddmmyyxxxx
 difftime
 dk
 doi
+dplyr
 ds
 eeptools
 eg
@@ -82,6 +83,7 @@ sapply
 stackoverflow
 strsplit
 teppo
+tibble
 vapply
 vec
 winP

man/add_padding.Rd

@@ -4,7 +4,14 @@
 \alias{add_padding}
 \title{Add padding to string}
 \usage{
-add_padding(d, length = NULL, after = FALSE, pad = "0")
+add_padding(
+  d,
+  length = NULL,
+  after = FALSE,
+  pad = "0",
+  lead = NULL,
+  tail = NULL
+)
 }
 \arguments{
 \item{d}{vector of strings or numbers}
@@ -14,6 +21,10 @@ add_padding(d, length = NULL, after = FALSE, pad = "0")
 \item{after}{if padding should be added after as opposed to default before}
 
 \item{pad}{padding string of length 1}
+
+\item{lead}{leading string for all. Number or character vector. Cycled.}
+
+\item{tail}{tailing string for all. Number or character vector. Cycled.}
 }
 \value{
 vector or character strings of same length.
@@ -22,5 +33,5 @@ vector or character strings of same length.
 Add padding to string
 }
 \examples{
-add_padding(sample(1:200,5))
+add_padding(sample(1:200,5),tail="AA",lead=c(2,3,"e"))
 }

man/ds2ical.Rd

@@ -0,0 +1,51 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ds2ical.R
+\name{ds2ical}
+\alias{ds2ical}
+\title{Convert data set to ical file}
+\usage{
+ds2ical(
+  data,
+  start = start,
+  end = end,
+  location = NULL,
+  summary.glue.string = "ID {id} [{assessor}]",
+  description.glue.string = NULL
+)
+}
+\arguments{
+\item{data}{data set}
+
+\item{start}{dplyr style event start datetime column name. Data or datetime
+object.}
+
+\item{end}{dplyr style event end datetime column name. Data or datetime
+object.}
+
+\item{location}{dplyr style event location column name}
+
+\item{summary.glue.string}{character string to pass to glue::glue() for event
+name (summary). Can take any column from data set.}
+
+\item{description.glue.string}{character string to pass to glue::glue() for
+event description. Can take any column from data set.}
+}
+\value{
+tibble of class "ical"
+}
+\description{
+Convert data set to ical file
+}
+\examples{
+df <- dplyr::tibble(
+  start = c(Sys.time(), Sys.time() + lubridate::days(2)),
+  id = c("1", 3), assessor = "A", location = "111", note = c(NA, "OBS")
+) |>
+  dplyr::mutate(end = start + lubridate::hours(2))
+df |> ds2ical()
+df |> ds2ical(summary.glue.string = "ID {id} [{assessor}] {note}")
+# Export .ics file: (not run)
+ical <- df |> ds2ical(start, end, location,
+description.glue.string = "{note}")
+# ical |> calendar::ic_write(file=here::here("calendar.ics"))
+}

man/mfi_domains.Rd

@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mfi_calc.R
+\name{mfi_domains}
+\alias{mfi_domains}
+\title{MFI domain score calculator}
+\usage{
+mfi_domains(
+  ds,
+  reverse = TRUE,
+  reverse.vars = c(2, 5, 9, 10, 13, 14, 16, 17, 18, 19)
+)
+}
+\arguments{
+\item{ds}{data set of MFI scores, 20 columns}
+
+\item{reverse}{reverse scoring}
+
+\item{reverse.vars}{variables/columns to reverse}
+}
+\value{
+tibble of domain scores
+}
+\description{
+MFI domain score calculator
+}
+\examples{
+mfi_mess <- data.frame(matrix(
+sample(c(" 1. ", "2. -A", "3.", " 4  ", "5.", NA),200,replace=TRUE),ncol=20))
+mfi_mess |> mfi_domains()
+}

man/multi_rev.Rd

@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/mfi_calc.R
+\name{multi_rev}
+\alias{multi_rev}
+\title{Title}
+\usage{
+multi_rev(d, var)
+}
+\arguments{
+\item{d}{data frame or tibble}
+
+\item{var}{numeric vector of indices of columns to reverse}
+}
+\value{
+data.frame or tibble depending on input
+}
+\description{
+Title
+}
+\examples{
+# rep_len(sample(1:5),length.out = 100) |> matrix(ncol=10) |> multi_rev(2:4)
+}

man/pase_calc.Rd

@@ -4,12 +4,14 @@
 \alias{pase_calc}
 \title{PASE score calculator}
 \usage{
-pase_calc(ds, adjust_work = FALSE)
+pase_calc(ds, adjust_work = FALSE, consider.missing = c("Not available"))
 }
 \arguments{
 \item{ds}{data set}
 
-\item{adjust_work}{flag to set whether to include 10b type 1.
+\item{adjust_work}{flag to set whether to include 10b type 1.}
+
+\item{consider.missing}{character vector of values considered missing.
 Default is TRUE.}
 }
 \value{
@@ -41,5 +43,6 @@ set \code{TRUE}.
 }
 \examples{
 summary(pase_calc(stRoke::pase)[,13])
+str(pase_calc(stRoke::pase))
 
 }

renv/.gitignore

@@ -0,0 +1,7 @@
+library/
+local/
+cellar/
+lock/
+python/
+sandbox/
+staging/

renv/settings.json

@@ -0,0 +1,19 @@
+{
+  "bioconductor.version": null,
+  "external.libraries": [],
+  "ignored.packages": [],
+  "package.dependency.fields": [
+    "Imports",
+    "Depends",
+    "LinkingTo"
+  ],
+  "ppm.enabled": null,
+  "ppm.ignored.urls": [],
+  "r.version": null,
+  "snapshot.type": "explicit",
+  "use.cache": true,
+  "vcs.ignore.cellar": true,
+  "vcs.ignore.library": true,
+  "vcs.ignore.local": true,
+  "vcs.manage.ignores": true
+}

vignettes/ds2dd.Rmd

@@ -1,87 +0,0 @@
----
-title: "ds2dd"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
-  %\VignetteIndexEntry{ds2dd}
-  %\VignetteEngine{knitr::rmarkdown}
-  %\VignetteEncoding{UTF-8}
----
-
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
-)
-```
-
-```{r setup}
-library(stRoke)
-```
-
-# Easy data set to data base workflow
-
-This function can be used as a simple tool for creating at data base metadata file for REDCap (called a DataDictionary) based on a given data set file.
-
-## Step 1 - Load your data set
-
-Here we'll use the sample TALOS dataset included with the package.
-
-```{r}
-data("talos")
-ds <- talos
-# As the data set lacks an ID column, one is added
-ds$id <- seq_len(nrow(ds))
-```
-
-## Step 2 - Create the DataDictionary
-
-```{r}
-datadictionary <- ds2dd(ds,record.id = "id",include.column.names = TRUE)
-```
-
-Now additional specifications to the DataDictionary can be made manually, or it can be uploaded and modified manually in the graphical user interface on the web page.
-
-The function will transform column names to lower case and substitute spaces for underscores. The output is a list with the DataDictionary and a vector of new column names for the dataset to fit the meta data.
-
-## Step 3 - Meta data upload
-
-Now the DataDictionary can be exported as a spreadsheet and uploaded or it can be uploaded using the `REDCapR` package (only projects with "Development" status).
-
-Use one of the two approaches below:
-
-### Manual upload
-
-```{r eval=FALSE}
-write.csv(datadictionary$DataDictionary,"datadictionary.csv")
-```
-
-### Upload with `REDCapR`
-
-```{r eval=FALSE}
-REDCapR::redcap_metadata_write(
-  datadictionary$DataDictionary,
-  redcap_uri = keyring::key_get("DB_URI"),
-  token = keyring::key_get("DB_TOKEN")
-)
-```
-
-In the ["REDCap R Handbook"](https://agdamsbo.github.io/redcap-r-handbook/) more is written on interfacing with REDCap in R using the `library(keyring)`to store credentials in [chapter 1.1](https://agdamsbo.github.io/redcap-r-handbook/access.html#sec-getting-access).
-
-## Step 4 - Data upload
-
-The same two options are available for data upload as meta data upload: manual or through `REDCapR`.
-
-Only the latter is shown here.
-
-```{r eval=FALSE}
-# new column names are applied
-colnames(ds) <- datadictionary$`Column names`
-
-REDCapR::redcap_write(
-  ds,
-  redcap_uri = keyring::key_get("DB_URI"),
-  token = keyring::key_get("DB_TOKEN")
-)
-```
-