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a new helper function has been introduced to help matching form names when special characters are in use.
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@ -88,13 +88,17 @@ REDCap_split <- function(records,
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# Process user input
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records <- process_user_input(records)
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metadata <-
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as.data.frame(process_user_input(metadata)) # See issue #12
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as.data.frame(process_user_input(metadata))
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# Process repeat instrument names to match the redcap naming
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records$redcap_repeat_instrument <- clean_redcap_name(records$redcap_repeat_instrument)
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# Get the variable names in the dataset
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vars_in_data <- names(records)
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# Match arg for forms
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forms <- match.arg(forms)
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forms <- match.arg(forms, c("repeating", "all"))
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# Check to see if there were any repeating instruments
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if (forms == "repeating" &&
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@ -76,8 +76,7 @@ read_redcap_tables <- function(uri,
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# Removes any extra characters other than a-z, 0-9 and "_", to mimic raw
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# instrument names.
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if ("redcap_repeat_instrument" %in% names(d)) {
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d$redcap_repeat_instrument <-
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gsub("[^a-z0-9_]", "", gsub(" ", "_", tolower(d$redcap_repeat_instrument)))
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d$redcap_repeat_instrument <- clean_redcap_name(d$redcap_repeat_instrument)
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}
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# Getting metadata
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@ -93,7 +92,7 @@ read_redcap_tables <- function(uri,
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l <- REDCap_split(d,
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m,
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forms = split_forms,
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primary_table_name = "nonrepeating")
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primary_table_name = "")
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# Sanitizing split list by removing completely empty rows apart from colnames
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# in "generics"
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19
R/utils.r
19
R/utils.r
@ -83,9 +83,24 @@ focused_metadata <- function(metadata, vars_in_data) {
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}
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#' clean_redcap_name
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#' @description
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#' Stepwise removal on non-alphanumeric characters, trailing white space,
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#' substitutes spaces for underscores and converts to lower case.
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#' Trying to make up for different naming conventions.
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#'
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#' @param x vector or data frame for cleaning
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#'
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#' @return vector or data frame, same format as input
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#' @export
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#'
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clean_redcap_name <- function(x){
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# function to convert the list of dataframes
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gsub(" ", "_",
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gsub("[' ']$","",
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gsub("[^a-z0-9' '_]", "",
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tolower(x)
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)))}
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#' Sanitize list of data frames
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