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