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clean.R contains a single function that takes the raw tibble from readData() and produces a standardized data frame ready for fitMetaprop(), fitSubMetaprop(), fitMetareg(), and applyCopas(). It renames columns, coerces types, classifies categorical variables, and computes the logit-prevalence outcome and its variance.

​
clean()

clean <- function(tbl, ...) {
  #' Clean the Data Frame
  #'
  #' Clean the data frame by standardizing the variables.
  #'
  #' @param tbl A data frame object
  #'
  #' @return A data frame object

  res <- tbl |>
    dplyr::mutate(
      "Age" = `Patient's age`,
      "Year" = as.integer(`Year of Publication`),
      "Prevalence" = gsub(x = Prevalence, ",", ".") |> as.numeric(),
      "Sample_size" = as.integer(`Sample size`),
      "Inappropriate_indication" = as.integer(`Inappropriate indication`),
      "Continent" = dplyr::case_when(
        grepl(x = Continent, "Asia") ~ "Asia",
        grepl(x = Continent, "Europe") ~ "Europe",
        grepl(x = Continent, "North America") ~ "North America",
        .default =  "Other"
      ) |> factor(levels = c("North America", "Asia", "Europe", "Other")),
      "Setting" = dplyr::case_when(
        grepl(x = Setting, "Hospital") ~ "Hospital Setting",
        .default = "Other"
      ) |> factor(levels = c("Hospital Setting", "Other")),
      "use_guideline" = ifelse(
        `Guideline` == "Yes", "Followed Guideline(s)", "No Guideline"
      ),
      "logit_prevalence" = log(Prevalence / (1 - Prevalence)),
      "var_logit_prevalence" = 1 / (Sample_size * logit_prevalence)
    ) %>%
    set_names(names(.) |> make.names())

  return(res)
}

​
Parameters

​
tbl
data.frame
required
Raw study-level data frame as returned by readData(). Must contain the original column names from the CSV: Patient's age, Year of Publication, Prevalence, Sample size, Inappropriate indication, Continent, Setting, and Guideline.
​
...
any
Additional arguments. Currently unused; reserved for future extensibility.

​
Returns

A data frame with standardized, R-compatible column names. All column names are passed through make.names() as a final step, replacing spaces and special characters with dots.

​
Transformations

Output columnSource columnOperation
AgePatient's ageRenamed; type unchanged
YearYear of PublicationCoerced to integer
PrevalencePrevalenceComma replaced with dot, coerced to numeric
Sample_sizeSample sizeCoerced to integer
Inappropriate_indicationInappropriate indicationCoerced to integer
ContinentContinentFuzzy-matched with grepl into four levels: "North America", "Asia", "Europe", "Other"; stored as ordered factor
SettingSettingDichotomized: rows matching "Hospital" → "Hospital Setting", all others → "Other"; stored as factor
use_guidelineGuideline"Yes" → "Followed Guideline(s)", all others → "No Guideline"
logit_prevalencePrevalenceComputed as log(Prevalence / (1 - Prevalence))
var_logit_prevalenceSample_size, logit_prevalenceComputed as 1 / (Sample_size * logit_prevalence)

​
Usage

targets::tar_load(tbl)
tbl_clean <- clean(tbl)
str(tbl_clean)
In _targets.R: 
tar_target(tbl_clean, clean(tbl))
var_logit_prevalence will be Inf when logit_prevalence equals 0 (i.e., when Prevalence is exactly 0.5). Rows with infinite variance must be excluded from meta-regression. fitMetareg() handles this automatically by subsetting with var_logit_prevalence != Inf before fitting the model.

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