R/datasummary_extract.R

In modelsummary: Summary Tables and Plots for Statistical Models and Data: Beautiful, Customizable, and Publication-Ready

#' Extract data from a `tables::tabular` object
#'
#' @noRd
datasummary_extract <- function(tab,
                                fmt = NULL,
                                sparse_header = TRUE,
                                data = NULL) {

  # arbitrary length of no formatting requested
  if (is.null(fmt)) fmt <- '%.50f'

  # formatting specified in the tabular formula
  idx <- unlist(attr(tab, 'format'))
  for (i in seq_along(tab)) {
    # use default where not specified in the tabular formula
    if (is.na(idx[i])) {
      if (is.numeric(tab[[i]])) {
        fmt <- sanitize_fmt(fmt)
        tab[[i]] <- fmt(tab[[i]])
      }
    }
  }

  # extract
  idx <- nrow(tables::colLabels(tab))
  mat <- as.matrix(tab)

  # header
  header <- as.matrix(tables::colLabels(tab))
  stub_width <- ncol(mat) - ncol(header) # before taking the full large header
  for (i in 1:nrow(header)) {
    header[i, ] <- carry_forward(as.vector(header[i,]))
  }

  tmp <- mat[1:nrow(header), , drop = FALSE]
  tmp[1:nrow(header), (stub_width + 1):ncol(mat)] <- header
  header <- tmp


  # main --- TODO: test if main has only one row
  main <- mat[(idx + 1):nrow(mat), , drop = FALSE]
  main <- data.frame(main)

  colnames(main) <- as.vector(mat[idx, ])

  # remove NAs, but not in stub or header
  for (i in (stub_width + 1):ncol(main)) {
    main[, i] <- trimws(main[, i])
    main[, i] <- ifelse(is.na(main[, i]) | is.nan(main[, i]), "", main[, i])
    main[, i] <- ifelse(main[, i] %in% c("NA", "NaN"), "", main[, i])
  }


  # labelled data
  variable_labels <- get_variable_labels_data(data)
  for (i in seq_along(variable_labels)) {
    n <- names(variable_labels)[i]
    v <- variable_labels[i]
    header[header == n] <- v
    for (j in 1:stub_width) {
      main[, j][main[, j] == n] <- v
    }
  }

  # stub_width attribute before return
  attr(main, 'stub_width') <- stub_width

  # escape latex column names
  colnames(main) <- escape_string(colnames(main))

  # 1 header level means colnames are sufficient. return output immediately.
  # this needs to go before definition of header_nocolnames
  if (nrow(header) == 1) {
    return(main)
  }

  # headers matrices without colnames
  header_nocolnames <- header[1:(nrow(header) - 1), , drop = FALSE]

  # header matrices: remove rows with a single label
  header_sparse <- sparsify(header)

  # header flat with sep="||||"
  if (nrow(header_sparse) > 1) {
      cols <- apply(header_sparse, 2, paste, collapse = "||||")
      # remove extraneous levels
      cols <- gsub("^\\|*$", "", cols)
      cols <- gsub("^\\|{4}", "", cols)
      cols <- pad(cols)
  } else {
      cols <- unlist(header_sparse)
  }
  colnames(main) <- cols

  # return
  return(main)

}

# fill-in spanning column labels horizontally
carry_forward <- function(x, empty = '') {
    x <- trimws(x)
    if (length(x) > 1) {
        for (i in 2:length(x)) {
            # carry foward when header is NA, but not "" because that indicates
            # a new higher level span
            if (is.na(x[i])) {
                if (!is.na(x[i - 1]) && !isTRUE(x[i - 1] == "")) {
                    x[i] <- x[i - 1]
                }
            }
        }
    }
    x
}

# utility functions
sparsify <- function(h) {
    unique_na <- function(x) length(unique(base::setdiff(x, ''))) > 1
    idx <- apply(h, 1, unique_na)
    out <- h[idx, , drop = FALSE]
    return(out)
}