R/shape_estimates.R

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

#' internal function to reshape grouped estimates
#'
#' @keywords internal
#' @noRd
shape_estimates <- function(estimates, shape, conf_level) {

    # default
    if (isTRUE(all.equal(shape$shape_formula, term + statistic ~ model))) {
        return(estimates)
    }

    shape_formula <- shape$shape_formula

    idx <- intersect(colnames(estimates), c("term", "statistic", "group", shape$group_name))

    # long
    out <- data.table::melt(data.table::data.table(estimates),
                            id.vars = idx,
                            variable.name = "model",
                            value.name = "estimate")

    if ("statistic" %in% shape$rhs) {
        out$statistic <- rename_statistics(out$statistic, conf_level = conf_level)
    }

    # use factors to preserve order in `dcast`
    for (col in c("part", "model", "term", shape$group_name, "statistic")) {
      if (col %in% colnames(out)) {
        out[[col]] <- factor(out[[col]], unique(out[[col]]))
      }
    }

    # wide
    out <- data.table::dcast(eval(shape_formula),
                             data = out,
                             value.var = "estimate",
                             sep = "||||")

    data.table::setDF(out)

    out[out == "NA"] <- ""
    out[is.na(out)] <- ""

    # empty columns
    idx <- sapply(out, function(x) !all(x == ""))
    out <- out[, idx, drop = FALSE]

    # empty rows
    idx <- setdiff(colnames(out), c("part", "term", "statistic", "model"))
    tmp <- out[, idx, drop = FALSE]
    idx <- apply(tmp, 1, function(x) !all(x == ""))
    out <- out[idx, ]

    return(out)
}