R/df_stats.R

Defines functions na.warn df_favstats df_stats cond2sum

Documented in df_stats na.warn

#' @importFrom tidyr gather
#' @importFrom dplyr %>% bind_rows
#' @importFrom rlang is_character f_rhs eval_tidy quos
#' @importFrom stats as.formula na.exclude
NA
utils::globalVariables(c("stat", "value"))


# crude way to convert | to + in formulas

cond2sum <- function(formula) {
  e <- environment(formula)
  res <- as.formula(sub("\\|", "+", format(formula)))
  environment(res) <- e
  res
}


#' Calculate statistics on a variable
#'
#' Creates a data frame of statistics calculated on one variable, possibly for each
#' group formed by combinations of additional variables.
#' The resulting data frame has one column
#' for each of the statistics requested as well as columns for any grouping variables.
#' @inheritParams stats::model.frame
#'
#' @param formula A formula indicating which variables are to be used.
#'   Semantics are approximately as in [lm()] since [stats::model.frame()]
#'   is used to turn the formula into a data frame.  But first conditions and `groups`
#'   are re-expressed into a form that [stats::model.frame()] can interpret.
#'   See details.
#' @param data A data frame or list containing the variables.
#' @param ... Functions used to compute the statistics.  If this is empty,
#'   a default set of summary statistics is used.  Functions used must accept
#'   a vector of values and return either a (possibly named) single value,
#'   a (possibly named) vector of values, or a data frame with one row.
#'   Functions can be specified with character strings, names, or expressions
#'   that look like function calls with the first argument missing.  The latter
#'   option provides a convenient way to specify additional arguments.  See the
#'   examples.
#'   Note: If these arguments are named, those names will be used in the data
#'   frame returned (see details).  Such names may not be among the names of the named
#'   arguments of `df_stats`().
#'
#'   If a function is specified using `::`, be sure to include the trailing
#'   parens, even if there are no additional arguments required.
#'
#' @param groups An expression to be evaluated in `data` and defining (additional) groups.
#'   This isn't necessary, since these can be placed into the formula, but it is provided
#'   for similarity to other functions from the \pkg{mosaic} package.
#' @param drop A logical indicating whether combinations of the grouping
#'   variables that do not occur in `data` should be dropped from the
#'   result.
#' @param fargs Arguments passed to the functions in `...`.
#' @param long_names A logical indicating whether the default names should include the name
#'   of the variable being summarized as well as the summarizing function name in the default
#'   case when names are not derived from the names of the returned object or
#'   an argument name.
#' @param nice_names A logical indicating whether [make.names()] should be
#'   used to force names of the returned data frame to by syntactically valid.
#' @param format One of `"long"` or `"wide"` indicating the desired shape of the
#'   returned data frame.
#' @param sep A character string to separate components of names.  Set to `""` if
#'   you don't want separation.
#' @param na.action A function (or character string naming a function) that determines how NAs are treated.
#'   Options include `"na.warn"` which removes missing data and emits a warning,
#'   `"na.pass"` which includes all of the data,
#'   `"na.omit"` or `"na.exclude"` which silently discard missing data,
#'   and `"na.fail"` which fails if there is missing data.
#'   See \code{link[stats]{na.pass}()} and [na.warn()] for details.
#'   The default is `"na.warn"` unless no function are specified in `...`, in which case
#'   `"na.pass"` is used since the default function reports the number of missing values.
#' @importFrom stats quantile
#'
#' @details
#' Use a one-sided formula to compute summary statistics for the left hand side
#' expression over the entire data.
#' Use a two-sided formula to compute summary statistics for the left hand expression
#' for each combination of levels of the expressions occurring on the right hand side.
#' This is most useful when the left hand side is quantitative and each expression
#' on the right hand side has relatively few unique values.  A function like
#' [mosaic::ntiles()] is often useful to create a few groups of roughly equal size
#' determined by ranges of a quantitative variable.  See the examples.
#'
#' Note that unlike \code{dplyr::\link[dplyr]{summarise}()}, `df_stats()` ignores
#' any grouping defined in `data` if `data` is a grouped `tibble`.
#'

#'
#' @section Cautions Regarding Formulas:
#'
#' The use of `|` to define groups is tricky because (a) [stats::model.frame()]
#' doesn't handle this sort of thing and (b) `|` is also used for logical or.  The
#' current algorithm for handling this will turn the first  occurrence of `|` into an attempt
#' to condition, so logical or cannot be used before conditioning in the formula.
#' If you have need of logical or, we suggest creating a new variable that contains the
#' results of evaluating the expression.
#'
#' Similarly, addition (`+`) is used to separate grouping variables, not for
#' arithmetic.
#'
#' @return A data frame. Names of columns in the resulting data frame consist of three
#'   parts separated by `sep`.
#'   The first part is the argument name, if it exists, else the function.
#'   The second part is the name of the variable being summarised if `long_names == TRUE` and
#'   the first part is the function name, else ""
#'   The third part is the names of the object returned by the summarizing function, if they
#'   exist, else a sequence of consecutive integers or "" if there is only one component
#'   returned by the summarizing function.
#'   See the examples.
#'
#' @examples
#' df_stats( ~ hp, data = mtcars)
#' # There are several ways to specify functions
#' df_stats( ~ hp, data = mtcars, mean, trimmed_mean = mean(trim = 0.1), "median",
#'   range, Q = quantile(c(0.25, 0.75)))
#' # When using ::, be sure to include parents, even if there are no additional arguments.
#' df_stats( ~ hp, data = mtcars, mean = base::mean(), trimmed_mean = base::mean(trim = 0.1))
#'
#' # force names to by syntactically valid
#' df_stats( ~ hp, data = mtcars, Q = quantile(c(0.25, 0.75)), nice_names = TRUE)
#' # shorter names
#' df_stats( ~ hp, data = mtcars, mean, trimmed_mean = mean(trim = 0.1), "median", range,
#'   long_names = FALSE)
#' # wide vs long format
#' df_stats( hp ~ cyl, data = mtcars, mean, median, range)
#' df_stats( hp ~ cyl, data = mtcars, mean, median, range, format = "long")
#' # More than one grouping variable -- 3 ways.
#' df_stats( hp ~ cyl + gear, data = mtcars, mean, median, range)
#' df_stats( hp ~ cyl | gear, data = mtcars, mean, median, range)
#' df_stats( hp ~ cyl, groups = gear, data = mtcars, mean, median, range)
#'
#' # because the result is a data frame, df_stats() is also useful for creating plots
#' if(require(ggformula)) {
#'   gf_violin(hp ~ cyl, data = mtcars, group = ~ cyl) %>%
#'   gf_point(mean_hp ~ cyl, data = df_stats(hp ~ cyl, data = mtcars, mean),
#'     color = ~ "mean") %>%
#'   gf_point(median_hp ~ cyl, data = df_stats(hp ~ cyl, data = mtcars, median),
#'     color = ~"median") %>%
#'   gf_labs(color = "")
#' }
#'
#' # magrittr style piping is also supported
#' if (require(ggformula)) {
#'   mtcars %>%
#'     df_stats(hp ~ cyl, mean, median, range)
#'   mtcars %>%
#'     df_stats(hp ~ cyl + gear, mean, median, range) %>%
#'     gf_point(mean_hp ~ cyl, color = ~ factor(gear)) %>%
#'     gf_line(mean_hp ~ cyl, color = ~ factor(gear))
#' }
#'
#' # can be used with a categorical response, too
#' if (require(mosaic)) {
#'   df_stats(sex ~ substance, data = HELPrct, table, prop_female = prop)
#' }
#' if (require(mosaic)) {
#'   df_stats(sex ~ substance, data = HELPrct, table, props)
#' }
#' @export
#' @importFrom rlang eval_tidy exprs expr quos new_quosure
#' @importFrom stats model.frame aggregate
#'
df_stats <- function(formula, data, ..., drop = TRUE, fargs = list(),
                     sep = "_",
                     format = c("wide", "long"), groups = NULL,
                     long_names = TRUE, nice_names = FALSE,
                     na.action = "na.warn") {

  qdots <- dplyr::quos(...)
  # dots <- rlang::exprs(...)
  format <- match.arg(format)

  if (length(qdots) < 1) {
    qdots <- list(dplyr::quo(df_favstats))
    names(qdots) <- ""
    na.action = "na.pass"
  }

  if (inherits(formula, "data.frame") && inherits(data, "formula")) {
    # switched at birth. Likely because input is piped in
    tmp <- data
    data <- formula
    formula <- tmp
  }
  if ( ! inherits(formula, "formula")) stop("first arg must be a formula")
  if ( ! inherits(data, "data.frame")) stop("second arg must be a data.frame")

  formula <- cond2sum(mosaic_formula_q(reop_formula(formula), groups = groups))

  if (identical(na.action, "na.warn")) na.action <- na.warn

  MF <- model.frame(formula, data, na.action = na.action)

  one_group <- FALSE
  if (ncol(MF) == 1) {
    one_group <- TRUE
    if ("group" %in% names(MF)) {
      MF[, "..group.."] <- 1
    } else {
      MF[, "group"] <- 1
    }
  }


#  if (is.null(fargs) || length(fargs) < 1L) {
  res <-
    lapply(
      qdots,
      function(f) {
        if (inherits(rlang::f_rhs(f), "call")) {
          df_aggregate(MF[, 1], by = MF[, -1, drop = FALSE],
                    FUN = function(x) eval(substitute(x %>% foo, list(foo = rlang::f_rhs(f)))),
                    drop = drop)
        } else {
          df_aggregate(MF[, 1], by = MF[, -1, drop = FALSE],
                    FUN = function(x) do.call(rlang::eval_tidy(f), c(list(x), fargs)),
                    drop = drop)
        }
      }
    )

  # extract argument names from names of list
  arg_names <- names(res)

  d <- ncol(MF) - 1
  groups <- res[[1]][, 1:d, drop = FALSE]

  # res[[i]]$x can have a variety of formats depending on the function.
  # so we have to do some work to get things into our desired format (a
  # traditional data frame with columns that are numeric vectors.

  # res <- lapply(res, function(x) data.frame(lapply(data.frame(x$x), unlist)))

  res0 <- res
  res1 <- lapply(res, function(x) make_df(x$x))
  res <- res1

  # extract result names from data frames just created.
  res_names <- lapply(res1, names)
  res_names <- lapply(res_names, function(x) if(all(x == ".")) NULL else x)

  ncols <- sapply(res, ncol)

  fun_names <-
    sapply(
      qdots,
      function(x) {
        if (rlang::is_character(rlang::f_rhs(x)))
          rlang::f_rhs(x)
        else
          deparse(rlang::f_rhs(x))
      }
      )

  # ignore function names if results are named.

  fun_names <- ifelse(sapply(res_names, is.null), fun_names, "")

  # part1: argument name if exists, else function name
  part1 <-
    rep(ifelse(arg_names == "", fun_names, arg_names), ncols)
  part1 <- gsub("df_favstats", "", part1)

  # part2: variable name or ""
  part2 <-
    rep(ifelse(arg_names == "" & long_names & ! fun_names == "df_favstats",
               deparse(formula[[2]]), ""),
        ncols)
  # no part2 if part1 is empty
  part2 <-
    ifelse(part1 == "", "", part2)

  # part3: res_names if they exist, else numbers or ""
  res_names <-
    ifelse(sapply(res_names, is.null), "", res_names)
  alt_res_names <- lapply(ncols, function(nc) if (nc > 1) format(1:nc) else "")
  part3 <-
    mapply(
      function(r, a) { if (is.null(r) || r == "") a else r },
      res_names, alt_res_names
    )
  part3 <- unlist(part3)

  # paste it all together
  final_names <-
    paste(part1, part2, part3, sep = sep)

  # remove unneccessary seperators
  final_names <- gsub(paste0(sep, sep), sep, final_names)
  final_names <- gsub(paste0(sep, "$"), "", final_names)
  final_names <- gsub(paste0("^", sep), "", final_names)

  # paste groups back in
  res <- do.call(cbind, c(list(groups), res))

  names(res) <- c(names(res)[1:d], unlist(final_names))
  if (nice_names) names(res) <- base::make.names(names(res), unique = TRUE)
  if (one_group) {
    res <- res[, -1, drop = FALSE]
  }
  row.names(res) <- NULL


  # return the appropriate format
  if (format == "long") {
    res %>% tidyr::gather(stat, value, !! -(1:d))
  } else {
    res
  }
}

df_favstats <- function (x, ..., na.rm = TRUE, type = 7)
{
  if (!is.null(dim(x)) && min(dim(x)) != 1)
    warning("Not respecting matrix dimensions.  Hope that's OK.")
  # x <- as.vector(x)
  if (! is.numeric(x)) {
    warning("Auto-converting ", class(x), " to numeric.")
    x <- as.numeric(x)
    if (!is.numeric(x)) stop("Auto-conversion to numeric failed.")
  }

  qq <- if (na.rm)
    stats::quantile(x, na.rm = na.rm, type = type)
  else
    rep(NA, 5)
  val <- data.frame(
    min=qq[1],
    Q1 = qq[2],
    median = qq[3],
    Q3 = qq[4],
    max = qq[5],
    mean = base::mean(x, na.rm = na.rm),
    sd = stats::sd(x, na.rm = na.rm),
    n = base::sum(! is.na(x)),
    missing = base::sum( is.na(x) )
  )
  rownames(val) <- ""
  return(val)
}

#' Exclude Missing Data with Warning
#'
#' Similar to [stats::na.exclude()] this function excludes missing data.
#' When missing data are excluded, a warning message indicating the number of excluded
#' rows is emited as a caution for the user.
#'
#' @export
#' @inheritParams stats::na.exclude

na.warn <- function(object, ...) {
  res <- stats::na.exclude(object, ...)
  n_excluded <- nrow(object) - nrow(res)
  if (n_excluded > 0L) {
    warning(paste0("Excluding ", n_excluded, " rows due to missing data [df_stats()]."), call. = FALSE)
  }
  res
}
ProjectMOSAIC/mosaicCore documentation built on Sept. 22, 2018, 5:27 p.m.