eliminate check of non-numeric 1st col for add_totals functions

See #57

R/add_totals.R

@@ -56,12 +56,12 @@ add_totals_row <- function(dat, fill = "-", na.rm = TRUE){
 add_totals_col <- function(dat, na.rm = TRUE){
 
   clean_dat <- clean_names(dat) # bad names will make select_if choke
-  if(ncol(dplyr::select_if(clean_dat, is.numeric)) == 0){stop("data.frame must contain at least one column of class numeric")}
+  if(sum(unlist(lapply(dat, is.numeric))) == 0){stop("input data.frame must contain at least one column of class numeric")}
   row_totals <- clean_dat %>%
     dplyr::select_if(is.numeric) %>%
     dplyr::transmute(Total = rowSums(., na.rm = na.rm))
 
-  dplyr::bind_cols(dat, row_totals) %>%
-    stats::setNames(c(names(dat), "Total")) # put back original names
+  dat$Total <- row_totals$Total
+  dat
 }
 

R/adorn_crosstab.R

@@ -3,7 +3,7 @@
 #' @description
 #' Designed to run on the output of a call to \code{crosstab}, this adds formatting, percentage sign, Ns, totals row/column, and custom rounding to a table of numeric values.  The result is no longer clean data, but it saves time in reporting table results.
 #'
-#' @param crosstab a data.frame with row names in the first column and numeric values in all other columns.  Usually the piped-in result of a call to  \code{crosstab} that included the argument \code{percent = "none"}.
+#' @param dat a data.frame with row names in the first column and numeric values in all other columns.  Usually the piped-in result of a call to  \code{crosstab} that included the argument \code{percent = "none"}.
 #' @param denom the denominator to use for calculating percentages.  One of "row", "col", or "all".
 #' @param show_n should counts be displayed alongside the percentages?
 #' @param digits how many digits should be displayed after the decimal point?
@@ -28,23 +28,23 @@
 
 # take result of a crosstab() call and print a nice result
 #' @export
-adorn_crosstab <- function(crosstab, denom = "row", show_n = TRUE, digits = 1, show_totals = FALSE, rounding = "half to even"){
+adorn_crosstab <- function(dat, denom = "row", show_n = TRUE, digits = 1, show_totals = FALSE, rounding = "half to even"){
   # some input checks
   if(! rounding %in% c("half to even", "half up")){stop("'rounding' must be one of 'half to even' or 'half up'")}
-  check_all_cols_after_first_are_numeric(crosstab)
+  dat[[1]] <- as.character(dat[[1]]) # for type matching when binding the word "Total" on a factor.  Moved up to this line so that if only 1st col is numeric, the function errors
+  if(sum(unlist(lapply(dat, is.numeric))) == 0){stop("input data.frame must contain at least one column of class numeric")} # changed from select_if as it can't handle numbers as col names
 
-  crosstab[[1]] <- as.character(crosstab[[1]]) # for type matching when binding the word "Total" on a factor
 
   showing_col_totals <- (show_totals & denom %in% c("col", "all"))
   showing_row_totals <- (show_totals & denom %in% c("row", "all"))
 
-  complete_n <- complete_n <- sum(crosstab[, -1], na.rm = TRUE) # capture for percent calcs before any totals col/row is added
+  complete_n <- complete_n <- sum(dat[, -1], na.rm = TRUE) # capture for percent calcs before any totals col/row is added
 
-  if(showing_col_totals){ crosstab <- add_totals_col(crosstab) }
-  if(showing_row_totals){ crosstab <- add_totals_row(crosstab) }
-  n_col <- ncol(crosstab)
+  if(showing_col_totals){ dat <- add_totals_col(dat) }
+  if(showing_row_totals){ dat <- add_totals_row(dat) }
+  n_col <- ncol(dat)
 
-  percs <- ns_to_percents(crosstab, denom, total_n = complete_n) # last argument only gets used in the "all" case = no harm in passing otherwise
+  percs <- ns_to_percents(dat, denom, total_n = complete_n) # last argument only gets used in the "all" case = no harm in passing otherwise
 
   # round %s using specified method, add % sign
   percs <- dplyr::mutate_at(percs, dplyr::vars(2:n_col), dplyr::funs(. * 100)) # since we'll be adding % sign - do this before rounding
@@ -56,7 +56,7 @@ adorn_crosstab <- function(crosstab, denom = "row", show_n = TRUE, digits = 1, s
 
     # paste Ns if needed
   if(show_n){
-    result <- paste_ns(percs, crosstab)
+    result <- paste_ns(percs, dat)
   } else{ result <- percs}
 
   as.data.frame(result) # drop back to data.frame from tibble

R/adorn_helpers.R

@@ -44,16 +44,4 @@ fix_parens_whitespace <- function(x){
          fixed = TRUE)
   }
 
-}
-
-# check that all columns in a data.frame beyond the first one are numeric
-check_all_cols_after_first_are_numeric <- function(x){
-  non_numeric_count <- x %>%
-    dplyr::select(-1) %>%
-    lapply(function(x) !is.numeric(x)) %>%
-    unlist %>%
-    sum
-  if(non_numeric_count > 0){
-    stop("all columns after the first one must be numeric")
-  }
-}
+}

R/ns_to_percents.R

@@ -25,10 +25,9 @@
 ns_to_percents <- function(dat, denom = "row", na.rm = TRUE, total_n = NULL){
   # catch bad inputs
   if(! denom %in% c("row", "col", "all")){stop("'denom' must be one of 'row', 'col', or 'all'")}
-  if(ncol(dplyr::select_if(clean_dat, is.numeric)) == 0){stop("data.frame must contain at least one column of class numeric")}
-
   numeric_cols <- which(unlist(lapply(dat, is.numeric)))
-  numeric_cols <- setdiff(numeric_cols, 1) # assume 1st column should not be included so remove it from numeric_cols
+  numeric_cols <- setdiff(numeric_cols, 1) # assume 1st column should not be included so remove it from numeric_cols. Moved up to this line so that if only 1st col is numeric, the function errors
+  if(length(numeric_cols) == 0){stop("input data.frame must contain at least one column of class numeric")}  
 
   if(!is.null(total_n)){
     if(!is.numeric(total_n)){stop("override_n must be numeric")}

tests/testthat/test-ns-to-percents.R

@@ -69,9 +69,9 @@ test_that("NAs handled correctly with na.rm = FALSE", {
   )
 })
 
-test_that("data.frames with non-numeric columns cause failure", {
+test_that("data.frames with no numeric columns beyond the first cause failure", {
   expect_error(ns_to_percents(data.frame(a = 1:2, b = c("hi", "lo"))),
-               "all columns after the first one must be numeric")
+               "input data.frame must contain at least one column of class numeric")
 })
 
 test_that("non-numeric argument to total_n fails", {