feat: handle both data.frame and numeric vector

R/ds-summary-stats.R

@@ -2,10 +2,13 @@
 #'
 #' @description Range of descriptive statistics for continuous data.
 #'
-#' @param data A \code{data.frame} or \code{tibble}.
+#' @param data An object of type \code{numeric} or \code{data.frame}.
 #' @param ... Column(s) in \code{data}.
 #'
 #' @examples
+#' # numeric data
+#' ds_summary_stats(mtcarz$mpg)
+#'
 #' # single variable
 #' ds_summary_stats(mtcarz, mpg)
 #'
@@ -25,87 +28,95 @@
 #'
 ds_summary_stats <- function(data, ...) {
 
-  check_df(data)
+  if (is.numeric(data)) {
+    print(ds_summary(data))
+  } else {
 
-  var <- rlang::quos(...)
+    check_df(data)
+    var <- rlang::quos(...)
 
-  if (length(var) < 1) {
-    is_num <- sapply(data, is.numeric)
-    if (!any(is_num == TRUE)) {
-      stop("Data has no continuous variables.", call. = FALSE)
+    if (length(var) < 1) {
+      is_num <- sapply(data, is.numeric)
+      if (!any(is_num == TRUE)) {
+        stop("Data has no continuous variables.", call. = FALSE)
+      }
+      data <- data[is_num]
+    } else {
+      data %<>%
+        dplyr::select(!!! var)
+      is_num <- sapply(data, is.numeric)
+      if (!any(is_num == TRUE)) {
+        stop("Data has no continuous variables.", call. = FALSE)
+      }
     }
-    data <- data[is_num]
-  } else {
-    data %<>%
-      dplyr::select(!!! var)
-    is_num <- sapply(data, is.numeric)
-    if (!any(is_num == TRUE)) {
-      stop("Data has no continuous variables.", call. = FALSE)
-    }
-  }
 
-  col_names <- names(data)
-  for (i in col_names) {
-    ds_rule(paste0('Variable: ', i))
-    cat('\n\n')
-    print(ds_summary(data, i))
-    cat('\n\n\n')
+    col_names <- names(data)
+    for (i in col_names) {
+      ds_rule(paste0('Variable: ', i))
+      cat('\n\n')
+      print(ds_summary(data, i))
+      cat('\n\n\n')
+    }
   }
-
 }
 
 ds_summary <- function(data, variable) UseMethod("ds_summary")
 
 ds_summary.default <- function(data, variable) {
 
-  check_df(data)
-  vary     <- rlang::enquo(variable)
-  var_name <- deparse(substitute(variable))
-  check_numeric(data, !! vary, var_name)
+  if (is.numeric(data)) {
+    odata <- data
+    sdata <- na.omit(data)
+  } else {
+    check_df(data)
+    vary     <- rlang::enquo(variable)
+    var_name <- deparse(substitute(variable))
+    check_numeric(data, !! vary, var_name)
 
-  odata <- dplyr::pull(data, !! vary)
+    odata <- dplyr::pull(data, !! vary)
 
-  sdata <-
-    data %>%
-    dplyr::pull(!! vary) %>%
-    na.omit()
+    sdata <-
+      data %>%
+      dplyr::pull(!! vary) %>%
+      na.omit()
+  }
 
   low      <- ds_tailobs(sdata, 5, "low")
   high     <- ds_tailobs(sdata, 5, "high")
   low_val  <- ds_rindex(sdata, low)
   high_val <- ds_rindex(sdata, high)
 
   result <-
-      list(obs      = length(odata),
-           missing  = sum(is.na(odata)),
-           avg      = mean(sdata),
-           tavg     = mean(sdata, trim = 0.05),
-           stdev    = sd(sdata),
-           variance = var(sdata),
-           skew     = ds_skewness(sdata),
-           kurtosis = ds_kurtosis(sdata),
-           uss      = stat_uss(sdata),
-           css      = ds_css(sdata),
-           cvar     = ds_cvar(sdata),
-           sem      = ds_std_error(sdata),
-           median   = median(sdata),
-           mode     = ds_mode(sdata),
-           range    = ds_range(sdata),
-           min      = min(sdata),
-           Max      = max(sdata),
-           iqrange  = IQR(sdata),
-           per99    = quantile(sdata, 0.99),
-           per90    = quantile(sdata, 0.90),
-           per95    = quantile(sdata, 0.95),
-           per75    = quantile(sdata, 0.75),
-           per25    = quantile(sdata, 0.25),
-           per10    = quantile(sdata, 0.10),
-           per5     = quantile(sdata, 0.05),
-           per1     = quantile(sdata, 0.01),
-           lowobs   = low,
-           highobs  = high,
-           lowobsi  = low_val,
-           highobsi = high_val)
+    list(obs      = length(odata),
+         missing  = sum(is.na(odata)),
+         avg      = mean(sdata),
+         tavg     = mean(sdata, trim = 0.05),
+         stdev    = sd(sdata),
+         variance = var(sdata),
+         skew     = ds_skewness(sdata),
+         kurtosis = ds_kurtosis(sdata),
+         uss      = stat_uss(sdata),
+         css      = ds_css(sdata),
+         cvar     = ds_cvar(sdata),
+         sem      = ds_std_error(sdata),
+         median   = median(sdata),
+         mode     = ds_mode(sdata),
+         range    = ds_range(sdata),
+         min      = min(sdata),
+         Max      = max(sdata),
+         iqrange  = IQR(sdata),
+         per99    = quantile(sdata, 0.99),
+         per90    = quantile(sdata, 0.90),
+         per95    = quantile(sdata, 0.95),
+         per75    = quantile(sdata, 0.75),
+         per25    = quantile(sdata, 0.25),
+         per10    = quantile(sdata, 0.10),
+         per5     = quantile(sdata, 0.05),
+         per1     = quantile(sdata, 0.01),
+         lowobs   = low,
+         highobs  = high,
+         lowobsi  = low_val,
+         highobsi = high_val)
 
   class(result) <- "ds_summary"
   return(result)

tests/testthat/test-summary-stats.R

@@ -2,11 +2,19 @@ context("test-summary-stats")
 
 ndata <- dplyr::select(mtcarz, mpg)
 
-test_that("output from ds_summary is as expected", {
+test_that("output from ds_summary is as expected when data is a data.frame", {
 
   actual <- round(ds_summary(mtcarz, mpg)$variance, 2)
-	expected <- 36.32
-	expect_equal(actual, expected)
+  expected <- 36.32
+  expect_equal(actual, expected)
+
+})
+
+test_that("output from ds_summary is as expected when data is numeric", {
+
+  actual <- round(ds_summary(mtcarz$mpg)$variance, 2)
+  expected <- 36.32
+  expect_equal(actual, expected)
 
 })