Remove n miss cumsum

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: naniar
 Type: Package
 Title: Data Structures, Summaries, and Visualisations for Missing Data
-Version: 0.3.3.9000
+Version: 0.3.3.9100
 Authors@R: c(
   person("Nicholas", "Tierney", 
          role = c("aut", "cre"),

NEWS.md

@@ -1,3 +1,11 @@
+# naniar 0.3.3.9100 (2018/07/31)
+
+## New feature
+
+* `miss_var_summary` and `miss_case_summary` now no longer provide the 
+cumulative sum of missingness in the summaries - this summary can be added back
+to the data with the option `add_cumsum = TRUE`. #186
+
 # naniar 0.3.3.9000 (2018/07/30)
 
 ## Breaking Changes

R/add-cols.R

@@ -65,7 +65,9 @@ add_shadow_shift <- function(data, ..., suffix = "shift"){
     # change names
     names(shadow_shifted_df) <- paste0(names(shadow_shifted_df), "_", suffix)
 
-    tibble::as_tibble(dplyr::bind_cols(data, shadow_shifted_df))
+    return(
+      tibble::as_tibble(dplyr::bind_cols(data, shadow_shifted_df))
+    )
 
   }
 
@@ -81,7 +83,9 @@ add_shadow_shift <- function(data, ..., suffix = "shift"){
   # change names
   names(shadow_shifted_df) <- paste0(names(shadow_shifted_df),"_",suffix)
 
-  tibble::as_tibble(dplyr::bind_cols(data, shadow_shifted_df))
+  return(
+    tibble::as_tibble(dplyr::bind_cols(data, shadow_shifted_df))
+  )
 
 }
 
@@ -141,7 +145,9 @@ add_any_miss <- function(data, ..., label = "any_miss"){
 
     names(stub_data_label) <- paste0(label,"_all")
 
+    return(
     dplyr::bind_cols(data, stub_data_label) %>% tibble::as_tibble()
+    )
 
   }
 
@@ -159,7 +165,9 @@ add_any_miss <- function(data, ..., label = "any_miss"){
 
   names(stub_data_label) <- paste0(label,"_vars")
 
+  return(
   dplyr::bind_cols(data, stub_data_label) %>% tibble::as_tibble()
+  )
 
 }
 

R/add-n-prop-miss.R

@@ -30,6 +30,8 @@ add_n_miss <- function(data, ..., label = "n_miss"){
 
   if (missing(...)) {
     data[[paste0(label, "_all")]] <- n_miss_row(data)
+
+    return(data)
   }
 
   quo_vars <- rlang::quos(...)
@@ -38,7 +40,7 @@ add_n_miss <- function(data, ..., label = "n_miss"){
 
   data[[paste0(label, "_vars")]] <- n_miss_row(selected_data)
 
-  data
+  return(data)
 }
 
 #' Add column containing proportion of missing data values
@@ -90,6 +92,8 @@ add_prop_miss <- function(data, ..., label = "prop_miss"){
 
   if (missing(...)) {
     data[[paste0(label, "_all")]] <- prop_miss_row(data)
+
+    return(data)
   }
 
   quo_vars <- rlang::quos(...)
@@ -98,5 +102,6 @@ add_prop_miss <- function(data, ..., label = "prop_miss"){
 
   data[[paste0(label, "_vars")]] <- prop_miss_row(selected_data)
 
-  data
+  return(data)
+
 }

R/miss-x-summary.R

@@ -7,6 +7,10 @@
 #' @param data a data.frame
 #' @param order a logical indicating whether to order the result by `n_miss`.
 #'     Defaults to TRUE. If FALSE, order of variables is the order input.
+#' @param add_cumsum logical indicating whether or not to add the cumulative
+#'   sum of missings to the data. This can be useful when exploring patterns
+#'   of nonresponse. These are calculated as the cumulative sum of the missings
+#'   in the variables as they are first presented to the function.
 #' @param ... extra arguments
 #'
 #' @note `n_miss_cumsum` is calculated as the cumulative sum of missings in the
@@ -31,7 +35,10 @@
 #'   miss_var_summary()
 #'
 #' @export
-miss_var_summary <- function(data, order = FALSE, ...) {
+miss_var_summary <- function(data,
+                             order = FALSE,
+                             add_cumsum = FALSE,
+                             ...) {
 
   test_if_null(data)
 
@@ -41,11 +48,19 @@ miss_var_summary <- function(data, order = FALSE, ...) {
 }
 
 #' @export
-miss_var_summary.default <- function(data, order = TRUE, ...) {
+miss_var_summary.default <- function(data,
+                                     order = TRUE,
+                                     add_cumsum = FALSE,
+                                     ...) {
+
   res <- purrr::map_df(data, n_miss) %>%
     tidyr::gather(key = "variable", value = "n_miss") %>%
-    dplyr::mutate(pct_miss = (n_miss / nrow(data) * 100),
-                  n_miss_cumsum = cumsum(n_miss))
+    dplyr::mutate(pct_miss = (n_miss / nrow(data) * 100))
+
+  if (add_cumsum) {
+   res <- res %>% dplyr::mutate(n_miss_cumsum = cumsum(n_miss))
+  }
+
   if (order) {
     return(dplyr::arrange(res, -n_miss))
   }
@@ -56,9 +71,15 @@ miss_var_summary.default <- function(data, order = TRUE, ...) {
 }
 
 #' @export
-miss_var_summary.grouped_df <- function(data, order = TRUE, ...) {
+miss_var_summary.grouped_df <- function(data,
+                                        order = TRUE,
+                                        add_cumsum = FALSE,
+                                        ...) {
 
-  group_by_fun(data, .fun = miss_var_summary, order = order)
+  group_by_fun(data,
+               .fun = miss_var_summary,
+               order = order,
+               add_cumsum = add_cumsum)
 
 }
 
@@ -72,10 +93,10 @@ miss_var_summary.grouped_df <- function(data, order = TRUE, ...) {
 #' @param order a logical indicating whether or not to order the result by
 #'     n_miss. Defaults to TRUE. If FALSE, order of cases is the order input.
 #' @param ... extra arguments
-#'
-#' @note `n_miss_cumsum` is calculated as the cumulative sum of missings in the
-#'     variables in the order that they are given in the data when entering
-#'     the function
+#' @param add_cumsum logical indicating whether or not to add the cumulative
+#'   sum of missings to the data. This can be useful when exploring patterns
+#'   of nonresponse. These are calculated as the cumulative sum of the missings
+#'   in the variables as they are first presented to the function.
 #'
 #' @return a tibble of the percent of missing data in each case.
 #'
@@ -93,7 +114,10 @@ miss_var_summary.grouped_df <- function(data, order = TRUE, ...) {
 #'
 #' miss_case_summary(airquality)
 #'
-miss_case_summary <- function(data, order = TRUE, ...){
+miss_case_summary <- function(data,
+                              order = TRUE,
+                              add_cumsum = FALSE,
+                              ...){
 
   test_if_null(data)
 
@@ -103,35 +127,56 @@ miss_case_summary <- function(data, order = TRUE, ...){
 }
 
 #' @export
-miss_case_summary.default <- function(data, order = TRUE, ...){
+miss_case_summary.default <- function(data,
+                                      order = TRUE,
+                                      add_cumsum = FALSE,
+                                      ...){
 
   res <- data
 
   res[["pct_miss"]] <- rowMeans(is.na(res))*100
   res[["n_miss"]] <- as.integer(rowSums(is.na(res)))
   res[["case"]] <- seq_len(nrow(res))
-  res[["n_miss_cumsum"]] <- cumsum(res[["n_miss"]])
 
-  res <- dplyr::as_tibble(res)
+  if (add_cumsum) {
+    res[["n_miss_cumsum"]] <- cumsum(res[["n_miss"]])
+    res <- dplyr::as_tibble(res)
+    res <- dplyr::select(res,
+                         case,
+                         n_miss,
+                         pct_miss,
+                         n_miss_cumsum)
+  }
+
+  if (!add_cumsum) {
+    res <- dplyr::as_tibble(res)
 
-  res <- dplyr::select(res,
-                       case,
-                       n_miss,
-                       pct_miss,
-                       n_miss_cumsum)
+    res <- dplyr::select(res,
+                         case,
+                         n_miss,
+                         pct_miss)
+
+  }
 
   if (order) {
     return(dplyr::arrange(res, -n_miss))
   }
 
-  return(res)
-
+  if (!order) {
+    return(res)
+  }
 }
 
 #' @export
-miss_case_summary.grouped_df <- function(data, order = TRUE, ...){
-
-  group_by_fun(data, .fun = miss_case_summary, order = order)
+miss_case_summary.grouped_df <- function(data,
+                                         order = TRUE,
+                                         add_cumsum = FALSE,
+                                         ...){
+
+  group_by_fun(data,
+               .fun = miss_case_summary,
+               order = order,
+               add_cumsum = add_cumsum)
 
 }
 

R/shadows.R

@@ -125,7 +125,9 @@ bind_shadow <- function(data, only_miss = FALSE){
 
     shadow_vars <- dplyr::select(data, !!!miss_vars) %>% as_shadow()
 
-    tibble::as_tibble(dplyr::bind_cols(data, shadow_vars))
+    return(
+      tibble::as_tibble(dplyr::bind_cols(data, shadow_vars))
+    )
 
   # if you want All the values to be added (the default behaviour)
   }
@@ -136,7 +138,9 @@ bind_shadow <- function(data, only_miss = FALSE){
 
     bound_shadow <- dplyr::bind_cols(data, data_shadow)
 
+    return(
     tibble::as_tibble(bound_shadow)
+    )
 
   }
 

tests/testthat/test-miss-case-summary.R

@@ -23,18 +23,39 @@ test_that("miss_case_summary on grouped_df returns a tibble", {
   expect_is(miss_case_summary(aq_group), "tbl_df")
 })
 
-test_that("grouped_df returns 1 more column than regular miss_case_summary", {
+test_that("miss_case_summary produces the right number of columns", {
+  expect_equal(ncol(miss_case_summary(airquality)),
+               3)
+})
+
+test_that("miss_case_summary grouped produces the right number of columns", {
+  expect_equal(ncol(miss_case_summary(aq_group)),
+               4)
+})
+
+test_that("grouped_df returns the same number of columns as regular miss_case_summary", {
   expect_equal(ncol(miss_case_summary(aq_group)),
-                   ncol(miss_case_summary(airquality))+1)
+               ncol(miss_case_summary(airquality)) + 1)
 })
 
 test_that("grouped_df returns a column named 'Month'", {
   expect_identical(names(miss_case_summary(aq_group)),
-                   c("Month", "case", "n_miss","pct_miss", "n_miss_cumsum"))
+                   c("Month", "case", "n_miss","pct_miss"))
 })
 
 test_that("grouped_df returns a column named 'Month' with the right levels", {
   expect_identical(unique(miss_case_summary(aq_group)$Month),
                    5:9)
 })
 
+# add testing for cumulative sum ----------------------------------------------
+
+test_that("miss_case_summary adds cumsum when add_cumsum = TRUE", {
+  expect_equal(names(miss_case_summary(airquality, add_cumsum = TRUE)),
+               c("case", "n_miss", "pct_miss", "n_miss_cumsum"))
+})
+
+test_that("miss_case_summary grouped adds cumsum when add_cumsum = TRUE", {
+  expect_equal(names(miss_case_summary(aq_group, add_cumsum = TRUE)),
+               c("Month", "case", "n_miss", "pct_miss", "n_miss_cumsum"))
+})