Merge pull request #40 from vbfelix/count_na

increment to 3.12.0

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: relper
 Title: Miscellaneous Functions for Data Preparation, Cleaning, and Visualization
-Version: 3.11.3
+Version: 3.12.0
 Authors@R: person("Vinícius", "Félix", email = "vinicius1b7f@gmail.com", role = c("aut", "cre"))
 Description: A collection of functions to help with data preparation, cleaning, and visualization. relper offers functions to: transform and format data (e.g., easily apply the percentage), compute metrics (e.g., coefficient of variation), check conditions (e.g., determine if a number is even or odd), supply lovely and varied color palettes (e.g., palettes inspired by TV series and movies), complement ggplot2 (e.g., add an identity line to a scatter plot), manipulate strings (e.g., keep only a type of character). The package's ultimate purpose is to simplify data analysis by providing functions that will shorten some steps.
 Depends: R (>= 3.6.0)

NAMESPACE

@@ -16,6 +16,7 @@ export(calc_mode)
 export(calc_peak_density)
 export(calc_perc)
 export(calc_skewness)
+export(count_na)
 export(cut_by_quantile)
 export(dttm_diff)
 export(dttm_vars)

NEWS.md

@@ -4,6 +4,12 @@
 >
 > *These additions transform the package into a comprehensive "Data Insight Toolbox," empowering users to gain deeper insights and perform advanced analyses on their datasets. Additionally, some functions have been renamed for better clarity and consistency.*
 
+## relper 3.12.0
+
+**Additions**
+
+-   count_na
+
 ## relper 3.11.3
 
 **Additions**

R/count_na.r

@@ -0,0 +1,25 @@
+#' Count the number of NA observations
+#'
+#' @description Count the number of NA observations
+#'
+#' @eval arg_value("x","")
+
+#' @return A numeric value
+#'
+#' @export
+#'
+#' @examples
+#'
+#' count_na(c(2,2,2))
+#'
+#' count_na(c(2,2,NA))
+#'
+
+
+count_na <- function(x){
+
+  output <- sum(is.na(x),na.rm = TRUE)
+
+  return(output)
+
+}

tests/testthat/test-count_na.R

@@ -0,0 +1,21 @@
+test_that("Expect equal", {
+
+  expect_equal(
+    object = count_na(1:3),
+    expected = 0
+  )
+  expect_equal(
+    object = count_na(NA),
+    expected = 1
+  )
+  expect_equal(
+    object = count_na(letters),
+    expected = 0
+  )
+  expect_equal(
+    object = count_na(NA_character_),
+    expected = 1
+  )
+
+})
+

tests/testthat/test-is_blank.R

@@ -17,12 +17,9 @@ test_that("Expect FALSE", {
 
   expect_false(object = is_blank("a"))
 
-})
-
-test_that("Expect error", {
+  expect_false(object = is_blank(TRUE))
 
-  expect_error(object = is_blank(TRUE))
-
-  expect_error(object = is_blank(1))
+  expect_false(object = is_blank(1))
 
 })
+

vignettes/other_functions.Rmd

@@ -20,6 +20,18 @@ library(dplyr)
 library(ggplot2)
 ```
 
+# count_na
+
+The goal of `count_na` is to count the number of missing observations.
+
+```{r count_na}
+count_na(c(2,2,NA))
+
+count_na(c(2,2,2))
+
+```
+
+
 # cut_by_quantile
 
 The goal of `cut_by_quantile` is to divide a numeric variable by a set of quantiles.