nothingness concept

concepts/nothingness/.meta/config.json

@@ -0,0 +1,5 @@
+{
+  "authors": ["colinleach"],
+  "contributors": [],
+  "blurb": "R uses NULL for complete absence of a value, NA for gaps in our knowledge of the data."
+}

concepts/nothingness/about.md

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+# About
+
+Many languages have a way such as `null` or `none` to indicate a non-existent value.
+Because R is designed to handle large volumes of (often messy) data, it has multiple forms of nothingness.
+
+The overall aim is to flag missing or suspect values as they are encountered, then continue without raising an exception.
+
+## NULL
+
+If a value really doesn't exist, it is repesented by `NULL`. This is probably closest to what C or Python might do.
+
+```R
+> v <- c() # zero-length vector
+> v
+NULL
+> is.null(v)
+[1] TRUE
+```
+
+In many contexts, `NULL` values are simply ignored:
+
+```R
+> c(2, 3, NULL, 5)
+[1] 2 3 5
+```
+
+## NA
+
+For situations where a value exists but we don't know what it is, `NA` is used. For example, when counting vehicles traveling on a road, human observers might go off sick or automatic sensors break down, but the traffic continues to flow.
+
+```R
+> v <- c(1, 2, NA, 4, 5)
+> v
+[1]  1  2 NA 4  5
+> is.na(v) # test for data gaps
+[1] FALSE FALSE  TRUE FALSE FALSE
+```
+
+Thus `NA` is a placeholder, warning humans that they need to make a decision about how to handle this.
+
+Statistical functions usually return `NA` by default if `NA` values are present. 
+If you want these values to be ignored, use the `na.rm` (NA remove) parameter to make this explicit:
+
+```R
+> v <- c(1, 2, NA, 4, 5)
+> mean(v)
+[1] NA
+> mean(v, na.rm = TRUE)
+[1] 3
+```
+
+## NaN
+
+Short for "Not a Number", this flags a computation problem in situations where a number was expected.
+
+```R
+> n <- c(0, 2, 4)
+> d <- c(0, 1, 2)
+> n / d
+[1] NaN   2   2  # 0/0 is mathematically undefined
+
+```
+
+This is not the only possibility for "invalid" calculations. 
+Dividing by zero will return `Inf` or `-Inf` for a non-zero numerator, without raising an error.

concepts/nothingness/introduction.md

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+# Introduction
+
+Many languages have a way such as `null` or `none` to indicate a non-existent value.
+Because R is designed to handle large volumes of (often messy) data, it has multiple forms of nothingness.
+
+The overall aim is to flag missing or suspect values as they are encountered, then continue without raising an exception.
+
+## NULL
+
+If a value really doesn't exist, it is repesented by `NULL`. This is probably closest to what C or Python might do.
+
+```R
+> v <- c() # zero-length vector
+> v
+NULL
+> is.null(v)
+[1] TRUE
+```
+
+In many contexts, `NULL` values are simply ignored:
+
+```R
+> c(2, 3, NULL, 5)
+[1] 2 3 5
+```
+
+## NA
+
+For situations where a value exists but we don't know what it is, `NA` is used. For example, when counting vehicles traveling on a road, human observers might go off sick or automatic sensors break down, but the traffic continues to flow.
+
+```R
+> v <- c(1, 2, NA, 4, 5)
+> v
+[1]  1  2 NA 4  5
+> is.na(v) # test for data gaps
+[1] FALSE FALSE  TRUE FALSE FALSE
+```
+
+Thus `NA` is a placeholder, warning humans that they need to make a decision about how to handle this.

concepts/nothingness/links.json

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+[
+  {
+    "url": "http://www.cookbook-r.com/Basics/Working_with_NULL_NA_and_NaN/",
+    "description": "Cookbook for R"
+  },
+  {
+    "url": "https://r02pro.github.io/null.html",
+    "description": "R Programming: Zero to Pro"
+  }
+]

config.json

@@ -569,6 +569,11 @@
       "uuid": "2751b6f2-7d71-4397-b063-9bf927a57756",
       "slug": "booleans",
       "name": "Booleans"
+    },
+    {
+      "uuid": "f6374c07-9244-44b2-b372-3e271b0d01be",
+      "slug": "nothingness",
+      "name": "Nothingness"
     }
   ],
   "key_features": [