6.3 Counting statistics
Next, we’ll move on to common counting functions for vectors with discrete or non-numeric data. Discrete data are those like gender, occupation, and monkey farts, that only allow for a finite (or at least, plausibly finite) set of responses. Common functions for discrete vectors are in Table 6.3. Each of these vectors takes a vector as an argument – however, unlike the previous functions we looked at, the arguments to these functions can be either numeric or character.
| Function | Description | Example | Result |
|---|---|---|---|
unique(x) |
Returns a vector of all unique values. | unique(c(1, 1, 2, 10)) |
1, 2, 10 |
table(x, exclude) |
Returns a table showing all the unique values as well as a count of each occurrence. To include a count of NA values, include the argument exclude = NULL |
table(c("a", "a", "b", "c")) |
2-"a", 1-"b", 1-"c" |
Let’s test these functions by starting with two vectors of discrete data:
vec <- c(1, 1, 1, 5, 1, 1, 10, 10, 10)
gender <- c("M", "M", "F", "F", "F", "M", "F", "M", "F")The function unique(x) will tell you all the unique values in the vector, but won’t tell you anything about how often each value occurs.
unique(vec)
## [1] 1 5 10
unique(gender)
## [1] "M" "F"The function table() does the same thing as unique(), but goes a step further in telling you how often each of the unique values occurs:
table(vec)
## vec
## 1 5 10
## 5 1 3
table(gender)
## gender
## F M
## 5 4If you want to get a table of percentages instead of counts, you can just divide the result of the table() function by the sum of the result:
table(vec) / sum(table(vec))
## vec
## 1 5 10
## 0.56 0.11 0.33
table(gender) / sum(table(gender))
## gender
## F M
## 0.56 0.44