npc: Create Net Promoter Categories from Likelihood to Recommend... In NPS: Convenience Functions and Tests for Working With the Net Promoter Score (NPS)

Description

This function produces Net Promoter Categories for `numeric` or `integer` Recommend data

Usage

 `1` ```npc(x, breaks = list(0:6, 7:8, 9:10)) ```

Arguments

 `x` A vector of Recommend scores `breaks` A `list` of length three, giving the integer Likert scale points for Detractors, Passives, and Promoters, respectively. The default is `list(0:6, 7:8, 9:10)`

Value

Net Promoter categories

Author(s)

Brendan Rocks [email protected]

`nps`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```# The command below will generate Net Promoter categories for each point # on a standard 0:10 Likelihood to Recommend scale npc(0:10) # Here's how scores and categories map out. Notice that scores which are # 'off the scale' drop out as missing/invalid data.frame(score = -2:12, category = npc(-2:12)) # When you have lots of data, summaries are useful rec <- sample(0:10, prob=c(0.02, 0.01, 0.01, 0.01, 0.01, 0.03, 0.03, 0.09, 0.22, 0.22, 0.35), 1000, replace=TRUE) # A Histrogram of the Likelihood to Recommend scores we just generated hist(rec, breaks=-1:10) # A look at the by nps category using summary summary(npc(rec)) # As above table(npc(rec)) # As a crosstabulation table(rec, npc(rec)) nps(rec) ```