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

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 [e-mail]

See Also

nps

Examples

# 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)

NPS documentation built on May 2, 2019, 8:36 a.m.