# nps: Calculate a Net Promoter Score In NPS: Convenience Functions and Tests for Working With the Net Promoter Score (NPS)

## Description

This function calculates a Net Promoter Score from a vector of Recommend scores, ideally `numeric` ones. An attempt will be made to coerce `factor`, or `character` data. `NA` values, either in the data, or generated by type coercion, are automatically omitted from the calculation. No warning is given in the former case. Net Promoter Scores generated are on a [-1,1] scale; you may want to multiply them by 100 (and perhaps round them!) prior to presentation.

## Usage

 `1` ```nps(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

a Net Promoter Score. Unrounded.

## Author(s)

Brendan Rocks rocks.brendan@gmail.com

`npc`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```# This will generate 1000 dummy Likelihood to Recommend reponses x <- 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) # Here are the proportions of respondents giving each Likelihood to # Recommend response prop.table(table(x)) # Here's a histrogram of the scores hist(x, breaks=-1:10, col=c(rep("red",7), rep("yellow",2), rep("green", 2))) # Here's a barplot. It's very similar, though for categorical responses # it's often slightly easier to interpret. barplot( prop.table(table(x)), col=c(rep("red",7), rep("yellow",2), rep("green", 2)) ) # Here's the nps nps(x) #You can round it if you like round(nps(x)) ; round(nps(x),1) ```

### Example output  ```x
0     1     2     3     4     5     6     7     8     9    10
0.012 0.013 0.012 0.011 0.004 0.027 0.034 0.094 0.197 0.232 0.364
 0.483
 0
 0.5
```

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