Description Usage Arguments Value Author(s) See Also Examples

View source: R/Documented_Small_NPS_Functions.r

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.

1 |

`x` |
A vector of |

`breaks` |
A |

a Net Promoter Score. Unrounded.

Brendan Rocks rocks.brendan@gmail.com

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)
``` |

```
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
[1] 0.483
[1] 0
[1] 0.5
```

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