R: dplyr - Error in (list: invalid subscript type 'double'

In my continued playing around with R I wanted to find the minimum value for a specified percentile given a data frame representing a cumulative distribution function (CDF).

e.g. imagine we have the following CDF represented in a data frame:

``````
library(dplyr)
df = data.frame(score = c(5,7,8,10,12,20), percentile = c(0.05,0.1,0.15,0.20,0.25,0.5))
``````

and we want to find the minimum value for the 0.05 percentile. We can use the filter function to do so:

``````
> (df %>% filter(percentile > 0.05) %>% slice(1))\$score
 7
``````

Things become more tricky if we want to return multiple percentiles in one go.

My first thought was to create a data frame with one row for each target percentile and then pull in the appropriate row from our original data frame:

``````
targetPercentiles = c(0.05, 0.2)
percentilesDf = data.frame(targetPercentile = targetPercentiles)
> percentilesDf %>%
group_by(targetPercentile) %>%
mutate(x = (df %>% filter(percentile > targetPercentile) %>% slice(1))\$score)

Error in (list(score = c(5, 7, 8, 10, 12, 20), percentile = c(0.05, 0.1,  :
invalid subscript type 'double'
``````

Unfortunately this didn't quite work as I expected - Antonios pointed out that this is probably because we're mixing up two pipelines and dplyr can't figure out what we want to do.

Instead he suggested the following variant which uses the do function

``````
df = data.frame(score = c(5,7,8,10,12,20), percentile = c(0.05,0.1,0.15,0.20,0.25,0.5))
targetPercentiles = c(0.05, 0.2)

> data.frame(targetPercentile = targetPercentiles) %>%
group_by(targetPercentile) %>%
do(df) %>%
filter(percentile > targetPercentile) %>%
slice(1) %>%
select(targetPercentile, score)
Source: local data frame [2 x 2]
Groups: targetPercentile

targetPercentile score
1             0.05     7
2             0.20    12
``````

We can then wrap this up in a function:

``````
percentiles = function(df, targetPercentiles) {
# make sure the percentiles are in order
df = df %>% arrange(percentile)

data.frame(targetPercentile = targetPercentiles) %>%
group_by(targetPercentile) %>%
do(df) %>%
filter(percentile > targetPercentile) %>%
slice(1) %>%
select(targetPercentile, score)
}
``````

which we call like this:

``````
df = data.frame(score = c(5,7,8,10,12,20), percentile = c(0.05,0.1,0.15,0.20,0.25,0.5))
> percentiles(df, c(0.08, 0.10, 0.50, 0.80))
Source: local data frame [2 x 2]
Groups: targetPercentile

targetPercentile score
1             0.08     7
2             0.10     8
``````

Note that we don't actually get any rows back for 0.50 or 0.80 since we don't have any entries greater than those percentiles. With a proper CDF we would though so the function does its job.