Reputation: 91
What I want to do is to have columns for the upper and lower confidence interval for a proportion.
Here is what I have done:
> #Create some sample data
> Frustration <- data.table(group = c('A','B','C'), trials = c(363,1398,139), surg = c(57,276,18))
> Frustration
group trials surg
1: A 363 57
2: B 1398 276
3: C 139 18
>
> #try to get confidence levels. what I am expecting is CI to be a list of 2 elements, for each value of group, but I don't think that is working
> F2 <- Frustration[, .(CI = prop.test(surg, trials, conf.level = .75)['conf.int']), by = .(group)]
> F2
group CI
1: A 0.1350140,0.1816828
2: B 0.1851178,0.2103210
3: C 0.09701967,0.16972056
>
> #lower is still a list here - I am stumped
> F3 <- F2[, .(lower = CI[[1]]), by = .(group)]
> F3
group lower
1: A 0.1350140,0.1816828
2: B 0.1851178,0.2103210
3: C 0.09701967,0.16972056
I think by confusion has to do with lists, and how data table handles the return.
Thanks for your help,
David
Upvotes: 4
Views: 1449
Reputation: 17725
The other solution works, but here's a simple one-liner:
library(data.table)
Frustration <- data.table(group = c('A','B','C'), trials = c(363,1398,139),
surg = c(57,276,18))
Frustration[, c("lower", "upper") :=
as.list(prop.test(surg, trials, conf.level = .75)$conf.int),
by=group][]
#> group trials surg lower upper
#> 1: A 363 57 0.13501400 0.1816828
#> 2: B 1398 276 0.18511780 0.2103210
#> 3: C 139 18 0.09701967 0.1697206
And even simpler, but you'll have to do some renaming:
Frustration[, as.list(prop.test(surg, trials, conf.level = .75)$conf.int), by=group]
#> group V1 V2
#> 1: A 0.13501400 0.1816828
#> 2: B 0.18511780 0.2103210
#> 3: C 0.09701967 0.1697206
Upvotes: 2
Reputation: 886938
The CI
is a list
column. We can use transpose
and assign (:=
)
library(data.table)
F2[, c('lower', 'upper') := data.table::transpose(CI)][, CI := NULL][]
#Key: <group>
# group lower upper
# <char> <num> <num>
#1: A 0.13501400 0.1816828
#2: B 0.18511780 0.2103210
#3: C 0.09701967 0.1697206
Upvotes: 3