Calculating length of 95%-CI using dplyr

Question

Last time I asked how it was possible to calculate the average score per measurement occasion (week) for a variable (procras) that has been measured repeatedly for multiple

Answer 1

You could do it manually using mutate a few extra functions in summarise

library(dplyr)
mtcars %>%
  group_by(vs) %>%
  summarise(mean.mpg = mean(mpg, na.rm = TRUE),
            sd.mpg = sd(mpg, na.rm = TRUE),
            n.mpg = n()) %>%
  mutate(se.mpg = sd.mpg / sqrt(n.mpg),
         lower.ci.mpg = mean.mpg - qt(1 - (0.05 / 2), n.mpg - 1) * se.mpg,
         upper.ci.mpg = mean.mpg + qt(1 - (0.05 / 2), n.mpg - 1) * se.mpg)

#> Source: local data frame [2 x 7]
#> 
#>      vs mean.mpg   sd.mpg n.mpg    se.mpg lower.ci.mpg upper.ci.mpg
#>   (dbl)    (dbl)    (dbl) (int)     (dbl)        (dbl)        (dbl)
#> 1     0 16.61667 3.860699    18 0.9099756     14.69679     18.53655
#> 2     1 24.55714 5.378978    14 1.4375924     21.45141     27.66287