Consider the following interactive example that generates a summary table:
library(dplyr)
tg <- ToothGrowth
ci_int <- 0.95
tg %>%
group_by(supp, dose) %>%
summarise(N = n(),
mean = mean(len, na.rm = T),
sd = sd(len, na.rm = T),
se = sd / sqrt(N),
ci = se * qt(ci_int / 2 + 0.50, N - 1))
# supp dose N mean sd se ci
# (fctr) (dbl) (int) (dbl) (dbl) (dbl) (dbl)
# 1 OJ 0.5 10 13.23 4.459709 1.4102837 3.190283
# 2 OJ 1.0 10 22.70 3.910953 1.2367520 2.797727
# 3 OJ 2.0 10 26.06 2.655058 0.8396031 1.899314
# 4 VC 0.5 10 7.98 2.746634 0.8685620 1.964824
# 5 VC 1.0 10 16.77 2.515309 0.7954104 1.799343
# 6 VC 2.0 10 26.14 4.797731 1.5171757 3.432090
I would like to convert this to a function and abstract away the data.frame, measure variable, groupvars grouping variables, and the conf.int. Here's a start:
library(lazyeval)
summarySE <- function(df, measure, groupvars, conf.int = 0.95) {
summary_dots <- list(
~ n(),
interp(~ mean(var, na.rm = T), var = as.name(measure)),
interp(~ sd(var, na.rm = T), var = as.name(measure))
)
df %>%
group_by_(.dots = groupvars) %>%
summarise_(.dots = setNames(summary_dots, c("N", "mean", "sd")))
}
summarySE(tg, "len", c("supp", "dose"))
Which yields:
# supp dose N mean sd
# (fctr) (dbl) (int) (dbl) (dbl)
# 1 OJ 0.5 10 13.23 4.459709
# 2 OJ 1.0 10 22.70 3.910953
# 3 OJ 2.0 10 26.06 2.655058
# 4 VC 0.5 10 7.98 2.746634
# 5 VC 1.0 10 16.77 2.515309
# 6 VC 2.0 10 26.14 4.797731
However, this doesn't feel very DRY? In addition, I'm not sure how to implement se and ci without getting overtly complex/verbose? Perhaps there's a better approach altogether or perhaps this should be split up into several functions?
How can I convert the summary table above to a function so that I can pass it any combination of a data.frame with different measure and groupvars with the "spirit" of dplyr?