r - Handling NA values in summary stats

Question

Situation

I am currently using ddply and numcolwise(summary) to return 5-summary stats (ie min , Q1 , Q2 , mean , Q3 and max ) for a given data frame.

However I can't figure out how to handle NA s (having tried various combinations of rm.na=TRUE .

Here is an example data frame and how I am using ddply and numcolwise(summary) .

library(dplyr)
id <- c(1, 2, 3, 4, 5)
name <- c("name1", "name2", "name3", "name4", "name5")
position <- c("AAA", "BBB", "CCC", "AAA", "BBB")
salary <- c(20, 30, 40, 50, 60)
bonus <- c(1, 1, 1, NA, 1)
sti <- c(2, 3, 4, 5, 6)
lti <- c(6, 5, 4, 3, 2)
other <- c(10, 11, 12, 13, 14)

df <- data.frame(id, name, position, salary, bonus, sti, lti, other)

df_out <- ddply(df, .(position), numcolwise(summary))

Question

Is it possible to use numcolwise(summary) this way that can handle NA s, or is there a method / function that will give me the 5-stats for each numerical column that can?

Notes

These functions all work

min(df[,"bonus"], na.rm=TRUE)
median(df[,"bonus"], na.rm=TRUE)
mean(df[,"bonus"], na.rm=TRUE)
quantile(df[,"bonus"], probs=(c(0.25, 0.5, 0.75)), type=7, na.rm=TRUE)
summary(df[,"bonus"], na.rm=TRUE)

Update

After some research one possible, but not very elegant solution is

df[,c("position", "salary","bonus","sti","lti","other")] %>% 
  group_by(position) %>% 
  summarise_each(funs(min, quantile(.,0.25, na.rm=TRUE), 
                      quantile(.,0.5, na.rm=TRUE), mean, quantile(., 0.75, na.rm=TRUE), max))

Answer 1

I can achieve the result using the %>% notation, summarise_each() and specifying the functions in the funs arguments.

df[,c("position", "salary","bonus","sti","lti","other")] %>% 
  group_by(position) %>% 
  summarise_each(funs(min, quantile(.,0.25, na.rm=TRUE), 
                  quantile(.,0.5, na.rm=TRUE), mean, quantile(., 0.75, na.rm=TRUE), max))