Mean excluding zero and na for all columns with dplyr

Question

I want do do a mean of my dataframe with the dplyr package for all my colums.

n = c(NA, 3, 5) 
s = c("aa", "bb", "cc") 
b = c(3, 0, 5) 
df = data.frame(n, s, b)

Here I want my function to get mean = 4 the n and b columns I tried mean(df$n[df$n>0]) buts it's not easy for a large dataframe. I want something like df %>% summarise_each(funs(mean)) ... Thanks

Answer 1

If you don't want 0s it's probably that you consider them as NAs, so let's be explicit about it, then summarize numeric columns with na.rm = TRUE :

library(dplyr)
df[df==0] <- NA
summarize_if(df, is.numeric, mean, na.rm = TRUE)
#   n b
# 1 4 4

As a one liner:

summarize_if(`[<-`(df, df==0, value= NA), is.numeric, mean, na.rm = TRUE)

and in base R (result as a named numeric vector)

sapply(`[<-`(df, df==0, value= NA)[sapply(df, is.numeric)], mean, na.rm=TRUE)

Answer 2

Cf elegant David Answer :

df %>% summarise_each(funs(mean(.[!is.na(.) & . != 0])), -s) 

Or

df %>% summarise_each(funs(mean(.[. != 0], na.rm = TRUE)), -s)