Making a new variable, means by group, conditional on value of another variable

Question

I want to find the most efficient way to create a new variable. Suppose I have this data frame:

set.seed(1234)
df <- data.frame(group = c(rep(1,4), rep(2,4)), X = rep(1:4, 2), G = sample(1:10, 8, replace = T) )

I want to make a new variable that is the mean of G within each group, conditional on X being 1 or 2. In the example df, then, the new variable would have the following values:

df$newvar <- c(rep(4.5, 4), rep(8, 4))

Is there a way to do this without resorting the dataframe and then filling down? That seems really cumbersome. Thanks!

akrun · Accepted Answer · 2018-08-12 15:11:32Z

4

After groupig by 'group', filter the 'G' elements based on the logical condition on 'X' and get the mean of those values to create a new column with mutate

library(dplyr)
df %>%
    group_by(group) %>% 
    mutate(newvar = mean(G[X %in% 1:2]))
# A tibble: 8 x 4
# Groups:   group [2]
#  group     X     G newvar
#  <dbl> <int> <int>  <dbl>
#1     1     1     2    4.5
#2     1     2     7    4.5
#3     1     3     7    4.5
#4     1     4     7    4.5
#5     2     1     9    8  
#6     2     2     7    8  
#7     2     3     1    8  
#8     2     4     3    8

Or using ave from base R

df$newvar <- with(df, ave(G * NA^(!X %in% 1:2), group, 
                FUN = function(x) mean(x, na.rm = TRUE)))

answered Aug 12, 2018 at 15:11

akrun

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3 Comments

Rui Barradas Over a year ago

Wow! That's a really nice trick, NA^(!X %in% 1:2). Didn't know that one.

akrun Over a year ago

@RuiBarradas With ave, we need the arguments to have the same length, so have to resort to something unusual

d.b Over a year ago

Also ave(replace(df$G, !df$X %in% 1:2, NA), df$group, FUN = function(x) mean(x, na.rm = TRUE))

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