Reputation: 8494
For a sample dataframe:
df1 <- structure(list(id = 1:21, region = structure(c(1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
4L), .Label = c("a", "b", "c", "d"), class = "factor"), weight = c(0.35,
0.65, 0.99, 1.5, 3.2, 2.1, 1.3, 3.2, 1.3, 2, 0.6, 0.6, 0.6, 0.45,
1, 1.2, 1.4, 2, 1.3, 1, 2), condition = c(0L, 1L, 0L, 1L, 0L,
0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L
)), .Names = c("id", "region", "weight", "condition"), class = "data.frame", row.names = c(NA,
-21L))
I wish to exclude the regions which do not have either the highest or lowest number of 1s in the result variable by region. For example, I would normally do:
summary <- setDT(df)[,.(.result = weighted.mean((condition==1),
w = weight)*100), by = region]
Which would give me: summary
region .result
1: a 61.60458
2: b 39.69466
3: c 50.56180
4: d 61.03896
Therefore I would subset regions c and d from the dataframe df.
Is it possible to do this in one step without having to manually look at a summary dataframe?
Upvotes: 0
Views: 741
Reputation: 15395
My understanding is that you wish to exclude all values that are not the highest and lowest values. It can't be done as a one liner, but if you add the following, you should get what you want:
incl <- summary[c(which.min(.result), which.max(.result)),region]
newdf <- df1[region %in% incl,]
newdf
id region weight condition
1: 5 b 3.20 0
2: 6 b 2.10 0
3: 7 b 1.30 0
4: 8 b 3.20 1
5: 9 b 1.30 0
6: 10 b 2.00 1
7: 1 a 0.35 0
8: 2 a 0.65 1
9: 3 a 0.99 0
10: 4 a 1.50 1
Upvotes: 3