Summarize with conditions in dplyr

You can do both sums in a single dplyr statement:

df1 <- df %>%
  group_by(ID) %>%
  summarize(sumB = sum(B),
            sumBfoo = sum(B[A=="foo"]))

And here is a data.table version:

library(data.table)

dt = setDT(df) 

dt1 = dt[ , .(sumB = sum(B),
              sumBfoo = sum(B[A=="foo"])), 
          by = ID]

dt1

   ID sumB sumBfoo
1:  1    6       1
2:  2   30      30
3:  3   54       0
4:  5  202       0

Writing up @hadley's comment as an answer

df_sqlite %>%
  group_by(ID) %>%
  mutate(Bfoo = if(A=="foo") B else 0) %>%
  summarize(sumB = sum(B),
            sumBfoo = sum(Bfoo)) %>%
  collect

If you want to do counting instead of summarizing, then the answer is somewhat different. The change in code is small, especially in the conditional counting part.

df1 <- df %>%
    group_by(ID) %>%
    summarize(countB = n(),
              countBfoo = sum(A=="foo"))

df1
Source: local data frame [4 x 3]

  ID countB countBfoo
1  1      2         1
2  2      2         2
3  3      1         0
4  5      1         0