Summarize with conditions in dplyr

走远了吗. 提交于 2019-11-26 18:41:51

You can do both sums in a single dplyr statement:

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

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

If you wanted to count the rows, instead of summing them, can you pass a variable to the function:

    df1 <- df %>%
group_by(ID) %>%
summarize(RowCountB = n(),
          RowCountBfoo = n(A=="foo"))

I get an error both with n() and nrow().

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