来自(哪里plyr
,你可能可以很容易地过渡到dplyr
。它不会像数据表那么快,但它会much比...快ddply
.
dat %>% group_by(membership_id) %>%
arrange(created_date) %>%
summarize(avg = as.numeric(mean(diff(created_date))))
# Source: local data frame [3 x 2]
#
# membership_id avg
# (int) (dbl)
# 1 12000000 555
# 2 12000001 262
# 3 12000003 391
无需任何更多的实际努力,您可以通过转换为更快的速度data.table
对象但仍然使用dplyr
命令。纯的data.table
仍然会更快。
(使用此数据)
dat = structure(list(membership_id = c(12000000L, 12000001L, 12000001L,
12000001L, 12000001L, 12000003L, 12000003L, 12000000L, 12000000L
), created_date = structure(c(16455, 15663, 15985, 16135, 16449,
15744, 16135, 16106, 15345), class = "Date")), .Names = c("membership_id",
"created_date"), row.names = c("1", "2", "3", "4", "5", "6",
"7", "8", "9"), class = "data.frame")