如何结合多个总结呼叫dplyr？
鉴于DF，
ww <- data.frame(
GM = c("A", "A", "A", "A", "A", "A",
"B", "B", "B", "B", "B", "B",
"C", "C", "C", "C", "C", "C"),
stanza = rep(c("Past", "Mid", "End"), 6),
change = c(1, 1.1, 1.4, 1, 1.3, 1.5, 1, 1.2, 1.4,
1.1, 1.2, 1.3, .9, 1.2, 1.3, .9, 1.3, 1.5))
我想计算每个GM的过去平均值，并根据GM特定的均值来计算“变化”中的每个值。我可以使用两个dplyr调用和一个连接函数进行以下操作：
past <- ww %>%
group_by(GM) %>%
filter(stanza == "Past") %>%
summarize(past.mean = mean(change))
ww <- left_join(ww, past, by = "GM")
ww %>%
group_by(GM, stanza) %>%
summarize(pr.change = change/past.mean)
但是必须有一种在一个dplyr调用中进行此操作的方法。
Given the df
ww <- data.frame(
GM = c("A", "A", "A", "A", "A", "A",
"B", "B", "B", "B", "B", "B",
"C", "C", "C", "C", "C", "C"),
stanza = rep(c("Past", "Mid", "End"), 6),
change = c(1, 1.1, 1.4, 1, 1.3, 1.5, 1, 1.2, 1.4,
1.1, 1.2, 1.3, .9, 1.2, 1.3, .9, 1.3, 1.5))
I would like to calculate the mean for Past for each GM and the divide each value in 'change' by the GM specific mean. I can do this with two dplyr calls and a join function as follows:
past <- ww %>%
group_by(GM) %>%
filter(stanza == "Past") %>%
summarize(past.mean = mean(change))
ww <- left_join(ww, past, by = "GM")
ww %>%
group_by(GM, stanza) %>%
summarize(pr.change = change/past.mean)
But there must be a way to do this in one dplyr call.
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data.table解决方案：A
data.tablesolution:无需加入，您可以直接在一个管道中计算它：
输出
No need to join, you can compute this directly in one pipe chain:
Output
continue
Using
base R-output