[英]Calculating values of a list of vectors by rows with purrr and reduce
I have this map here that returns a list of vectors of lags, using purrr:map. 我在这里有这张地图,使用purrr:map返回了滞后向量的列表。
purrr:map(0:2,~ lag(1:10, .x))
[[1]] [1] 1 2 3 4 5 6 7 8 9 10
[[1]] [1] 1 2 3 4 5 6 7 8 9 10
[[2]] [1] NA 1 2 3 4 5 6 7 8 9
[[2]] [1]不适用1 2 3 4 5 6 7 8 9
[[3]] [1] NA NA 1 2 3 4 5 6 7 8
[[3]] [1]不适用不适用1 2 3 4 5 6 7 8
I'm interested in calculating averages for rows if those vectors were combined into a tibble. 如果这些向量合并成小节,我有兴趣计算行的平均值。
I know I can sum rows using rows. 我知道我可以使用行求和。 So, for example,
因此,例如
reduce(map(0:2,~ lag(1:10, .x)), `+`)
[1] NA NA 6 9 12 15 18 21 24 27
[1]不适用不适用6 9 12 15 18 21 24 27
However, when I try: 但是,当我尝试:
reduce(map(0:2,~ lag(1:10, .x)), ~ mean(.x, na.rm=T))
5.5
5.5
This is not the answer I'm interested in. How do I do that using purrr? 这不是我感兴趣的答案。如何使用purrr做到这一点?
You could use a variant of pmap
to loop through all three vectors simultaneously. 您可以使用
pmap
的变体同时遍历所有三个向量。 Because mean
takes a vector of numbers, though, I used an anonymous function to concatenate the three elements together via c
. 由于
mean
是一个数字向量,因此我使用了一个匿名函数通过c
将三个元素连接在一起。
pmap_dbl
returns a vector of numbers. pmap_dbl
返回数字向量。
map(0:2, ~lag(1:10, .x) ) %>%
pmap_dbl( function(a, b, c) mean( c(a, b, c), na.rm = TRUE) )
[1] 1.0 1.5 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0
The development version has added ..1
type coding with the tilde to refer to each list. 开发版本添加了
..1
类型编码,并带有波浪号以引用每个列表。
map(0:2, ~lag(1:10, .x) ) %>%
pmap_dbl( ~mean( c(..1, ..2, ..3), na.rm = TRUE) )
中间数据帧看起来不太漂亮,但仍可以按预期工作:
purrr::map(0:2,~ lag(1:10, .x)) %>% as.data.frame() %>% rowMeans(na.rm=TRUE)
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