这是上一个问题的续篇:Apply function over every entry one table to every entry of another
我有以下表loss.tib
和bandstib
以及函数bandedlossfn
library(tidyverse)
set.seed(1)
n <- 5
loss.tib <- tibble(lossid = seq(n),
loss = rbeta(n, 1, 10) * 100)
bandstib <- tibble(bandid = seq(4),
start = seq(0, 75, by = 25),
end = seq(25, 100, by = 25))
bandedlossfn <- function(loss, start, end) {
pmin(end - start, pmax(0, loss - start))
}
可以使用bandstib
作为参数在loss.tib
上应用此函数:
loss.tib %>%
mutate(
result = map(
loss, ~ tibble(result = bandedlossfn(.x, bandstib$start,
bandstib$end))
)
) %>% unnest
但是,我想在map中添加一个索引,如下所示:
loss.tib %>%
mutate(
result = map(
loss, ~ tibble(result = bandedlossfn(.x, bandstib$start,
bandstib$end)) %>%
mutate(bandid2 = row_number())
)
) %>% unnest
但它似乎并没有像预期的那样工作。我还想在映射函数中添加filter(!near(result,0))
,以实现高效的内存管理。
我期望的结果是:
lossid loss bandid result
1 21.6691088 1 21.6691088
2 6.9390647 1 6.9390647
3 0.5822383 1 0.5822383
4 5.5671643 1 5.5671643
5 27.8237244 1 25.0000000
5 27.8237244 2 2.8237244
谢谢。
发布于 2019-04-22 00:56:41
这里有一种可能:首先嵌套bandstib
并将其添加到loss.tib
。这样,id就符合您的计算方式:
bandstib <- tibble(bandid = seq(4),
start = seq(0, 75, by = 25),
end = seq(25, 100, by = 25)) %>%
nest(.key = "data")
set.seed(1)
n <- 5
result <- tibble(loss = rbeta(n, 1, 10) * 100) %>%
bind_cols(., slice(bandstib, rep(1, n))) %>%
mutate(result = map2(loss, data, ~bandedlossfn(.x, .y$start, .y$end))) %>%
unnest()
https://stackoverflow.com/questions/55784212
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