我试图了解pmap的工作原理。下面的小标题包含一个列表列function1 <- function (x, y, train, test){
a<- train[[x]]
b<- train[[y]]
c<- test[[x]]
d<- test[[y]]
return(list(a,b,c,d))
}
。我想创建一个新列values，这取决于New列中的相应元素是否为NULL。由于未对is.null进行矢量化处理，因此我最初想到先使用values，然后再遇到rowwise()。
在pmap()之前使用rowwise()可以得到期望的结果，如下所示：
mutate()但是，tbl = as.data.frame(do.call(rbind, pars)) %>%
rowwise() %>%
mutate(New = ifelse(is.null(values), paste(id, default), paste(id, values, collapse=", ")))
> tbl
Source: local data frame [2 x 6]
Groups: <by row>
# A tibble: 2 x 6
id lower upper values default New
<list> <list> <list> <list> <list> <chr>
1 <chr [1]> <dbl [1]> <dbl [1]> <NULL> <dbl [1]> a 5
2 <chr [1]> <NULL> <NULL> <list [3]> <chr [1]> b 0, b 1, b 2
不会：
pmap()如果我使用匿名函数代替代字号，它似乎可以正常工作：
tbl = as.data.frame(do.call(rbind, pars)) %>%
mutate(New = pmap(., ~ifelse(is.null(values), paste(id, default), paste(id, values, collapse=", "))))
> tbl
id lower upper values default New
1 a 1 10 NULL 5 a NULL, b list("0", "1", "2")
2 b NULL NULL 0, 1, 2 1 a NULL, b list("0", "1", "2")
但是我不明白为什么波浪号版本会失败？我宁愿不必完全指定参数，因为我需要在多个列上映射函数。我要去哪里错了？
答案 0 :(得分：0)
我正要问一个非常类似的问题。基本上，询问如何在pmap中使用mutate，而不必多次使用变量名。
相反，我将其作为“答案”发布在这里，因为它包含一个reprex和许多我发现的选项，这些选项都不令我完全满意。
希望其他人能够按照要求回答如何做。
在使用带有列表列的data.frame时，我经常想在purrr::pmap内使用dplyr::mutate。
有时，这涉及到很多重复的变量名。
我希望能够使用匿名函数更简洁地执行此操作，以便在传递给pmap的{{1}}参数时，变量仅使用一次。
以这个小型数据集为例：
.f说我要应用于每一行的函数是
library('dplyr')
library('purrr')
df <- tribble(
~x, ~y, ~z,
c(1), c(1,10), c(1, 10, 100),
c(2), c(2,20), c(2, 20, 200),
)
在实践中，该函数将更加复杂，并包含许多变量。 该函数只需要使用一次，因此我不希望不必显式命名它并阻塞脚本和工作环境。
以下是选项。每个创建完全相同的data.frame，但以不同的方式。包含func <- function(x, y, z){c(sum(x), sum(y), sum(z))}
.. 1 avg`` will be come clear.
Note I'm not considering position matching using .. 2`等的原因很容易弄乱。
,据我所知，这些是选项，不包括位置匹配。
理想情况下，可能会发生以下类似情况，其中函数# Explicitly create a function for `.f`.
# This requires using the variable names (x, y, z) three times.
# It's completely clear what it's doing, but needs a lot of typing.
# It might sometimes fail - see https://github.com/tidyverse/purrr/issues/280
df_explicit <- df %>%
mutate(
avg = x - mean(x),
a = pmap(.l = list(x, y, z), .f = function(x, y, z){ c(sum(x), sum(y), sum(z)) })
)
# Pass the whole of `df` to `.l` and add `...` in an explicit function to deal with any unused columns.
# variable names are used twice.
# `df` will have to be passes explicitly if not using pipes (eg, `mutate(.data = df, a = pmap(.l = df, ...`).
# This is probably inefficient for large datasets.
df_dots <- df %>%
mutate(
avg = x - mean(x),
a = pmap(.l = ., .f = function(x, y, z, ...){ c(sum(x), sum(y), sum(z)) })
)
# Use `pryr::f` (as discussed in https://stackoverflow.com/a/51123520/4269699).
# Variable names are used twice.
# Potentially unexpected behaviour.
# Not obvious to the casual reader why the extra `pryr::f` is needed and what it's doing
df_pryrf <- df %>%
mutate(
avg = x - mean(x),
a = pmap(.l = list(x,y,z), .f = pryr::f({c(sum(x), sum(y), sum(z))} ))
)
# Use `rowwise()` similar to this: https://stackoverflow.com/a/47734073/4269699
# Variable names are used once.
# It will mess up any vectorised functions used elsewhere in mutate, hence the two `mutate()`s
df_rowwise <- df %>%
mutate( avg = x - mean(x) ) %>%
rowwise() %>%
mutate( a = list( {c(sum(x), sum(y), sum(z))} ) ) %>%
ungroup()
# Use Romain Francois' neat {rap} package.
# Variable names used once.
# Like `rowwise()` it will mess up any vectorised functions so it needs two `mutate()`s for this particular problem
#
library('rap') #devtools::install_github("romainfrancois/rap")
df_rap <- df %>%
mutate( avg = x - mean(x) ) %>%
rap( a = ~ c(sum(x), sum(y), sum(z)) )
# Another solution discussed here https://stackoverflow.com/a/51123520/4269699 doesn't seem to work inside `mutate()`, but maybe could be tweaked?
# Like the `pryr::f` solution, it's not immediately obvious what the purpose of the `with(list(...` bit is.
df_with <- df %>%
mutate(
avg = x-mean(x),
a = pmap(.l = list(x,y,z), .f = ~with(list(...), { c(sum(x), sum(y), sum(z))} ))
)
知道从传递的对象中查找（行式）变量qmap，x和y z的{{1}}参数。
mutate但是我不知道该怎么做，所以只考虑部分答案。
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