有人可以告诉我如何使用dplyr将带有参数名称的向量传递给函数吗？
library("dplyr", quietly = TRUE, warn.conflicts = FALSE) # version 0.8.0.1
# Does not work
iris %>% rowwise() %>% mutate(v1 = mean( as.name(names(iris)[-5]) ) )
iris %>% rowwise() %>% mutate(v1 = mean( !!(names(iris)[-5]) ) )
iris %>% rowwise() %>% mutate(v1 = mean( enquo(names(iris)[-5]) ) )
iris %>% rowwise() %>%
mutate(v1 = mean( c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width") ) )
# This works and is the intended result
iris %>% rowwise() %>%
mutate(v1 = mean( c(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width ) ) )
关键是要使函数（均值或任何函数）与names(iris)[-5]或具有变量名称的向量一起使用。
我看这里没有成功： dplyr mutate_each_ standard evaluation; dplyr: Standard evaluation and enquo()
我的会话信息：
R version 3.5.3 (2019-03-11)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)
Matrix products: default
locale:
[1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252
[3] LC_MONETARY=French_France.1252 LC_NUMERIC=C
[5] LC_TIME=French_France.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.1.0 visdat_0.5.3 lubridate_1.7.4 naniar_0.4.2
[5] dplyr_0.8.0.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.1 rstudioapi_0.10 magrittr_1.5 tidyselect_0.2.5
[5] munsell_0.5.0 colorspace_1.4-0 R6_2.4.0 rlang_0.3.4
[9] fansi_0.4.0 stringr_1.4.0 plyr_1.8.4 tools_3.5.3
[13] grid_3.5.3 packrat_0.5.0 gtable_0.2.0 utf8_1.1.4
[17] cli_1.1.0 withr_2.1.2 digest_0.6.18 lazyeval_0.2.2
[21] assertthat_0.2.0 tibble_2.1.1 crayon_1.3.4 tidyr_0.8.3
[25] purrr_0.3.2 glue_1.3.1 labeling_0.3 stringi_1.4.3
[29] compiler_3.5.3 pillar_1.3.1 scales_1.0.0 pkgconfig_2.0.2
谢谢！
答案 0 :(得分：5)
使用map2_dbl
library(tidyverse)
iris %>% mutate(v1 = map2_dbl(Sepal.Length, Sepal.Width, ~mean(c(.x, .y)))) %>% head
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species v1
#1 5.1 3.5 1.4 0.2 setosa 4.30
#2 4.9 3.0 1.4 0.2 setosa 3.95
#3 4.7 3.2 1.3 0.2 setosa 3.95
#4 4.6 3.1 1.5 0.2 setosa 3.85
#5 5.0 3.6 1.4 0.2 setosa 4.30
#6 5.4 3.9 1.7 0.4 setosa 4.65
或者如果您想获取mean的某些列。
cols <- c("Sepal.Length", "Sepal.Width")
iris %>% mutate(v1 = rowMeans(.[cols])) %>% head
答案 1 :(得分：3)
我们可以在rowMeans中使用base R
cols <- c("Sepal.Length", "Sepal.Width")
iris$v1 <- rowMeans(iris[cols])
或者在tidyverse
library(tidyverse)
iris %>%
mutate(v1 = select(., cols) %>% reduce(`+`)/length(cols)) %>%
head
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species v1
#1 5.1 3.5 1.4 0.2 setosa 4.30
#2 4.9 3.0 1.4 0.2 setosa 3.95
#3 4.7 3.2 1.3 0.2 setosa 3.95
#4 4.6 3.1 1.5 0.2 setosa 3.85
#5 5.0 3.6 1.4 0.2 setosa 4.30
#6 5.4 3.9 1.7 0.4 setosa 4.65
或者另一个选择是pmap（在多于两列的情况下也应该起作用）
iris %>%
mutate(v1 = pmap_dbl(.[cols], ~ mean(c(...))))
答案 2 :(得分：1)
感谢@Ronak Shah和@akrun的回答。我的问题也许从一开始就没有很好地解决，而寻找的是pmap：
cols <- names(iris)[-5]
library(dplyr, quietly = TRUE, warn.conflicts = FALSE)
iris %>% mutate(v1 = rowMeans(.[cols])) %>% head # ok with mean per rows
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species v1
#> 1 5.1 3.5 1.4 0.2 setosa 2.550
#> 2 4.9 3.0 1.4 0.2 setosa 2.375
#> 3 4.7 3.2 1.3 0.2 setosa 2.350
#> 4 4.6 3.1 1.5 0.2 setosa 2.350
#> 5 5.0 3.6 1.4 0.2 setosa 2.550
#> 6 5.4 3.9 1.7 0.4 setosa 2.850
# Creating a custom stat function
set.seed(123)
w0 <- rnorm(n = 10)
mystat <- function(x, w = w0[1:length(x)]) sum(x*w)/length(x)
iris[1, cols] %>% mystat # test value
#> [1] -0.3669384
# Tests
iris %>% mutate(v1 = mystat(.[cols])) %>% head # ko
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species v1
#> 1 5.1 3.5 1.4 0.2 setosa 109.1179
#> 2 4.9 3.0 1.4 0.2 setosa 109.1179
#> 3 4.7 3.2 1.3 0.2 setosa 109.1179
#> 4 4.6 3.1 1.5 0.2 setosa 109.1179
#> 5 5.0 3.6 1.4 0.2 setosa 109.1179
#> 6 5.4 3.9 1.7 0.4 setosa 109.1179
library(purrr, quietly = TRUE, warn.conflicts = FALSE)
iris %>% mutate(v1 = map_dbl(list(.[cols]), mystat)) %>% head # ko
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species v1
#> 1 5.1 3.5 1.4 0.2 setosa 109.1179
#> 2 4.9 3.0 1.4 0.2 setosa 109.1179
#> 3 4.7 3.2 1.3 0.2 setosa 109.1179
#> 4 4.6 3.1 1.5 0.2 setosa 109.1179
#> 5 5.0 3.6 1.4 0.2 setosa 109.1179
#> 6 5.4 3.9 1.7 0.4 setosa 109.1179
iris %>% mutate(v1 = pmap_dbl(.[cols], ~ mystat(c(...)))) %>% head # OK mean
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species v1
#> 1 5.1 3.5 1.4 0.2 setosa -0.3669384
#> 2 4.9 3.0 1.4 0.2 setosa -0.3101425
#> 3 4.7 3.2 1.3 0.2 setosa -0.3325953
#> 4 4.6 3.1 1.5 0.2 setosa -0.2348935
#> 5 5.0 3.6 1.4 0.2 setosa -0.3586810
#> 6 5.4 3.9 1.7 0.4 setosa -0.3115633