Question

如果我有以下代码：

my_func <- function(var1, var2, var3, var4) {
    ... (side effect included) 
}

df <- crossing(
    nesting(var1=...,var2=....)
    nesting(var3=...,var4=....)
)

在每一行df上应用my_func最优雅的方法是什么？另外my_func不是纯函数，它设计用于执行一些副作用（IO，plot ...）

方法1

my_func_wrapper <- function(row) {
  my_func(row['var1'], row['var2'], row['var3'], row['var4'])
}

# Vector coercion is a problem, if variables are not the same type.
apply(df, 1, my_func_wrapper)

方法2

df %>%
  rowwise() %>%
  do(result=invoke(my_func, .)) %>% #If it ends here, I will be pretty happy.
  .$result # Relying auto print feature to plot or trigger some side effect

方法3

#This looks pretty good on its own but it does not play well with the pipe %>%
foreach(row=iter(df, by='row'))  %do% invoke(my_func, row)

#Method 3.1 (With Pipe)
 df %>%
   (function(df) foreach(row=iter(df, by='row'))  %do% invoke(my_func, row))

#Method 3.2 this does not work
# df %>%
#   foreach(row=iter(., by='row'))  %do% invoke(my_func, row)

#Method 3.3 this does not work
#I am trying to get this work with purrr's simplified anonymous function, but it does not work.
# df %>%
#    as_function(~ foreach(row=iter(., by='row'))  %do% invoke(my_func, row))

有没有更好的方法，与%>%一起玩，这样做？

Answer 1

老实说，我会使用purr的pmap::pmap

library(tidyverse)

df = data.frame(
  x = rnorm(10),
  y = runif(10)
)
df %>% 
  pmap_dbl(function(x, y) {
    min(x,y)
  })

使用不纯函数循环遍历数据帧行的最优雅方法是什么？

1 个答案: