如何以最优雅的方式循环遍历一个含有不纯函数的数据框的行?

4
如果我有以下代码片段:
my_func <- function(var1, var2, var3, var4) {
    ... (side effect included) 
}

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

什么是将my_func应用于df的每一行的最优雅方法? 此外,my_func不是一个纯函数,它设计用于执行一些副作用(IO、绘图等)。
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,可以很好地使用%>% ,来做这件事?


1
不要忘记包含你正在使用的软件包。我还会在你的问题标签中添加这些软件包的名称(我假设是 dplyr?也许是 foreach?),以增加它被回答的机会。 - J.Con
2个回答

2
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)
  })


0

我发现在许多这类操作中,tidyverse提供的功能仍然比plyr差。例如:

> library(plyr)
> library(tidyverse)
> #dummy function
> your_function = function(...) {do.call(args = list(..., sep = " + "), what = str_c)}
> alply(mpg[1:5, ], .margins = 1, .fun = function(row) {
+   your_function(row$manufacturer, row$cyl, row$trans)
+ }) %>% unlist()
                      1                       2                       3                       4                       5 
  "audi + 4 + auto(l5)" "audi + 4 + manual(m5)" "audi + 4 + manual(m6)"   "audi + 4 + auto(av)"   "audi + 6 + auto(l5)" 

我不知道你想从这个函数中收集什么,但可能你会想要使用alply()adply()


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