嵌套for循环到函数和lappy

我试图为嵌套for循环编写函数和调用代码。下面的代码，我可以很容易地把它与for循环和我的函数运行。但我试图在函数中避免for循环，而选择lappy。如何使用lappy创建函数及其相应的调用代码

带有for循环的代码：

df <- data.frame(actual=c("reaok_oc giade_len","reaok_oc giade_len reaok_oc giade_len"),
                  Predicted = c("giade_len","reaok_oc giade_len reaok_oc giade_len"))

df[] <- lapply(df, as.character)
str(df)

all_acc<-NULL
for(s in 1:nrow(df)){
  sub_df1<-df[s,]
  actual_words<-unlist(strsplit(sub_df1$actual," "))
  all_count<-0
  for(g in 1:length(actual_words)){
    count_len<-ifelse(grep(actual_words[g],sub_df1$Predicted),1,0)
    all_count<-sum(all_count,count_len)
  }
  sub_acc<-all_count/length(actual_words)
  all_acc<-c(all_acc,sub_acc)
}

df$trans_acc<-all_acc
sensitivity=sum(df$trans_acc)/nrow(df)
sensitivity

---

df也许，我们可以使用
分隔行

library(dplyr)
library(tidyr)
library(stringr)
df %>%
   separate_rows(actual, sep="_") %>%
   summarise(perc = mean(str_detect(Predicted, actual)))
#  perc
#1 0.75

它可以包装成一个函数

f1 <- function(data, act, pred) {
   data %>%
       separate_rows({{act}}, sep="_") %>%
       summarise(perc = mean(str_detect({{pred}}, {{act}})))
 }
f1(df, actual, Predicted)
#   perc
#1 0.75

actual_in_predicted <- function(actual, predicted) {
    sum(actual %in% predicted)
}

f1%
单独的_行（{act}，sep=“”）%>%
总结（perc=平均值（str_detect（{{pred}}，{{{act}）））
}
f1（df、实际值、预测值）
#perc
#1 0.75
在基本R中，通常最好找到“矢量化”（仅一个R函数调用）而不是“迭代”（每个元素一个调用）的解决方案。比如说

for(s in 1:nrow(df)){
    sub_df1<-df[s,]
    actual_words<-unlist(strsplit(sub_df1$actual," "))
    ...

只涉及一个，但执行相同的转换

我也认为当你说

    for(g in 1:length(actual_words)){
        count_len<-ifelse(grep(actual_words[g],sub_df1$Predicted),1,0)
        all_count<-sum(all_count,count_len)
    }

“for”循环可能会迭代实际和预测的每个元素

all_count <- integer()
for (i in 1:nrow(df))
    all_count[[i]] <- actual_in_predicted(actual[[i]], predicted[[i]])

变量
all_acc
是这个数字向量除以每次比较中的实际单词数

all_acc <- all_count / lengths(actual)

我想做的所有动作都是功能性的。你能帮忙吗
actual_in_predicted <- function(actual, predicted) {
    sum(actual %in% predicted)
}

all_count <- integer()
for (i in 1:nrow(df))
    all_count[[i]] <- actual_in_predicted(actual[[i]], predicted[[i]])

all_count <- mapply(actual_in_predicted, actual, predicted)

all_acc <- all_count / lengths(actual)

actual_in_predicted <- function(actual, predicted) {
    sum(actual %in% predicted)
}

actual <- strsplit(df$actual, " ")
predicted <- strsplit(df$Predicted, " ")

all_count <- mapply(actual_in_predicted, actual, predicted)
all_acc <- all_count / lengths(actual)

df$trans_acc <- all_acc
sensitivity <- sum(df$trans_acc) / nrow(df)