R-如何设置快速自定义德语 - 英语词典

Question

，因为我的输入经常使用德语，但我希望代码是纯英语，我想拥有一个简短的自定义词典 - 基本上是由工作日和几个月的缩写组成。因此，我想创建一个快速的英语 - 德语（和Vise Versa）词典 - 理想情况下是具有parent environment = .GlobalEnv的环境。但是，当我将代码放在函数中时，dict_g2e字典已不复存在。

 set_dict <- function() { # Delete this line and ...
   dict_g2e <- new.env(hash = TRUE, size = 7)
   from <- c("So", "Mo", "Di", "Mi", "Do", "Fr", "Sa")
   to <- c("Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat")
   for (i in 1:19) {
     assign(x = from[i], value = to[i], envir = dict_g2e)
   } # this line and the code is working as expected

测试：

> get("So", env = dict_g2e) # ran without the set_dict <- function() {...} part
[1] "Sun"

• 错误在哪里？
• 我会使用dict_e2g做同样的事情。有更快的速度吗？做这个较短的方法？
• 是否有比get("So", env = dict_g2e)更好的命令？是否有任何反对g2e <- function(wd) {get(wd, envir = dict_g2e)}
的论点

在@Roland和@Alexis_Laz 的评论后进行编辑：

df_dict <- function() {
  df <- data.frame(german = c("So", "Mo", "Di", "Mi", "Do", "Fr", "Sa"),
    english = c("Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat"),
    stringsAsFactors = F)
  return(df)
}
df <- df_dict()
df_g2e <- function(wd) {
  df$english[which(df$german == wd)]
}

微学分：

print(summary(microbenchmark::microbenchmark(
  g2e("So"),
  df_g2e("So"),
  times = 1000L, unit = "us")))
}

和结果：

       expr    min     lq      mean median     uq    max neval
   g2e("So")  1.520  2.280  2.434178  2.281  2.661 17.106  1000
df_g2e("So") 12.545 15.205 16.368450 15.966 16.726 55.500  1000

Answer 1

您可以使用闭合：

dict <- function() { # Delete this line and ...
  dict_g2e <- new.env(hash = TRUE, size = 7)
  from <- c("So", "Mo", "Di", "Mi", "Do", "Fr", "Sa")
  to <- c("Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat")
  for (i in 1:19) {
    assign(x = from[i], value = to[i], envir = dict_g2e)
  }
  function(from) {
    dict_g2e[[from]]
  }
}
wdays1 <- dict()
wdays1("So")
#[1] "Sun"

但是，向量子集更快：

wdays2 <- setNames(c("Sun", "Mon", "Tues", "Wed", "Thurs", "Fri", "Sat"), 
                   c("So", "Mo", "Di", "Mi", "Do", "Fr", "Sa"))

并在全球环境中定义环境的速度更快：

wdays3 <- list2env(as.list(wdays2), hash = TRUE)
library(microbenchmark)
microbenchmark(for (i in seq_len(1e3)) wdays1("Mi"), 
               for (i in seq_len(1e3)) wdays2[["Mi"]], 
               for (i in seq_len(1e3)) wdays3[["Mi"]])
#Unit: microseconds
#                                    expr     min      lq     mean   median       uq      max neval cld
#   for (i in seq_len(1000)) wdays1("Mi") 434.045 488.205 520.6626 507.0265 516.2455 2397.108   100   c
# for (i in seq_len(1000)) wdays2[["Mi"]] 182.324 211.005 214.6720 215.9985 217.9190  239.173   100  b 
# for (i in seq_len(1000)) wdays3[["Mi"]] 141.609 164.143 167.1088 168.2410 169.7770  190.007   100 a 

但是，向量方法有明显的优势：它已被矢量化。

wdays2[c("So", "Do")]
#     So      Do 
#  "Sun" "Thurs"

如果您想在两个方向上翻译，使用data.frame将是自然方法，但是数据帧子集相当慢。您可以使用两个命名向量，一个用于每个方向。