R 加速用于创建方便的映射和图表组的函数 背景

以下是SO同事就以下方面进行的有益讨论和提供的帮助：

• ,
• ,
• 及

我结合了一个方便的功能。它接受一个数值向量，并生成与组相关的分解向量

作用 函数体如下所示

nice.cuts <- function(variable, cuts = 10, thousands.separator = FALSE) {

  # Load required packages (useful when used independently of context)
  Vectorize(require)(package = c("gsubfn", "Hmisc", "scales"),
                     character.only = TRUE)

  # Destring this variable
  destring <- function(x) {
    ## convert factor to strings
    if (is.character(x)) {
      as.numeric(x)
    } else if (is.factor(x)) {
      as.numeric(levels(x))[x]
    } else if (is.numeric(x)) {
      x
    } else {
      stop("could not convert to numeric")
    }
  }

  # Apply function
  variable <- destring(variable)

  # Check whether to disable scientific notation
  if (mean(variable) > 100000) {
    options(scipen = 999)
  } else {
    options(scipen = 0)
  }

  # Create pretty breaks
  cut_breaks <- pretty_breaks(n = cuts)(variable)

  # Round it two decimal places
  variable <- round(variable, digits = 2)

  # Develop cuts according to the provided object
  cuts_variable <- cut2(x = variable, cuts = cut_breaks)

  # Check if variable is total or with decimals
  if (all(cut_breaks %% 1 == 0)) {
    # Variable is integer
    clean_cuts <- gsubfn('\\[\\s*(\\d+),\\s*(\\d+)[^0-9]+',
                         ~paste0(x, '-',as.numeric(y)-1),
                         as.character(cuts_variable))
  } else {
    # Variable is not integer
    # Create clean cuts
    clean_cuts <- gsubfn('\\[\\s*([0-9]+\\.*[0-9]*),\\s*(\\d+\\.\\d+).*',
                         ~paste0(x, '-', as.numeric(y)- 0.01),
                         as.character(cuts_variable))
  }

  # Clean Inf
  clean_cuts <- gsub("Inf", max(variable), clean_cuts)

  # Clean punctuation
  clean_cuts <- sub("\\[(.*), (.*)\\]", "\\1 - \\2", clean_cuts)

  # Replace strings with spaces
  clean_cuts <- gsub("-"," - ",clean_cuts, fixed = TRUE)

  # Trim white spaces
  clean_cuts <- trimws(clean_cuts)

  # Order factor before returning
  clean_cuts <- factor(clean_cuts, levels = unique(clean_cuts[order(variable)]))

  if (thousands.separator == TRUE) {
    levels(clean_cuts) <- sapply(strsplit(levels(clean_cuts), " - "),
                                 function(x) paste(prettyNum(x,
                                                             big.mark = ",",
                                                             preserve.width = "none"),
                                                   collapse = " - "))
  }

  # Return
  return(clean_cuts)
}

---

nice.cuts完成完整输入向量上标签的所有清理：首先在
cut2
之后生成一个字符向量，然后在此向量上执行大量正则表达式。但是，您只是在修改标签

因此，在生成
cut\u breaks
之后，我将首先以正确的格式生成标签：
cut\u labels
。我在下面新版本的
cut.labels
中这样做了。与原始版本相比，基准测试显示了巨大的改进：

> require(microbenchmark)
> dta <- data.frame(values=floor(runif(1000, 10000,90000)))
> microbenchmark(nice.cuts(dta$values, thousands.separator = TRUE),
+   nice.cuts2(dta$values, thousands.separator = TRUE))
Unit: milliseconds
                                               expr      min        lq     mean    median        uq        max neval cld
  nice.cuts(dta$values, thousands.separator = TRUE) 720.1378 815.51782 902.9218 923.97881 968.39036 1208.00434   100   b
 nice.cuts2(dta$values, thousands.separator = TRUE)  11.4147  15.18232  16.6196  16.46937  17.05305   29.91089   100  a 
> 

这似乎更适合姐妹网站。如果你想把它放在这里，你应该把它归结为代码问题，即更简单的问题。@Roland谢谢你的建议，我很乐意删除并在CR上重新发布。也许我会等几分钟，看看SO社区的成员是否感兴趣。我被标签的可用性误导了，因为这就是我的问题所在。@非常感谢您的建议。事实上，我对构建有序因子向量感兴趣。实际上，我经常对形状文件中的
data.frames
after
ggfortify
中已有的值使用该函数，因此我希望为所有具有括号值而非核心值的观察值设置一个因子列。基本理念是在地图/图表图例上使用一组整洁的颜色。非常感谢您表现出的兴趣和宝贵的贡献。您是否愿意发布基准测试结果以显示速度的提高？@Konrad添加了新版本的
nice.cuts
，并将其与原始版本进行了基准测试。非常感谢您的评论，这对性能来说是一个非常显著的提高！
> dta$cuts <- nice.cuts(dta$values, thousands.separator = TRUE)
> t(t(table(dta$cuts))) #' t() for presentation

                  [,1]
  10,000 - 19,999    9
  20,000 - 29,999   11
  30,000 - 39,999   12
  40,000 - 49,999   20
  50,000 - 59,999    6
  60,000 - 69,999   15
  70,000 - 79,999   17
  80,000 - 89,999   10

> require(microbenchmark)
> dta <- data.frame(values=floor(runif(100, 10000,90000)))
> microbenchmark(nice.cuts(dta$values, thousands.separator = TRUE))
Unit: milliseconds
                                              expr      min       lq     mean   median       uq      max neval
 nice.cuts(dta$values, thousands.separator = TRUE) 32.67988 58.25709 99.26317 95.25195 136.7998 222.2178   100

> dta <- data.frame(values=floor(runif(1000, 10000,90000)))
> microbenchmark(nice.cuts(dta$values, thousands.separator = TRUE),
+                times = 10)
Unit: milliseconds
                                              expr      min       lq     mean   median       uq      max neval
 nice.cuts(dta$values, thousands.separator = TRUE) 428.6821 901.2123 1154.097 1068.845 1679.052 1708.836    10

> require(microbenchmark)
> dta <- data.frame(values=floor(runif(1000, 10000,90000)))
> microbenchmark(nice.cuts(dta$values, thousands.separator = TRUE),
+   nice.cuts2(dta$values, thousands.separator = TRUE))
Unit: milliseconds
                                               expr      min        lq     mean    median        uq        max neval cld
  nice.cuts(dta$values, thousands.separator = TRUE) 720.1378 815.51782 902.9218 923.97881 968.39036 1208.00434   100   b
 nice.cuts2(dta$values, thousands.separator = TRUE)  11.4147  15.18232  16.6196  16.46937  17.05305   29.91089   100  a 
> 

nice.cuts2 <- function(variable, cuts = 10, thousands.separator = FALSE) {

  # Load required packages (useful when used independently of context)
  Vectorize(require)(package = c("gsubfn", "Hmisc", "scales"),
                     character.only = TRUE)

  # Destring this variable
  destring <- function(x) {
    ## convert factor to strings
    if (is.character(x)) {
      as.numeric(x)
    } else if (is.factor(x)) {
      as.numeric(levels(x))[x]
    } else if (is.numeric(x)) {
      x
    } else {
      stop("could not convert to numeric")
    }
  }

  # Apply function
  variable <- destring(variable)

  # Check whether to disable scientific notation
  if (mean(variable) > 100000) {
    options(scipen = 999)
  } else {
    options(scipen = 0)
  }

  # Create pretty breaks
  cut_breaks <- pretty_breaks(n = cuts)(variable)

  # Round it two decimal places
  variable <- round(variable, digits = 2)

  # Develop cuts according to the provided object
  cuts_variable <- cut2(x = variable, cuts = cut_breaks)

  cuts_labels <- levels(cuts_variable)

  # Check if variable is total or with decimals
  if (all(cut_breaks %% 1 == 0)) {
    # Variable is integer
    cuts_labels <- gsubfn('\\[\\s*(\\d+),\\s*(\\d+)[^0-9]+',
                         ~paste0(x, '-',as.numeric(y)-1),
                         as.character(cuts_labels))
  } else {
    # Variable is not integer
    # Create clean cuts
    cuts_labels <- gsubfn('\\[\\s*([0-9]+\\.*[0-9]*),\\s*(\\d+\\.\\d+).*',
                         ~paste0(x, '-', as.numeric(y)- 0.01),
                         as.character(cuts_labels))
  }

  # Clean Inf
  cuts_labels <- gsub("Inf", max(variable), cuts_labels)

  # Clean punctuation
  cuts_labels <- sub("\\[(.*), (.*)\\]", "\\1 - \\2", cuts_labels)

  # Replace strings with spaces
  cuts_labels <- gsub("-"," - ",cuts_labels, fixed = TRUE)

  # Trim white spaces
  cuts_labels <- trimws(cuts_labels)


  if (thousands.separator == TRUE) {
    cuts_labels <- sapply(strsplit(cuts_labels, " - "),
                                 function(x) paste(prettyNum(x,
                                                             big.mark = ",",
                                                             preserve.width = "none"),
                                                   collapse = " - "))
  }

  levels(cuts_variable) <- cuts_labels
  cuts_variable
}