是否有矢量化并行max（）和min（）？

I have a data.frame with columns "a" and "b". I want to add columns called "high" and "low" that contain the highest and the lowest among columns a and b.

我有一个带有“a”和“b”列的data.frame。我想添加名为“high”和“low”的列，其中包含a和b列中的最高和最低。

Is there a way of doing this without looping over the lines in the dataframe?

有没有办法在没有循环数据框中的行的情况下执行此操作？

edit: this is for OHLC data, and so the high and low column should contain the highest and lowest element between a and b on the same line, and not among the whole columns. sorry if this is badly worded.

编辑：这是针对OHLC数据的，因此高和低列应包含同一行上a和b之间的最高和最低元素，而不是整列中的最高和最低元素。对不起，如果措辞严厉的话。

4 个解决方案

#1

Sounds like you're looking for pmax and pmin ("parallel" max/min):

听起来你正在寻找pmax和pmin（“并行”最大/分钟）：

Extremes                 package:base                  R Documentation

Maxima and Minima

Description:

     Returns the (parallel) maxima and minima of the input values.

Usage:

     max(..., na.rm = FALSE)
     min(..., na.rm = FALSE)

     pmax(..., na.rm = FALSE)
     pmin(..., na.rm = FALSE)

     pmax.int(..., na.rm = FALSE)
     pmin.int(..., na.rm = FALSE)

Arguments:

     ...: numeric or character arguments (see Note).

   na.rm: a logical indicating whether missing values should be
          removed.

Details:

     ‘pmax’ and ‘pmin’ take one or more vectors (or matrices) as
     arguments and return a single vector giving the ‘parallel’ maxima
     (or minima) of the vectors.  The first element of the result is
     the maximum (minimum) of the first elements of all the arguments,
     the second element of the result is the maximum (minimum) of the
     second elements of all the arguments and so on.  Shorter inputs
     are recycled if necessary.  ‘attributes’ (such as ‘names’ or
     ‘dim’) are transferred from the first argument (if applicable).

#2

If your data.frame name is dat.

如果您的data.frame名称是dat。

dat$pmin <- do.call(pmin,dat[c("a","b")])
dat$pmax <- do.call(pmax,dat[c("a","b")])

#3

Another possible solution:

另一种可能的方案

set.seed(21)
Data <- data.frame(a=runif(10),b=runif(10))
Data$low <- apply(Data[,c("a","b")], 1, min)
Data$high <- apply(Data[,c("a","b")], 1, max)

#4

Here's a version I implemented using Rcpp. I compared pmin with my version, and my version is roughly 3 times faster.

这是我使用Rcpp实现的版本。我将pmin与我的版本进行了比较，我的版本大约快了3倍。

library(Rcpp)

cppFunction("
  NumericVector min_vec(NumericVector vec1, NumericVector vec2) {
    int n = vec1.size();
    if(n != vec2.size()) return 0;
    else {
      NumericVector out(n);
      for(int i = 0; i < n; i++) {
        out[i] = std::min(vec1[i], vec2[i]);
      }
      return out;
    }
  }
")

x1 <- rnorm(100000)
y1 <- rnorm(100000)

microbenchmark::microbenchmark(min_vec(x1, y1))
microbenchmark::microbenchmark(pmin(x1, y1))

x2 <- rnorm(500000)
y2 <- rnorm(500000)

microbenchmark::microbenchmark(min_vec(x2, y2))
microbenchmark::microbenchmark(pmin(x2, y2))

The microbenchmark function output for 100,000 elements is:

100,000个元素的微基准功能输出是：

> microbenchmark::microbenchmark(min_vec(x1, y1))
Unit: microseconds
            expr     min       lq     mean  median       uq
 min_vec(x1, y1) 215.731 222.3705 230.7018 224.484 228.1115
     max neval
 284.631   100
> microbenchmark::microbenchmark(pmin(x1, y1))
Unit: microseconds
         expr     min       lq     mean  median      uq      max
 pmin(x1, y1) 891.486 904.7365 943.5884 922.899 954.873 1098.259
 neval
   100

And for 500,000 elements:

对于500,000个元素：

> microbenchmark::microbenchmark(min_vec(x2, y2))
Unit: milliseconds
            expr      min       lq     mean   median       uq
 min_vec(x2, y2) 1.493136 2.008122 2.109541 2.140318 2.300022
     max neval
 2.97674   100
> microbenchmark::microbenchmark(pmin(x2, y2))
Unit: milliseconds
         expr      min       lq     mean   median       uq
 pmin(x2, y2) 4.652925 5.146819 5.286951 5.264451 5.445638
      max neval
 6.639985   100

So you can see the Rcpp version is faster.

所以你可以看到Rcpp版本更快。

You could make it better by adding some error checking in the function, for instance: check that both vectors are the same length, or that they are comparable (not character vs. numeric, or boolean vs. numeric).

您可以通过在函数中添加一些错误检查来使其更好，例如：检查两个向量是否相同，或者它们是否可比较（不是字符与数字，或布尔与数字）。

#1