根据其他列中的值将 R 函数应用于行 [英] Apply R-function to rows depending on value in other column

问题描述

我有以下函数可以为一列中的变量构建股票效应.该变量在 B 列中创建一个值，该值采用 A 列中的值，并从 B 列中的先前观察结果中添加一个结转(例如 0.5).

I have the following function to build a stock effect for a variable in one column. The variable creates a value in Column B that takes the value in ColumnA and adds a carry over (like e.g. 0.5) from the previous observation in Column B.

constructZ <- function(lag, N) {
  r <- lag^(seq_len(N)-1)
  m <- matrix(rep(r,N),nrow=N)
  z <- matrix(0,nrow=N,ncol=N)
  z[lower.tri(z,diag=TRUE)] <- m[row(m) <= (N+1-col(m))]
  z
}

我的问题是现在我有一个面板数据集，其中包含针对许多不同情况的一列观察结果.每个案例都有一个特定的指标(数字).数据如下:

My problem is now that I have a panel data set that has in one column observations for many different cases. Each case has a specific indicator (numeric). Data looks like:

ColumnA      Indicator         Time
1            1                 1
0            1                 2
0            1                 3
4            2                 1
5            2                 2
0            2                 3
4            3                 1
0            3                 2
2            3                 3

我现在希望将函数应用于所有观察(时间)的每个案例(指标).

I now want the function to be applied to each case (Indicator) for all observations (Time).

知道如何实现这一目标吗?输出应如下所示:

Any idea how to achieve this? The Output should then look like:

ColumnA      Indicator         Time          ColumnB
    1            1                 1         1
    0            1                 2         0.5
    0            1                 3         0.25
    4            2                 1         4
    5            2                 2         7
    0            2                 3         3.5
    4            3                 1         4
    0            3                 2         2
    2            3                 3         3

非常感谢任何帮助或支持！

Any help or support is highly appreciated!

非常感谢！

推荐答案

这里是另一种无循环/函数式编程解决方案.我们将使用 Reduce() 函数，该函数对向量中的每对项目应用二元函数.

Here is an alternative loop-free/functional programming solution. We are going to use the Reduce() function which applies a binary function over every pair of items in a vector.

例如，Reduce(`+`, xs) 计算向量中值的总和.如果我们设置 accumulate = TRUE，我们会得到一个滚动/累积和.

For example, Reduce(`+`, xs) computes the sum of values in vector. If we set accumulate = TRUE, we get a rolling/cumulative sum.

Reduce(`+`, 1:6)
#> [1] 21

# What Reduce is doing here, basically
((((((1) + 2) + 3) + 4) + 5) + 6)
#> [1] 21

# Keep each intermediate sum
Reduce(`+`, 1:6, accumulate = TRUE)
#> [1]  1  3  6 10 15 21

(purrr 包将这两种行为分为不同的函数:reduce() 和 accumulate().)

(The purrr package separates these two behaviors into different functions: reduce() and accumulate().)

我们可以使用 Reduce() 来实现结转/缩放功能.首先，定义一个处理一对值的函数，然后使用 Reduce() 来执行它的滚动版本.

We can use Reduce() to implement the carry-over/scaling function. First, define a function that works on a pair of values, then use Reduce() to perform a rolling version of it.

rolling_scale <- function(xs, scale_factor) {
  scale_pair <- function(x1, x2) x2 + scale_factor * x1
  Reduce(scale_pair, xs, accumulate = TRUE)
}

rolling_scale(c(4, 5, 0), .5)
#> [1] 4.0 7.0 3.5

现在，我们可以使用 dplyr 并将此滚动功能应用于每个指标组.

Now, we can use dplyr and apply this rolling function to each indicator group.

library(dplyr)

raw <- data.frame(
  ColumnA = c(1, 0, 0, 4, 5, 0, 4, 0, 2), 
  Indicator = rep(x = 1:3, each = 3), 
  Time = 1:3)

raw %>% 
  group_by(Indicator) %>% 
  mutate(ColumnB = rolling_scale(ColumnA, .5)) %>% 
  ungroup()
#> # A tibble: 9 × 4
#>   ColumnA Indicator  Time ColumnB
#>     <dbl>     <int> <int>   <dbl>
#> 1       1         1     1    1.00
#> 2       0         1     2    0.50
#> 3       0         1     3    0.25
#> 4       4         2     1    4.00
#> 5       5         2     2    7.00
#> 6       0         2     3    3.50
#> 7       4         3     1    4.00
#> 8       0         3     2    2.00
#> 9       2         3     3    3.00

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