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
这篇关于根据其他列中的值将 R 函数应用于行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！