问题描述
我具有以下功能，可以在一列中为变量建立股票效应.变量会在B列中创建一个值，该值会在ColumnA中获得该值，并从B列中的上一个观察值中结转(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，现在我有了一个面板数据集，该数据集在一列中观察到了许多不同的情况.每个案例都有一个特定的指示符(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)的每种情况(指标).
I now want the function to be applied to each case (Indicator) for all observations (Time).
任何想法如何实现这一目标?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
任何帮助或支持都将受到高度赞赏！
Any help or support is highly appreciated!
非常感谢！
推荐答案
这是替代的无循环/功能编程解决方案.我们将使用 Reduce()函数，该函数将二进制函数应用于向量中的每对项目.
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)计算向量中的值之和.如果设置 accumulate = TRUE ，我们将获得滚动/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软件包将这两种行为分为不同的功能: 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，定义一个适用于一对值的函数，然后使用 Reduce()对其进行滚动版本.
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
这篇关于将R函数应用于行，具体取决于其他列中的值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！