我正在嘗試g_it在分組資料框中創建一個名為的新列，其中每個組的第一個值將是呼叫列中的初始值，exp后續值是(1 - 0.1) * lag(g_it) exp.
我相信這purrr:accumulate是我正在尋找的，但我不知道如何設定它。
我的資料是：
structure(list(group = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 4), exp = c(0.493735461892577, 0.501836433242221, 0.4916437138759,
0.515952808021378, 0.503295077718154, 0.49179531615882, 0.504874290524285,
0.507383247051292, 0.505757813516535, 0.496946116128436, 0.515117811684508,
0.503898432364114, 0.493787594194582, 0.477853001128225, 0.511249309181431,
0.499550663909848)), class = "data.frame", row.names = c(NA,
-16L))
預期輸出：
group exp g_it
1 0.4937355 0.4937355
1 0.5018364 0.94619835
1 0.4916437 1.343222215
1 0.5159528 1.724852794
2 0.5032951 0.5032951
2 0.4917953 0.94476089
2 0.5048743 1.355159101
2 0.5073832 1.727026391
3 0.5057578 0.5057578
3 0.4969461 0.95212812
3 0.5151178 1.372033108
3 0.5038984 1.738728197
4 0.4937876 0.4937876
4 0.477853 0.92226184
4 0.5112493 1.341284956
4 0.4995507 1.70670716
uj5u.com熱心網友回復：
如果您accumulate使用~語法提供函式，.x則為“累積”（上一個）值并且.y是“下一個”值。
df <- structure(list(group = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 4), exp = c(0.493735461892577, 0.501836433242221, 0.4916437138759,
0.515952808021378, 0.503295077718154, 0.49179531615882, 0.504874290524285,
0.507383247051292, 0.505757813516535, 0.496946116128436, 0.515117811684508,
0.503898432364114, 0.493787594194582, 0.477853001128225, 0.511249309181431,
0.499550663909848)), class = "data.frame", row.names = c(NA,
-16L))
library(dplyr, warn.conflicts = F)
library(purrr)
df %>%
group_by(group) %>%
mutate(g_it = accumulate(exp, ~ (1 - 0.1)*.x .y))
#> # A tibble: 16 × 3
#> # Groups: group [4]
#> group exp g_it
#> <dbl> <dbl> <dbl>
#> 1 1 0.494 0.494
#> 2 1 0.502 0.946
#> 3 1 0.492 1.34
#> 4 1 0.516 1.72
#> 5 2 0.503 0.503
#> 6 2 0.492 0.945
#> 7 2 0.505 1.36
#> 8 2 0.507 1.73
#> 9 3 0.506 0.506
#> 10 3 0.497 0.952
#> 11 3 0.515 1.37
#> 12 3 0.504 1.74
#> 13 4 0.494 0.494
#> 14 4 0.478 0.922
#> 15 4 0.511 1.34
#> 16 4 0.500 1.71
由reprex 包于 2022-01-10 創建(v2.0.1)
uj5u.com熱心網友回復：
使用ave 的基本 R 選項Reduce
transform(
df,
g_it = ave(
exp,
group,
FUN = function(v) {
Reduce(
function(x, y) 0.9 * x y,
v,
accumulate = TRUE
)
}
)
)
給
group exp g_it
1 1 0.4937355 0.4937355
2 1 0.5018364 0.9461983
3 1 0.4916437 1.3432222
4 1 0.5159528 1.7248528
5 2 0.5032951 0.5032951
6 2 0.4917953 0.9447609
7 2 0.5048743 1.3551591
8 2 0.5073832 1.7270264
9 3 0.5057578 0.5057578
10 3 0.4969461 0.9521281
11 3 0.5151178 1.3720331
12 3 0.5038984 1.7387283
13 4 0.4937876 0.4937876
14 4 0.4778530 0.9222618
15 4 0.5112493 1.3412850
16 4 0.4995507 1.7067071
uj5u.com熱心網友回復：
另一種可能的解決方案，僅基于dplyrand cumsum：
library(dplyr)
df %>%
group_by(group) %>%
mutate(g_it = cumsum((1 - 0.1)^(row_number() - 1) * exp)) %>% ungroup
#> # A tibble: 16 × 3
#> group exp g_it
#> <dbl> <dbl> <dbl>
#> 1 1 0.494 0.494
#> 2 1 0.502 0.945
#> 3 1 0.492 1.34
#> 4 1 0.516 1.72
#> 5 2 0.503 0.503
#> 6 2 0.492 0.946
#> 7 2 0.505 1.35
#> 8 2 0.507 1.72
#> 9 3 0.506 0.506
#> 10 3 0.497 0.953
#> 11 3 0.515 1.37
#> 12 3 0.504 1.74
#> 13 4 0.494 0.494
#> 14 4 0.478 0.924
#> 15 4 0.511 1.34
#> 16 4 0.500 1.70
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