根据另一列-R-Data.table中值的变化来减少列值的好方法

我正在寻找一种简单易行的方法，分别根据第1列中每个国家第3列的变化和第2列中每年的变化来减少第4列的值。这应该在R中使用data.table对象完成

数据：

我想要的是这样的东西：

数据：

---

下面是一种使用

dplyr

和

tidyr

的方法：

library(tidyverse)
data %>%
  separate(1, sep = ",", into = c("country","year","var1","var2")) %>%
  mutate(across(year:var2, as.numeric)) %>%
  group_by(country) %>%
  mutate(var2 = var2 * ((2*min(var1))-var1)/100)
## A tibble: 6 x 4
## Groups:   country [2]
#  country   year  var1  var2
#  <chr>    <dbl> <dbl> <dbl>
#1 country1  2020   100   1  
#2 country1  2025   120   0.8
#3 country1  2030   140   0.6
#4 country2  2020   100   1  
#5 country2  2025   150   0.5
#6 country2  2030   180   0.2

---

data您所说的“减少值”具体是什么意思？您能否使用
dput
向我们展示预期的输出并重新格式化您的数据，以便于加载？请看。我包括了我的预期输出。但您没有确切解释这两列之间的关系。例如，为什么我们在最后一行从.5到.42？这只是一个例子。我还不知道确切的关系。我只是在寻找一个好的、方便的方法，在其他值的基础上减少这个值。在这种情况下，精确的参数是不可逆的。理想情况下，有一个解，线性衰减，一个指数衰减，一个随机衰减。这将是完美的，但并非无关紧要。我们建议的代码将实现一种形式的衰减，因此如果您想要一个“完整”的答案，您需要指定一个“完整”的问题。
country1,2020,100,1
country1,2025,120,0.8
country1,2030,140,0.6
country2,2020,100,1
country2,2025,150,0.5
country2,2030,180,0.2

library(tidyverse)
data %>%
  separate(1, sep = ",", into = c("country","year","var1","var2")) %>%
  mutate(across(year:var2, as.numeric)) %>%
  group_by(country) %>%
  mutate(var2 = var2 * ((2*min(var1))-var1)/100)
## A tibble: 6 x 4
## Groups:   country [2]
#  country   year  var1  var2
#  <chr>    <dbl> <dbl> <dbl>
#1 country1  2020   100   1  
#2 country1  2025   120   0.8
#3 country1  2030   140   0.6
#4 country2  2020   100   1  
#5 country2  2025   150   0.5
#6 country2  2030   180   0.2

library(data.table)
setDT(data)
data[, c("country","year","var1","var2") := tstrsplit(V1,",")]
data[,V1 := NULL]
data[,c("year","var1","var2") := lapply(.SD,as.numeric),.SDcol = c("year","var1","var2")]
data[,var2 := .(var2 * (2*min(var1)-var1)/100), by = "country"]
data
#    country year var1 var2
#1: country1 2020  100  1.0
#2: country1 2025  120  0.8
#3: country1 2030  140  0.6
#4: country2 2020  100  1.0
#5: country2 2025  150  0.5
#6: country2 2030  180  0.2

data <- structure(list(V1 = c("country1,2020,100,1", "country1,2025,120,1", 
"country1,2030,140,1", "country2,2020,100,1", "country2,2025,150,1", 
"country2,2030,180,1")), class = "data.frame", row.names = c(NA, 
-6L))