>我有以下数据框包含多个项目的现金流。例如:
test <- data.frame(ID = c(rep("A",3), rep("B",4)),
time = c("y3","y2","y1","y4","y3","y2","y1"),
Cfs= c(rep(1,3),rep(2,4)),
interest = c(rep(0.1,3),rep(0.05,4)))
ID time CFs interest
A y3 1 0.1
A y2 1 0.1
A y1 1 0.1
B y4 2 0.05
B y3 2 0.05
B y2 2 0.05
B y1 2 0.05
我想为每个项目生成每个时间点的净现值,因此最终输出应如下所示:
ID time CFs interest NPV
A y3 1 0.1 2.487
A y2 1 0.1 1.736
A y1 1 0.1 0.909
B y4 2 0.05 7.092
B y3 2 0.05 5.446
B y2 2 0.05 3.719
B y1 2 0.05 1.905
通过阅读一些旧帖子,我能够计算出每个项目的总现金流的净现值,但我不确定在每个时间段如何做到这一点。另外,由于实际数据集非常大(300k+(,我也试图避免循环。
谢谢
您可能会发现其中一些帮助程序函数很有用
dcf <- function(x, r, t0=FALSE){
# calculates discounted cash flows (DCF) given cash flow and discount rate
#
# x - cash flows vector
# r - vector or discount rates, in decimals. Single values will be recycled
# t0 - cash flow starts in year 0, default is FALSE, i.e. discount rate in first period is zero.
if(length(r)==1){
r <- rep(r, length(x))
if(t0==TRUE){r[1]<-0}
}
x/cumprod(1+r)
}
npv <- function(x, r, t0=FALSE){
# calculates net present value (NPV) given cash flow and discount rate
#
# x - cash flows vector
# r - discount rate, in decimals
# t0 - cash flow starts in year 0, default is FALSE
sum(dcf(x, r, t0))
}
现在,我们可以应用dplyr
的力量
library(dplyr)
test %>% mutate_if(is.factor, as.character) %>%
arrange(ID, time) %>%
group_by(ID) %>%
mutate(DCF=cumsum(dcf(x=Cfs, r=interest)))
#> # A tibble: 7 x 5
#> # Groups: ID [2]
#> ID time Cfs interest DCF
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 A y1 1 0.10 0.9090909
#> 2 A y2 1 0.10 1.7355372
#> 3 A y3 1 0.10 2.4868520
#> 4 B y1 2 0.05 1.9047619
#> 5 B y2 2 0.05 3.7188209
#> 6 B y3 2 0.05 5.4464961
#> 7 B y4 2 0.05 7.0919010
这个问题很旧,但我在这里写下我的答案,也许对某人有帮助:
您需要在for()
循环中使用cumsum()
和cumprod()
计算每个 ID 的 NPV,如下所示:
test <- test %>% mutate(npv = -1)
for(j in unique(test$ID)){
x <- (test %>% filter(ID == j))$Cfs
irr <- (test %>% filter(ID == j))$interest
npv <- cumsum(x/cumprod(1+irr)) %>% round(3)
test$npv[test$ID==j] <- npv[length(npv):1]
}
test
结果如下:
ID time Cfs interest npv
1 A y3 1 0.10 2.487
2 A y2 1 0.10 1.736
3 A y1 1 0.10 0.909
4 B y4 2 0.05 7.092
5 B y3 2 0.05 5.446
6 B y2 2 0.05 3.719
7 B y1 2 0.05 1.905