我有以下数据:
Company Year Variables Data
ABC 2000 Revenue 10
ABC 2001 Revenue 15
ABC 2002 Revenue 12
ABC 2003 Revenue 25
ABC 2004 Revenue 30
CDE 2000 Revenue 5
CDE 2001 Revenue 8
CDE 2002 Revenue 17
CDE 2003 Revenue 9
CDE 2004 Revenue 34
#etc
我想计算过去3年的复合年增长率(CAGR)。
例如,每家公司的3年CAGR结果将是:
Company Year Variables Data CAGR
ABC 2000 Revenue 10 NA
ABC 2001 Revenue 15 NA
ABC 2002 Revenue 12 6.27%
ABC 2003 Revenue 25 18.56%
ABC 2004 Revenue 30 35.72%
CDE 2000 Revenue 5 NA
CDE 2001 Revenue 8 NA
CDE 2002 Revenue 17 50.37%
CDE 2003 Revenue 9 4.00%
CDE 2004 Revenue 34 25.99%
我按年使用以下数据:
CAGR for 2004=((LastYear/PreviousYear)^(1/n))-1
For example for n = 2
LastYear =2004
PreviousYear =2004-2 = 2002
尝试计算2004年与2002年的复合年增长率的R代码:
library(tibble)
library(dplyr)
library(lubridate)
year<-c(rep(2000:2004,2))
company<-rep(c("ABC","CDE"),5)
variable<-rep("revenue",10)
data<-c(10,15,12,25,30,5,8,17,9,34)
tibdf<-tibble(company,year,variable,data)
View(tibdf)
#revenue2004<-tibdf%>%filter(year==2004)%>%select(company,data)
#revenue2002<-tibdf%>%filter(year==2001)%>%select(company,data)
计算复合年增长率(来自Plot Compound Annual Growth Rate (3 independent variables) in R)
annual.growth.rate <- function(a){
T1 <- max(a$year) - min(a$year)+1
FV <- a[which(a$year == max(a$year)),"data"]
SV <- a[which(a$year == min(a$year)),"data"]
cagr <- ((FV/SV)^(1/T1)) -1
}
使用tibdf作为函数。 不幸的是,我无法将功能应用于我的数据。
感谢您的帮助。
答案 0 :(得分:1)
此函数计算n
的不同值的CAGR:
calc_cagr <- function(df, n) {
df <- df %>%
arrange(company, year) %>%
group_by(company) %>%
mutate(cagr = ((data / lag(data, n)) ^ (1 / n)) - 1)
return(df)
}
calc_cagr(tibdf, 2)
# A tibble: 10 x 5
# Groups: company [2]
# company year variable data cagr
# <chr> <int> <chr> <dbl> <dbl>
# 1 ABC 2000 revenue 10.0 NA
# 2 ABC 2001 revenue 15.0 NA
# 3 ABC 2002 revenue 12.0 0.0954
# 4 ABC 2003 revenue 25.0 0.291
# 5 ABC 2004 revenue 30.0 0.581
# 6 CDE 2000 revenue 5.00 NA
# 7 CDE 2001 revenue 8.00 NA
# 8 CDE 2002 revenue 17.0 0.844
# 9 CDE 2003 revenue 9.00 0.0607
# 10 CDE 2004 revenue 34.0 0.414
然而,我得到的结果与您不同,但您的问题对于是否除以n
或n+1
有点模棱两可。
数据强>
tibdf <- tibble(company = rep(c("ABC", "CDE"), each = 5),
year = rep(2000:2004, 2),
variable = rep("revenue", 10),
data = c(10, 15, 12, 25, 30, 5, 8, 17, 9, 34))