data("Grunfeld",package = "AER")
library("plm")
gr <- subset(Grunfeld, firm %in% c("General Electric","General Motors"))
pgr <- plm.data(gr,index = c("firm","year"))
row.names firm year invest value capital
1 1 General Motors 1935 317.6 3078.5 2.8
2 2 General Motors 1936 391.8 4661.7 52.6
3 3 General Motors 1937 410.6 5387.1 156.9
4 4 General Motors 1938 257.7 2792.2 209.2
5 5 General Motors 1939 330.8 4313.2 203.4
6 6 General Motors 1940 461.2 4643.9 207.2
7 7 General Motors 1941 512.0 4551.2 255.2
8 8 General Motors 1942 448.0 3244.1 303.7
9 9 General Motors 1943 499.6 4053.7 264.1
10 10 General Motors 1944 547.5 4379.3 201.6
11 11 General Motors 1945 561.2 4840.9 265.0
12 12 General Motors 1946 688.1 4900.9 402.2
13 13 General Motors 1947 568.9 3526.5 761.5
14 14 General Motors 1948 529.2 3254.7 922.4
15 15 General Motors 1949 555.1 3700.2 1020.1
16 16 General Motors 1950 642.9 3755.6 1099.0
17 17 General Motors 1951 755.9 4833.0 1207.7
18 18 General Motors 1952 891.2 4924.9 1430.5
19 19 General Motors 1953 1304.4 6241.7 1777.3
20 20 General Motors 1954 1486.7 5593.6 2226.3
21 41 General Electric 1935 33.1 1170.6 97.8
22 42 General Electric 1936 45.0 2015.8 104.4
23 43 General Electric 1937 77.2 2803.3 118.0
24 44 General Electric 1938 44.6 2039.7 156.2
25 45 General Electric 1939 48.1 2256.2 172.6
26 46 General Electric 1940 74.4 2132.2 186.6
27 47 General Electric 1941 113.0 1834.1 220.9
28 48 General Electric 1942 91.9 1588.0 287.8
29 49 General Electric 1943 61.3 1749.4 319.9
30 50 General Electric 1944 56.8 1687.2 321.3
31 51 General Electric 1945 93.6 2007.7 319.6
32 52 General Electric 1946 159.9 2208.3 346.0
33 53 General Electric 1947 147.2 1656.7 456.4
34 54 General Electric 1948 146.3 1604.4 543.4
35 55 General Electric 1949 98.3 1431.8 618.3
36 56 General Electric 1950 93.5 1610.5 647.4
37 57 General Electric 1951 135.2 1819.4 671.3
38 58 General Electric 1952 157.3 2079.7 726.1
39 59 General Electric 1953 179.5 2371.6 800.3
40 60 General Electric 1954 189.6 2759.9 888.9
以上是数据，然后我想计算投资的增长率，因此通用汽车的增长率的第一个值将是NA，而通用电气的第一个价值将是 同样，这意味着我想按组计算增长率。
如果我使用以下命令：
pgr$invest_growth <- NA
pgr$invest_growth<- c(NA,diff(invest)/invest[-length(invest)])
我会得到一个结果，但对于41号，我得到了通用汽车和通用电气之间的增长率，而对于我的问题，我希望将41号值设为NA。
就像
一样id firm invest
1 A 2
2 A 1
3 A 4
4 A 3
1 B 2
2 B 5
3 B 2
4 B 1
然后
id firm invest growth rate
1 A 1 NA
2 A 2 1
3 A 3 0.5
4 A 4 0.3333
1 B 5 NA
2 B 6 0.2
3 B 7 0.1666.
4 B 8 0.14
那么这种情况的命令是什么？非常感谢。
答案 0 :(得分：1)
我会在基础R中使用ave并编写我自己的函数。
data("Grunfeld", package = "AER")
Grunfeld <- Grunfeld[order(Grunfeld$firm, Grunfeld$year), ]
myGR <- function(x, n=1) {
c(rep(NA, n), diff(x, n) / head(x, -1*n))
}
Grunfeld$grInvest <- ave(Grunfeld$invest, Grunfeld$firm, FUN=myGR)
编辑：如果您错过了多年（例如，这种方法不知道年份，只知道相对位置），这会失败，但我通常用expand.grid填写这些
fullPanel <- expand.grid(unique(Grunfeld$firm), min(Grunfeld$year):max(Grunfeld$year))
names(fullPanel) <- c("firm", "year")
GrunfeldAlt <- Grunfeld[-5, ]
GrunfeldAlt <- merge(GrunfeldAlt, fullPanel, all=TRUE)
答案 1 :(得分：1)
使用true软件包：
dplyr答案 2 :(得分：0)
编辑：在QuantMod包中，您可以找到命令PercChange。如果您不想要百分比，也可以使用slide命令。
data(mtcars)
x<-split(mtcars,f=as.factor(mtcars$gear))
for(i in names(x)){
x[[i]]<-PercChange(data=x[[i]],Var='mpg')
}
data<-unsplit(x,f=as.factor(mtcars$gear))
答案 3 :(得分：0)
有很多功能可以计算回报：TTR::ROC，quantmod::dailyReturn，quantmod::Delt等，但我会用你的。
myReturn <- function(x) c(NA, diff(x)/x[-length(x)])
至少有十几种方法可以做到这一点。搜索“split，apply，combine”，“group by”，“by”，“tapply”，“ave”。以下是几种方式。
library(data.table)
setDT(pgr) # convert to a data.table
pgr[, invest_growth:=myReturn(invest), by=firm]
# if you don't want to create a column, pgr[, myReturn(invest), by=firm] will do.
pgr$invest_growth <- unlist(lapply(split(pgr, pgr$firm), function(x) myReturn(x$invest)))
答案 4 :(得分：0)
collapse R软件包和通用函数fgrowth以及相关的增长算子G现在提供了一个解决此问题的快速解决方案。它还具有用于plm对象的方法：
data("Grunfeld",package = "AER")
library("plm")
gr <- subset(Grunfeld, firm %in% c("General Electric","General Motors"))
pgr <- pdata.frame(gr, index = c("firm","year"))
library(collapse)
# This computes the proper panel growth rate of all numeric variables
G(pgr)
head(G(pgr))
firm year G1.invest G1.value G1.capital
General Motors-1935 General Motors 1935 NA NA NA
General Motors-1936 General Motors 1936 23.362720 51.427643 1778.571429
General Motors-1937 General Motors 1937 4.798367 15.560847 198.288973
General Motors-1938 General Motors 1938 -37.238188 -48.168774 33.333333
General Motors-1939 General Motors 1939 28.366317 54.473175 -2.772467
General Motors-1940 General Motors 1940 39.419589 7.667161 1.868240
# You could add those growth rates to the data.frame using
add_vars(pgr) <- G(pgr, keep.ids = FALSE)
# You can only compute the growth rate of invest and turn off the automatic renaming with
G(pgr, cols = "invest", stubs = FALSE)
firm year invest
General Motors-1935 General Motors 1935 NA
General Motors-1936 General Motors 1936 23.362720
General Motors-1937 General Motors 1937 4.798367
General Motors-1938 General Motors 1938 -37.238188
General Motors-1939 General Motors 1939 28.366317
# You could also compute the growth rate of the panel-series directly:
G(pgr$invest)
General Motors-1935 General Motors-1936 General Motors-1937 General Motors-1938 General Motors-1939 General Motors-1940
NA 23.362720 4.798367 -37.238188 28.366317 39.419589
General Motors-1941 General Motors-1942 General Motors-1943 General Motors-1944 General Motors-1945 General Motors-1946
11.014744 -12.500000 11.517857 9.587670 2.502283 22.612259
General Motors-1947 General Motors-1948 General Motors-1949 General Motors-1950 General Motors-1951 General Motors-1952
-17.323064 -6.978379 4.894180 15.816970 17.576606 17.899193
General Motors-1953 General Motors-1954 General Electric-1935 General Electric-1936 General Electric-1937 General Electric-1938
46.364452 13.975774 NA 35.951662 71.555556 -42.227979
General Electric-1939 General Electric-1940 General Electric-1941 General Electric-1942 General Electric-1943 General Electric-1944
7.847534 54.677755 51.881720 -18.672566 -33.297062 -7.340946
General Electric-1945 General Electric-1946 General Electric-1947 General Electric-1948 General Electric-1949 General Electric-1950
64.788732 70.833333 -7.942464 -0.611413 -32.809296 -4.883011
General Electric-1951 General Electric-1952 General Electric-1953 General Electric-1954
44.598930 16.346154 14.113160 5.626741
fgrowth / G完全基于C ++，因此在速度方面胜过所有其他解决方案，也可以应用于更大的面板：
library(microbenchmark)
microbenchmark(G(pgr))
Unit: microseconds
expr min lq mean median uq max neval
G(pgr) 47.302 48.642 54.48262 49.533 50.6495 186.086 100
顺便说一句：flag / L / F使用fdiff / D和collapse，还为滞后，超前，滞后/超前和迭代差异实现了类似的解决方案。