我在集团 - 州 - 品牌级别以长格式提供年度时间序列数据。我想应用一个函数来计算每个级别的增长率。
基本上是（currentvalue / previous value）-1
在下面找到数据的摘录：
Grp Sta Brnd Yr Sls
A AL Ben's 2012 29770
A AL Ben's 2013 23357
A AL Ben's 2014 22442
A AL Ben's 2015 21848
A AL Ben's 2016 13799
B CA Scott's 2012 1079
B CA Scott's 2013 11178
B CA Scott's 2014 14778
B CA Scott's 2015 15241
B CA Scott's 2016 10569
C TX Joey's 2012 1673
C TX Joey's 2013 1290
C TX Joey's 2014 899
C TX Joey's 2015 732
C TX Joey's 2016 294
基本上，每个独特的grp-state-brand级别为5行。
Grp Sta Brnd Yr Sls Grwth
A AL Ben's 2012 29770
A AL Ben's 2013 23357 -22%
A AL Ben's 2014 22442 -4%
A AL Ben's 2015 21848 -3%
A AL Ben's 2016 13799 -37%
B CA Scott's 2012 1079
B CA Scott's 2013 11178 936%
B CA Scott's 2014 14778 32%
B CA Scott's 2015 15241 3%
B CA Scott's 2016 10569 -23%
C TX Joey's 2012 1673
C TX Joey's 2013 1290 -23%
C TX Joey's 2014 899 -30%
C TX Joey's 2015 732 -19%
C TX Joey's 2016 294 -60%
答案 0 :(得分：2)
使用base R
df$Grwth <- ave(df$Sls, df$Grp, df$Sta, df$Brnd, FUN = function(x)
round((x/lag(x) - 1)*100))
df
# Grp Sta Brnd Yr Sls Grwth
#1 A AL Bens 2012 29770 NA
#2 A AL Bens 2013 23357 -22
#3 A AL Bens 2014 22442 -4
#4 A AL Bens 2015 21848 -3
#5 A AL Bens 2016 13799 -37
#6 B CA Scotts 2012 1079 NA
#7 B CA Scotts 2013 11178 936
#8 B CA Scotts 2014 14778 32
#9 B CA Scotts 2015 15241 3
#10 B CA Scotts 2016 10569 -31
#11 C TX Joeys 2012 1673 NA
#12 C TX Joeys 2013 1290 -23
#13 C TX Joeys 2014 899 -30
#14 C TX Joeys 2015 732 -19
#15 C TX Joeys 2016 294 -60
答案 1 :(得分：1)
df=data.frame(Grp = c(rep("A",5),rep("B",5),rep("C",5)), Sta = c(rep("AL",5),rep("CA",5),rep("TX",5)),
Brnd = c(rep("Ben's",5),rep("Scott's",5),rep("Joey's",5)),
Yr=rep(c(2012,2013,2014,2015,2016),3),
Sls = c(29770,23357,22442,21848,13799,1079,11178,14778,15241,10569,1673,1290,899,732,294))
ddply(df, .(Grp,Sta,Brnd),mutate, y = sprintf("%.2f%%",c(NA,100*diff(Sls)/Sls[-length(Sls)])))
Grp Sta Brnd Yr Sls y
1 A AL Ben's 2012 29770 NA%
2 A AL Ben's 2013 23357 -21.54%
3 A AL Ben's 2014 22442 -3.92%
4 A AL Ben's 2015 21848 -2.65%
5 A AL Ben's 2016 13799 -36.84%
6 B CA Scott's 2012 1079 NA%
7 B CA Scott's 2013 11178 935.96%
8 B CA Scott's 2014 14778 32.21%
9 B CA Scott's 2015 15241 3.13%
10 B CA Scott's 2016 10569 -30.65%
11 C TX Joey's 2012 1673 NA%
12 C TX Joey's 2013 1290 -22.89%
13 C TX Joey's 2014 899 -30.31%
14 C TX Joey's 2015 732 -18.58%
15 C TX Joey's 2016 294 -59.84%