我有一张表，其中有五列Year，Revenue，Pensions，Income和Wages。使用此表，我使用以下代码进行了计算：
library(dplyr)
#DATA
TEST<-data.frame(
Year= c(2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021),
Revenue =c(8634,5798,6022,6002,6266,6478,6732,7224,6956,6968,7098,7620,7642,8203,9856,20328,22364,22222,23250,25250,26250,27250),
Pensions =c(8734,5798,7011,7002,7177,7478,7731,7114,7957,7978,7098,7710,7742,8203,9857,10328,11374,12211,13150,15150,17150,17150),
Income =c(8834,5898,6033,6002,6366,6488,6833,8334,6956,6968,8098,8630,8642,8203,9856,30328,33364,32233,33350,35350,36350,38350),
Wages =c(8834,5598,8044,8002,8488,8458,8534,5444,8958,8988,5098,5840,5842,8203,9858,40328,44384,42244,43450,45450,48450,45450)
)
#FUNCTION
fun1 <- function(x){ ((x - lag(x))/lag(x))*100}
#CALCULATION
ESTIMATION_0<-mutate(TEST,
Nominal_growth_Revenue=fun1(Revenue),
Nominal_growth_Pensions=fun1(Pensions),
Nominal_growth_Income=fun1(Income),
Nominal_growth_Wages=fun1(Wages)
)
但是我的目的是优化此代码并使用apply函数（或类似方法）进行此计算。也就是说，为了进行此计算，我编写了4条代码行，但是我喜欢用一条代码行来完成此操作。那么有人可以帮助我解决这个问题吗？
答案 0 :(得分：5)
假设您有一个带有相关列的字符向量：
cols <- c("Revenue", "Pensions", "Income", "Wages")
使用apply()：
TEST[paste0('nomial_growth', cols)] <- apply(TEST[cols], 2, fun1)
或data.table：
library(data.table)
setDT(TEST)
TEST[, (paste0('nomial_growth', cols)) := lapply(.SD, fun1), .SDcols = cols]
答案 1 :(得分：3)
您可以这样做：
vars_names <- paste0("Nominal_groth", names(select(TEST, -Year)))
TEST %>%
bind_cols( (TEST %>% mutate_at(vars(-Year), ~fun1(.x))) %>% select(-Year) %>% set_names(vars_names) )
Year Revenue Pensions Income Wages Nominal_grothRevenue Nominal_grothPensions Nominal_grothIncome Nominal_grothWages
1 2000 8634 8734 8834 8834 NA NA NA NA
2 2001 5798 5798 5898 5598 -32.8468844 -33.6157545 -33.2352275 -36.63119765
3 2002 6022 7011 6033 8044 3.8634012 20.9210072 2.2889115 43.69417649
4 2003 6002 7002 6002 8002 -0.3321156 -0.1283697 -0.5138405 -0.52212829
5 2004 6266 7177 6366 8488 4.3985338 2.4992859 6.0646451 6.07348163
6 2005 6478 7478 6488 8458 3.3833387 4.1939529 1.9164310 -0.35344015
7 2006 6732 7731 6833 8534 3.9209633 3.3832576 5.3175092 0.89855758
8 2007 7224 7114 8334 5444 7.3083779 -7.9808563 21.9669252 -36.20810874
9 2008 6956 7957 6956 8958 -3.7098560 11.8498735 -16.5346772 64.54812638
10 2009 6968 7978 6968 8988 0.1725129 0.2639186 0.1725129 0.33489618
11 2010 7098 7098 8098 5098 1.8656716 -11.0303334 16.2169920 -43.27992879
12 2011 7620 7710 8630 5840 7.3541843 8.6221471 6.5695233 14.55472734
13 2012 7642 7742 8642 5842 0.2887139 0.4150454 0.1390498 0.03424658
14 2013 8203 8203 8203 8203 7.3410102 5.9545337 -5.0798426 40.41424170
15 2014 9856 9857 9856 9858 20.1511642 20.1633549 20.1511642 20.17554553
16 2015 20328 10328 30328 40328 106.2500000 4.7783301 207.7110390 309.08906472
17 2016 22364 11374 33364 44384 10.0157418 10.1278079 10.0105513 10.05752827
18 2017 22222 12211 32233 42244 -0.6349490 7.3588887 -3.3898813 -4.82155732
19 2018 23250 13150 33350 43450 4.6260463 7.6897879 3.4653926 2.85484329
20 2019 25250 15150 35350 45450 8.6021505 15.2091255 5.9970015 4.60299194
21 2020 26250 17150 36350 48450 3.9603960 13.2013201 2.8288543 6.60066007
22 2021 27250 17150 38350 45450 3.8095238 0.0000000 5.5020633 -6.19195046