我正在寻找一种有效的方法来操作数据框中的多个变量。现在我正在使用 dplyr，但是随着变量的增多，这会变得很麻烦。 假设我有以下数据框，其中 brd 是汽车品牌，ye 是年份，type 是汽车类型，cy 和 hp 是类型特征。
brd <-c("BMW","BMW","BMW","Volvo","Volvo", "Volvo","BMW","BMW","BMW","Volvo","Volvo","Volvo")
ye <- c(99,99,99,99,99,99,98,98,98,98,98,98)
type <- c(1,2,3,1,2,3,1,2,3,1,2,3)
cy <- c(1895,1991,1587,2435,2435,1596,1991,1588,1984,1596,1991,1588)
hp <- c(77,110,80,103,103,75,110,77,93,75,110,77)
df <- as.data.frame(brd)
df$ye <- ye
df$type <- type
df$cy <- cy
df$hp <- hp
df
brd ye type cy hp
1 BMW 99 1 1895 77
2 BMW 99 2 1991 110
3 BMW 99 3 1587 80
4 Volvo 99 1 2435 103
5 Volvo 99 2 2435 103
6 Volvo 99 3 1596 75
7 BMW 98 1 1991 110
8 BMW 98 2 1588 77
9 BMW 98 3 1984 93
10 Volvo 98 1 1596 75
11 Volvo 98 2 1991 110
12 Volvo 98 3 1588 77
每年，我想计算同一品牌的所有其他产品的产品特征总和，并将其作为新变量添加到数据框中。现在，我正在使用 dplyr，如下所示:
library(dplyr)
df <- df %>% group_by(brd, ye) %>%
mutate(sumall_cy = sum(cy),
sumall_hp = sum(hp))
df <- df %>%
mutate(sumother_cy = sumall_cy-cy,
sumother_hp = sumall_li-hp)
这样我就得到了
brd ye type cy hp sumall_cy sumall_hp sumother_cy sumother_hp
<fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 BMW 99 1 1895 77 5473 267 3578 190
2 BMW 99 2 1991 110 5473 267 3482 157
3 BMW 99 3 1587 80 5473 267 3886 187
4 Volvo 99 1 2435 103 6466 281 4031 178
5 Volvo 99 2 2435 103 6466 281 4031 178
6 Volvo 99 3 1596 75 6466 281 4870 206
7 BMW 98 1 1991 110 5563 280 3572 170
8 BMW 98 2 1588 77 5563 280 3975 203
9 BMW 98 3 1984 93 5563 280 3579 187
10 Volvo 98 1 1596 75 5175 262 3579 187
11 Volvo 98 2 1991 110 5175 262 3184 152
12 Volvo 98 3 1588 77 5175 262 3587 185
有没有更有效的方法？我正在考虑像这样的 stata 代码循环:
foreach x of varlist hp cy {
bysort ye: egen sumall_`x'= sum(`x')
gen sumother_`x'=(sumall_`x' -`x')}
如有任何建议，我们将不胜感激。
最佳答案
这是一个解决方案 non-standard evaluation ，group_by 操作只需执行一次，并且当您有更多列需要处理时也可以工作:
library(dplyr) # 0.7.0
library(rlang) # required for the `syms` function
varlist <- c('cy', 'hp')
# make a list of quos of opertions
ops <- sapply(syms(varlist), function(x) quo(sum(UQ(x)) - UQ(x)) )
# set new variable name
names(ops) <- paste('sumother', varlist, sep = '_')
# get results
df %>% group_by(brd, ye) %>% mutate(!!!ops) %>% ungroup()
# # A tibble: 12 x 7
# brd ye type cy hp sumother_cy sumother_hp
# <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BMW 99 1 1895 77 3578 190
# 2 BMW 99 2 1991 110 3482 157
# 3 BMW 99 3 1587 80 3886 187
# 4 Volvo 99 1 2435 103 4031 178
# 5 Volvo 99 2 2435 103 4031 178
# 6 Volvo 99 3 1596 75 4870 206
# 7 BMW 98 1 1991 110 3572 170
# 8 BMW 98 2 1588 77 3975 203
# 9 BMW 98 3 1984 93 3579 187
# 10 Volvo 98 1 1596 75 3579 187
# 11 Volvo 98 2 1991 110 3184 152
# 12 Volvo 98 3 1588 77 3587 185
如果我们想保留 sumall_ 列，我们可以尝试:
ops <- sapply(syms(varlist), function(x) list(quo(sum(UQ(x))), quo(sum(UQ(x)) - UQ(x))) )
names(ops) <- paste(
rep(c('sumall', 'sumother'), length(varlist)),
rep(varlist, each = 2), sep = '_')
df %>% group_by(brd, ye) %>% mutate(!!!ops) %>% ungroup()
# # A tibble: 12 x 9
# brd ye type cy hp sumall_cy sumother_cy sumall_hp sumother_hp
# <fctr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 BMW 99 1 1895 77 5473 3578 267 190
# 2 BMW 99 2 1991 110 5473 3482 267 157
# 3 BMW 99 3 1587 80 5473 3886 267 187
# 4 Volvo 99 1 2435 103 6466 4031 281 178
# 5 Volvo 99 2 2435 103 6466 4031 281 178
# 6 Volvo 99 3 1596 75 6466 4870 281 206
# 7 BMW 98 1 1991 110 5563 3572 280 170
# 8 BMW 98 2 1588 77 5563 3975 280 203
# 9 BMW 98 3 1984 93 5563 3579 280 187
# 10 Volvo 98 1 1596 75 5175 3579 262 187
# 11 Volvo 98 2 1991 110 5175 3184 262 152
# 12 Volvo 98 3 1588 77 5175 3587 262 185
关于r - 在循环中汇总并生成多个变量，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/44870255/