我需要为3个不同的数据集运行非常相似的代码。我当前的代码如下:
## data a
a_dat2 <- merge(a_dat, zip, by = "zip", all.x = T)
a_dat2 <- a_dat2 %>%
group_by(zip) %>%
summarize(dist_a_min = min(dist))
## data b
b_dat2 <- merge(b_dat, zip, by = "zip", all.x = T)
b_dat2 <- b_dat2 %>%
group_by(zip) %>%
summarize(dist_b_min = min(dist))
## data c
c_dat2 <- merge(c_dat, zip, by = "zip", all.x = T)
c_dat2 <- c_dat2 %>%
group_by(zip) %>%
summarize(dist_c_min = min(dist))
3个数据集的代码相同,除了数据名称不同:a_dat
,b_dat
,c_dat
。变量名称dist
也有所不同:dist_a_min
,dist_b_min
,dist_c_min
。可以使用什么函数/循环来缩短代码,这样就不必分别复制和粘贴每个数据集了?
答案 0 :(得分:3)
一种选择是将元素与list
放在mget
中,通过list
遍历imap
,将'?left_join
)与' zip”数据集,按“ zip”分组,并在基于标识符名称子字符串的列名称创建过程中获得{dist}的min
library(tidyverse)
mget(ls(pattern = "_dat2$")) %>%
imap(~ left_join(.x, zip, by = 'zip') %>%
group_by(zip) %>%
summarise((! str_c('dist_', substr(.y, 1, 1), '_min') := min(dist)))
或者另一个选择是为重复的任务创建一个函数
joinSumm <- function(dat, groupName, colName, data2) {
groupName <- enquo(groupName)
colName <- enquo(colName)
nm1 <- str_c('dist_', str_sub(rlang::as_name(enquo(dat)), 1, 1), '_min')
dat %>%
left_join(data2, by = rlang::as_name(groupName)) %>%
group_by(!! groupName) %>%
summarise((!! nm1) := min(!! colName))
}
joinSumm(a_dat2, zip, dist, zip)
joinSumm(b_dat2, zip, dist, zip)
带有内置数据集iris
(无连接部分)的可复制示例
list(a_dat = iris, b_dat = iris, c_dat = iris) %>%
imap(~ .x %>%
group_by(Species) %>%
summarise(!! str_c('dist_', substr(.y, 1, 1), '_min') := min(Sepal.Length)))
#$a_dat
# A tibble: 3 x 2
# Species dist_a_min
# <fct> <dbl>
#1 setosa 4.3
#2 versicolor 4.9
#3 virginica 4.9
#$b_dat
# A tibble: 3 x 2
# Species dist_b_min
# <fct> <dbl>
#1 setosa 4.3
#2 versicolor 4.9
#3 virginica 4.9
$c_dat
# A tibble: 3 x 2
# Species dist_c_min
# <fct> <dbl>
#1 setosa 4.3
#2 versicolor 4.9
#3 virginica 4.9