purrr查找最小值，然后在

我有两个数据集。第一个包含城市列表及其与目的地的距离（以英里为单位）。第二个列表包含目的地。我想使用purrr将最近目的地的名称放入第一个数据集中的新列中

这是第一个数据集（包含合成数据/距离）：

库（tidyverse）
数据1我们可以
收集
为“长”格式，按“城市”分组，
切片
最小值为“val”的行，
左键与“数据2”连接以获得“最近的”

library(tidyverse)
data1 %>% 
   gather(key, val, starts_with("dist")) %>% 
   group_by(city) %>% 
   slice(which.min(val)) %>% 
   ungroup %>%
   transmute(city, key = str_remove(key, 'dist_')) %>% 
   left_join(data2 %>% 
   mutate(key = word(destination, 1))) %>%
   select(city, nearest = destination) %>% 
   left_join(data1)

使用
tidyverse
的解决方案

library(tidyverse)

data3 <- data1 %>%
  mutate(City = apply(data1 %>% select(-city), 1, function(x) names(x)[which.min(x)])) %>%
  mutate(City = str_remove(City, "^dist_")) %>%
  left_join(data2 %>%
              separate(destination, into = c("City", "Country"), sep = " ", remove = FALSE),
            by = "City") %>%
  select(-City, -Country) %>%
  rename(nearest = destination)

data3
# # A tibble: 3 x 6
#   city    dist_Rome dist_Miami dist_Singapore dist_Toronto nearest            
#   <chr>       <dbl>      <dbl>          <dbl>        <dbl> <chr>              
# 1 Atlanta      1000        400           3000          900 Miami United States
# 2 Tokyo        2000       3000            600         3200 Singapore Singapore
# 3 Paris         300       1500           2000         1900 Rome Italy

库（tidyverse）
数据3%
mutate（City=apply（data1%>%select（-City），1，函数（x）名称（x）[which.min（x）]）%>%
变异（城市=str_删除（城市，“^dist”））%>%
左联合（数据2%>%
分离（目的地，进入=c（“城市”、“国家”），sep=“”，remove=FALSE），
by=“City”）%%>%
选择（-City，-Country）%%>%
重命名（最近=目的地）
数据3
##tibble:3 x 6
#城市区罗马区迈阿密区新加坡区多伦多最近
#                                                 
#1亚特兰大1000 400 3000 900美国迈阿密
#2东京2000 3000 600 3200新加坡新加坡
#3巴黎300 1500 2000 1900意大利罗马

library(tidyverse)
solution <- tibble(city = c("Atlanta", "Tokyo", "Paris"),
                   dist_Rome = c(1000, 2000, 300),
                   dist_Miami = c(400, 3000, 1500),
                   dist_Singapore = c(3000, 600, 2000),
                   dist_Toronto = c(900, 3200, 1900),
                   nearest = c("Miami United States", "Singapore Singapore", "Rome Italy"))

library(tidyverse)
solution <- data1 %>%
  mutate(nearest_hub = map(select(., contains("dist")), ~
                                  case_when(which.min(c(...)) ~ data2$destination),
                                TRUE ~ "NA"))
Error in which.min(c(...)) : 
  (list) object cannot be coerced to type 'double'

library(tidyverse)
data1 %>% 
   gather(key, val, starts_with("dist")) %>% 
   group_by(city) %>% 
   slice(which.min(val)) %>% 
   ungroup %>%
   transmute(city, key = str_remove(key, 'dist_')) %>% 
   left_join(data2 %>% 
   mutate(key = word(destination, 1))) %>%
   select(city, nearest = destination) %>% 
   left_join(data1)

library(tidyverse)

data3 <- data1 %>%
  mutate(City = apply(data1 %>% select(-city), 1, function(x) names(x)[which.min(x)])) %>%
  mutate(City = str_remove(City, "^dist_")) %>%
  left_join(data2 %>%
              separate(destination, into = c("City", "Country"), sep = " ", remove = FALSE),
            by = "City") %>%
  select(-City, -Country) %>%
  rename(nearest = destination)

data3
# # A tibble: 3 x 6
#   city    dist_Rome dist_Miami dist_Singapore dist_Toronto nearest            
#   <chr>       <dbl>      <dbl>          <dbl>        <dbl> <chr>              
# 1 Atlanta      1000        400           3000          900 Miami United States
# 2 Tokyo        2000       3000            600         3200 Singapore Singapore
# 3 Paris         300       1500           2000         1900 Rome Italy