R语言每当解释性变量发生变化时，就在变量后面附加一个计数器

xdyibdwo 于 2023-05-04 发布在其他

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我在r中有一个这样的 Dataframe ：

df <- data.frame(policy_ID = c("P1","P1","P1","P2","P2","P3","P4","P4","P4","P4"), 
                 City = c("Copenhagen", "Copenhagen", "London", "LA", "LA",
                          "Tokyo", "Madrid", "Madrid", "Rome", "Milan"),
                 Floor = c("1","1","1","3","4","2","1","1","4","4"))

如果City或Floor的值发生更改，我希望policy_ID发生一些更改。期望输出如下：

df <- data.frame(policy_ID = c("P1","P1","P1_2","P2","P2_2","P3","P4","P4","P4_2","P4_3"), 
                 City = c("Copenhagen", "Copenhagen", "London", "LA", "LA",
                          "Tokyo", "Madrid", "Madrid", "Rome", "Milan"),
                 Floor = c("1","1","1","3","4","2","1","1","4","4"))

有没有人知道如何做到这一点？
我已经成功地生成了这段代码，如果进行了更改，它会输出1：

df %>%
  group_by(policy_ID) %>%
  mutate(City_or_Floor_changed = ifelse(City != lag(City, default = first(City)) | 
                                        Floor != lag(Floor, default = first(Floor)), 
                                      1, 0)) %>%
  ungroup()

但是，我正在努力弄清楚如何按照我的意愿更改policy_ID。

来源：https://stackoverflow.com/questions/76149286/append-a-counter-to-a-variable-whenever-explanatory-variables-change

3条答案

按热度按时间

yhxst69z1#

第二种选择是使用dplyr::consecutive_id，它是在dplyr 1.1.0中引入的，并受到data.table::rleid的启发：

library(dplyr, warn = FALSE)

df |>
  mutate(run = consecutive_id(City, Floor), .by = policy_ID) |>
  mutate(
    policy_ID = if_else(run > 1,
      paste(policy_ID, run, sep = "_"),
      policy_ID
    )
  )
#>    policy_ID       City Floor run
#> 1         P1 Copenhagen     1   1
#> 2         P1 Copenhagen     1   1
#> 3       P1_2     London     1   2
#> 4         P2         LA     3   1
#> 5       P2_2         LA     4   2
#> 6         P3      Tokyo     2   1
#> 7         P4     Madrid     1   1
#> 8         P4     Madrid     1   1
#> 9       P4_2       Rome     4   2
#> 10      P4_3      Milan     4   3

赞(0）回复(0）举报 2023-05-04

nukf8bse2#

我们可以使用rleid来创建索引

library(dplyr)
library(data.table)
library(stringr)
df %>%
   mutate(policy_ID2 = policy_ID) %>%
   mutate(tmp = rleid(City, Floor), 
   policy_ID = case_when(tmp > 1 ~ str_c(policy_ID, '_', tmp), 
      TRUE ~ policy_ID), .by = policy_ID2) %>% 
   select(-tmp, - policy_ID2)

输出

policy_ID       City Floor
1         P1 Copenhagen     1
2         P1 Copenhagen     1
3       P1_2     London     1
4         P2         LA     3
5       P2_2         LA     4
6         P3      Tokyo     2
7         P4     Madrid     1
8         P4     Madrid     1
9       P4_2       Rome     4
10      P4_3      Milan     4

赞(0）回复(0）举报 2023-05-04

z31licg03#

只是为了 * lafts *，这里有一个使用lag和cumsum的答案：

library(dplyr)

df %>% 
  mutate(tmp = cumsum(City != lag(City, default = "null") | 
                      Floor != lag(Floor, default = "null")), 
         policy_ID = case_when(tmp == 1 ~ policy_ID,
                               TRUE ~paste(policy_ID, tmp, sep = "_")),
         .by = policy_ID) %>% 
  select(-tmp)

#>    policy_ID       City Floor
#> 1         P1 Copenhagen     1
#> 2         P1 Copenhagen     1
#> 3       P1_2     London     1
#> 4         P2         LA     3
#> 5       P2_2         LA     4
#> 6         P3      Tokyo     2
#> 7         P4     Madrid     1
#> 8         P4     Madrid     1
#> 9       P4_2       Rome     4
#> 10      P4_3      Milan     4

赞(0）回复(0）举报 2023-05-04

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R语言每当解释性变量发生变化时，就在变量后面附加一个计数器

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R语言 每当解释性变量发生变化时，就在变量后面附加一个计数器

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R语言每当解释性变量发生变化时，就在变量后面附加一个计数器