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从多个列中选择组中的值

data.table dplyr r

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user5249203 · 技术社区 · 5 年前

我的数据具有以下结构。对于每个国家和id,从装运列(中国和香港)中, 如果一个组同时具有中国和香港,则新列装运应具有香港如果一个组有HK,那么新的列装运应该有HK 如果是NA,应该是NA

shipment_hk 或 shipment_china . 以便用户可读。而且,季度值在ifelse条件下似乎不起作用。

library(dplyr)
sample_df %>% tibble::as.tibble() %>% 
dplyr::group_by(country,id) %>% 
dplyr::mutate(periods = ifelse(Monthly == "Monthly", "monthly", ifelse(Quarterly == "Quarterly", "quarterly", ifelse(Yearly == "Yearly", "yearly", "")))) %>%
dplyr::mutate(shipment = any(shipment_hk %in% "send to hk")) %>% 
dplyr::select(country,id, type,periods,shipment)
#> # A tibble: 20 x 5
#> # Groups:   country, id [7]
#>    country id     type        periods shipment
#>    <chr>   <chr>  <chr>       <chr>   <lgl>   
#>  1 group_1 2.1    ""          <NA>    FALSE   
#>  2 group_1 2.1    "bar"       monthly FALSE   
#>  3 group_1 2.1    "chocolate" monthly FALSE   
#>  4 group_1 2.17   ""          <NA>    FALSE   
#>  5 group_1 2.17   "bar"       monthly FALSE   
#>  6 group_1 2.17   "chocolate" monthly FALSE   
#>  7 group_1 2.2    ""          <NA>    TRUE    
#>  8 group_1 2.2    ""          <NA>    TRUE    
#>  9 group_1 2.2    "bar"       <NA>    TRUE    
#> 10 group_1 2.2    "chocolate" <NA>    TRUE    
#> 11 group_2 1      ""          <NA>    TRUE    
#> 12 group_2 1      ""          <NA>    TRUE    
#> 13 group_2 1      "bar"       monthly TRUE    
#> 14 group_2 2.1    ""          <NA>    FALSE   
#> 15 group_2 2.1    "bar"       monthly FALSE   
#> 16 group_2 2.12.1 ""          <NA>    TRUE    
#> 17 group_2 2.12.1 ""          <NA>    TRUE    
#> 18 group_2 2.12.1 "donut"     <NA>    TRUE    
#> 19 group_2 2.12.2 ""          <NA>    FALSE   
#> 20 group_2 2.12.2 "bar"       <NA>    FALSE

^{于2020-11-03由

reprex package}

dput(sample_df)
structure(list(country = c("group_1", "group_1", "group_1", "group_1", 
"group_1", "group_1", "group_1", "group_1", "group_1", "group_1", 
"group_2", "group_2", "group_2", "group_2", "group_2", "group_2", 
"group_2", "group_2", "group_2", "group_2", "group_3", "group_3", 
"group_3", "group_3", "group_3", "group_3"), id = c("2.1", "2.1", 
"2.1", "2.17", "2.17", "2.17", "2.2", "2.2", "2.2", "2.2", "1", 
"1", "1", "2.1", "2.1", "2.12.1", "2.12.1", "2.12.1", "2.12.2", 
"2.12.2", "2.17", "2.17", "2.17", "2.18", "2.18", "2.18"), type = c("", 
"bar", "chocolate", "", "bar", "chocolate", "", "", "bar", "chocolate", 
"", "", "bar", "", "bar", "", "", "donut", "", "bar", "tiles", 
"tiles", "tiles", "tiles", "tiles", "tiles"), shipment_china = c("send to china", 
NA, NA, "send to china", NA, NA, "send to china", NA, NA, NA, 
"send to china", NA, NA, "send to china", NA, "send to china", 
NA, NA, "send to china", NA, NA, NA, NA, NA, NA, NA), shipment_hk = c(NA, 
NA, NA, NA, NA, NA, NA, "send to hk", NA, NA, NA, "send to hk", 
NA, NA, NA, NA, "send to hk", NA, NA, NA, NA, NA, NA, NA, NA, 
NA), Monthly = c(NA, "Monthly", "Monthly", NA, "Monthly", "Monthly", 
NA, NA, NA, NA, NA, NA, "Monthly", NA, "Monthly", NA, NA, NA, 
NA, NA, NA, "Monthly", NA, NA, NA, NA), Quarterly = c(NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Quarterly", 
NA, "Quarterly", NA, NA, NA, "Quarterly", NA, NA), Yearly = c(NA, 
NA, NA, NA, NA, NA, NA, NA, "Yearly", "Yearly", NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)), class = "data.frame", row.names = c(NA, 
-26L))

0 回复 | 直到 5 年前

1

Ben 5 年前

coalesce 以及使用 case_when ifelse :

library(dplyr)

sample_df %>%
  group_by(country, id) %>%
  mutate(periods = coalesce(Monthly, Quarterly, Yearly),
         shipment = case_when(
           any(shipment_hk == "send to hk") ~ "send to hk",
           any(shipment_china == "send to china") ~ "send to china",
           TRUE ~ NA_character_
         ))

这将优先考虑 shipment_hk shipment_china 在你的新 shipment 列。