僅在R中特定范圍的行索引中洗掉列中的重復值

我有一個測驗資料框df，我想從中洗掉Hits列中的重復值，但不洗掉與重復值關聯的行。然而，條件是僅在行索引的某些特定范圍內進行洗掉。

df <- data.frame(
  Hits = c("#a", "#ID:987129470", "#b", "Hit1", "Hit1", "Hit2", "Hit3", "Hit3", "#a", "#ID:6971324987", "#b", "Hit1", "Hit2", "Hit2", "Hit3"),
  Category1 = c(NA, NA, NA, 0.001, 0.001, 0.002, 0.003, 0.003, NA, NA, NA, 0.023, 0.341, 0.341, 0.569),
  Category2 = c(NA, NA, NA, 100, 100, 99, 98, 98, NA, NA, NA, 100, 95, 95, 97),
  Category3 = c(NA, NA, NA, 100, 100, 99, 98, 98, NA, NA, NA, 98, 97, 97, 92))

df 看起來像這樣

在這種情況下，要執行洗掉操作的行索引的范圍是4:8和12:15。基本上，每個 ID 下的重復命中將被洗掉，保持其他列中的關聯值完整無缺。輸出應該是這樣的

在原始資料幀（有 ~100k 行！）中，無法指定范圍。我該如何解決這個問題？

uj5u.com熱心網友回復：

首先通過開始使用#a 然后使用ifelse陳述句來創建組。

library(dplyr)
df %>% 
  group_by(id_Group = cumsum(Hits=="#a")) %>% 
  mutate(Hits = ifelse(duplicated(Hits), "", Hits)) %>% 
  ungroup() %>% 
  select(-id_Group)

  Hits             Category1 Category2 Category3
   <chr>                <dbl>     <dbl>     <dbl>
 1 "#a"                NA            NA        NA
 2 "#ID:987129470"     NA            NA        NA
 3 "#b"                NA            NA        NA
 4 "Hit1"               0.001       100       100
 5 ""                   0.001       100       100
 6 "Hit2"               0.002        99        99
 7 "Hit3"               0.003        98        98
 8 ""                   0.003        98        98
 9 "#a"                NA            NA        NA
10 "#ID:6971324987"    NA            NA        NA
11 "#b"                NA            NA        NA
12 "Hit1"               0.023       100        98
13 "Hit2"               0.341        95        97
14 ""                   0.341        95        97
15 "Hit3"               0.569        97        92

uj5u.com熱心網友回復：

另一種可能的解決方案：

library(tidyverse)

df <- data.frame(
  Hits = c("#a", "#ID:987129470", "#b", "Hit1", "Hit1", "Hit2", "Hit3", "Hit3", "#a", "#ID:6971324987", "#b", "Hit1", "Hit2", "Hit2", "Hit3"),
  Category1 = c(NA, NA, NA, 0.001, 0.001, 0.002, 0.003, 0.003, NA, NA, NA, 0.023, 0.341, 0.341, 0.569),
  Category2 = c(NA, NA, NA, 100, 100, 99, 98, 98, NA, NA, NA, 100, 95, 95, 97),
  Category3 = c(NA, NA, NA, 100, 100, 99, 98, 98, NA, NA, NA, 98, 97, 97, 92))

df %>% 
  group_by(Hits, if_else(str_detect(Hits, "Hits*"), 1, 0) %>% data.table::rleid(.)) %>% 
  mutate(Hits = if_else(row_number() > 1 & str_detect(Hits, "Hits*"), "", Hits)) %>%
  ungroup %>% select(-last_col())

#> # A tibble: 15 × 4
#>    Hits             Category1 Category2 Category3
#>    <chr>                <dbl>     <dbl>     <dbl>
#>  1 "#a"                NA            NA        NA
#>  2 "#ID:987129470"     NA            NA        NA
#>  3 "#b"                NA            NA        NA
#>  4 "Hit1"               0.001       100       100
#>  5 ""                   0.001       100       100
#>  6 "Hit2"               0.002        99        99
#>  7 "Hit3"               0.003        98        98
#>  8 ""                   0.003        98        98
#>  9 "#a"                NA            NA        NA
#> 10 "#ID:6971324987"    NA            NA        NA
#> 11 "#b"                NA            NA        NA
#> 12 "Hit1"               0.023       100        98
#> 13 "Hit2"               0.341        95        97
#> 14 ""                   0.341        95        97
#> 15 "Hit3"               0.569        97        92

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