當數據框的幾列中的值在另一個特定列中具有相同的值時，如何更改它們？

Question

所以我有一個這樣的數據框，其中包含來自三個不同國家的幾個物種：

 Species           |  Country_A | Country_B  | Country_C   
-----------------------------------------------------
Tilapia guineensis |   yes      |   no       |  no
Tilapia guineensis |   no       |   yes      |  no
Tilapia zillii     |   no       |   no       |  yes
Tilapia zillii     |   no       |   no       |  no
Eutrigla gurnardus |   no       |   yes      |  no
Eutrigla gurnardus |   yes      |   no       |  no 
Sprattus sprattus  |   yes      |   no       |  no 
Sprattus sprattus  |   yes      |   no       |  no
Sprattus sprattus  |   no       |   yes      |  no
Aetobatus narinari |   no       |   no       |  yes
Aetobatus narinari |   yes      |   no       |  no
Aetobatus narinari |   yes      |   no       |  no 
Aetobatus narinari |   yes      |   no       |  no 

我想基本上更改國家/地區的列，如果該物種的任何出現在該國家/地區的其他行中具有“是”，則更改為“是”。 如果我感到困惑，我很抱歉。 我想做的是這樣的：

 Species           |  Country_A | Country_B  | Country_C   
-----------------------------------------------------
Tilapia guineensis |   yes      |   yes      |  no
Tilapia zillii     |   no       |   no       |  yes
Eutrigla gurnardus |   yes      |   yes      |  no
Sprattus sprattus  |   yes      |   yes      |  no 
Aetobatus narinari |   yes      |   no       |  yes

提前感謝您的任何回答。

Answer 1

也許嘗試重塑數據，然后保留所需的值，管理重復的行並重塑為寬：

library(dplyr)
library(tidyr)
#Code
new <- df %>% pivot_longer(-Species) %>%
  filter(value!='no') %>%
  group_by(Species) %>%
  filter(!duplicated(name)) %>%
  pivot_wider(names_from = name,values_from=value,values_fill='no')

Output：

# A tibble: 5 x 4
# Groups:   Species [5]
  Species            Country_A Country_B Country_C
  <chr>              <chr>     <chr>     <chr>    
1 Tilapia guineensis yes       yes       no       
2 Tilapia zillii     no        no        yes      
3 Eutrigla gurnardus yes       yes       no       
4 Sprattus sprattus  yes       yes       no       
5 Aetobatus narinari yes       no        yes      

使用的一些數據：

#Data
df <- structure(list(Species = c("Tilapia guineensis", "Tilapia guineensis", 
"Tilapia zillii", "Tilapia zillii", "Eutrigla gurnardus", "Eutrigla gurnardus", 
"Sprattus sprattus", "Sprattus sprattus", "Sprattus sprattus", 
"Aetobatus narinari", "Aetobatus narinari", "Aetobatus narinari", 
"Aetobatus narinari"), Country_A = c("yes", "no", "no", "no", 
"no", "yes", "yes", "yes", "no", "no", "yes", "yes", "yes"), 
    Country_B = c("no", "yes", "no", "no", "yes", "no", "no", 
    "no", "yes", "no", "no", "no", "no"), Country_C = c("no", 
    "no", "yes", "no", "no", "no", "no", "no", "no", "yes", "no", 
    "no", "no")), row.names = c(NA, -13L), class = "data.frame")

Answer 2

我們可以通過dplyr中的操作輕松完成此操作

library(dplyr)
df %>%
    group_by(Species) %>%
    summarise(across(everything(), ~ if(any(. == 'yes')) 'yes'
           else 'no'), .groups = 'drop')

-輸出

# A tibble: 5 x 4
#  Species            Country_A Country_B Country_C
#  <chr>              <chr>     <chr>     <chr>    
#1 Aetobatus narinari yes       no        yes      
#2 Eutrigla gurnardus yes       yes       no       
#3 Sprattus sprattus  yes       yes       no       
#4 Tilapia guineensis yes       yes       no       
#5 Tilapia zillii     no        no        yes