如何根据 R 中其他列的特定条件创建新列？

Question

I have the following data frame regarding a predator, prey interaction, SS=surf.smelt, SL = sandlance and H = herring.

Essentially all I need is another column that states whether there was more than one species available for the predator during the interaction. For example, If you look at index 12, the Prey was SL, but there was 1000 sandlance and 100 herring available, I need a column that can simply show with a 1 or 0 if there were more than 2 species available for the predator.

If possible, I would also like to show what other species was available in long format

Date frame I have:

index   date     site  Pred  Prey  attack  passive surf.smelt  sandlance  herring
 17   2015-06-06  cb    JCK   SS     0       1         20         0          0
 26   2015-07-05  cb    JCK   SS     0       1         100        0          0
 12   2016-07-26  cb    JCK   SL     1       0         0         1000       100
 88   2016-07-26  cb    JCK   H      1       0         0         1000       1000
 89   2016-07-26  cb    JCK   H      1       0         0          0         100
 90   2018-08-21  cb    JCO   SL     1       0        100        500         0
100   2018-08-26  cb    JCO   SL     1       0         0         1000       100
108   2019-06-22  cb    JCO   SS     0       1        50          0         100

Data frame I want:

index   date     site  Pred  Prey  attack  passive surf.smelt  sandlance  herring OtherPrey?
 17   2015-06-06  cb    JCK   SS     0       1         20         0          0       0
 26   2015-07-05  cb    JCK   SS     0       1         100        0          0       0
 12   2016-07-26  cb    JCK   SL     1       0         0         1000       100      1  
 88   2016-07-26  cb    JCK   H      1       0         0         1000       1000     1
 89   2016-07-26  cb    JCK   H      1       0         0          0         100      0
 90   2018-08-21  cb    JCO   SL     1       0        100        500         0       1
100   2018-08-26  cb    JCO   SL     1       0         0         1000       100      1
108   2019-06-22  cb    JCO   SS     0       1        50          0         100      1

And if possible I would want to define the other species available:

Data frame I want:

index   date     site  Pred  Prey  attack  passive surf.smelt  sandlance  herring OtherAvailable
 17   2015-06-06  cb    JCK   SS     0       1         20         0          0       0
 26   2015-07-05  cb    JCK   SS     0       1         100        0          0       0
 12   2016-07-26  cb    JCK   SL     1       0         0         1000       100      H  
 88   2016-07-26  cb    JCK   H      1       0         0         1000       1000     SL
 89   2016-07-26  cb    JCK   H      1       0         0          0         100      0
 90   2018-08-21  cb    JCO   SL     1       0        100        500         0       SS
100   2018-08-26  cb    JCO   SL     1       0         0         1000       100      H
108   2019-06-22  cb    JCO   SS     0       1        50          0         100      H

Answer 1

We can use dplyr::if_any :

library(dplyr)

df %>% mutate(other_prey = +if_any(surf.smelt:herring))

For the "OtherAvailable", we can use toString

library(dplyr)

df %>% rowwise %>%
       mutate(OtherAvailable = toString(names(across(surf.smelt:herring))[as.logical(surf.smelt:herring)]))

Answer 2

With dplyr :

df %>% 
   rowwise() %>% 
   mutate(Other_Prey=ifelse(sum(c_across(surf.smelt:herring)>0)>=2,1,0))
# A tibble: 8 x 11
# Rowwise: 
  index date       site  Pred  Prey  attack passive surf.smelt sandlance herring Other_Prey
  <int> <chr>      <chr> <chr> <chr>  <int>   <int>      <int>     <int>   <int>      <dbl>
1    17 2015-06-06 cb    JCK   SS         0       1         20         0       0          0
2    26 2015-07-05 cb    JCK   SS         0       1        100         0       0          0
3    12 2016-07-26 cb    JCK   SL         1       0          0      1000     100          1
4    88 2016-07-26 cb    JCK   H          1       0          0      1000    1000          1
5    89 2016-07-26 cb    JCK   H          1       0          0         0     100          0
6    90 2018-08-21 cb    JCO   SL         1       0        100       500       0          1
7   100 2018-08-26 cb    JCO   SL         1       0          0      1000     100          1
8   108 2019-06-22 cb    JCO   SS         0       1         50         0     100          1

Answer 3

It's not very elegant, but here's a shot.

library(dplyr)
library(stringr)


df <- data.frame(Prey = c("SS", "SS", "SL", "H", "H", "SL", "SL", "SS"),
             SS = c(20, 100, 0, 0, 0, 100, 0 ,50),
             SL = c(0,0,1000, 1000, 0, 500, 1000, 0),
             H = c(0,0,100,1000,100,0,100,100) )

tb <- df %>%
    mutate(SS = SS > 0, SL = SL > 0, H  = H > 0,
    OtherPrey =  (rowSums(across(where(is.logical))))-1)
    
tb1 <- which(as.matrix(tb[,2:4]), arr.ind = TRUE)
tb$Available <- tapply(names(tb[,2:4])[tb1[,2]], tb1[,1], paste, collapse=",")        


tb <- tb %>%
    mutate(other = str_replace(Available,Prey,""),
           AvailableOther = str_replace(other,",","")) %>%
    select(Prey, OtherPrey, AvailableOther) 

df_new <- cbind(df, tb)

df_new