running loop with my own function

[help]

remove_animal <- c("Ant", "Ann", "Jim", "Rita")

animal_function <- function(select_animal) {
  
 animal_df <- all_animals %>%
  filter((!animal_name %in% remove_animal & animal_name != select_animal),
         animal_type != "cow")
 
 return(animal_df)
 }


animal_data_loop <- lapply(select_animal = remove_animal, animal_function)

I want to run the function I created through a loop for each element within the remove animal list I created. each element should be a different dataframe. any help would be so so appreciated.

here is the data sample

structure(list(animal_type = c("cow", "dog", "dog", "dog", "cow", 
"monkey", "giraffe", "giraffe", "cow"), animal_name = c("Ant", 
"Sophia", "Ann", "Jill", "Jim", "Jim", "Rita", "Anna", "Rita"
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-9L))

[AyushBipinPatel]

Hello @help ,

I have tried to address your question. If I have understood correctly, you need 4 different data frames after looping through the function you have written. Please see the below code and output, it may be of some use.

> library(tidyverse)
> 
> # data from post
> 
> structure(list(animal_type = c("cow", "dog", "dog", "dog", "cow", 
+                                "monkey", "giraffe", "giraffe", "cow"), 
+                animal_name = c("Ant","Sophia", "Ann", "Jill", "Jim", "Jim", 
+                                "Rita", "Anna", "Rita"
+                                )), 
+           class = c("tbl_df", "tbl", "data.frame"), 
+           row.names = c(NA, -9L)) ->  all_animals
> 
> 
> remove_animal <- c("Ant", "Ann", "Jim", "Rita")
> 
> 
> animal_function <- function(select_animal) {
+   
+   animal_df <- all_animals %>%
+     filter((!animal_name %in% remove_animal & animal_name != select_animal), # this condition is written in a manner that gegnerates the samem out put no matter what is provided as an input for select_animal
+            animal_type != "cow")
+   
+   return(animal_df)
+ }
> 
> 
> ## Possible solutions/approaches
> 
> lapply(X = remove_animal,FUN = animal_function) # returns a list of data, you were super close 
[[1]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[2]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[3]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[4]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

> 
> purrr::map(remove_animal,animal_function) # does the same using purrr
[[1]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[2]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[3]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[4]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

> 
> ## using a loop
> 
> list_of_df <-  vector(mode='list', length=length(remove_animal)) # create an empty list 
> 
> for (i in seq_along(remove_animal)) {
+   
+   list_of_df[[i]] <- animal_function(remove_animal[i])
+   
+ }
> 
> list_of_df
[[1]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[2]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[3]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

[[4]]
# A tibble: 3 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 giraffe     Anna       

> 
> ## maybe this is the function that works better
> 
> 
> animal_function2 <- function(select_animal) {
+   
+   animal_df <- all_animals %>%
+     filter((animal_name != select_animal), # edited condition
+            animal_type != "cow")
+   
+   return(animal_df)
+ }
> 
> lapply(X = remove_animal,FUN = animal_function2) 
[[1]]
# A tibble: 6 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 monkey      Jim        
5 giraffe     Rita       
6 giraffe     Anna       

[[2]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 monkey      Jim        
4 giraffe     Rita       
5 giraffe     Anna       

[[3]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 giraffe     Rita       
5 giraffe     Anna       

[[4]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 monkey      Jim        
5 giraffe     Anna       

> 
> purrr::map(remove_animal,animal_function2) # does the same using purrr
[[1]]
# A tibble: 6 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 monkey      Jim        
5 giraffe     Rita       
6 giraffe     Anna       

[[2]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 monkey      Jim        
4 giraffe     Rita       
5 giraffe     Anna       

[[3]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 giraffe     Rita       
5 giraffe     Anna       

[[4]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 monkey      Jim        
5 giraffe     Anna       

> 
> ## using a loop
> 
> list_of_df <-  vector(mode='list', length=length(remove_animal)) # create an empty list 
> 
> for (i in seq_along(remove_animal)) {
+   
+   list_of_df[[i]] <- animal_function2(remove_animal[i])
+   
+ }
> 
> list_of_df
[[1]]
# A tibble: 6 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 monkey      Jim        
5 giraffe     Rita       
6 giraffe     Anna       

[[2]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Jill       
3 monkey      Jim        
4 giraffe     Rita       
5 giraffe     Anna       

[[3]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 giraffe     Rita       
5 giraffe     Anna       

[[4]]
# A tibble: 5 × 2
  animal_type animal_name
  <chr>       <chr>      
1 dog         Sophia     
2 dog         Ann        
3 dog         Jill       
4 monkey      Jim        
5 giraffe     Anna       

Hope this helps,
Ayush

[help]

this is so helpful. but im not getting the desired output with my filter which is upsetting. I essentially want to remove observations with animal_name as "Ann", "Jim", or "Rita" in the first dataframe. in the second dataframe, I want to remove observations with animal_name as "Ant", "Jim", or "Rita"... so on and so forth. ur assistance on editing the filter would be so helpful.

[technocrat]

The problem appears still to be underspecified. Here is one interpretation

d <- data.frame(animal_type = c(
  "cow", "dog", "dog", "dog", "cow",
  "monkey", "giraffe", "giraffe", "cow"
), animal_name = c(
  "Ant",
  "Sophia", "Ann", "Jill", "Jim", "Jim", "Rita", "Anna", "Rita"
))

d
#>   animal_type animal_name
#> 1         cow         Ant
#> 2         dog      Sophia
#> 3         dog         Ann
#> 4         dog        Jill
#> 5         cow         Jim
#> 6      monkey         Jim
#> 7     giraffe        Rita
#> 8     giraffe        Anna
#> 9         cow        Rita
drops <- c("Ant", "Ann", "Jim", "Rita")
kine <- "cow"

# no cows anywhere, which also knocks out Ant
tip_cow <- function(x) x[which(x[1] != kine),]
tip_cow(d)
#>   animal_type animal_name
#> 2         dog      Sophia
#> 3         dog         Ann
#> 4         dog        Jill
#> 6      monkey         Jim
#> 7     giraffe        Rita
#> 8     giraffe        Anna
# no names not in drop list (i.e., Sophie)
keepers <- function(x) x[which(x[2][[1]] %in% drops),]
keepers(d)
#>   animal_type animal_name
#> 1         cow         Ant
#> 3         dog         Ann
#> 5         cow         Jim
#> 6      monkey         Jim
#> 7     giraffe        Rita
#> 9         cow        Rita
# no cows and only names in the drop list
tip_cow(d) |> keepers(x = _)# separate into dataframes
#>   animal_type animal_name
#> 3         dog         Ann
#> 6      monkey         Jim
#> 7     giraffe        Rita
# splits, too lazy to parameterize d
herd <- function(x) (tip_cow(d) |> keepers(x = _))[x,]
herd(1)
#>   animal_type animal_name
#> 3         dog         Ann
# list of three data frames; to lazy to parameterize 1:3
lapply(1:3,herd)
#> [[1]]
#>   animal_type animal_name
#> 3         dog         Ann
#> 
#> [[2]]
#>   animal_type animal_name
#> 6      monkey         Jim
#> 
#> [[3]]
#>   animal_type animal_name
#> 7     giraffe        Rita

Created on 2023-01-04 with reprex v2.0.2