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.
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
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.
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
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.