使用%in%进行直接子集化，而不是使用==

subset(data, facility_id %in% facilities_id_list)

在OP的代码中，存在多个问题- 1）输入参数为facilities_id_list，其中与lapply一样，我们正在对序列i进行循环。，2）facility_id==facility应为data$facility_id==facility，因为我们正在使用[，并且没有数据绑定。3）我们需要指定使用行索引作为子集，默认情况下不使用任何,，它将被视为data.frame中的列索引

facility_counts<-function(i){
  facility<-facilities_id_list[[i]]
  data[data$facility_id == facility, ]
}
> do.call(rbind, lapply(seq_along(facilities_id_list), facility_counts))
  facility_id patient_number test_result
1         123           1000          25
2         123           1000          30
3          25           1001          12
4          25           1002          67
5          25           1010          75

下面是一个使用普通的filter的示例，然后是另一个使用do.call()的自定义函数的选项：

library(dplyr)

# data
df <- tibble::tribble(
  ~facility_id, ~patient_number, ~test_result,
  123L,           1000L,          25L,
  123L,           1000L,          30L,
  25L,           1001L,          12L,
  25L,           1002L,          67L,
  25L,           1010L,          75L,
  65L,           1009L,           8L,
  22L,           1222L,          95L,
  22L,           1223L,          89L
)

facilities_id_list<-c(123, 25)

# simplest solution: just using filter
df %>% 
  filter(facility_id %in% facilities_id_list)
#> # A tibble: 5 × 3
#>   facility_id patient_number test_result
#>         <int>          <int>       <int>
#> 1         123           1000          25
#> 2         123           1000          30
#> 3          25           1001          12
#> 4          25           1002          67
#> 5          25           1010          75

# using custom function + do.call
custom_filter <- function(data) {
  data %>% 
    filter(facility_id %in% facilities_id_list)
}

do.call(custom_filter, list(df))
#> # A tibble: 5 × 3
#>   facility_id patient_number test_result
#>         <int>          <int>       <int>
#> 1         123           1000          25
#> 2         123           1000          30
#> 3          25           1001          12
#> 4          25           1002          67
#> 5          25           1010          75