Фильтр для многоуровневой логики по столбцам — значения выше или ниже по типу

Вы можете попробовать:

### Packages
library(dplyr)
library(tidyr)

### Data
dat <- data.frame(group=c("AB","AB","AB","AB","BC","BC","B","B","AD","AD","AD","G"),
                  type=c("B","L","N","N","N","L","N","N","B","L","L","L"),
                  value=c(2,4,3,2,5,3,8,9,4,3,9,7))

### We add the number of L and N for each group
dat2=dat %>%
  group_by(group) %>%
  mutate(nb_L = sum (type == "L"),
         nb_N = sum (type == "N")) %>%
  ungroup()

### We create 3 dataframes that respect your conditions
a=dat2 %>% group_by(group) %>% filter(nb_L>1&type= = "L") %>% slice_min(value,n = 1) %>% ungroup()
b=dat2 %>% group_by(group) %>% filter(nb_N>1&type= = "N") %>% slice_max(value,n=1) %>% ungroup()
c=dat2 %>% group_by(group) %>% filter(type= = "B"|(nb_L<=1&type= = "L")|(nb_N<=1&type= = "N")) %>% ungroup()

### We stack the dataframes
dat2=bind_rows(a,b,c) %>% ungroup()

### We add the value of L and N for each group
### We remove the rows regarding the rest of your criterias
dat2=dat2 %>%
  group_by(group) %>%
  mutate(val_L = ifelse(type == "L", value, NA_real_),
         val_N = ifelse(type == "N", value, NA_real_)) %>%
  fill(c(val_L,val_N), .direction = "downup") %>%
  mutate(across(c(val_L,val_N),~replace_na(.x,0)),
         keep=case_when(nb_L>0&type= = "N"&val_N>val_L~"remove",.default = "keep")) %>% 
  filter(keep= = "keep") %>%
  select(group,type,value) %>% 
  arrange(group,type) %>% 
  ungroup()

Выход :

# A tibble: 8 × 3
  group type  value
  <chr> <chr> <dbl>
1 AB    B         2
2 AB    L         4
3 AB    N         3
4 AD    B         4
5 AD    L         3
6 B     N         9
7 BC    L         3
8 G     L         7

Вот возможное решение с помощью подхода pivot_wider --> map_ --> pivot_longer.
(данные об игрушке в конце)

library(tidyverse) # purrr, tidyr

new_df <- my_df %>% 
  pivot_wider(id_cols = group, names_from = type, values_from = value, values_fn = list) %>% 
  
  mutate(
    L =  map(L, \(x) if (!is.null(x)) min(x) else NA_real_),          
    N =  map(N, \(x) if (!is.null(x)) max(x) else NA_real_),                          
    N = map2(N, L, \(x, y) if (x > y & !is.na(x) & !is.na(y)) NA_real_ else x)) %>%  
  
  pivot_longer(-group, names_to = "type", values_drop_na = TRUE) %>% 
  unnest_longer(value) %>% 
  filter(!is.na(value))

Выход:

> new_df
# A tibble: 8 × 3
  group type  value
  <chr> <chr> <dbl>
1 AB    B         2
2 AB    L         4
3 AB    N         3
4 BC    L         3
5 B     N         9
6 AD    B         4
7 AD    L         3
8 G     L         7

Вот и все, надеюсь, это поможет!

Данные игрушки:

# Toy data
my_df <- tibble::tribble(
  ~group, ~type, ~value,
    "AB",   "B",      2, #
    "AB",   "L",      4, # 
    "AB",   "N",      3, #
    "AB",   "N",      2,
    "BC",   "N",      5,
    "BC",   "L",      3, #
     "B",   "N",      8,
     "B",   "N",      9, #
    "AD",   "B",      4, #
    "AD",   "L",      3, #
    "AD",   "L",      9,
     "G",   "L",      7) #

^{Created on 2024-05-20 with reprex v2.1.0}