根据另一个 tibble 过滤 mutate 中的 tibble？

Question

I have two tibbles, ranges and sites. The first contains a set of coordinates (region, start, end, plus other character variables) and the other contains a sites (region, site). I need to get all sites in the second tibble that fall within a given range (row) in the first tibble. Complicating matters, the ranges in the first tibble overlap.

# Range tibble
  region start end var_1 ... var_n
1  A     1     5   
2  A     3     10
3  B     20    100
# Site tibble
  region site 
1  A     4        
2  A     8    
3  B     25

The ~200,000 ranges can be 100,000s long over about a billion sites, so I don't love my idea of a scheme of making a list of all values in the range, unnesting, semi_join'ing, grouping, and summarise(a_list = list(site))'ing.

I was hoping for something along the lines of:

range_tibble %>%
  rowwise %>%
  mutate(site_list = site_tibble %>%
                filter(region.site == region.range, site > start, site < end) %>%
      .$site %>% as.list))

to produce a tibble like:

# Final tibble
 region start   end    site_list  var_1 ... var_n    
  <chr>  <dbl> <dbl>   <list>     <chr>     <chr>
  1 A          1     5 <dbl [1]>
  2 A          3    10 <dbl [2]>
  3 B         20   100 <dbl [1]>

I've seen answers using "gets" of an external variable (ie filter(b == get("b")), but how would I get the variable from the current line in the range tibble? Any clever pipes or syntax I'm not thinking of? A totally different approach is great, too, as long as it plays well with big data and can be turned back into a tibble.

Answer 1

Use left_join() to merge two data frames and summarise() to concatenate the sites contained in the specified range.

library(dplyr)

range %>%
  left_join(site) %>%
  filter(site >= start & site <= end) %>% 
  group_by(region, start, end) %>%
  summarise(site = list(site))

#   region start   end site     
#   <fct>  <dbl> <dbl> <list>   
# 1 A          1     5 <dbl [1]>
# 2 A          3    10 <dbl [2]>
# 3 B         20   100 <dbl [1]>

---

Data

range <- data.frame(region = c("A", "A", "B"), start = c(1, 3, 20), end = c(5, 10, 100))
site <- data.frame(region = c("A", "A", "B"), site = c(4, 8, 25))