Interpolation of values from list

Robert Wilson's answer gave me an idea. Rather than mapping over nested dataframes the rank function from base can get to the same result

df %>% 
  mutate(new_place = rank(place, ties.method = "first")) %>% 
  mutate(score = points[new_place]) %>% 
  group_by(place) %>% 
  summarize(score = mean(score)) %>% 
  inner_join(df)

  place score name 
  <dbl> <dbl> <chr>
1     1     4 a    
2     2     2 b    
3     2     2 c    
4     4     0 d   

This can be accomplished in few lines with an ifelse() statement inside of a mutate():

df %>% 
  group_by(place) %>%
  mutate(n_ties = n()) %>%
  ungroup %>%
  mutate(score = (points[place] + ifelse(n_ties > 1, 1, 0))/ n_ties)


# A tibble: 4 x 4
  name  place n_ties score
  <chr> <dbl>  <int> <dbl>
1 a         1      1     4
2 b         2      2     2
3 c         2      2     2
4 d         4      1     0

A solution using nested data frames and purrr.

library(dplyr)
library(tidyr)
library(purrr)
df <- data.frame(name = letters[1:4],
             place = c(1, 2, 2, 4))

points <- c(4, 3, 1, 0)
names(points) <- 1:4
# a function to help expand the dataframe based on the number of ties
expand_all <- function(x,n){
  x:(x+n-1)
}

df %>% 
  group_by(place) %>% 
  tally() %>% 
  mutate(new_place = purrr::map2(place,n, expand_all)) %>% 
  unnest(new_place) %>% 
  mutate(score = points[new_place]) %>% 
  group_by(place) %>% 
  summarize(score = mean(score)) %>% 
  inner_join(df)