如何使用tidyverse实现基于时间范围从DataFrame提取对应索引？

Absolutely! There’s a straightforward tidyverse-based approach to map each time in df2 to the corresponding index from df1 where the time falls within the start-end range. Here are two reliable methods, depending on your dataset size:

Method 1: Fuzzy Join (Best for Larger Datasets) #

Using fuzzyjoin (a tidyverse-compatible package) is efficient and clean, especially if you’re working with bigger datasets. It avoids creating a full cross-product of rows, which saves memory.

First, load the required packages:

library(tidyverse)
library(fuzzyjoin)

Then run the code to generate df3:

# Your original data
df1 <- data.frame(index = c(1,2,3,4), start = c(5,10,15,20), end = c(10,15,20,25))
df2 <- data.frame(time = c(11,17,18,5,5,22))

# Match times to their corresponding ranges
df3 <- df2 %>%
  fuzzy_left_join(
    df1,
    by = c("time" = "start", "time" = "end"),
    match_fun = list(`>=`, `<=`)  # Check time >= start AND time <= end
  ) %>%
  select(time, index) %>%  # Keep only the columns we need
  arrange(match(time, df2$time))  # Preserve original order of df2's times

This will produce exactly the df3 you specified:

> df3
  time index
1   11     2
2   17     3
3   18     3
4    5     1
5    5     1
6   22     4

Method 2: Rowwise Processing (Simple for Small Datasets) #

If you’re working with a small dataset and prefer to stick strictly to core dplyr functions, you can use row-wise processing to check each time against df1’s ranges:

df3 <- df2 %>%
  rowwise() %>%
  mutate(index = df1$index[start <= time & end >= time]) %>%
  ungroup()

This works by evaluating each row in df2 individually, finding the matching index from df1, and then ungrouping to return a standard tibble/data frame.

内容的提问来源于stack exchange，提问作者Al Mac