Introduction to Adding Rows for Missing Intervals between Existing Intervals in R

In this article, we’ll delve into the process of adding rows to a dataset that contains interval data with start and end dates. The goal is to include potential gaps between these intervals (per group), even when existing intervals may overlap.

Background on Interval Data

Interval data is a type of data that consists of a range or an open-ended interval, such as “open” or “closed.” In this context, we’re dealing with open-ended intervals, denoted by square brackets []. The start date and end date represent the boundaries of these intervals.

Converting to Date Objects

To work with dates, we need to convert them from date-time objects to date objects. This is achieved using the as.Date() function in R.

Making End Dates Exclusive

In the desired output format, the end dates should be exclusive, meaning they don’t include the upper boundary of the interval. We achieve this by adding 1 to the end_date value when converting it to a date object.

Computing Interval Complement

The complement of an interval is the set of all real numbers that are not in the interval. In this context, we want to compute the complement of each apartment’s intervals to obtain the empty dates (i.e., gaps between existing intervals).

Bounding and Sorting

We’ll combine the original data with the computed complements, arrange them by apartment and range, and then sort the resulting dataset.

Using the ivs Package

The ivs package provides an efficient way to work with interval vectors. We can use its functions to simplify the process of computing interval complements and bounding intervals.

Example Data

We start with a sample dataset containing interval data for apartments:

example_dates <- structure(list(apartment = c("A", "A", "A", "A", "B", "B", 
                                          "B", "C", "C", "C"), 
                               start_date = structure(c(1640995200, 1642291200, 
                                                        1649980800, 1655769600, 1644451200, 1646092800, 
                                                        1659312000, 1646438400, 1649376000, 1664582400), 
                                                       class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
                               end_date = structure(c(1642204800, 1648166400, 1655683200, 
                                                      1668643200, 1655251200, 1653868800, 1667260800, 1654819200, 
                                                      1661385600, 1668470400), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
                               status = c("person in apartment", "person in apartment", 
                                          "person in apartment", "person in apartment", "person in apartment", 
                                          "person in apartment", "person in apartment", "person in apartment", 
                                          "person in apartment", "person in apartment")), 
                           class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -10L))

Converting Dates to Date Objects

We convert the start_date and end_date columns to date objects:

example_dates <- example_dates %>% 
  mutate(start_date = as.Date(start_date), end_date = as.Date(end_date)) 

Making End Dates Exclusive

We add 1 to the end_date value to make it exclusive:

example_dates <- example_dates %>% 
  mutate(end_date = end_date + 1)

Computing Interval Complement

We compute the complement of each apartment’s intervals using the iv_complement() function from the ivs package:

library(ivs)

empty_dates <- example_dates %>% 
  group_by(apartment) %>% 
  summarise(range = iv_complement(range))

Bounding and Sorting

We combine the original data with the computed complements, arrange them by apartment and range, and then sort the resulting dataset:

result <- bind_rows(example_dates, empty_dates) %>% 
  arrange(apartment, range) %>% 
  mutate(start_date = iv_start(range), end_date = iv_end(range))

Conclusion

We’ve demonstrated how to add rows to a dataset containing interval data with start and end dates. By converting the dates to date objects, making the end dates exclusive, computing the interval complement, bounding, and sorting the results, we can efficiently obtain the desired output format.

This approach can be applied to various scenarios where interval data needs to be processed and transformed for analysis or visualization purposes.

Last modified on 2023-08-15