Creating a Flag Indicating if Year Variable is in the Range of Start:end Variables in data.table

In this article, we will explore how to create a new variable in a data.table that indicates whether a year variable falls within a specified range defined by start and end variables. We will delve into different approaches, discuss their advantages and disadvantages, and provide benchmarks for each method.

Introduction

data.tables are a powerful toolset for data manipulation in R, providing efficient and flexible data structures for various operations. One of the challenges when working with date variables is to create conditions that involve multiple date fields. In this article, we will tackle the specific problem of creating a flag indicating if a year variable falls within a range defined by start and end variables.

The Problem

Let’s examine the problem using an example:

test <- data.table(year = 2001:2003,
                   start = c(2003, 2002, 2000),
                   end   = c(2003, 2004, 2002),
                   x_desired = c(F, T, F))

We want to create a new variable x that indicates whether the year is in the range defined by start and end variables for each row.

Initial Approach

The initial approach would be:

test[, x := year %in% start:end]

However, this method does not produce the expected results because it assumes that the ranges are defined globally rather than row-by-row. We need a way to define the range within each row.

Alternative Approaches

There are several alternative approaches to achieve our goal:

1. Using between Function with List of Start and End Values

DT[, x := FALSE ]
DT[ year %between% list(start, end), x := TRUE]

This method uses the between function in combination with a list of start and end values to create the desired range for each row. The first line initializes the variable x as FALSE for all rows, while the second line updates the value of x to TRUE if the year falls within the specified range.

2. Using Rowwise Operations

DT[, x := between(year, start, end)]

This approach utilizes rowwise operations to create the desired range for each row. The between function is applied element-wise to compare each year with both the start and end values of its respective row.

3. Using Map Function

DT[, x := unlist(do.call(Map, c(f = between, unname(.SD)))), .SDcols = year:end]

This method employs the map function in combination with a vectorized version of the between function to create the desired range for each row. The .SDcols = year:end argument ensures that only the start and end values are considered when defining the range.

4. Using pmap Function

DT[, x := purrr::pmap_lgl(.SD[, .(x = year, left = start, right = end)], between)]

Similar to the map function approach but utilizes the pmap function for parallelized evaluation.

5. Using mapply Function

DT[, col := mapply(between, year, start, end)]

This method uses the mapply function in combination with a vectorized version of the between function to create the desired range for each row.

Benchmarking the Approaches

To compare the performance of these methods, we can use microbenchmarking:

n = 1000000
set.seed(123)
dt <- data.table(year = sample(2001:2003, n, replace = TRUE),
                 start=sample(c(2003, 2002, 2000), n, replace = TRUE),
                 end  =sample(c(2003, 2004, 2002), n, replace = TRUE))

microbenchmark(
  wimpel = {
    DT <- copy(dt)
    DT[, x := FALSE ]
    DT[ year %between% list(start,end), x := TRUE] 
  },
  akrun_nrow = {
    DT <- copy(dt)
    DT[, x := between(year, start, end), 1:nrow(DT)]
  },
  akrun_map = {
    DT <- copy(dt)
    DT[, x := unlist(do.call(Map, c(f = between, unname(.SD)))), .SDcols = year:end]
  },
  akrun_pmap = {
    DT <- copy(dt)
    DT[, x := purrr::pmap_lgl(.SD[, .(x = year, left = start, right = end)], between)]
  },
  markus = {
    DT <- copy(dt)
    DT[, col := mapply(between, year, start, end)]
  },
  times = 3
)

The results from the microbenchmarking exercise show that the wimpel method is the fastest approach.

Conclusion

In this article, we explored different methods for creating a flag indicating if a year variable falls within a range defined by start and end variables in data.table. We discussed the advantages and disadvantages of each approach, provided benchmarks for comparison, and concluded with the wimpel method as the most efficient solution. This conclusion is based on the specific use case presented and may vary depending on the actual characteristics of your dataset.

References

• data.table package (2020) in R: https://CRAN.R-project.org/package=data.table
• R for Data Science (2013): https://r4ds.hadley.org/

By understanding the intricacies of data manipulation in R and utilizing the efficient tools available, you can streamline your workflow and unlock greater insights from your data.

Last modified on 2023-07-24