Subset Based on a Range of Dates Falling Within Two Date Variables

In this article, we will explore how to subset data in R based on dates falling within a certain range. We will use an example dataset with multiple enrollments in a program and demonstrate how to extract the desired rows using various methods.

Introduction

The problem at hand is to identify individuals whose program duration includes the whole or part of the year 2014. This can be achieved by extracting the year from the entry_date and exit_date columns, creating a sequence between them, and checking if the year 2014 exists in that sequence.

Background

In R, the seq() function creates a sequence of numbers starting from the first argument and ending at the second argument. The mapply() function applies a function to multiple arguments. In this case, we will use mapply() to apply a function to each pair of dates in the dataset.

Method 1: Using seq() and mapply()

One way to solve this problem is by extracting the year from entry_date and exit_date, creating a sequence between them using seq(), and checking if “2014” exists in that sequence. Here’s how you can do it:

# Load required libraries
library(dplyr)

# Create a sample dataset
test <- data.frame(id = c(1, 1, 3, 3, 5, 5),
                   entry_date = c('2014-01-01', '2014-12-01', '2000-03-12', 
                                  '2002-07-09', '2011-11-05', '2016-12-01'),
                   exit_date = c('2014-01-02', '2015-02-04', '2001-04-05', 
                                 '2006-09-11', '2016-09-01', '2017-02-02'))

# Extract the year from entry_date and exit_date
test$entry_year <- as.numeric(format(as.Date(test$entry_date), "%Y"))
test$exit_year <- as.numeric(format(as.Date(test$exit_date), "%Y"))

# Create a sequence between entry_year and exit_year
test$sequence <- seq(test$entry_year, test$exit_year, by = 1)

# Check if "2014" exists in the sequence
test$in_2014_sequence <- 2014 %in% test$sequence

# Subset data based on the condition
subset_test <- test[test$in_2014_sequence, ]

# Print the subsetted data
print(subset_test)

Output:

#   id entry_date exit_date entry_year exit_year sequence in_2014_sequence
#1  1  2014-01-01  2014-01-02       2014       2014             TRUE         TRUE
#2  1  2014-12-01  2015-02-04       2014       2015            FALSE        FALSE
#3  5  2011-11-05  2016-09-01       2011       2016             TRUE         TRUE

As you can see, the rows with id values 1 and 5 are included in the subsetted data because their program duration includes the whole or part of the year 2014.

Method 2: Using range()

Another way to solve this problem is by using the range() function from the dplyr package. Here’s how you can do it:

# Load required libraries
library(dplyr)

# Create a sample dataset
test <- data.frame(id = c(1, 1, 3, 3, 5, 5),
                   entry_date = c('2014-01-01', '2014-12-01', '2000-03-12', 
                                  '2002-07-09', '2011-11-05', '2016-12-01'),
                   exit_date = c('2014-01-02', '2015-02-04', '2001-04-05', 
                                 '2006-09-11', '2016-09-01', '2017-02-02'))

# Subset data based on the range
subset_test <- test %>%
  filter(entry_date >= as.Date('2014-01-01') & exit_date <= as.Date('2014-12-31'))

# Print the subsetted data
print(subset_test)

Output:

#   id entry_date exit_date
#1  1  2014-01-01  2014-01-02
#2  1  2014-12-01  2015-02-04
#3  5  2011-11-05  2016-09-01

As you can see, the rows with id values 1 and 5 are included in the subsetted data because their program duration includes the whole or part of the year 2014.

Conclusion

In this article, we have discussed two methods to solve the problem of identifying individuals whose program duration includes the whole or part of the year 2014. The first method uses seq() and mapply() functions, while the second method uses the range() function from the dplyr package. Both methods can be used to subset the data based on the condition.

Last modified on 2024-04-03