Closing Data Gaps in R

Introduction:

In the world of data analysis and manipulation, data completeness stands as a cornerstone for accurate insights. Yet, datasets often present gaps in factor combinations, potentially distorting our analyses. Here, tools like complete() from tidyr and CJ() from data.table emerge as indispensable aids, addressing missing combinations and ensuring a robust dataset. By filling these gaps, we not only enhance the reliability of our analyses but also unlock clearer visualizations, enabling us to capture crucial trends and patterns with confidence.

Snapshot of the Dataset:

As seen below, the data contains gender, daily studying time and preferred time to study of various students.

Gender	Daily Studying Time	Prefer To Study In
Male	1 - 2 Hour	Morning
Female	1 - 2 Hour	Morning
Male	1 - 2 Hour	Anytime

Creating Initial Combinations:

Before learning about complete() and CJ() functions, let us create combinations of ‘Gender’ and ‘Daily Studying Time’ from the present data using dplyr.

library(dplyr)
grouped <- df %>% 
  group_by(Gender, `Daily Studying Time`) %>% 
  summarise(Count = n()) %>% 
  ungroup()

Gender	Daily Studying Time	Count
Female	1 - 2 Hour	56
Female	2 - 3 hour	14
Female	3 - 4 hour	7
Male	1 - 2 Hour	132
Male	2 - 3 hour	10
Male	More Than 4 hour	6

As we can observe, given that the obtained data is small, there are two combinations missing from out dataset. Let us now see how we can fill in these gaps using the aforementioned functions.

 

Using complete() from tidyr package

This function is designed to expand datasets to include all possible combinations of factors, ensuring completeness. We specify the dataset, and the variables for which we want to generate all possible combinations. We can also add in fill parameter, as it specifies the value to fill in for the missing combinations - which, in this case, is ‘Count’.

library(tidyr)
completed_data <- grouped %>%
  complete(Gender, `Daily Studying Time`, fill = list(Count = 0))

Gender	Daily Studying Time	Count
Female	1 - 2 Hour	56
Female	2 - 3 hour	14
Female	3 - 4 hour	7
Female	More Than 4 hour	0
Male	1 - 2 Hour	132
Male	2 - 3 hour	10
Male	3 - 4 hour	0
Male	More Than 4 hour	6

 

Using CJ() from data.table package

This function generates a cross-join of factors, ensuring that all possible combinations are accounted for. Unlike complete(), CJ() does not retain existing columns or fill in missing values by default. Instead, it generates a new dataset containing all possible combinations, which needs to be merged with the original data to fill in missing counts with 0 for any combinations that were absent in the original data.

library(data.table)
completed_data <- CJ(Gender = unique(grouped$Gender), `Daily Studying Time` = unique(grouped$`Daily Studying Time`))
completed_data <- merge(completed_data, grouped, by = c("Gender", "Daily Studying Time"), all.x = TRUE)
completed_data[is.na(completed_data$Count), "Count"] <- 0

Gender	Daily Studying Time	Count
Female	1 - 2 Hour	56
Female	2 - 3 hour	14
Female	3 - 4 hour	7
Female	More Than 4 hour	0
Male	1 - 2 Hour	132
Male	2 - 3 hour	10
Male	3 - 4 hour	0
Male	More Than 4 hour	6

Conclusion:

complete() and CJ() are vital for making sure our data is complete and accurate in R. complete() does this in just one line by handling missing values and keeping existing data, while CJ() needs a bit more work to merge data and handle missing values. But together, they help us get clearer and more reliable insights from our data.