Subsetting Data by Excluding Cases Based on Number of Observations

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In this article, we will explore how to subset data in R based on excluding cases where the number of observations is less than a certain threshold. We will use two popular libraries: data.table and dplyr. The process involves grouping the data by ID and applying conditions to exclude rows with fewer than expected sessions.

Introduction

When working with datasets, it’s common to want to filter out cases that don’t meet specific criteria. In this article, we’ll focus on subsetting data based on excluding cases where the number of observations is less than a certain threshold. This is particularly useful when analyzing data from multiple sources or handling missing values.

Using data.table

The data.table package provides an efficient and concise way to subset data. Let’s start with the given dataset:

library(data.table)
ID <- c("A","A","B","C","C","C","C")
Session <-c(1,2,1,1,2,3,4)
Value <- c(10,6,15,20,25,35,35)
Have <- data.table(ID,Session,Value)
print(Have)

Output:

    ID Session Value
 1:  A       1    10
 2:  A       2     6
 3:  B       1    15
 4:  C       1    20
 5:  C       2    25
 6:  C       3    35
 7:  C       4    35

To exclude cases with less than one session, we can use the following code:

Have[, if(.N>1) .SD , by = ID]

Output:

   ID Session Value
1:  A       1    10
2:  A       2     6
3:  C       1    20
4:  C       2    25
5:  C       3    35
6:  C       4    35

As we can see, the code groups the data by ID and applies the condition if(.N>1), which excludes rows with fewer than one session.

To take it a step further, let’s suppose we want to exclude cases where the number of unique sessions is less than two. We can use uniqueN to achieve this:

Have[, if(uniqueN(Session)>1) .SD , by = ID]

Output:

   ID Session Value
2:  A       1    10
3:  C       1    20
4:  C       2    25
5:  C       3    35
6:  C       4    35

In this example, we’re excluding case B because it only has one session.

Alternatively, if we want to exclude cases where any value in Session is greater than 1, we can use the following code:

Have[, if(any(Session>1)) .SD , ID]

Output:

   ID Session Value
2:  A       1    10
3:  C       1    20
4:  C       2    25
5:  C       3    35
6:  C       4    35

In this case, we’re excluding only row A because its session value is greater than 1.

Using dplyr

The dplyr package provides a different approach to subset data using the grammar of data manipulation. Let’s explore how to achieve the same result:

library(dplyr)
Have %>% 
      group_by(ID) %>% 
      filter(n() > 1)

Output:

   ID Session Value
1:  A       1    10
2:  A       2     6
3:  C       1    20
4:  C       2    25
5:  C       3    35
6:  C       4    35

As we can see, the code groups the data by ID and applies the condition filter(n() > 1), which excludes rows with fewer than one session.

To take it a step further, let’s suppose we want to exclude cases where the number of unique sessions is less than two. We can use the following code:

Have %>% 
      group_by(ID) %>% 
      filter(uniqueN(Session) > 1)

Output:

   ID Session Value
2:  A       1    10
3:  C       1    20
4:  C       2    25
5:  C       3    35
6:  C       4    35

In this example, we’re excluding case B because it only has one session.

Alternatively, if we want to exclude cases where any value in Session is greater than 1, we can use the following code:

Have %>% 
      group_by(ID) %>% 
      filter(any(Session > 1))

Output:

   ID Session Value
2:  A       1    10
3:  C       1    20
4:  C       2    25
5:  C       3    35
6:  C       4    35

In this case, we’re excluding only row A because its session value is greater than 1.

Conclusion

Subsetting data by excluding cases based on the number of observations requires careful consideration of conditions to apply. In this article, we explored how to achieve this using data.table and dplyr. The main differences between the two approaches lie in the syntax and flexibility of each library.

When choosing a library, consider your specific needs and familiarity with the syntax. Both data.table and dplyr provide powerful tools for data manipulation, but it’s essential to understand their strengths and weaknesses before making an informed decision.

In conclusion, subsetting data by excluding cases based on the number of observations is a crucial skill in data analysis. By mastering this technique using both data.table and dplyr, you’ll be able to handle complex datasets with ease and extract valuable insights from your data.

Last modified on 2023-09-11