Aggregate Across Multiple Vectors: Retain Entries with Missing Values

In this post, we’ll delve into the world of data aggregation and explore how to handle missing values when aggregating across multiple vectors. We’ll use R as our primary programming language, but the concepts and techniques discussed here can be applied to other languages as well.

Overview

When working with datasets containing missing values, it’s essential to understand how these values affect various analyses, including aggregation. In this post, we’ll examine the differences between aggregating across multiple vectors with and without considering missing values.

Example Dataset

To illustrate our discussion, let’s create a sample dataset in R:

site <- c(12, 12, 12, 12, 45, 45, 45, 45)
horizon <- c('A', 'A', 'B', 'C', 'A', 'A', 'B', 'C')
value1 <- c(19, 14, 3, 2, 18, 19, 4, 5)
value2 <- c(NA, NA, 3, 2, NA, NA, 4, 5)

data <- data.frame(site, horizon, value1, value2)

This dataset contains three variables: site, horizon, and value1 (and an additional value2). We’ll use this dataset to demonstrate how aggregation works with and without missing values.

Aggregating Across Multiple Vectors

When aggregating across multiple vectors using the aggregate() function in R, the behavior of missing values depends on the FUN argument. Let’s explore two scenarios:

Scenario 1: Default Behavior

By default, if an aggregation operation encounters a row with missing values for all variables being aggregated, it will be ignored.

# Aggregate by site and horizon (without considering missing values)
aggregate(value1 ~ site + horizon, data = data, FUN = mean)

  site horizon value
1   12       A  16.5
2   45       A  18.5
3   12       B   3.0
4   45       B   4.0
5   12       C   2.0
6   45       C   5.0

In this example, the row with site = 12 and horizon = A is ignored because there are missing values in both variables being aggregated.

Scenario 2: Considering Missing Values

To include rows with missing values in the aggregation results, you can specify the na.action argument. By default, na.action = "drop", which means that missing values will be dropped from the calculation.

# Aggregate by site and horizon (considering missing values)
aggregate(value1 ~ site + horizon, data = data, FUN = mean, na.action = na.pass)

  site horizon value1 value2
1   12       B      3      3
2   45       C      4      4

In this revised example, the row with site = 12 and horizon = A is included in the results because we specified na.action = "pass", which means that missing values will be passed to the FUN function without being dropped.

How to Use `na.action = na.pass`

To use this approach, you can modify the aggregate() call as shown above. The na.action argument allows you to choose how R should handle missing values during aggregation:

na.action = "drop" (default): Drop rows with missing values.
na.action = "mean": Use the mean value for each variable being aggregated.
na.action = "median": Use the median value for each variable being aggregated.
na.action = "first": Return the first observation for each variable being aggregated, regardless of whether it has missing values or not.
na.action = "last": Return the last observation for each variable being aggregated, regardless of whether it has missing values or not.
na.action = "ifany": If any value is missing, return NA. Otherwise, return the mean/median/mode/etc.

Conclusion

In conclusion, when aggregating across multiple vectors in R, you must be mindful of how missing values affect your results. By understanding how to handle missing values using the na.action argument and the aggregate() function, you can ensure that your aggregation results accurately reflect the data.

Remember to choose an appropriate value for na.action based on your specific use case:

Use na.action = "drop" if you want to exclude rows with missing values.
Use na.action = na.pass (or another variant) if you want to include rows with missing values and calculate the result using a custom function.

By applying these techniques, you can effectively aggregate your data while handling missing values.

Last modified on 2024-01-22