Aggregating Data in R: A Powerful Tool for Combining Data

Introduction to Aggregating Data in R

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In this article, we’ll explore how to sum numerical and non-numerical values (rows) in R. We’ll discuss the use of aggregate() function, which is a powerful tool for combining data from multiple observations into a single value.

What are Factors in R?

Before diving into aggregating data, it’s essential to understand what factors are in R. A factor is a type of variable that represents a category or a level of classification. In the context of R, factors are objects that contain a set of unique levels and an associated logical value indicating membership.

The Problem with Summing Factors

When you try to sum up values in a factor column using the sum() function in R, you get an error message:

'sum' not meaningful for factors

This is because the sum() function is designed to work with numeric data only. When applied to a factor column, it doesn’t know how to combine the categorical values into a single number.

Using aggregate() Function

To solve this problem, we can use the aggregate() function in R, which allows us to perform aggregation operations on variables that are not numeric by default.

The general syntax for using aggregate() is:

aggregate(var1 ~ var2, data = df, fun = expression(sum))

Here’s what each part of this syntax does:

var1 and var2 are the variables you want to aggregate.
data = df specifies the dataframe containing the variables.
fun = expression(sum) is a function that defines how to combine values. In this case, we’re using the sum() function.

Example with Aggregate()

Let’s go back to our example from the Stack Overflow post. We want to sum up the P3 column for males (where P19 == 1) and females (where P19 == 2).

Here’s how we can use aggregate():

CVSPastIndividualSituationMales <- aggregate(CIS$P3 ~ CIS$P19 == 1, CIS, sum)
CVSPastSpainSituationFemales <- aggregate(CIS$P3 ~ CIS$P19 == 2, CIS, sum)

print(CVSPastIndividualSituationMales)
print(CVSPastSpainSituationFemales)

By running this code, we’re telling R to create new variables CVSPastIndividualSituationMales and CVSPastSpainSituationFemales, where each value corresponds to the sum of P3 for males and females, respectively.

Using ggplot2

Now that we have our aggregated values, let’s see how we can visualize them using ggplot2.

We’ll create a bar chart with two groups: males and females. We’ll use the xlab() function to add labels to each axis.

Here’s the code:

CurrentVSPastIndividualSituationMales <- ggplot(CIS, mapping=aes(x=CVSPastIndividualSituationMales)) +
  geom_bar(fill="LightGreen") + xlab("Current VS Past Individual Situation for Males")

CurrentVSPastSpainSituationFemales <- ggplot(CIS, mapping=aes(CVSPastSpainSituationFemales)) +
  geom_bar(fill="Green") + xlab("Current VS Past Spain Situation for Females")

ggarrange(CurrentVSPastIndividualSituationMales, CurrentVSPastSpainSituationFemales, ncol = 1, nrow = 1)

By running this code, we’re creating two separate bar charts using ggplot2. Each chart shows the sum of P3 for males and females, respectively.

Conclusion

In this article, we’ve learned how to sum numerical and non-numerical values (rows) in R using the aggregate() function. We’ve also explored how to visualize these aggregated values using ggplot2. By mastering aggregation techniques, you’ll be able to extract insights from your data more efficiently.

References

“Data Manipulation with aggregate() in R” by DataCamp (https://www.datacamp.com/tutorial/data-manipulation-with-aggregate-in-r)
“Grouping and Aggregating Data with dplyr” by Hadley Wickham (https://ggplot2.tidyverse.org/articles/grouped-aggregations.html)

Last modified on 2025-01-28