Aggregate / summarize multiple variables per group (e.g. sum, mean)

📝 Title: How to Aggregate and Summarize Multiple Variables per Group in R

👋 Hey there, data enthusiasts! Today, we're going to tackle a common problem in data analysis: How can we easily aggregate and summarize multiple variables simultaneously by groups? If you've been scratching your head over this, don't worry – I've got you covered!

🔍 The Problem: Let's say you have a data frame, and you want to aggregate multiple variables (such as x1 and x2) by year and month. You may have already tried using the aggregate() function, but you're not sure how to aggregate both variables at the same time. Sound familiar? Keep reading!

🤔 The Solution: Fortunately, there is a simple solution to this problem. Just follow these steps:

1. Use the aggregate() function to aggregate one of the variables. In our case, we'll start with x1. Here's an example:

   df2 <- aggregate(x1 ~ year+month, data=df1, sum, na.rm=TRUE)

   This code will aggregate the x1 variable by year and month, using the sum function to calculate the aggregate value. Feel free to replace sum with other functions like mean or max, depending on your needs.

2. Now, here's the trick to simultaneously aggregate another variable, such as x2. Instead of creating a separate code block, you can simply add the second variable to the aggregate() function. Here's how it's done:

   df2 <- aggregate(cbind(x1, x2) ~ year+month, data=df1, sum, na.rm=TRUE)

   By using cbind(x1, x2) inside the aggregate() function, you're indicating that both x1 and x2 should be aggregated simultaneously.

3. Finally, enjoy the fruits of your labor! Now you have a data frame (df2) where both x1 and x2 are aggregated by year and month.

🌟 Example Output: To give you a better idea of what the resulting data frame (df2) might look like, here's a sneak peek from the sample data:

year month        x1          x2
1 2000     1 0.6583751 -1.08492036
2 2000     2 1.3153057 -0.55643705
3 2000     3 0.4033004  2.02541647
4 2000     4 1.0003529  1.48516108
5 2000     5 0.6584187  2.57812204
6 2000     6 0.6483203  3.51737437

As you can see, the aggregated x1 and x2 values are grouped by year and month.

🔥 Call-to-Action: Congratulations! You've just leveled up your data aggregation skills. 🎉 Now it's time for you to put this knowledge into practice. Take a moment to try out the code with your own data and see the magic unfold.

Got any questions or suggestions? I'd love to hear from you in the comments below. Let's geek out together and turn data into valuable insights! 💡💪

Keep exploring, stay curious! Until next time! 🚀✨