How to Multiply Matching Values in Two Data Frames in R using dplyr and data.table

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Multiplying Matching Values in Two Data Frames in R

Are you working with multiple data frames in R that have matching column names and need to perform multiplication on specific columns? If so, you are in the right place. This guide will guide you through how to multiply values of certain columns from two data frames where the columns have matching names. Let’s dive into the problem and explore the solution step-by-step.

Understanding the Problem

You have two data frames with the following characteristics:

Both data frames share the same column names.

The combination of the "Period" and "Code" columns serves as a key for matching rows.

Your objective is to multiply the values in column a from the first data frame (dataframe1) with the values from column a in the second data frame (dataframe2). This operation should be repeated for column c as well.

Here’s a brief look at what your data frames look like:

Sample Data Frames

DataFrame1:

PeriodCodeac2014-01-13ABC182015-01-13ABC292015-01-13LMZ3102016-01-13LMZ411DataFrame2:

PeriodCodeac2014-01-13ABC0.10.22015-01-13ABC0.50.92015-01-13EKZ0.90.32015-01-13LMZ0.40.22016-01-13LMZ0.30.4Your end goal is to create a new data frame that merges and multiplies these values.

The Solution

Method 1: Using dplyr

Load the dplyr Library:

Make sure you have the dplyr package installed and loaded:

[[See Video to Reveal this Text or Code Snippet]]

Join the Data Frames:

Use left_join to merge the data frames based on the "Period" and "Code" columns and then multiply the columns of interest:

[[See Video to Reveal this Text or Code Snippet]]

Output:

Execute the above code, and you will get the desired results:

[[See Video to Reveal this Text or Code Snippet]]

Convert Data Frames to Data Tables:

[[See Video to Reveal this Text or Code Snippet]]

Output:

You will achieve the same output as with dplyr:

PeriodCodeac2014-01-13ABC0.11.62015-01-13ABC1.08.12015-01-13LMZ1.22.02016-01-13LMZ1.24.4Conclusion

Feel free to try out these methods on your own data frames and see how powerful R can be for data manipulation.