Selecting IDs Based on Conditional Matching in R

Introduction

As data analysts and scientists, we often find ourselves dealing with complex data sets and trying to make sense of them. In the context of recommendation systems, identifying individuals who possess specific skills or attributes is crucial for making accurate recommendations. This blog post delves into how to select IDs based on conditional matching in R.

Background

Recommendation systems are designed to suggest items that a user may be interested in based on their past behavior and preferences. In this case, we’re dealing with a recommender system that recommends skills to people based on historical data of skills owned by individuals working on specific activities.

We have a dataset containing information about regions, activity IDs, recommended skills for each activity, person IDs, and the skills possessed by each person. Our goal is to associate these recommended skills with specific people who possess at least 50% of these skills.

The Challenge

The question asks us to determine how to select the right people based on their matched skills and the conditions specified (i.e., at least 50% matching). We have a dataset that looks like this:

Region	ActivityID	Recommended_Skills	PersonID	Skills_Person
France	123	Python, PowerPoint, R, Word	ABC	R
France	123	PowerPoint, R, Word	ABC	Mikado
France	123	R	ABC	Python
France	123	Word	ABC	Photoshop
France	123	Python	XYZ	Finance
France	123	PowerPoint	XYZ	Powerpoint
France	123	R	XYZ	Law
France	123	Word	XYZ	Analytics
Spain	789	JavaScript, PowerPoint, UI, Office	DEF	PowerPoint
Spain	789	PowerPoint, Office	DEF	Word
Spain	789	UI	DEF	R
Spain	789	Office	DEF	Finance
Spain	789	Python	CVB	JavaScript
Spain	789	PowerPoint	CVB	Office

Our expected dataframe should look like this:

Region	ActivityID	PersonID
France	123	ABC
Spain	789	CVB

The Solution

One approach to solving this problem is to use the tidyverse library in R. Here’s a step-by-step explanation of how we can achieve our goal:

Step 1: Load the Necessary Libraries and Data

First, let’s load the required libraries and dataset.

# Load necessary libraries
library(tidyverse)

# Load the dataset
df <- read.csv("your_data_file.csv")

Replace “your_data_file.csv” with the path to your actual CSV file.

Step 2: Group by Region and ActivityID

Next, we’ll group our data by region and activity ID. This will allow us to find the recommended skills for each combination of these two factors.

# Group the data by Region and ActivityID
df %&gt;%
  group_by(Region, ActivityID)

Step 3: Find the Person ID with Maximum Matching Skills

Now, we’ll use the map_dbl function to calculate the total matching skills for each person. We’ll then find the person ID that has the maximum number of matching skills using the which.max function.

# Calculate the total matching skills for each person
df %&gt;%
  summarise(PersonID = names(which.max(map_dbl(split(Skills_Person, PersonID), 
                                               ~sum(Recommended_Skills %in% .)))))

This will give us a dataframe with the desired output.

Example Walkthrough

Let’s walk through an example using our France activity ID:

Region	ActivityID	Recommended_Skills	PersonID	Skills_Person
France	123	Python, PowerPoint, R, Word	ABC	R

We want to find the person who possesses at least 50% of these recommended skills. In this case:

Person A (ABC) has R, which is one of the recommended skills.
Person B (Mikado) has R, but also some other unrelated skills.

We’ll use our formula to calculate the matching skills for each person and find the maximum:

# Group by Region and ActivityID
df %&gt;%
  group_by(Region, ActivityID) %&gt;%
  
  # Calculate the total matching skills for each person
  summarise(PersonID = names(which.max(map_dbl(split(Skills_Person, PersonID), 
                                               ~sum(Recommended_Skills %in% .)))))

This will give us:

Region	ActivityID	PersonID
France	123	ABC

So, the person with ID ABC in our France activity is recommended.

Conclusion

In this blog post, we’ve explored how to select IDs based on conditional matching in R. We’ve walked through an example using a sample dataset and shown how to use the tidyverse library to achieve our goal.

By following these steps, you should be able to develop your own recommender system that accurately recommends skills to people based on historical data of their skills.

Last modified on 2023-11-15