Correlating DataFrame Columns with Last Activity

Introduction

The problem at hand involves correlating data frame columns based on their last activity. The goal is to create a new column that matches the “Student ID” with the corresponding “Question ID” and “Option ID” based on the latest “Activity” timestamp.

In this article, we will explore how to achieve this using the plyr package in R and provide step-by-step explanations along with code examples.

Background

Before diving into the solution, it’s essential to understand the data structure and data types involved. The provided dataset consists of three columns: “Student ID,” “Question ID,” and “Option ID.” These columns are represented as strings, but we can convert them to numeric values using the as.numeric() function.

The “Activity” column contains a single character value “save,” indicating that it represents the timestamp. However, since it’s not an actual date or time value, we cannot directly compare it. Instead, we’ll assume that the order of appearance in the dataset corresponds to the latest activity for each combination of Student ID, Question ID, and Option ID.

Problem Statement

Given a DataFrame with the following columns:

• Student ID (str)
• Question ID (str)
• Option ID (str)
• Activity (str)

Create a new column that matches the Student ID with the corresponding Question ID and Option ID based on the last Activity timestamp, ensuring that each combination is only shown once.

Solution

To solve this problem, we’ll follow these steps:

1. Convert the non-numeric columns to numeric values.
2. Create a new column that combines the Student ID, Question ID, and Option ID using the + operator.
3. Use the aggregate() function from the plyr package to create a group-by operation based on the combined column.
4. Apply the tail() function to get only the latest activity for each combination.

Code Example

library(plyr)

# Sample data
df <- data.frame(
  S.ID = c("00011", "00011", "00011", "00011", "00011", "00012", "00012", "00012", "00013", "00013", "00013", "00012", "00013"),
  Q.ID = c(55525, 55525, 55526, 55526, 55527, 55527, 55528, 55528, 55529, 55529, 55522, 55522, 55522),
  O.ID = c(7896, 7896, 7898, 7898, 7897, 7897, 7898, 7898, 7899, 7899, 7892, 7892, 7892),
  activity = rep("save", 13), stringsAsFactors = F
)

# Convert non-numeric columns to numeric values
df$S.ID <- as.numeric(as.character(df$S.ID))
df$Q.ID <- as.numeric(as.character(df$Q.ID))
df$O.ID <- as.numeric(as.character(df$O.ID))

# Create a new column that combines Student ID, Question ID, and Option ID
df$combined_column <- df$S.ID + df$Q.ID + df$O.ID

# Use aggregate() to create a group-by operation based on the combined column
result <- aggregate(S.ID ~ O.ID + Q.ID, data = df, tail, n = 1)

# Print the result
print(result)

Result

The resulting DataFrame should have the following structure:

Explanation

In the code example above, we first convert the non-numeric columns to numeric values using as.numeric(as.character()). We then create a new column called “combined_column” by adding the Student ID, Question ID, and Option ID together. This combined value serves as the key for our group-by operation.

We use the aggregate() function with the ~ operator to specify the grouping columns (O.ID, Q.ID) and the aggregation function (tail). The n = 1 argument ensures that we get only the latest activity for each combination.

Finally, we print the resulting DataFrame to verify that our solution produces the desired output.

Conclusion

In this article, we’ve explored how to correlate DataFrame columns with their last activity using the plyr package in R. By converting non-numeric columns to numeric values and creating a new column that combines the Student ID, Question ID, and Option ID, we can group data by these combined values and get only the latest activity for each combination.

Last modified on 2023-12-07