Deleting Duplicates in R and Changing Remainder: A Practical Approach with Sample Data

Deleting Duplicates in R and Changing Remainder

In this article, we’ll explore how to delete duplicate rows from a data frame in R, and then change the remaining unique row based on the number of duplicates that were deleted. We’ll use a specific example using a dataset containing directors and their associated companies.

Understanding the Problem

The problem statement involves removing duplicate rows for each director, where a director’s presence is counted across multiple company boards. The resulting data frame should have three new columns: one for the number of board seats held by each director (which will be the total number of duplicates removed), another for the names of all companies on which they sit, and finally, a column to indicate if the director’s primary affiliation is with their “home company” or an outsider.

Solution Overview

To achieve this, we’ll use several R packages:

plyr: Provides functions like ddply for data manipulation.
data.frame: For creating and storing our dataset.
merge: To combine multiple datasets based on common columns.

Here’s a step-by-step breakdown of the solution, with code examples:

Step 1: Import Necessary Libraries and Load Data

# Load necessary libraries
library(plyr)
library(data.frame)

# Create our sample data frame
Name <- c('Abbot, F', 'Abdool-Samad, T', 'Abedian, I', 'Abrahams, F', 'Abrahams, F', 'Abrahams, F')
Position <- c('Executive Director', 'Outsider', 'Outsider', 'Executive Director','Outsider',  'Outsider')
Companies <- c('ARM', 'R', 'FREIT', 'FG', 'CG', 'LG')
NoBoards <- c(1,1,1,1,1,1)

df <- data.frame(Name, Position, Companies, NoBoards)

Step 2: Remove Duplicate Rows Based on Director’s Name

We’ll use the ddply function to group our data by director name and perform summary operations.

# Group by 'Name' and summarize positions and companies for each director
sumPosition <- ddply(df, .(Name), summarize, Position = paste(Position, collapse=", "))
sumCompanies <- ddply(df, .(Name), summarize, Companies = paste(Companies, collapse=", "))

# Merge the results into a single data frame
df2 <- merge(sumPosition, sumCompanies, by = 'Name')

Step 3: Summarize Number of Boards for Each Director

To get the number of boards each director is associated with, we need to count their presence across multiple boards.

# Aggregate df to count the number of boards per Name
names_NoBoards <- aggregate(df$NoBoards, by = list(df$Name), sum)
names(names_NoBoards) <- c('Name', 'NoBoards')

# Merge this result with df2 for easier access to director's name and board counts
df3 <- merge(df2, names_NoBoards, by = 'Name')

Step 4: Determine Director’s Primary Affiliation

To determine if a director is affiliated primarily with their “home company” or an outsider, we can compare the count of boards they’re involved in (NoBoards) to the total number of boards they appear on (sumPosition$Position).

However, this step needs careful consideration because our data only provides one position for each director (whether it’s executive or an outsider), and there isn’t clear guidance from the problem statement about how we should handle directors who are not explicitly listed as executives.

For the sake of completeness and illustrative purposes, let’s proceed under a simplified assumption that this step could be approached by looking at the ratio of their “home company” appearances (Companies) to their total board count (NoBoards), assuming directors with fewer boards than companies appear predominantly in one category (e.g., they’re likely not executives or outsiders).

# Assuming a simplistic interpretation where 'ExecutiveDirector' holds only those positions listed as executive,
# and the rest of their presence is in 'Companies', calculate director's primary affiliation.
ExecutiveDirector <- df[Position == 'Executive Director', c(1,3)]

df4 <- merge(df3, ExecutiveDirector, by = 'Name', all.x = TRUE)

Step 5: Handling Potential Issues with Simplification

It’s essential to recognize that this approach may not accurately represent directors’ primary affiliations due to the simplistic nature of our assumption and potential inconsistencies in data.

Final Data Frame Output

After these steps, df4 would contain all necessary information about each director, including their board counts, company affiliations, and a simplified indicator of whether they’re primarily affiliated with their “home company” or an outsider. However, note that this simplistic approach assumes the presence of executives is directly tied to directors’ board appearances, which might not always be accurate.

# Final data frame df4 after merging director information with board counts and affiliations
df4 <- merge(df3, ExecutiveDirector, by = 'Name', all.x = TRUE)

In conclusion, while this example provides a practical approach to removing duplicate rows in R based on specific conditions, the actual implementation may vary depending on the nature of your data and the requirements of your problem.

Last modified on 2023-05-27