Merging Two CSV Files to Remove Duplicates from Output File Using Dplyr - R

Introduction

In this article, we will explore a common problem in data analysis: merging two CSV files while removing duplicates. We’ll use the popular R programming language and its dplyr package to achieve this goal. The process involves reading both datasets into memory, identifying unique rows based on a shared column, and then returning only those rows from one of the original datasets.

Prerequisites

This article assumes you have a basic understanding of R and its data manipulation libraries, including the dplyr package. Familiarity with SQL concepts, such as GROUP BY and JOINs, is also helpful but not required.

Background on Datasets and Data Manipulation in R

In R, datasets are typically represented as tables or vectors. For this example, we’ll assume two CSV files have been read into memory using read.csv(). The first file (df1) contains all individuals with their contact information, while the second file (df2) holds survey responses.

## Load required libraries and data
library(dplyr)
# assuming df1 and df2 are in the global environment

# dummy data (replace with your actual datasets)
df1 <- data.frame("customer_ID" = 1:100, 
                 "address" = rep(c("saturn", "mars"), 25))

df2 <- data.frame("customer_ID" = c(1:25, 75:99), 
                 "likes_apples" = rep(c(TRUE, FALSE), 50))

Merging Two Datasets Using `anti_join()` from dplyr

The most straightforward way to merge two datasets in R is using the anti_join() function from the dplyr package. This function returns all rows from the first dataset (df1) that do not have matching rows in the second dataset (df2).

## Anti-join df2 onto df1 based on "customer_ID"
merged_data <- anti_join(df1, df2, by = "customer_ID")

# view merged data
merged_data

In this code snippet:

anti_join() is the function used to merge datasets.
The first argument (df1) represents the original dataset that contains all rows we’re interested in.
The second argument (df2) specifies the dataset containing survey responses.
The by parameter indicates which column (customer_ID) should be used for matching.

Filtering Unique Rows from df2

Alternatively, if you want to explicitly filter unique rows from df2, you can use the following approach:

## Filter unique rows from df2 based on "customer_ID"
unique_rows <- df2 %>% 
  group_by(customer_ID) %>% 
  filter(n() == 1)

# view unique customer IDs
unique_rows$customer_ID

## Join filtered data with df1
final_data <- merge(df1, unique_rows, by = "customer_ID", all.x = TRUE)

# view final merged data
final_data

However, the anti_join() method is generally more efficient and recommended for large datasets.

Advanced Merging Techniques Using SQL Join

For situations requiring advanced joins or filtering based on multiple conditions, consider using an SQL approach:

## Use SQL JOIN to merge df1 with df2 where customer_ID matches
merged_data_sql <- inner_join(df1, df2, by = "customer_ID")

# view merged data
merged_data_sql

In this example, inner_join() performs a matching operation between both datasets based on the shared column (customer_ID).

Handling Missing Values

When working with real-world datasets, it’s not uncommon to encounter missing values. To avoid errors during merging, make sure to address any missing value issues before proceeding:

## Check for missing values and fill them (if necessary)
df1 <- na.omit(df1) # remove rows with missing values from df1
df2 <- na.omit(df2)  # remove rows with missing values from df2

# merge datasets using anti_join()
merged_data <- anti_join(df1, df2, by = "customer_ID")

Conclusion

Merging two CSV files to remove duplicates can be efficiently achieved in R using the dplyr package’s anti_join() function. By following this tutorial and adapting it to your specific dataset needs, you’ll be able to effectively merge datasets while avoiding duplicate entries.

By mastering the art of data manipulation, you’ll unlock a wide range of possibilities for your future projects, whether they involve cleaning, analyzing, or visualizing data. Happy coding!

Last modified on 2023-12-09