Merging Duplicated Rows from Two Dataframes in R with dplyr

Merging Duplicated Rows from Two Dataframes in R

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In this article, we will explore how to merge duplicated rows from two dataframes in R. Both dataframes share many columns, but not all. The goal is to merge these two dataframes while keeping the status only of the more up-to-date dataframe.

Introduction

Dataframe merging is a common operation in data analysis and visualization. When working with multiple data sources, it’s often necessary to combine them into a single dataset for further processing or analysis. However, sometimes the dataframes share duplicated rows, which can lead to inconsistencies and errors.

In this article, we’ll focus on merging duplicated rows from two dataframes in R, using the dplyr library.

The Problem

We have two dataframes, df1 and df2, which share many columns but not all. Both dataframes have an ID-column, some of which are common to both dataframes. We want to merge these two dataframes while keeping the status only of the more up-to-date dataframe.

The Solution

To solve this problem, we’ll use a combination of dplyr functions: full_join(), match(), and nomatch = 0.

Step 1: Full Join the Dataframes

First, we need to perform a full join on both dataframes. This will create a new dataframe with all rows from both dataframes.

library(dplyr)
df1 <- df1 %>% full_join(df2, by = c("ID", "status", "date"))

In this step, we use full_join() to combine the two dataframes on the common columns (ID, status, and date). The resulting dataframe will contain all rows from both dataframes.

Step 2: Match IDs and Update Status

Next, we need to match the IDs between both dataframes and update the status only of the more up-to-date dataframe.

df1$status[match(df2$ID, df1$ID, nomatch = 0)] <- df2$status[match(df1$ID, df2$ID, nomatch = 0)]

In this step, we use match() to find the positions of matching IDs in both dataframes. We then update the status only for the rows with matched IDs.

Step 3: Handle Missing Values

Finally, we need to handle missing values in the someColumnA and someColumnB columns. Since we don’t have any values in these columns that are common to both dataframes, we can simply replace them with a dash (-) or another suitable value.

df1 %>% 
  mutate(someColumnA = ifelse(is.na(someColumnA), "-", someColumnA),
         someColumnB = ifelse(is.na(someColumnB), "-", someColumnB))

In this step, we use mutate() to create new columns with missing values replaced.

Example Use Case

Let’s create a sample dataframe and demonstrate the merging process:

# Create sample dataframes
df1 <- data.frame(
  ID = c(1, 2, 3),
  status = c("open", "closed", "pending"),
  date = c("01.01.2020", "01.01.2020", "01.01.2020"),
  someColumnA = c("A", "B", "C")
)

df2 <- data.frame(
  ID = c(1, 2, 4),
  status = c("closed", "closed", "pending"),
  date = c("01.01.2020", "01.01.2020", "01.01.2020"),
  someColumnB = c("rr", "tt", "zz")
)

# Merge the dataframes
df1 <- df1 %>% full_join(df2, by = c("ID", "status", "date"))

# Match IDs and update status
df1$status[match(df2$ID, df1$ID, nomatch = 0)] <- df2$status[match(df1$ID, df2$ID, nomatch = 0)]

# Handle missing values
df1 %>% 
  mutate(someColumnA = ifelse(is.na(someColumnA), "-", someColumnA),
         someColumnB = ifelse(is.na(someColumnB), "-", someColumnB))

# Print the resulting dataframe
print(df1)

Conclusion

In this article, we demonstrated how to merge duplicated rows from two dataframes in R using dplyr. We performed a full join on both dataframes and matched IDs to update the status only of the more up-to-date dataframe. Finally, we handled missing values in the resulting dataframe.

By following these steps, you can easily merge duplicated rows from multiple dataframes in R and create a unified dataset for further analysis or visualization.

Last modified on 2024-04-14