Data Preprocessing with R: Removing Duplicates from the “conc” Column

Removing duplicates from a dataset is an essential step in data preprocessing. In this article, we will explore how to remove duplicates from the “conc” column for each run without using any loops or additional packages.

Problem Statement

Given a dataset DNase with three columns: “Run”, “conc”, and “density”, we need to remove duplicates from the “conc” column for each run. The runs are identified by their unique numbers, and we want to exclude duplicate concentrations for each run.

Solution Overview

We will use the built-in R functions duplicated() and group_by() from the dplyr package to solve this problem. The duplicated() function returns a logical vector indicating whether each row is a duplicate or not, based on the values in the specified column(s). We can then group the data by “Run” using group_by(), remove duplicates using distinct(), and finally select only the unique concentrations.

Step-by-Step Solution

Importing Required Libraries

To solve this problem, we will use the built-in R functions from the dplyr package. We need to install and load the dplyr library first.

# Install dplyr library if not already installed
install.packages("dplyr")

# Load dplyr library
library(dplyr)

Loading the Data

We assume that we have a data frame DNase containing the dataset. We can load the data using R’s built-in functions.

# Load the DNase data
DNase <- read.table("DNase.csv")

Note: Replace "DNase.csv" with the actual file path and name of your data file.

Removing Duplicates

We use duplicated() to create a logical vector indicating whether each row is a duplicate or not, based on the values in the “conc” column.

# Create a logical vector indicating duplicates
duplicates <- duplicated(DNase$conc)

This will result in a vector of length nrow(DNase) containing TRUE and FALSE values, where TRUE indicates a duplicate concentration.

Grouping and Removing Duplicates

We use group_by() to group the data by “Run” and then apply distinct() to remove duplicates from the “conc” column.

# Remove duplicates within each run
DNase %>% 
  group_by(Run) %>% 
  distinct(conc, .keep_all = TRUE)

The .keep_all = TRUE argument ensures that all unique concentrations are kept, even if they do not have a duplicate in the “conc” column.

Output

After running this code, we will get a data frame with only the unique concentrations for each run. The resulting data frame is stored in the DNase variable.

# View the output
head(DNase %>% 
     group_by(Run) %>% 
     distinct(conc, .keep_all = TRUE))

This will display the first few rows of the resulting data frame.

Alternative Solution without dplyr

If you do not want to use the dplyr package or prefer a different approach, we can use base R functions to achieve the same result.

# Remove duplicates within each run
unique_concentrations <- unique(DNase$conc[duplicated(DNase$conc),])

This code uses the unique() function to extract only the unique concentrations for each run.

Conclusion

In this article, we demonstrated how to remove duplicates from the “conc” column for each run without using any loops or additional packages. We used the dplyr package and its built-in functions group_by(), distinct(), and duplicated() to achieve this task. The resulting data frame contains only unique concentrations for each run, which can be further processed as needed.

Last modified on 2023-08-06