R Code for Filtering Data

Data filtering is a fundamental process in data analysis, allowing analysts to extract relevant information from large datasets based on specific criteria or conditions.

Whether working with spreadsheets, databases, or programming languages like R, data filtering enables users to focus on subsets of data that are essential for their analysis or decision-making.

Method 1 – Filter data using subset() function

This method demonstrates a simple way to filter data in R using the subset() function. You can adjust the condition inside the subset() function to filter data based on different criteria, and you can also filter based on multiple conditions using logical operators like & for AND and | for OR.

The following is an example of R code for filtering data.

# Create a sample dataframe
data <- data.frame(
  ID = c(1, 2, 3, 4, 5),
  Name = c("John", "Emma", "Michael", "Sophia", "William"),
  Age = c(25, 30, 22, 35, 28),
  Gender = c("M", "F", "M", "F", "M")
)

# Filtering data where Age is greater than 25
filtered_data <- subset(data, Age > 25)

# Print the filtered data
print(filtered_data)

Let me explain the above code,

First, create a dataframe named ‘data’ with columns ID, Name, Age, and Gender. This dataframe contains sample data of individuals with their respective attributes.
We use the subset() function in R to filter the data based on a condition. In this example, we’re filtering the data where Age is greater than 25.
This creates a new dataframe named ‘filtered_data’ containing only the rows where the Age column satisfies the condition.
Finally, print the filtered data to see the result.

Method 2 – Using dplyr package

Another commonly used method for filtering data in R is by using the dplyr package, which provides a more intuitive and efficient way to manipulate data frames.

Let me show you an example code using dplyr.

# Load the dplyr package
library(dplyr)

# Create a sample dataframe
data <- data.frame(
  ID = c(1, 2, 3, 4, 5),
  Name = c("John", "Emma", "Michael", "Sophia", "William"),
  Age = c(25, 30, 22, 35, 28),
  Gender = c("M", "F", "M", "F", "M")
)

# Filtering data where Age is greater than 25 using dplyr
filtered_data <- filter(data, Age > 25)

# Print the filtered data
print(filtered_data)

I’ll also explain the above code to you.

Load the dplyr package using the library() function. This package provides a set of functions for data manipulation.
Same as before, we create a dataframe named ‘data’ with columns ID, Name, Age, and Gender containing sample data.
Use the filter() function from the dplyr package to filter the data. Inside the filter() function, we specify the dataframe (data) and the condition for filtering (Age > 25). This filters the rows where the Age column satisfies the condition.
Print the filtered data to see the result.

Using dplyr makes the code more readable and concise compared to base R functions like subset(). Additionally, dplyr provides a set of functions for various data manipulation tasks, making it a powerful tool for data analysis in R.