How to Select Top 10 Rows of a DataFrame by Date in R?

This recipe will help you learn how to select top 10 rows from your DataFrame by date in R to optimize your data processing and analysis workflow.

Recipe Objective - How to Select Top 10 Rows of a DataFrame by Date in R? 

In R programming, extracting the top rows of a dataframe based on a specific criterion is a common task. This recipe guide will walk you through the process of selecting the top 10 rows of a dataframe in R, with a focus on using dates as the criteria. We'll utilize the powerful top_n() function and also showcase an alternative method using the head() function.

Stepwise Guide to Get First 10 Rows of DataFrame in R 

Explore the following steps to get a comprehensive understanding to display first 10 rows in R- 

Step 1 - Install and Load the Required Library

Before we dive into selecting the top rows of a DataFrame, it's crucial to ensure we have the necessary library installed. In this case, we're using the tidyr library. If you haven't installed it yet, the command install.packages("tidyr") will take care of that. Following the installation, we load the library into our R environment using library(tidyr). 

install.packages("tidyr")

library(tidyr)

Step 2 - Define a DataFrame

Here, we create a simple DataFrame named df, consisting of student names and their corresponding birth dates. This dataset will be used for our illustration of selecting the top rows based on date criteria. 

df <- data.frame(

  student_name = c("A", "B", "C", "D", "E"),

  birth_date = c('1988-01-14', '1998-10-02', '2008-12-31', '2004-06-15', '1997-05-25')

)

print("Dataframe:")

print(df)

"Dataframe is:"

  student_name birth_date

1            A 1988-01-14

2            B 1998-10-02

3            C 2008-12-31

4            D 2004-06-15

5            E 1997-05-25

Step 3 -  Use top_n() to Select Top ‘n’ Rows by Date

Syntax : top_n(x,n,col_name) 

where, x - input dataframe n - number of rows to be returned col_name - which columns to be returned(optional)

The top_n() in R allows us to select the top 'n' rows based on a specified column, which, in this case, is the birth_date. We showcase two methods: one directly using top_n() and the other using the pipe operator %>% for a more streamlined approach.

x <- top_n(df,2,birth_date)

print(x)

"Output of code:"

  student_name birth_date

1            C 2008-12-31

2            D 2004-06-15

x <- df %>% top_n(2) # can use the method for returning top_n as well print(x)

"Output of code:"

Selecting by birth_date

  student_name birth_date

1            C 2008-12-31

2            D 2004-06-15

{"mode":"full","isActive":false}

The above example illustrates displaying the top 2 rows of the DataFrame. Similarly, we can replace n to 10 to get the top 10 rows of the DataFrame. 

Alternative Method: Using head() Function in R

In addition to top_n(), R provides the head() function for selecting the first 'n' rows of a DataFrame. Here, we demonstrate how to use head() to achieve the same result.

# Using head() function to select the first 10 rows

head_rows <- head(df, 10)

print("Output using head() function:")

print(head_rows)

The output of the above code will also display the top 10 rows of the dataframe df. 

This step-by-step guide introduces you to the process of selecting the top rows of a DataFrame in R, focusing on the birth date as the criterion. Whether you prefer the flexibility of top_n() or the simplicity of head(),these techniques empower you to efficiently manipulate data frames based on your specific needs.

Dive Deeper into DataFrame Operations in R with ProjectPro!

Selecting the top rows of a DataFrame by date in R is crucial for efficient data manipulation and analysis. Real-world applications often demand such precise data handling, emphasizing the significance of practical experience. ProjectPro offers a diverse repository of over 250+ solved data science and big data projects. Engaging in these projects helps you to apply and reinforce the knowledge gained, ensuring a comprehensive understanding of the intricacies involved in working with data in R.

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

ML Model Deployment on AWS for Customer Churn Prediction
MLOps Project-Deploy Machine Learning Model to Production Python on AWS for Customer Churn Prediction

Azure Text Analytics for Medical Search Engine Deployment
Microsoft Azure Project - Use Azure text analytics cognitive service to deploy a machine learning model into Azure Databricks

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

Model Deployment on GCP using Streamlit for Resume Parsing
Perform model deployment on GCP for resume parsing model using Streamlit App.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Natural language processing Chatbot application using NLTK for text classification
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.

Build a Multi ClassText Classification Model using Naive Bayes
Implement the Naive Bayes Algorithm to build a multi class text classification model in Python.

Build an Image Segmentation Model using Amazon SageMaker
In this Machine Learning Project, you will learn to implement the UNet Architecture and build an Image Segmentation Model using Amazon SageMaker

Build an AI Chatbot from Scratch using Keras Sequential Model
In this NLP Project, you will learn how to build an AI Chatbot from Scratch using Keras Sequential Model.

Classification Projects on Machine Learning for Beginners - 2
Learn to implement various ensemble techniques to predict license status for a given business.