Categories: Data Science

Learn R – How to Create Data Frames using Existing Data Frame

This article represents commands that could be used to create data frames using existing data frame. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

Following is a list of command summary for creating data frames by extracting multiple columns from existing data frame based on following criteria, whose sample is provided later in this article:

  • Column indices
  • Column names
  • Subset command
  • Data.frame command
6 Techniques for Extracting Data Frame from Existing Data Frames

Following commands have been based on diamonds data frame which is loaded as part of loading ggplot2 library.

 

Following is how the diamonds data frame looks like:

#1: Create data frame with selected columns using column indices
# Displays column carat, cut, depth
dfnew1 <- diamonds[,c(1,2,5)]

#2: Create data frame with selected columns using column indices with sequences
# Displays column carat, cut, color, depth, price, x
dfnew2 <- diamonds[, c(1:3, 5, 7:8)]

#3: Create data frame with selected columns using data.frame command
# Displays column carat, cut, color
dfnew3 <- data.frame(diamonds$carat, diamonds$cut, diamonds$color)
names(dfnew3) <- c("Carat", "Cut", "Color")

#4: Create data frame using selected columns and column names
# Displays column carat, depth, price
dfnew4 <- diamonds[,c("carat", "depth", "price")]

#5: Create data frame using subset command and column names
# Displays column color, carat, price
dfnew5 <- subset(diamonds, select=c("color", "carat", "price"))

#6: Create data frame using subset command and column indices
# Displays column carat, cut, color, depth
dfnew6 <- subset(diamonds, select=c(1:3, 5))
Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Agentic Reasoning Design Patterns in AI: Examples

In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…

2 months ago

LLMs for Adaptive Learning & Personalized Education

Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…

2 months ago

Sparse Mixture of Experts (MoE) Models: Examples

With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…

3 months ago

Anxiety Disorder Detection & Machine Learning Techniques

Anxiety is a common mental health condition that affects millions of people around the world.…

3 months ago

Confounder Features & Machine Learning Models: Examples

In machine learning, confounder features or variables can significantly affect the accuracy and validity of…

3 months ago

Credit Card Fraud Detection & Machine Learning

Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…

3 months ago