What happens when you mutiply 2 vectors of unequal length in R?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

What happens when you mutiply 2 vectors of unequal length in R?

What happens when you mutiply 2 vectors of unequal length in R?

This recipe explains what happens when you mutiply 2 vectors of unequal length in R

Recipe Objective**

What happens when you multiply 2 vectors of unequal length in R? When two vectors of unequal length are multiplied, the vector with shorter length will be recycled in such a way that it will match the length of the longer vector and then perform the multiplication operation. This recycling of the shorter vector is known as the recycling rule. This recipe performs multiplication of unequal vector lengths.

Step 1- Define 2 vectors

Two vectors (for e.g : numeric) of unequal lengths are defined , here the length of vector b is a multiple of length of vector b

a <- c(1:10) b <- c(1:5) print(length(a)) print(length(b)) print(a) print(b)

The shorter vector b gets recycled and forms a vector of length (1:10) in order to match the length of vector a..

Step 2 - Multiple the two vectors

print(a*b)

Two vectors (for e.g - numeric) of unequal lengths are defined, here the length of vector b is not a multiple of the length of vector b.

x <- c(1:10) y <- c(1:4) print(length(x)) print(length(y)) print(x) print(y)
"Output of the code is:"
[1]  1  2  3  4  5  6  7  8  9 10
[1] 1 2 3 4  

A warning message is displayed when the length of the two vectors are not in proportion —

print(x*y)
"Output of the code is"
Warning message in x * y:
“longer object length is not a multiple of shorter object length”
 [1]  1  4  9 16  5 12 21 32  9 20 
 

Relevant Projects

Machine learning for Retail Price Recommendation with Python
Use the Mercari Dataset with dynamic pricing to build a price recommendation algorithm using machine learning in Python to automatically suggest the right product prices.

Loan Eligibility Prediction in Python using H2O.ai
In this loan prediction project you will build predictive models in Python using H2O.ai to predict if an applicant is able to repay the loan or not.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

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.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.

Ola Bike Rides Request Demand Forecast
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Word2Vec and FastText Word Embedding with Gensim in Python
In this NLP Project, you will learn how to use the popular topic modelling library Gensim for implementing two state-of-the-art word embedding methods Word2Vec and FastText models.