What is rowmeans and colmeans in R?

What is rowmeans and colmeans in R?

What is rowmeans and colmeans in R?

This recipe explains what is rowmeans and colmeans in R


Recipe Objective

Exploratory Data Analysis is a crucial step before building any machine learning model on a dataset. This also includes gathering statistical inferences from the data. There are a few main terms in statistics which describes the central tendency of the variables i.e. means and medians. R gives us the flexibilty to calculate these measures row-wise and column-wise.

This recipe focuses on using rowMeans() and colMeans() functions.

rowMeans() function calculates the means of of all the rows in the dataset and displays the output

colMeans() function calculates the means of all the columns in the dataset and displays the output

Step 1: Importing libraries and loading dataset

# Data manipulation package library(tidyverse) ​ # reading a dataset customer_seg = read.csv('R_77_Mall_Customers.csv') ​ glimpse(customer_seg)
Rows: 200
Columns: 5
$ CustomerID              1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1...
$ Gender                  Male, Male, Female, Female, Female, Female, ...
$ Age                     19, 21, 20, 23, 31, 22, 35, 23, 64, 30, 67, ...
$ Annual.Income..k..      15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 19, ...
$ Spending.Score..1.100.  39, 81, 6, 77, 40, 76, 6, 94, 3, 72, 14, 99,...

Dataset description: It is the basic data about the customers going to the supermarket mall. We are interested in all the numeric variables in the dataset.

Step 2: Using rowMeans()

# calculating means of every row apart ignoring the values in the 2nd column as it is categorical rowMeans(customer_seg[,-2])

Step 3: Using colMeans()

# calculating means of every column apart ignoring the values in the 2nd column as it is categorical in nature colMeans(customer_seg[,-2])

Relevant Projects

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Customer Market Basket Analysis using Apriori and Fpgrowth algorithms
In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Data Science Project on Wine Quality Prediction in R
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

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.

Predict Credit Default | Give Me Some Credit Kaggle
In this data science project, you will predict borrowers chance of defaulting on credit loans by building a credit score prediction model.

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.