## Install Packages in R : A Quick Tutorial

In this tutorial, we’ll learn how to install packages in R.

We’ll start with what R packages are and then learn how to install packages in R. After that we’ll discuss how to use R packages and finally we’ll learn about some useful packages and commands.

## ggplot2 in R – A Tutorial

In this tutorial we’ll study data visualization using the ggplot2 package in R. The ggplot2 package is one of the most popular packages in data science.

First we’ll study how to construct various bar charts and graphs using ggplot2 and then we’ll study how to plot regression lines and trend lines in R.

## Skewness and Kurtosis in R – an explanation and examples

In an earlier tutorial we focused on measures of central tendency and variation. Now we’ll look at some other measures that are equally important for preliminary data analysis.

## Descriptive Statistics in R – An Introduction

Before diving into Descriptive Statistics in R we will first look at the different sources and types of data and focus on data measurement scales. To summarise data we will study various measures of central tendency and measures of variation.

## Data Types in R – A Quick Tutorial

There are a wide variety of data types in R. First we will try to understand what the different data types are and then we will move on to their applications.

So, let us get into the types of data.

## K Nearest Neighbors – R vs Python

## K-Nearest Neighbors – a simple example using R and Python

The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems.

KNN stores all available cases and classifies (or gives expected values of) new cases based on a similarity measure. Here we look at a simple example using both R and Python.