This is part 4 of a series on “Handling Categorical Data in R” where we are learning to read, store, summarize, reshape & visualize categorical data. Below are the links to the other articles of this series: Part 1 - Introduction to Factor Par...

This is part 3 of a series on “Handling Categorical Data in R where we are learning to read, store, summarize, visualize & manipulate categorical data..” In part 1 and part 2 of this series, we understood how R stores categorical data using f...

This is part 2 of a series on “Handling Categorical Data in R where we are learning to read, store, summarize, visualize & manipulate categorical data..” In part 1 of this series, we understood what categorical data is, how R stores it using fa...

This is part 1 of a series on “Handling Categorical Data in R.” Almost every data science project involves working with categorical data, and we should know how to read, store, summarize, visualize & manipulate such data. Working with categor...

We’re excited to announce the release of rfm 0.2.2 on CRAN! rfm provides tools for customer segmentation using Recency Frequency Monetary value analysis. It includes a Shiny app for interactive segmentation. You can install rfm with:

install.packages("rfm")

In this blog post, we will summarize the changes implemented in the current (0.2.2) ...

In this tutorial, we will learn to handle date & time in R. We will start off by learning how to get current date & time before moving on to understand how R handles date/time internally and the different classes such as Date & POSIXct/lt. We will spend some time exploring ...

In this new series Handling Date & Time in R, we will learn to handle date & time in R. We will start off by learning how to get current date & time before moving on to understand how R handles date/time internally and the different c...

We are excited to announce the nse2r package. NSE (National Stock Exchange) is the leading stock exchange of India, located in the city of Mumbai. While users can manually download data from NSE through a browser, importing this data into R becomes cumbersome. The nse2r R package implements ...

In this tutorial, you will be introduced to the command line. We have selected a set of commands we think will be useful in general to a wide range of audience. We have created a RStudio Cloud Project to ensure that all readers are using the same environment while going ...

Introduction In a previous post, we had briefly looked at connecting to databases from R and using dplyr for querying data. In this new expanded post, we will focus on the following: connect to & explore database read & write data use RStudio SQL script & knitr SQL engine query data using dplyr ...

Introduction In a previous post, we had introduced our R package rfm but did not go into the conceptual details of RFM analysis. In this post, we will explore RFM in much more depth and work through a case study as well. RFM (Recency, Frequency & Monetary) analysis is a behavior ...

Motivation There are several wonderful tools for retrieving information about R packages, some of which are listed below: cranlogs, dlstats and packageRank for R package download stats pkgsearch and packagefinder for searching CRAN R packages crandb provides API for programatically accessing meta-data cchecks for CRAN check results We have used ...

Introduction Ever wondered why items are displayed in a particular way in retail/online stores. Why certain items are suggested to you based on what you have added to the cart? Blame it on market basket analysis or association rule mining. Resources Below are the links to all the resources ...

Introduction Are you trying to compare price of products across websites? Are you trying to monitor price changes every hour? Or planning to do some text mining or sentiment analysis on reviews of products or services? If yes, how would you do that? How do you get the details available ...

We are excited and happy to share a set of shiny apps built for interactive data analysis and teaching at Rsquared Academy. The apps are part of our R packages and presently cover the following topics: Descriptive Statistics Probability Distributions...

We are happy to introduce the vistributions package, a set of tools for visually exploring probability distributions. Installation

# Install release version from CRAN
install.packages("vistributions")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/vistributions")

Shiny App vistributions includes a shiny app which can be launched using

vdist_launch_app()

or try the live version here. Read on to learn more about the features of vistributions, or see ...

Introduction In this post, we will learn about the different methods of getting help in R. Often, we get stuck while doing some analysis as either we do not know the correct function to use or its syntax. It is important for anyone who is new to R to know ...

We are pleased to introduce the blorr package, a set of tools for building and validating binary logistic regression models in R, designed keeping in mind beginner/intermediate R users. The package includes: comprehensive regression output variable selection procedures bivariate analysis, model fit statistics and model validation tools various plots ...