Rsquared Academy Blog

Explore..Discover..Learn

Visually explore Probability Distributions with vistributions

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 the vistributions website for detailed documentation on using the package.

Binning Data with rbin

We are happy to introduce the rbin package, a set of tools for binning/discretization of data, designed keeping in mind beginner/intermediate R users. It comes with two RStudio addins for interactive binning. Installation # Install release version from CRAN install.packages("rbin") # Install development version from GitHub # install.packages("devtools") devtools::install_github("rsquaredacademy/rbin") RStudio Addins rbin includes two RStudio addins for manually binning data. Below is a demo: Read on to learn more about the features of rbin, or see the rbin website for detailed documentation on using the package.

Getting Help in R

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 the right place to look for help. There are two ways to look for help in R: built in help system online In the first section, we will look at various online resources that can supplement the built in help system.

Logistic regression in R using blorr package

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 and underlying data If you know how to build models using glm(), you will find blorr very useful. Most of the functions use an object of class glm as input.

Descriptive/Summary Statistics with descriptr

We are pleased to introduce the descriptr package, a set of tools for generating descriptive/summary statistics. Installation # Install release version from CRAN install.packages("descriptr") # Install development version from GitHub # install.packages("devtools") devtools::install_github("rsquaredacademy/descriptr") Shiny App descriptr includes a shiny app which can be launched using ds_launch_shiny_app() or try the live version here. Read on to learn more about the features of descriptr, or see the descriptr website for detailed documentation on using the package.

Descriptive/Summary Statistics with descriptr

We are pleased to announce the descriptr package, a set of tools for generating descriptive/summary statistics. Installation # Install release version from CRAN install.packages("descriptr") # Install development version from GitHub # install.packages("devtools") devtools::install_github("rsquaredacademy/descriptr") Shiny App descriptr includes a shiny app which can be launched using ds_launch_shiny_app() or try the live version here. Read on to learn more about the features of descriptr, or see the descriptr website for detailed documentation on using the package.

RFM Analysis in R

We are pleased to announce the rfm package, a set of tools for recency, frequency and monetary value analysis, designed keeping in mind beginner/intermediate R users. It can handle: transaction level data customer level data Installation # Install release version from CRAN install.packages("rfm") # Install development version from GitHub # install.packages("devtools") devtools::install_github("rsquaredacademy/rfm") Shiny App rfm includes a shiny app which can be launched using

Introducing olsrr

I am pleased to announce the olsrr package, a set of tools for improved output from linear regression models, designed keeping in mind beginner/intermediate R users. The package includes: comprehensive regression output variable selection procedures heteroskedasticiy, collinearity diagnostics and measures of influence various plots and underlying data If you know how to build models using lm(), you will find olsrr very useful. Most of the functions use an object of class lm as input.

SQL for Data Science - Part 2

Introduction This is the fourth post in the series R & Databases. You can find the links to the other two posts of this series below: Quick Guide: R & SQLite Data Wrangling with dbplyr SQL for Data Science - Part 1 In this post, we will learn to aggregate data order data and group data Libraries, Code & Data We will use the following libraries in this post:

SQL for Data Science - Part 1

Introduction This is the third post in the series R & Databases. You can find the links to the other two posts of this series below: Quick Guide: R & SQLite Data Wrangling with dbplyr SQL for Data Science - Part 2 In this post, we will learn to: select single column multiple columns distinct values in a column limit the number of records returned handle NULL values and filter columns using the following operators WHERE AND, or & NOT BETWEEN IN LIKE Libraries, Code & Data We will use the following libraries in this post: