Rsquared Academy Blog

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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:

Data Wrangling with dbplyr

Introduction This is the second post in the series R & Databases. You can find the links to the first post of this series below: Quick Guide: R & SQLite In this post, we will learn to query data from a database using dplyr. Libraries, Code & Data We will use the following libraries in this post: DBI RSQLite dbplyr dplyr All the data sets used in this post can be found here and code can be downloaded from here.

Quick Guide: R & SQLite

Introduction This is the first post in the series R & Databases. You can find the links to the other two posts of this series below: Data Wrangling with dbplyr SQL for Data Science - Part 1 SQL for Data Science - Part 2 In this post, we will learn to: connect to a SQLite database from R display database information list tables in the database query data read entire table read few rows read data in batches create table in database overwrite table in database append data to table in database remove table from database generate SQL query close database connection Libraries, Code & Data We will use the following libraries in this post:

Categorical Data Analysis using forcats

Introduction In this post, we will learn to work with categorical/qualitative data in R using forcats. Let us begin by installing and loading forcats and a set of other pacakges we will be using. Libraries & Code We will use the following packages: forcats dplyr magrittr ggplot2 tibbe purrr and readr The codes from here. library(forcats) library(tibble) library(magrittr) library(purrr) library(dplyr) library(ggplot2) library(readr) Case Study We will use a case study to explore the various features of the forcats package.

Working with Date and Time in R

Introduction In this post, we will learn to work with date/time data in R using lubridate, an R package that makes it easy to work with dates and time. Let us begin by installing and loading the pacakge. Libraries, Code & Data We will use the following packages: lubridate dplyr magrittr readr The data sets can be downloaded from here and the codes from here. library(lubridate) library(dplyr) library(magrittr) library(readr) Quick Intro Origin Let us look at the origin for the numbering system used for date and time calculations in R.