R_tips

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r_tips offers R programming tips for data cleaning, data visualization, statistical modeling, and machine learning. It provides guidance on setting up Positron IDE and covers topics like ggplot2 and SQL <> R workflows.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is r_tips?

r_tips is a comprehensive GitHub repository providing R programming tips for various data science tasks. It covers essential areas such as data cleaning, data visualization, statistical modeling, and machine learning. Users can find practical guidance on setting up a Positron IDE, an introduction to ggplot2 for creating volcano plots, and instructions for using DiagrammeR to draw flow charts. The repository also delves into data cleaning techniques using data.table, tidyverse, or Python Pandas, and explains how to clean strings with regular expressions. For productionization, it offers tutorials on creating SQL <> R workflows and automating R Markdown report generation. The resource is designed to help developers and data scientists enhance their R programming skills.

Best used for

Ideal for developers and data scientists who need to learn and apply R programming for data cleaning, data visualization, and statistical modeling. Especially valuable for those looking for practical tutorials on ggplot2, regular expressions, and SQL <> R workflow automation.

Common actions

learn R programming

clean data

visualize data

model statistics

implement machine learning

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Capabilities

Key features

R programming tips
Data cleaning tutorials
Data visualization guides
Statistical modeling insights
Machine learning resources
SQL <> R workflows

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What specific R programming topics are covered in r_tips?

r_tips covers a range of topics including setting up Positron IDE, data visualization with ggplot2 and DiagrammeR, data cleaning using data.table, tidyverse, and regular expressions, and productionization techniques like SQL <> R workflows and R Markdown report generation.

Does r_tips provide resources for both statistical modeling and machine learning?

Yes, r_tips aims to provide resources for both statistical modeling and machine learning. While some sections are marked as 'TBC' (To Be Confirmed), the repository's scope includes these advanced data science areas.

Are there any specific style guidelines for contributing to the r_tips repository?

Yes, the repository outlines specific style guidelines for file naming, code style, and comment formatting. This includes using hyphens for tutorial topics, underscores for whitespace in filenames, and adhering to the tidyverse style guide for comments.

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