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How to get started with R programming
The document outlines the importance of R programming for data science, highlighting its integration with various data science packages and tools like Tableau. It provides resources for learning R and R Studio, including free tutorials and paid courses, as well as recommended books. The document emphasizes linear regression as a fundamental and popular technique in data analysis.
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Introduction to the basics of R programming and its relevance for data science.
R's integration with data science packages, open-source benefits, and no cost for clients.
Focus on mastering R, especially for creating linear regression models.
Instructions for downloading and configuring R and R Studio as a development environment.
Available tutorials and educational resources for learning R Studio basics at no cost.
Access to paid courses for learning R Studio through Pluralsight.
Overview of advanced subjects in R such as machine learning, data visualization, and hands-on courses.
A suggestion for a key book focused on R programming.
Objective to create linear regression models using tools like Spotfire and Alteryx.
Resources for predictive analysis in Tableau utilizing R integration.
Importance of Github as a community resource for R code and projects.
Kaggle as an online platform for data scientists to share and collaborate.
How to get started with R programming
- 1. How to getstarted with R Programming Ramon E. Salazar – Business Intelligence Professional
- 2. The case forR programming • Almost all data science packages integrate with R, that’s why it is so important to use it. That includes our main partner, Tableau. • Additionally, R is free (open source), meaning that we could provide our consulting services even without • Requiring the client to invest in a predictive analytical tool (if they do not have one)
- 3. Why Learn R? Objective:Gain knowledge in the most popular programming language for Data Scientist. Being able to Create a simple linear regression model with R.
- 4. R Studio R ProgrammingLanguage Libraries In order to use R, you first need to learn R Studio. Please download from the two links below R programming/ R Studio Objective: Install and configure R development environment
- 5. Learning R StudioIDE and basics Free options for learning R studio: • R studio official online learning website with tutorials, webminars and tips • UCLA R studio tutorial • Princeton R Studio Introduction (PDF) • Introduction to R studio Free Video Objective: Learning R studio and basic R
- 6. Pluralsight courses (paid) Paidoption for Learning R Studio (Pluralsight) Link to Rstudio course in Pluralsight
- 7. Advanced • Understanding MachineLearning with R • Mastering Data Visualization with R Visualization Beginning Data Visualization with R Multivariate Data Visualization with R Start with R Programming Fundamental Data Science with R Exploratory Data Analysis with R Pluralsight has a good selection of hands-on R courses, listed below Pluralsight courses (paid)
- 8. Books One of thebest books for R programming is
- 9. Hands on withcommercial tools. Objective: Complete at least one linear regression model using commercial tools: Spotfire, Alteryx and others.. Why linear regression? . From all the data science techniques, this is the easiest and most popular one.
- 10. In Tableau, Predictiveanalysis done via R integration. Resource #1 White Paper “ Advanced Analytics with Tableau” Link White Paper Advanced Analytics with Tableau Link Tableau manual - Pass Expressions to R Resource #2 – Video Link Video Advanced Analytics with Tableau Tableau (R integration)
- 11. Github-online community withcode and repositories
- 12. Kaggle-online community forData Scientist