What is R and Why Learning R Programming
What is R?
R can be used to compute a large variety of classical statistic tests including:
- Student’s t-test comparing the means of two groups of samples
- Wilcoxon test, a non parametric alternative of t-test
- Analysis of variance (ANOVA) comparing the means of more than two groups
- Chi-square test comparing proportions/distributions
- Correlation analysis for evaluating the relationship between two or more variables
It’s also possible to use R for performing classification analysis such as:
- Principal component analysis
- clustering
Many types of graphs can be drawn using R, including: box plot, histogram, density curve, scatter plot, line plot, bar plot, …
Why learning R?
R is open source, so it’s free.
R is cross-plateform compatible, so it can be installed on Windows, MAC OSX and Linux
R provides a wide variety of statistical techniques and graphical capabilities.
R provides the possibility to make a reproducible research by embedding script and results in a single file.
R has a vast community both in academia and in business
R is highly extensible and it has thousands of well-documented extensions (named R packages) for a very broad range of applications in the financial sector, health care,…
It’s easy to create R packages for solving particular problems
Infos
This analysis has been performed using R software (ver. 3.2.3).
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Recommended for You!
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This section contains the best data science and self-development resources to help you on your path.
Books - Data Science
Our Books
- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)
Others
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet
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