Programming with R
- Research and Academics: Statisticians and students use R to perform various statistical computations and analyses. R is also used for machine learning research and deep learning.
- Banking: Banks often use R along with other proprietary software like SAS. It is also used for fraud detection, loan stress test simulation, client assessment, and much more.
- Social media: Social media companies like Facebook use R for behavior and sentiment analysis. They can alter and improve their suggestions to users based on the user’s history, and the mood and tone of their recent posts and viewed content. R is also used to analyze traffic, user sessions, and content, all to improve user experience.
- Manufacturing: Big companies like Modelez, Ford, and John Deere utilize R programming to interpret customer opinions. This assists them in optimizing their product according to the customers’ trending interests and matching their production volume to changing market interest. They also utilize R programming to reduce the cost of production and maximize profits.
What is R?
R is a programming language designed for statistical analysis. It was developed by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand in 1995, and is currently developed by the R Core Group.
R is an open-source environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering and others.
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- R Programming course is suitable for everyone, no coding experience or a statistics background is needed
- Learning R can be hard because there are many special cases in R to remember. R is the best user of memory. It has thousands of packages, designed, maintained, and widely used by statisticians.
- R programmers are a good fit for the research-oriented industry for statistical model implementation for data analysis.
- Install R and RStudio and create R script and be able to save your work in R project
- Be able to differentiate between different R data structures such as: string, number, vector, matrix, data frame, factor, date and time object, and many more
- Write R program for executing repetitive tasks using loops and vectorized code
- Write your own user-defined functions and create simulations inside R environment
- Visualize your data using base R graphics