Introduction to R Language

Course overview

Uplatz offers extensive course on R Programming for Data Science & Machine Learning. It is a self-paced video course. You will be awarded Course Completion Certificate at the end of the course.

R is a high-level programming language for statistical analysis, graphics representation and reporting. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science.

R provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical data representation. It also offers various data manipulation and cleaning tools, as well as a wide range of add-on packages for specialized tasks.

R programming language is a popular language among statisticians, data analysts, and researchers who deal with large amounts of data. Here are some key characteristics of R programming language:

1. Open source: R is free and open-source software, which means that it can be used, modified, and distributed by anyone.

2. Graphics: R provides a wide range of graphical capabilities, including static and interactive visualizations, which can be used to analyze and communicate data effectively.

3. Large community: R has a large community of users and developers, which means that there are many resources available for learning and troubleshooting.

4. Packages: R has over 18,000 packages available on the Comprehensive R Archive Network (CRAN) that extend its functionality and can be used for a wide variety of applications.

To learn R programming language, here are some steps you can follow:

1. Install R: You can download and install R from the Comprehensive R Archive Network (CRAN) website. It is available for Windows, Mac, and Linux.

2. Choose an Integrated Development Environment (IDE): An IDE makes it easier to write and run R code. Some popular IDEs for R are RStudio, Visual Studio Code, and Jupyter Notebook.

3. Learn the basics: Start by learning the basics of R syntax, data types, and data structures. You can find many online resources, such as tutorials, books, and videos.

4. Practice coding: Start coding small programs and practice coding with real-world data sets. This will help you to get familiar with R and its capabilities.

5. Explore packages: R has a vast library of packages that can help you to perform specific tasks. You can explore these packages and find the ones that are relevant to your work.

6. Join the community: Join R user groups, forums, and mailing lists to connect with other R users and get help with your coding challenges.

Overall, learning R programming language can be challenging but rewarding, and it can help you to unlock the full potential of your data analysis projects.

In this R Programming for Beginnerscourse, you'll be learning about the basics of R, and you'll end with the confidence to start writing your own R scripts. But this isn't your typical textbook introduction to R. You're not just learning about R fundamentals, you'll be using R to solve problems related to movies data. Using a concrete example makes the learning painless. You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R's most important data structures -- vectors! From vectors, you'll then learn about lists, matrix, arrays and data frames. Then you'll jump into conditional statements, functions, classes and debugging. Once you've covered the basics - you'll learn about reading and writing data in R, whether it's a table format (CSV, Excel) or a text file (.txt). Finally, you'll end with some important functions for character strings and dates in R.