What is R, and why use it?

R is a widely used and versatile programming language used in finance, academia, and data analysis. It offers a range of powerful libraries and frameworks that address issues in statistics and mathematics, making it an ideal choice for tackling problems in these areas.

R is open-source software that can be accessed and used for free by anyone and is utilized by over two million people, providing a large and supportive community. Its strong integration with other software packages makes it great for developing custom applications and supporting machine learning and AI projects. Furthermore, having proficiency in R is increasingly required by employers, so learning it can open up new career opportunities.

Learning R gives users the following skills:

  • Understanding data better
  • Creating data-driven reports
  • Developing robust statistical models
  • Creating data-driven visualized insights

About the course

This course provides an overview of the techniques used to analyze, manipulate, and visualize data using the R programming language. We emphasized interactive case studies, assessments, and code examples to provide an optimal learning experience.

After completing this course, you will acquire a comprehensive understanding of the widely known practices of data analysis and possess the technical skills necessary to solve data-related problems.

Sections of the course

Before we delve into the course, let’s look at some reasons to invest time in the R programming language. To ensure a successful learning journey, let’s first look at the necessary prerequisites, like the requisite libraries, how to load them, and how to utilize them.

Note: This course presumes that the participants already have a fundamental understanding of the R language.

This course includes five major sections where we cover different aspects of data analysis. The overview of each section is given below.

1. File management

File management is an essential skill when working with R. It enables efficient and effective organization, management, and data storage. Proper file management ensures that data is stored and organized in such a way that accessing and retrieving it is effortless when needed. Additionally, by mastering file management techniques, we can reduce the risk of data loss and ensure that data is secure and protected.

Throughout this section, we will learn how to save files, access data using different formats, and use file management techniques to ensure that our data is efficiently organized.

2. Data structures

Gaining a good understanding of data types is essential for effective data exploration, manipulation, and management. Knowing how to work with data structures in R helps to ensure data integrity and accuracy while minimizing errors in our analysis. Furthermore, having a good grasp of data structures in R facilitates efficient coding, debugging, and code maintenance. This section provides us with the necessary information to develop these skills.

3. Data cleaning and transformation

Data cleaning and transformation in R allows users to prepare raw data for further analysis. Data cleaning involves removing any errors, outliers, duplicates, or missing values from data. Data transformation involves various skills to make the data easier to analyze and visualize, such as formatting dates and times, transposing columns and rows, and merging datasets. By learning data cleaning and transformation in R, we can ensure that our data is accurate and ready for analysis.

4. Statistical analysis

Statistical analysis helps us uncover powerful insights from our data and make effective decisions. With R, we can use statistics to identify trends, unveil hidden patterns, and detect outliers. We can employ advanced statistical techniques to draw meaningful conclusions from our data and make confident decisions regarding the data analysis process.

5. Data visualization

Using data visualization methods in R makes interpreting complex data easier and helps extract meaningful insights. It allows us to quickly and effectively communicate complex information in a visually appealing and understandable way. With a wide range of charts and graphs in R, we can create visuals to identify patterns, trends, and outliers to make informed decisions with a glimpse of the data.