Gain insights into data manipulation, analysis, and visualization with R. Learn statistical concepts, create insightful visualizations, and empower decision-making to excel in data-centric roles.

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This course delves into data manipulation, analysis, and visualization with R. It demonstrates the power of R as open-source statistical software, unlocking valuable insights for decision-making across diverse fields.

You'll begin by mastering the basics of R programming, fundamental data structures in R, and file management. You will learn fundamental statistical concepts such as hypothesis testing and regression analysis. You will also learn to leverage well-structured data for creating insightful visualizations using R's robust graphics libraries.

After completing this course, you'll confidently approach data-driven challenges. With proficiency in R, you can efficiently manipulate and analyze data, positioning yourself as a sought-after asset in data-centric roles. Your ability to interpret statistical results and create compelling data visualizations will empower improved decision-making, paving the way for new opportunities for career growth and success across various industries.

Introduction to Data Analysis and Visualization with R

Values from a data type can be converted into another as long as the desired data type can be represented by the value that is being converted, as in the case of converting `TRUE` to a string. This type of conversion between data types is called **coercion**.

Data coercion is accomplished using the `as.<datatype>()` command.



Possible Coercions

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The table above illustrates the directions for supported data coercions in R. The "From" column indicates which data types can be converted into the types listed in the "To" column.  
There are numerous coercion scenarios. However, we will only cover the most important ones in this lesson.

## Coercion to character/string

All data types can be converted and stored as characters. Converting a data type to characters/strings is as easy as enclosing it inside quotation marks (`""`).
Here are some examples of doing this in code:

From	To
All	Character
Character (if appropriate) - Double - Logical - Complex - Factor	Integer
Character (if appropriate) - Integer - Logical - Complex - Factor	Double
Character (if appropriate) - Integer - Double - Complex	Logical
Integer - Double - Logical - Factor	Complex
All	Factor

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Uber Data Analysis Using the R Language

Conclusion

Evaluation

Netflix Shows

Data Coersion in R

Convert data types

Possible Coercions

Coercion to character/string