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


Data elements have specific characteristics depending on their format. Although two pieces of data may look the same, the way they act can be marginally different. Therefore, we need to understand the features of data types and learn how to use these features when needed.

Utilizing the appropriate data types in a project can assist us in conserving both time and energy. 
Knowing how to work with data types and how to modify them as needed are vital skills for a competent analyst---this will set you apart from those with less experience.

We can use the `class()` method to see the type of variable that stores the data.

# Check the data type of the variable
myNumber <- 4
print(class(myNumber))

# Check the type of the data itself
class(4)

## Strings (characters)

**Strings** in R can be composed of any kind of character, as long as they are surrounded by `''` or `""`. 
For example, even a numeric character like 3 converts to a string if wrapped with quotation marks.
```R
my_string <- '3' # Numeric data wrapped with quotation marks.
```

This form of data storage can accommodate numbers, operands, braces, and other nonalphabetical characters in addition to text.

The `is.character()` function returns `TRUE` if the object is a string.

> **Note:** The terms `character` and `string` are interchangeable and refer to the same data type.

Learn about the functionalities and characteristics of various data types used in R.

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 Types in R

Data types in R

Strings (characters)