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

## The factor data type
Factors are beneficial when we store data as categorical variables and construct a hierarchical structure among values. The classical use case for factor data is when values in a dataset are duplicated many times, and there are a limited number of unique values.
 
Here is an example:
```R
c('Small', 'Big',' Medium', 'Small', 'Big', 'Small', 'Big', 'Medium', 'Big') 
# or 
c(2,3,2,1,1,3,1,1,3,2,3,2) 
```


### Create factors

The `factor()` function converts containers into factor objects. This function determines unique values as distinct levels (categories). A factor object does not allow the addition of elements outside the established categories.
The `levels()` function displays the levels, which are the unique values that exist in the object.

# The factor data type
Factors are beneficial when we store data as categorical variables and construct a hierarchical structure among values. The classical use case for factor data is when values in a dataset are duplicated many times, and there are a limited number of unique values.
 
Here is an example:
```R
c('Small', 'Big',' Medium', 'Small', 'Big', 'Small', 'Big', 'Medium', 'Big') 
# or 
c(2,3,2,1,1,3,1,1,3,2,3,2) 
```


## Create factors

The `factor()` function converts containers into factor objects. This function determines unique values as distinct levels (categories). A factor object does not allow the addition of elements outside the established categories.
The `levels()` function displays the levels, which are the unique values that exist in the object.

Learn about factor objects in R and their use cases.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Factors in R

The factor data type

Create factors