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

In data analysis, **encoding** refers to converting data from one format into another. Encoding is often applied to prepare data for processing and modeling or to make it more suitable for a particular purpose.
Various encoding methods can be used in data analysis depending on our specific needs and the characteristics of the data. 

In data analytics, one-hot encoding, label encoding, and ordinal encoding are the typical methods to encode the data for further processing. 



## One-hot encoding

**One-hot encoding (OHE)** is a type of numeric encoding method that is often used to represent categorical data. In one-hot encoding, each category is represented with a binary column. The newly created binary columns hold 1 where the class in the encoded column and the corresponding column match, and other rows take 0.



We prefer this method when the categorical data does not have a hierarchical characteristic. For example, we can use OHE when user locations are recorded because geographical areas have no superiority. Neither Paris nor Lahore is numerically superior to the other.

In the example below, we will use the one-hot encoding method to encode the `feed` column of the `chickwts` dataset.  

We will utilize the `ifelse()` and `mutate()` functions in the process.
Here is the structure we will use. This converts the categorical data into logical data.
```R
# Syntax structure
mutate(<column> = ifelse(<category1> = ifelse(<column> == '<category1>',1,0))) 
```
Check out the example to learn how to apply the OHE method: 

Learn the details of ordinal encoding and one-hot encoding using 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

Encode Categorical Data Using R

What is encoding

One-hot encoding