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

R is a powerful programming language that provides various tools and functions for transforming data. **Data transformation** involves manipulating data in a dataset to prepare it for further analysis or to make it more suitable for a particular task or purpose. 
This lesson covers the various uses of the `mutate()` function, like making calculations, deriving new columns, and cleaning data.


## Optimize incorrect numerical data

Data cleaning is time-consuming and laborious. The `mutate()` function makes this process smoother for us. It helps us modify, replace, and update data.

> **Note:** Since this lesson aims to teach you how to clean imperfect data, we will use a custom dataset with errors so we can show you how to optimize it.

In the example below, we notice negative price values in the data frame. This is not reasonable because we can't have a negative price for a product. Therefore, we assume the negative values are mistakenly recorded. So, we convert the negative values to their positive versions using the `abs()` function. 
It takes the data as input and always outputs the numerical distance between the value and 0.
Let's use it in the example below.


Learn how to clean and preprocess data by fixing misspelled values and creating new features 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

Mutate Data Using R

Transformation of data

Optimize incorrect numerical data