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R_lang

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

## Location matters

We are trying to find out if the locations of stores affect sales performance. We do this by comparing the means and variances of data collected from different places. In this case, we will use the ANOVA method since there are three categories of location in the data. We would use the Student's t-test if the category number were two. 

We research the effect of location on sales performance. Therefore, our independent variable is `Location`, and the dependent variable is `Sales_score`.
Now that we've defined our test statistics and the variables, we can conduct the analysis using code.


# Location matters

We are trying to find out if the locations of stores affect sales performance. We do this by comparing the means and variances of data collected from different places. In this case, we will use the ANOVA method since there are three categories of location in the data. We would use the Student's t-test if the category number were two. 

We research the effect of location on sales performance. Therefore, our independent variable is `Location`, and the dependent variable is `Sales_score`.
Now that we've defined our test statistics and the variables, we can conduct the analysis using code.


Check the answer to the solution of the "Lookup Case Study" challenge and compare it to your solution.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Solution: Lookup Case Study

Location matters