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

## Test the differences
Lookup, a retail business, experiences a drop in sales performance and wants to find the reason behind it. They suspect that the drop is related to the geographical location of the stores. They want to know if their suspicion can be statistically proven.

You are in Lookup's analyst team. The team suggests collecting data from stores across the country. The data contains the location and performance scores of the stores in the `Sales_score` and `Location` columns. The locations of retail stores are categorized into three different groups. The number of stores in each location group varies. 

What would be your strategy after collecting the data?

Please conduct a statistical test and decide if the geographical location affects the sales performance by following the instructions below:

- Read the CSV file to load the data from this location: 
```R
"Documents/Lookup.csv"
```
- Choose the type of statistical test.
- Test the relationship between the `Sales_score` and `Location` columns.
- Name the output of the test `result`.

Please use the test container below.
The test container interprets the test results automatically.

# Test the differences
Lookup, a retail business, experiences a drop in sales performance and wants to find the reason behind it. They suspect that the drop is related to the geographical location of the stores. They want to know if their suspicion can be statistically proven.

You are in Lookup's analyst team. The team suggests collecting data from stores across the country. The data contains the location and performance scores of the stores in the `Sales_score` and `Location` columns. The locations of retail stores are categorized into three different groups. The number of stores in each location group varies. 

What would be your strategy after collecting the data?

Please conduct a statistical test and decide if the geographical location affects the sales performance by following the instructions below:

- Read the CSV file to load the data from this location: 
```R
"Documents/Lookup.csv"
```
- Choose the type of statistical test.
- Test the relationship between the `Sales_score` and `Location` columns.
- Name the output of the test `result`.

Please use the test container below.
The test container interprets the test results automatically.

Follow the instructions to solve the challenge.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Challenge: Lookup Case Study

Test the differences