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

**ANOVA (analysis of variance)** is a statistical method used to compare the means of three or more groups or populations, one of which is the dependent variable. It is used to determine whether the differences between the averages of the groups are statistically significant or stem from luck. ANOVA and Student's t-tests probe into the same issue. The difference between them is that the t-test analyzes differences between two groups, while ANOVA does it between three groups or more. 



We use different variations of ANOVA in different circumstances. In a **one-way ANOVA**, there is only one independent categorical variable. The goal is to determine if the means of the dependent variables are affected by the independent variable.



Assume that we analyze the longevity of three different smartphone products. 
We have three sets of data available for each brand, and each dataset contains records of the time elapsed between the sale of a product and the end of its use.
Our goal is to find if the brands are distinguishable from each other in terms of product lifetime. In such a situation, we use the one-way ANOVA.



 To conduct an ANOVA in R, the `aov()` function is used. The function takes three arguments: 
- Dependent variable
- Independent variable
- Data name

```R
aov(<dependent variable> ~ <independent variable>,<name of data>)
```

Please notice that we use the `~` sign to define the relationship between the variables.

After making calculations using the `aov()` function, we can also utilize the `summary()` and `TukeyHSD()` functions to analyze the results and see more details.


Learn about one-way and two-way ANOVA tests.  

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

ANOVA (Analysis of Variance)

One-way ANOVA