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

# Blog traffic dashboard

Suppose you are analyzing website traffic data for a blog over a week, and you want to create a visualization that shows the number of daily page views as well as the average time spent on the site each day. To do this, you can create a bar chart to show the number of daily page views and a line chart to show the average time spent on the site each day. 

Follow the steps to create the chart:
1. Read the data from the CSV file, located here: 
``` R
"Documents/blog_traffic.csv"
```
2. Create a `ggplot()` layer and define the common variables that will be used in both chart types. 
3. Add the `geom_bar()` layer and define the columns that differ from the line layer. Also, specify the value for the `stat` argument.

4. Add a `geom_line()` layer and define the distinct column. Please scale the data to be able to show the line in the chart. Also, specify that the data comes from one category. 

5. Add a `geom_point()` layer that uses the same data as the line plot. We create this layer to increase the visibility of the points on the line chart. 

6. Add a secondary y-axis to the chart and scale it appropriately. Make sure that the secondary axis correctly shows the average time spent.  

> **Note:** Use different colors for each layer. Try to improve the interpretability and visibility of the chart. Feel free to experiment on different configuration arguments.







Use the concepts you learned about data visualization to complete the task. 

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

Challenge: Visualize the Data

Blog traffic dashboard