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

## Explore the solution

The detailed solution for the challenge is given below. The layers are added one by one. 

library(ggplot2)
traffic <- read.csv('Documents/blog_traffic.csv')
ggplot(data = traffic, aes(x = days)) + 
  geom_bar(aes(y = pageviews), stat = 'identity', color = 'darkblue', fill = 'cyan')+
  geom_line(aes( y = time_spent*1000), group = 1, color = 'black', linewidth = 3) + 
  geom_point(aes(y = time_spent*1000), color = 'purple', size = 6) +  
  scale_y_continuous(name = "Pageviews", sec.axis = sec_axis(~ . / 1000, name = "Time Spent"))

- **Line 2:** We read the dataset from the given location and name it `traffic`.

- **Line 3:** We define the common variables in the `ggplot()` function so that we do not repeat them in other layers. The data is `traffic`, and the x-axis represents the data from the `days` column.

- **Line 4:** We create a barplot layer. The y-axis represents the `pageviews` data. The calculation method of the function is `identity`, meaning that it takes the data directly from the column. The color of the outline of the bars is `darkblue`, and the fill color is `cyan`.

- **Line 5:** We create a line layer. To approximate the numbers from the `pageviews` and `time_spent`, we multiply the `time_spent` data by 1,000. We state that the data comes from only one category with `group = 1`. The color of the line is `black` and `linewidth` is 3. 

- **Line 6:** We create a scatter plot layer that uses the same data as the line layer. They overlap on the chart making the points on the line more visible. The color of the points is `purple,` and the size is increased to 6. 

- **Line 7:** We create a secondary axis and scale it using the `scale_y_continuous()` function. Because we multiplied the `time_spent` values by 1,000, we need to divide the range of the scale by 1,000 to show the original value range on the new axis. We use this statement to scale the range `~./1000`. Then we label the secondary axis. 






# Explore the solution

The detailed solution for the challenge is given below. The layers are added one by one. 

Compare your answer with the solution in this lesson.

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

Solution: Visualize the Data

Explore the solution