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

# Feature

Area plots, also referred to as stacked area charts, are a form of data visualization that exhibit the evolution in the distribution of a whole over time. These plots share similarities with line charts but differ in that the space below the line is filled with color or shading to represent the data.



We use area plots to compare the relative proportions of different categories or variables over time. For example, we could use an area plot to show the changing proportions of varying income levels in a population over several years.


# Area charts with `plot()`

We use the `polygon()` function to create area charts in base R. We first create an empty chart and then add the layer created using the `polygon()` function. To create an empty chart, we use the `type = 'n'` argument in the `plot()` function.

The `polygon()` function takes two containers including a series of numbers. The numbers in the first variable are the x-coordinates, and the numbers in the second variable are the y-coordinates of the corner points. The function automatically creates an area by attaching the points in clockwise order. Then, the chart is created by painting the area. 

```R
plot(<central coordinate>, type = 'n', xlim = <range> , ylim = <range> )
polygon(<variable1>,<variable2>, col = <color>)
```
Let's create a simple area chart.

# We create a custom dataset in this exercise
var1 <- c(1,2,4,4)   # Create the coordinate numbers for the x-axis
var2 <- c(3,5,6,4)  # Create the coordinate numbers for the y-axis

plot(0, type = 'n',  # Create an empty template
    xlim = c(min(var1),max(var1)),  # The range of the x-axis is defined
    ylim = c(min(var2),max(var2)))  # The range of the y-axis is defined
polygon(var1,var2, col = 'cyan')   # Add the polygon layer to the template
points(var1,var2, pch = 15, lwd = 3 )  # Show the points on the chart

- **Lines 2–3:**  We create a variable holding a series of numbers that represent the coordinates on the x and y axes. The coordinates of the numbers are `(1,3)`, `(2,5)`, `(4,6)`, and `(4,4)`. 

- **Line 5:** We create an empty template by using the `type='n'` command so that we can add the polygon layer later.
- **Lines 6–7:** We define the ranges of the axes for the chart. We define the ranges using the minimum and maximum points of the coordinates. `xlim` and `ylim` specify the range from the minimum and maximum values of `var1` and `var2`. 
 
- **Line 8**: We create a triangle by using the points created from `var1` and `var2`. We fill the area using the `col` argument. 

- **Line 9**: We show the points that are used while generating the area plot. 

> **Note:** We can add more polygons to the chart above.

## Combining polygons and lines

To create a chart that covers the area below a line, we pair the points that create the line with their projections on the x-axis. Later, we create a polygon layer using these points.

 Suppose we have data on car sales for 10 years, and we want to create a chart that visualizes the area below the sales line. To achieve this, we need to create a polygon that represents the region between the sales line and the x-axis.

To create this polygon, we first need to generate coordinates for each point that borders the area. We can use the coordinates of the sales line to do this, but we also need to synthesize the coordinates for their projections onto the x-axis. To synthesize these points, we simply set the x-coordinate to 0, which represents the x-axis.

Once we have all the coordinates, we order them in a clockwise direction to create a polygon that fully encloses the area between the sales line and the x-axis. To ensure that the polygon is filled correctly, we use the `rev()` function to reverse the order of the x-coordinates so that they are in decreasing order.

For example, if our sales data is `c(3, 8, 2)`, our polygon would have the coordinates `(1,3)`, `(2,8)`, `(3,2)`, `(3,0)`, `(2,0)`, and `(1,0)` in clockwise order. This polygon would fully enclose the area between the sales line and the x-axis, providing a clear visualization of the sales data.

Get introduced to area plots and learn how to generate them. 

Area Plots

Get introduced to area plots and learn how to generate them.

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

Area Plots

Feature

Area charts with `plot()`

Uber Data Analysis Using the R Language

Evaluation

Netflix Shows

Area Plots

Feature

Area charts with plot()

Area charts with `plot()`