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

# Features

Scatter plots show data points on a chart by crossing at least two features of an element. 

Scatter plots show the relationship between two variables, each represented on different axes. We can identify trends, correlations, and outliers by looking at the scatter plots. We do not prefer scatter plots when two datasets are not related.






# Scatter plot with `plot()`

The built-in function `plot()` generates scatter plots in R. It takes at least two variables that contain the data to be represented. We define the variables as `x` and `y`. 

```R
# Syntax structure
plot(x = <variable1>, y = <variable2>)
```

Let's create a simple scatter plot using the `mtcars` dataset.

# We use the `mtcars` dataset in this exercise
head(mtcars)   # Preview of the dataset
plot(x = mtcars$mpg, y = mtcars$disp)  # Define the variables in the function.

- **Line 3**: The data in the `mpg` column is reflected on the `x` axis, and `disp` is reflected on the `y` axis. 

We can state that the data in these columns are negatively correlated because as `mpg` increases, `disp` decreases.


We can configure the chart using the following arguments:


- `col`: This changes the colors of the points on the chart. This argument takes string values, either color names or hexadecimal codes.

- `lwd`: This changes the thickness of the points. It takes an integer number. The higher the number, the thicker the circle lines.

- `cex`: This adjusts the size of the circles. It takes an integer number. The size increases as the number increases.

- `pch`: This changes the shape of the points on the chart. It takes integers between 1 and 25. 

- `bg`: This adjusts the fill color of the shapes on the chart. It takes string values. 

Let's modify the chart above. Feel free to change the values in the code to understand the functionality of the arguments.






Explore the features of scatter plots and learn how to create 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

Scatter Plots

Features

Scatter plot with `plot()`