Completion requirementsOpened: Monday, March 30, 2026, 11:59 PM

Due: Sunday, April 5, 2026, 11:59 PM

The library, ggplot, is one of the most powerful and widely used data visualization tools in the R programming language. In this assignment, we will get up to speed using this library.

For this assignment, we will expand upon the ggplot assignment by using real world data.

HMEQ Scrubbed Data set provided below.

You have complete freedom to decide upon the graphs you generate. The only condition is that you must use ggplot to create the graph.

As you create each graph, either download it to your computer or take a screen shot of it.

Hand in the following:

• A File with your R program. This file should contain only the code (no output) and must have the typical r extension. No other file extensions will be accepted. The reason is that the assignment be graded based on your R code and not the output file. The output file will be used to verify the code commands. Also, please make sure that all comments, discussion, and conclusions regarding results are also annotated as part of your code.
• A PDF/DOC file with your output code. We are giving you more flexibility regarding how you want to present your output (tables, plots, etc.). You can either use RMD files that combine code, narrative txt, and plots or you can use word document with copy and paste from the R platform you are using. However, please remember that all output (tables, plots, comments, conclusions, etc.) shown in this file has to be generated by the same R code that you submit. This is important! Output shown that is generated using a separate code or output shown that is not supported by the submitted code will not be graded. Screenshots will not be accepted.
• Under each graph, describe in a few sentences what the graph is presenting. The sentences must be useful and understandable to a manager who does not understand technical concepts. Interesting and advanced graphs with good descriptions will get more points. So push yourself!

Essential Activities:

1. Watch all the training videos
2. Execute the example code while watching the training videos.

Notes:

1. You may only use the HMEQ data set. If you use any other data, you will not receive any points.
2. This assignment is due Sunday at 11:59 PM EST

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