6  Mastering R through Use Case Projects

R, like any programming language, is best understood not just through theory but through application. Once learners grasp the foundational elements of R, it’s crucial to transition into real-world projects that allow for deeper understanding and retention of the material. This is where use case projects come in.

6.1 Why Use Case Projects?

  1. Application of Theory: Practical projects allow learners to apply the theoretical knowledge they’ve acquired. This transition from theory to application often solidifies understanding.

  2. Problem-Solving Skills: Real-world projects present unforeseen challenges. By working through these, learners enhance their problem-solving skills and become adept at troubleshooting.

  3. Comprehensive Understanding: Use case projects often require the integration of various R functions and techniques. This holistic approach ensures a deeper and more comprehensive grasp of R.

  4. Confidence Building: Successfully completing a use-case project boosts confidence, giving students the assurance that they can tackle real-world data problems using R.

  5. Portfolio Building: Projects can be added to a student’s portfolio, showcasing their skills to potential employers or collaborators.

6.2 Sample Use Case Project: Televison Client Analysis

6.2.1 Background

A small television company is interested in understanding the factors that impact viewers’ ratings of the company. Data has been collected from viewers who rated how highly they regard the television company (regard) and provided other related measures.

6.2.2 Data Structure

Variables in the dataset:

  • regard: Viewer rating of the television company (higher ratings indicate higher regard).

  • gender: Gender the viewer identifies with.

  • views: Number of views.

  • online: Number of times accessed bonus online material.

  • library: Number of times browsed the online library.

  • Show1 to Show4: Scores for four different shows.

Download the dataset here.

6.2.3 Tasks

  1. Data Import and Cleaning

    • Import the dataset into R.

    • Perform data cleaning: handle missing values, detect outliers, and ensure correct data types.

  2. Exploratory Data Analysis (EDA)

    • Produce descriptive statistics and exploratory graphics for regard, focusing on patterns by gender.

    • Analyze scores for each of the four shows, highlighting differences by gender.

  3. Derived Variables

    • Create a new variable mean_show, calculated as the mean of Show1 to Show4.

  4. Correlation Analysis

    • Test for a statistically significant linear correlation between mean_show and regard.

  5. Recommendation

    • Write a short report (around 100 words) outlining your findings.

    • Include insights on regard, relevance to the client, and any limitations of your analysis.

6.3 Exercise 6.1: Analyzing a Rape Survey for the Federal Government of Nigeria

6.3.1 Project Overview

You have been consulted by the Federal Government of Nigeria to analyze a recent rape survey. As a data analyst with no specific instructions, your task is to use your analytical skills to uncover insights that will be valuable for the government.

6.3.2 Dataset

Download the dataset here.

6.3.3 Your Task

  • Data Analysis: Perform a comprehensive analysis of the survey data.
  • Visualization: Create visualizations that effectively communicate your findings.
  • Insights: Identify key issues, trends, and patterns that are important for the government to understand.
  • Recommendations: Provide suggestions or action items based on your analysis.

6.3.4 Presentation

Prepare to present your data product, explaining:

  • Functionality: How you analyzed the data and what tools you used.
  • Design Choices: Why you chose specific methods or visualizations.
  • Findings: The key insights from your analysis.
  • Future Improvements: How the analysis could be expanded or refined.
Wrap-Up

By engaging in these exercises and projects, you’re not just learning R—you’re mastering it. The key to proficiency is consistent practice and application. These real-world scenarios will challenge you, enhance your problem-solving skills, and prepare you for future data analysis tasks. Keep exploring, stay curious, and continue to build upon the foundation you’ve established.