Table of Contents

1. Challenge

   Introduction

   Visualize Data with ggplot2 in R

   Hello learners!

   In this lab, you will utilize your R skills to transform tabular data into insightful visualization. This lab will take you through guided steps on to write ggplot2 functions and customize them to your own needs.

   You will use a medical patients dataset 🩺🥼💉to achieve your lab objectives.

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   Key Concepts

   1. Basic Plot Creation

      • Bar Chart
      • Line Chart
      • Histogram

   2. Data Manipulation

      • Age Calculation

   3. Chart Customization

      • Custom Colors
      • Labels and Titles
      • Scale Adjustment

   4. Multi-variable Plots

      • Facet Grids
      • Scatter Plot
      • Trend Line
      • Multiple Variables

   5. Chart Formatting and Styling

      • Custom Themes
      • Coordinate Flipping
      • Annotations

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   Project Structure

   The FILETREE holds the following folders and files:

   1. data: It contains the data.csv file which has the following columns:
      • patient_id
      • patient_name
      • gender
      • date_of_birth
      • visit_date
      • symptoms
      • diagnosis
      • medication
      • dosage
   2. src: It contains the following four files where you will write your code:
      • step1.R
      • step2.R
      • step3.R
      • step4.R
   3. Rplots.pdf: Whenever you generate a plot, it will be saved inside this PDF file. You can download it to view the resultant plot.

   All set, let’s move! 🏃🏻‍♀️‍➡️

2. Challenge

   Creating basic charts

   Your first lab objective includes a series of tasks, ranging from loading the dataset to creating bar charts, line charts, and histograms.

   For this step, you will work in the src/step1.R file, which contains the required libraries and a pre-filled data_with_month function to assist you.

   Begin with the first task to kick off your journey! > ℹ️ NOTE

   You can save R files with either the .r or .R extension, as R recognizes both as valid script formats. However, if you attempt to reference a xyz.r file within an abc.r file using xyz.R, it will fail because R treats the extensions as different.

3. Challenge

   Customize charts

   In this step, you will customize the bar chart and line chart that you have created in the step1.R file.

   You will work in the src/step2.R file, which already includes the ggplot2 library and the src/step1.R file. This file retrieves the data frame using the load_data function and stores the result in the data variable.

4. Challenge

   Create multivariate charts

   Up to this point, you have created charts that rely on a single column. Now, you will explore how to build charts using multiple variables.

   For this step, you will work in the src/step3.R file, which includes the required library and imports. This file also contains two pre-defined functions:

   1. filtered_data – Returns a subset of the data frame containing only four diagnoses: Viral Infection, Chronic Insomnia, Hay Fever, and Allergic Rhinitis.
   2. add_age_month – Returns a data frame with the corresponding patient age and visit month.
5. Challenge

   Format and style charts

   In the final step, you will learn to format and style charts. You will perform the tasks inside the src/step4.R file which has the necessary library and imports. In this lab, you explored how to create both basic and multivariate charts, from simple plots to those with multiple subplots. Additionally, you learned how to enhance their aesthetics for better readability and tailor them to your specific needs.

   Keep up the learning! ✌