Welcome to our class on line plots! Line plots illustrate how a variable changes over time by connecting points on a graph, a process similar to forming a constellation by connecting the stars. Our objective today is to demonstrate how to create meaningful patterns from data using the R programming language. By the end of this course, you will be capable of crafting stories using line plots, understanding the necessary data preparation, and customizing your plot to use ggplot2, a powerful plotting library in R.
A line plot can be likened to a method of tracking your progress in a video game over a week. Each day, you mark down your score and connect the scores with a line, thereby creating a plot. This subsequent plot serves as a visual representation of the rise and fall of your scores — a depiction of your journey through the game's levels.
Attributes of a well-crafted line plot include:
- An X-axis (a horizontal line) representing your timeline.
- A Y-axis (a vertical line) that indicates what you're measuring, such as your game score.
- Data points that register each day's score.
- Lines that connect these data points, forming a visual 'route' throughout your gaming week.
Line plots excel at identifying trends at a glance and find utility in numerous scenarios, spanning from finance to fitness tracking.
Before plotting, it's imperative to have our data ready — akin to gathering ingredients before baking cookies. We need matched pairs of time and the characteristic that we're tracking (similar to different cookie types and their quantities). If we were to plot the growth of a garden plant:
- Record the plant heights at regular intervals.
- Verify the absence of errors, such as missing dates or unrealistic growth spurts.
