Code
install.packages("ggplot2")28 Line Plots
Line plots (or line graphs) are a staple of data visualization, particularly useful for displaying trends and variation over time. They help analysts see how data points connect across a period or sequence — essential for spotting trends, cycles, and anomalies in something like a season’s rainfall or a commodity’s price.
28.1 Utility of Line Plots in Data Analysis
Trend identification: line plots are excellent for observing trends over time — rainfall across a season, or mandi prices across months.
Comparison: plotting multiple lines on one graph makes it easy to compare trends across categories — rainfall across several districts, side by side.
Temporal changes: line plots suit continuously changing, sequentially ordered data, particularly time series.
Smoothing and forecasting: a moving average can smooth out short-term noise to make the underlying trend clearer, and support simple forecasting.
28.2 Single-Series Line Plot
In R, the ggplot2 package provides a flexible way to build line plots. Make sure it’s installed first:
The line makes the season’s monsoon peak in August, and the sharp drop by October, immediately visible — a shape that would take much longer to spot in a table of five numbers.
28.3 Multi-Line Comparison
Plotting several lines on the same axes — one per group — makes it possible to compare trends directly, such as how rainfall varied across three districts over the same months.
Coimbatore tracks consistently wetter than Warangal across every month shown — a comparison that a single-district line plot couldn’t make visible on its own.
28.4 Line Plot with a Moving Average
Real time series are noisy. A moving average — the mean of each point and its neighbors — smooths that noise so the underlying trend stands out, which is useful for something like a weekly mandi price series that jumps around from day to day.
The pale line is the raw weekly price; the green line is its 3-week moving average, smoothing the week-to-week noise so the broader trend across the 12 weeks is easier to read.
Summary
| Concept | Description |
|---|---|
| Line Plots | |
| Utility of Line Plots | Line plots identify trends, support comparison across groups, suit sequential/time-series data, and support smoothing and forecasting |
| Single-Series Line Plot | Plots one variable against time, e.g., monthly rainfall across a season |
| Multi-Line Comparison | Plots several groups' lines on one graph for direct comparison, e.g., rainfall by district |
| Line Plot with a Moving Average | Overlays a moving average on a noisy series to reveal the underlying trend, e.g., a mandi price series |