Line plots are an underutilized chart in data visualization. I’ve personally used them many times to identify hidden patterns in equipment performance data. It’s amazing how frequently a basic line plot will uncover key insights that more advanced dashboards overlook. Allow me to explain why these charts are so effective for continuous improvement efforts.
Understanding Line Plots
Line plots are one of the most powerful data visualizations. I’ve personally used line plots countless times throughout my career as an engineer to analyze equipment performance trends. A line plot is essentially a number line with X’s or dots to represent data points and a title that describes what the data is.
You’ll likely find line plots most helpful when you want to:
- Display the frequency of small data sets
- Track how data changes over time
- Compare the distributions of data from multiple groups
- Identify any patterns or clusters within the data
Line plots are particularly effective for understanding the shape and spread of a data distribution. They’re also one of the best visualizations for small data sets where each specific value matters.
In my experience, I like line plots for the following reasons:
- Simple to generate and understand
- Clearly shows how the data is clustering
- Easily identifies any outliers
- You can quickly compare data from multiple data sets
When I worked as an engineer analyzing machine performance metrics, line plots were one of my favorite visualizations. This is because they allow you to see subtle changes in the data, which often were the earliest signs that a piece of equipment was about to fail.
Crafting a Time Series Graph: Step-by-Step Guide
Drawing an accurate line plot requires some strategy and precision. Here’s how I create a line plot that is both accurate and insightful:
- Gather and organize your data. Double-check that it’s accurate and relevant.
- Decide on the appropriate scale and range for your number line. It needs to cover all your data points with a bit of extra room on each side.
- Draw a horizontal number line and label it with the right increments.
- Plot each data point above the value on the number line it represents. You can use an X or a dot.
- If you have the same value repeated multiple times, plot multiple X’s or dots directly on top of one another.
- Give the line plot a title that clearly communicates what the data is.
Here are the most important things to remember when plotting:
- Keep consistent spacing between X’s or dots (if using a dot)
- Make sure each X or dot is directly above the value it represents
- If you’re drawing the line plot by hand, use a ruler to keep everything straight
- If you have more than one data set on the same plot, consider using different colors to distinguish them from each other
I once used this process to analyze vibration data from a set of industrial pumps, and after plotting the data, the resulting line explained the entire story of increased vibration leading up to each failure.
Types of Data Suitable for Line Plots
Line plots are most effective when visualizing discrete data sets—data points that fit into specific categories or values. I’ve used line plots to visualize:
- Equipment failure frequencies
- Production defect counts
- Maintenance task durations
Visualizing continuous data in a line plot can be tricky. You’d have to bin continuous data into discrete categories, which may cause you to lose important details.
There are a few downsides to using line plots. It’s not the best choice for:
- Very large data sets (too many data points)
- Data with a high number of unique values (too crowded)
- Time series data (use a line graph)
For example, I once made the mistake of attempting to create a line plot of hourly temperature readings. It was a disaster—there were too many data points. A line graph was a much better choice in that situation.
Line Plots in Education
Line plots are one of the first data visualizations students learn in elementary math. For this reason, line plots evolve in complexity as students progress through grade levels:
- 2nd grade: Basic line plots using whole numbers
- 3rd-4th grade: Including fractional and decimal values
- 5th-6th grade: Working with multiple data sets, comparative data analysis, and more
The most common student mistakes I’ve observed when teaching line plots include:
- Believing they need to create a y-axis
- Incorrectly scaling the number line
- Forgetting to include axis labels and a title
To effectively teach line plots, always use real-world data examples the students can relate to, such as test scores or sports statistics. This strategy has always made the lesson more engaging in my experience.
Analyzing Graph Trends
Identifying the mode (most common value) is as simple as identifying the tallest stack of X’s or dots. And finding the median is as simple as identifying the middle value when the points are ordered.
Here are the main takeaways you can get from a line plot:
- Typical or common values
- Range or distribution of the data
- Subgroups in the data
- Outliers or unusual values
For example, I once used a line plot to analyze durations of machine downtime. I discovered a bimodal distribution, which ultimately helped us identify that we were experiencing two different types of failures.
Sophisticated Usage of Linear Graphs
Line plots are another excellent option in more advanced analyses. I’ve used line plots to compare multiple data sets by plotting different colors on the same number line. This allowed me to see that a set of similar machines actually performed slightly differently.
Incorporating fractions and decimals makes line plots more versatile. I’ve used line plots to analyze fine measurements in a quality control process.
In scientific research, line plots are effective for visualizing experimental results or sampling distributions. In business analysis, line plots are useful for showing sales frequencies or even customer satisfaction scores.
The main professional software options for creating line plots are:
- Microsoft Excel
- Python with Matplotlib
- R with ggplot2
- Tableau
These tools have more advanced features (e.g., custom styling, interactive hover effects, and easy connections to data).
Frequent Errors to Sidestep in Data Visualization Graphs
In my experience, I’ve seen plenty of poorly designed line plots. Here are the most common mistakes I’ve seen:
- Incorrectly scaling the number line
- Forgetting to label the axes or add a title
- Misleading data points
- Inappropriate data sets
These are the mistakes I often see students make:
- Believing a y-axis is necessary
- Incorrectly scaling the number line
- Forgetting to label the axes or add a title
- Confusing line plots with other types of graphs
I remember once reviewing a report where the analyst used a line plot to display time-series data. Unfortunately, it was impossible to discern any trend from the line plot as it jumped around too much. A basic histogram would have been much more effective.
Line Plots vs. Other Graph Types
Selecting the most appropriate graph type is key to effective data communication. Here’s how line plots stack up against other common graph types:
| Graph Type | Best For | Not Suitable For |
|---|---|---|
| Line Plot | Small discrete data sets, Frequency distributions | Large data sets, Time series data |
| Histogram | Continuous data, Large data sets | Small data sets, Discrete categories |
| Bar Graph | Comparing categories, Discrete data | Showing distributions, Continuous data |
| Scatter Plot | Relationship between two variables | Single variable distributions |
Line plots are ideal when you want to visualize the distribution of a small set of discrete values. They are a different graph from histograms, which group continuous data into bins.
One use case where I’ve found line plots to be particularly helpful is quality control data when each data point is a discrete measurement. In contrast, bar graphs are more ideal for comparing discrete categories, such as monthly sales numbers.
Final Thoughts
Line plots are one of the most effective data visualizations. They are an excellent visual cue to identify patterns, trends, and outliers. Yet I’ve watched many engineers and analysts underestimate the power of line plots. Don’t be that person. Use line plots when you want to display the frequency distribution of discrete data.
They are particularly helpful for small datasets. Just ensure that you scale your number line appropriately and clearly label everything. With a little practice, you’ll produce line plots that tell you something interesting about your data. So take this information and plot something!]
