A line graph is a unique graph which is commonly used in statistics. It represents the change in a quantity with respect to another quantity. For example, the price of different flavours of chocolates varies, which we can represent with the help of this graph. This variation is usually plotted in a two-dimensional XY plane. If the relation including any two measures can be expressed utilizing a straight line in a graph, then such graphs are called linear graphs. Thus, the line graph is also called a linear graph. Here, we are going to discuss what a line graph is, its types, procedure to draw a line graph, and examples in detail.
Table of Contents:
Line Graph Definition
A line graph or line chart or line plot is a graph that utilizes points and lines to represent change over time. It is a chart that shows a line joining several points or a line that shows the relation between the points. The graph represents quantitative data between two changing variables with a line or curve that joins a series of successive data points. Linear graphs compare these two variables in a vertical axis and a horizontal axis.
Types of Line Graphs
The following are the types of the line graph. They are:
- Simple Line Graph: Only one line is plotted on the graph.
- Multiple Line Graph: More than one line is plotted on the same set of axes. A multiple line graph can effectively compare similar items over the same period of time.
- Compound Line Graph: If information can be subdivided into two or more types of data. This type of line graph is called a compound line graph. Lines are drawn to show the component part of a total. The top line shows the total and line below shows part of the total. The distance between every two lines shows the size of each part.
Vertical Line Graph
Vertical line graphs are graphs in which a vertical line extends from each data point down to the horizontal axis. Vertical line graph sometimes also called a column graph. A line parallel to the y-axis is called a vertical line.
Horizontal Line Graph
Horizontal line graphs are graphs in which a horizontal line extends from each data point parallel to the earth. Horizontal line graph sometimes also called a row graph. A line parallel to the x-axis is called a vertical line
Straight Line Graph
A line graph is a graph formed by segments of straight lines that join the plotted points that represent given data. The line graph is used to solve changing conditions, often over a certain time interval. A general linear function has the form y = mx + c, where m and c are constants.
The fundamental rule at the rear of sketching a linear graph is that we require only two points to graph a straight line. The subsequent procedure is followed in drawing linear graphs:
- By substituting two dissimilar values for x in the equation y = mx + c, we get two values for y. Thus, we get two points (x1, y1) and (x2, y2) on the line.
- Plot the horizontal line and vertical line and select the suitable scale for both the axes.
- If the given table values are large choose the scale for that particular value. It depends on the given value.
- Plot the two points in the Cartesian plane of the paper. Join the two points using a line segment and extend to two directions. The closed figure obtained is the required linear graph.
Different Parts of Line Graph
Title: The title explains what graph is to be plotted.
Scale: The scale is the numbers that explain the units utilized on the linear graph.
Labels: Both the side and the bottom of the linear graph have a label that indicates what kind of data is represented in the graph. X-axis describes the data points on the line and the y-axis shows the numeric value for each point on the line.
Bars: They measure the data number.
Data values: they are the actual numbers for each data point.
How to Make a Line Graph?
If we have created data tables, then we draw linear graphs using the data tables. These graphs are plotted as a series of points, which are later joined with straight lines to provide a simple way to review data collected over time. It offers an excellent visual format of the outcome data collected over time.
To plot a linear/line graph follow the below steps:
- Use the data from the data-table to choose a suitable scale.
- Draw and label the scale on the vertical (y-axis) and horizontal (x-axis) axes.
- List each item and place the points on the graph.
- Join the points with line segments.
Double Line Graph
A double line graph is a line graph with two lines. A graph that compares two different subjects over a period of time. A double line graph shows how things change over a period of time. The double line graph shows two line graphs within one chart. Double line graphs are used to compare trends and patterns between two subjects.
Steps to Make a Double Line Graph:
- Use the data from the table to choose an appropriate scale.
- Draw and label the scale on the vertical and horizontal axis.
- List each item and locate the points on the graph for both the lines.
- Connect the points with line segments separately of both the lines.
- Draw two line graphs within one chart.
Example: See the graph below.
The points on the double line graph show the average monthly rainfall in the two cities (city 1, city 2). The separate lines that are made by connecting the points for each city.
Uses of Line Graph
The important use of line graph is to track the changes over the short and long period of time. It is also used to compare the changes over the same period of time for different groups. It is always better to use the line than the bar graph, whenever the small changes exist. For example, in a company finance team wants to plot the changes in the cash amount that the company has on hand over time. In that case, they use the line graph plotting the points over the horizontal and the vertical axis. It usually represents the time period of the data.
Line Graph Example
Question: Sketch the solution on number line |x + 2| ≤ 5.
Solution:
Given, inequality |x + 2| ≤ 5
Step 1: Change the inequality into a compound inequality
– 5 ≤ x + 2 ≤ 5
Step 2: Subtract 2 from all three sides to get
– 5 – 2 ≤ x + 2 – 2 ≤ 5 – 2
= – 7 ≤ x ≤ 3
Step 3: Place this value on a number line
To learn different types of graphs used in Statistics, register with BYJU’S – The Learning App and download the app to get Maths-related videos.
Generally, line graphs or line charts are used to track variations over time, which may be long-term or short-term. We can also use line graphs to compare changes over the same period for more than one group.
To make a linear/line graph, follow the below steps: Step 1: Observe the data from the data-table to choose a suitable scale. Step 2: Draw and label the scale on x and y axes, i.e. the horizontal and vertical axes, respectively. Step 3: List each item and place the points (taking x-value as the x-coordinate and y-value as the y-coordinate) on the graph.
Step 4: Finally, join the plotted points with line segments to get a line chart.
A line graph helps understand the performance or comparisons over time by observing the line’s steepness on a graph sheet.
The five major parts of a line graph include the following: Title Scale Labels Bars
Data values
It is essential to know that all line graphs must have a title part. A line graph possesses two axes, namely x and y. The events and the categories required to compare for a given time should be taken on the x-axis of a line graph. The y-axis describes the scale, which expresses the data and is organized into regular intervals.
A line chart (aka line plot, line graph) uses points connected by line segments from left to right to demonstrate changes in value. The horizontal axis depicts a continuous progression, often that of time, while the vertical axis reports values for a metric of interest across that progression.
The line chart above shows the exchange rate between two fictional currencies over a six month period. As time progresses from left to right, points connect the daily exchange rates. We can read from the general slope of the line and its vertical positions that the rate improved from about 0.75 to 0.78 between March and early April, then fell gradually to about 0.765 in late May and June.
When you should use a line chart
You will use a line chart when you want to emphasize changes in values for one variable (plotted on the vertical axis) for continuous values of a second variable (plotted on the horizontal). This emphasis on patterns of change is sold by line segments moving consistently from left to right and observing the slopes of the lines moving up or down.
On the horizontal axis, you need a variable that depicts continuous values that have a regular interval of measurement. Very commonly, this variable is a temporal one, generating an observation every minute, hour, day, week, or month. The choice of interval size, or bin, is a decision that the analyst will usually need to make for the data, rather than it being an inherent data characteristic.
On the vertical axis, you will report the value of a second numeric variable for points that fall in each of the intervals defined by the horizontal-axis variable. Often, this will be a statistical summary like a total or average value across events within each bin.
Multiple lines can also be plotted in a single line chart to compare the trend between series. A common use case for this is to observe the breakdown of the data across different subgroups. The ability to plot multiple lines also provides the line chart a special use case where it might not usually be selected. Normally, we would use a histogram to depict the frequency distribution of a single numeric variable. However, since it’s tricky to plot two histograms on the same set of axes, the line chart serves as a good mode of comparison as a substitute. Line charts used to depict frequency distributions are often called frequency polygons.
Example of data structure
| 2019-05-01 | 19 | 103 |
| 2019-05-02 | 22 | 105 |
| 2019-05-03 | 20 | 98 |
| 2019-05-04 | 26 | 83 |
| … | … | … |
To use a line chart, data often needs to be aggregated into a table with two or more columns. Values in the first column indicate positions for points on the horizontal axis for each line to be plotted. Each following column indicates the vertical position for points of a single line.
Certain tools create line charts from a different data format where three columns are expected regardless of how many lines to plot. In these cases, the columns specify the horizontal values, vertical values, and to which line to each row will be assigned.
| 2019-03-01 | Guest | 23 |
| 2019-03-01 | Subscriber | 102 |
| 2019-03-02 | Guest | 24 |
| 2019-03-03 | Subscriber | 77 |
| … | … | … |
Best practices for using a line chart
Choose an appropriate measurement interval
An important aspect of creating a line chart is selecting the right interval or bin size. For temporal data, a too-broad of a measurement interval may mean that it takes too long to see where the data trend is leading, hiding away the useful signal. On the flip side of the coin, a too-short a measurement interval may only reveal noise rather than signal.
Testing out different intervals or relying on your domain knowledge about what data is being recorded can inform you of a good choice of bin size. It can also be possible to use multiple lines, with one line for a fine-grained interval, and then a second line for the overall trend, averaging over a rolling window.
Don’t plot too many lines
With great power comes great responsibility, so while there is the technical capacity to put many lines onto a single line chart, it is a good idea to be judicious in the amount of data that you plot. A good rule of thumb is to limit yourself to five or fewer lines, lest the plot end up looking like an unreadable tangle. However, if the lines are well-separated, you can still plot all of the values you wish to track.
If you find the need to plot more lines than can be read in a single axis, then you might consider faceting the plots into a grid of smaller line charts. It will be more difficult to see details in these plots, so it’s a good idea to sort them by some important characteristic (like average or final value) to help draw out important points. If you are using a tool that allows for interactive plots, another alternative is to be able to highlight individual lines or grey out lines to be out of focus as the reader desires.
Common misuses
Strictly using a zero-value baseline
Despite the zero baseline for the vertical axis being a requirement for bar charts and histograms, you do not need to include a zero baseline for a line chart. Recall that the main goal of a line chart is to emphasize changes in value, rather than the magnitude of the values themselves. In cases where a zero line is not meaningful or useful, it’s fine to zoom the vertical axis range into what will make the changes in value most informative.
There is one use case where a zero baseline is still necessary, however. When a line chart being used to display frequency distributions, then it is being used in a capacity equivalent to bar charts and histograms. Thus, it will follow the same requirement of needing to include a zero-value baseline as an anchor for the line chart’s heights.
Failing to identify uneven gaps between points
When the line chart is missing information for certain bins, gaps in the record may be interpreted as phantom values if the line does not include distinct dots at each observation. When there aren’t many points to plot, try showing all of the points and not just the line. If including the points would muddy up the interpretability of the plot, another alternative is to include a gap in the line to show where there are missing values.
Interpolating a curve between points
In a standard line chart, each point is connected to the next with a straight line segment, from first to last. However, there may be the aesthetic temptation to try and link all of the points smoothly, fitting a curve that goes through all of the points at once. You should absolutely resist this temptation! As seen in the example below, attempting this kind of fitting will be assured of distorting perception of trends in the data. The direction and steepness of the line is supposed to be indicative of change in value, and so the curve may end up implying the presence of additional data points between the actual measurements that do not exist.
Using a misleading dual axis
Examples of line charts with multiple lines have thus far had each line be part of the same domain, and thus plottable on the same axis. There’s nothing that limits each line to depict values on the same units, however. When a line plot includes two series, each depicting a summary of a different variable, then we end up with a dual axis plot.
The problem with a dual-axis plot is that it can easily be manipulated to be misleading. Depending on how each axis is scaled, the perceived relationship between the two lines can be changed. In the two plots below, the number of weekly trials and subscriptions are plotted in dual-axis plots. The data is exactly the same for each, but due to the choice of vertical scaling for each variable, the inferred relationship between the variables will change.
While many visualization tools are capable of creating dual-axis charts, common recommendations suggest against this, regardless of if the two axes are in the same or separate domains. Instead, faceting the two lines into separate plots still allows for the general patterns of change to be observed for both variables, while reducing the temptations to compare them in misleading ways.
Common line chart options
Include additional lines to show uncertainty
When we have a line that depicts a statistical summary like an average or median, we can also have an option to add to the plot to display uncertainty or variability in the data at each plotted point. One way of doing this is through the addition of error bars at each point to show standard deviation or some other uncertainty measure. Another alternative is to add supporting lines above or below the line to show certain bounds on the data. These lines might be rendered as shading to show the most common data values, as in the example below.
Sparkline
A special use case for the line chart is the sparkline. A sparkline is essentially a small line chart, built to be put in line with text or alongside many values in a table. Because of its small size, it will not include any labeling. Statistics can be placed next to the sparkline to indicate starting and ending values, or perhaps minimum or maximum values. The main point of a sparkline is to show change over a period of time, and is often seen in financial contexts.
Ridgeline plot
One variant chart type for a line chart with multiple lines is the ridgeline plot. In a ridgeline plot, each line is plotted on a different axis, slightly offset from each other vertically. This slight offset can save on space compared to a complete faceting of plots. Like the sparkline, vertical axis markings are typically eschewed: it would be difficult to read those values on the different axes. Ridgeline plots are mainly used to compare lots of groups on their frequency distributions. This is most useful when a clear pattern is visible when the lines are ordered in some way.
Bar chart
If the variable we want to show on the horizontal axis is not numeric or ordered, but instead categorical, then we need to use a bar chart instead of a line chart. The bars in a bar chart are usually separated by small gaps, which help to emphasize the discrete nature of the categories plotted. Note, however, when our horizontal axis is numeric or ordered, we aren’t restricted against using a bar chart, as seen in the example below.
Dot plot
Another chart type we can use when the horizontal axis variable is categorical is the dot plot, or Cleveland dot plot. The dot plot is like a line plot, except that there are no line segments connecting consecutive points. This lack of line segments frees the points from their sequential progression, and so the order of labels and points can be freely adjusted like a bar chart. The major advantage of using a dot plot over a bar chart is that a dot plot, like a line chart, is not beholden to include a zero-baseline. If we have values over levels of a categorical variable, but associated values do not have a meaningful zero-baseline, then the dot plot can be a good chart type option.
Histogram
When the vertical axis of a line chart depicts information about a frequency distribution, we have an option to visualize the data as a histogram instead. One of the main benefits of the histogram is that the bars are a more consistent display of frequency within each bin. Frequency judgments can be misleading in a line chart, especially in the peaks and troughs of a distribution. However, a line chart does have one advantage for visualizing frequency distributions: if we need to compare two different groups, this is very difficult for a histogram. As seen in an earlier section when using a line chart, we can just plot the two groups’ lines on the same axes with little issue.
Density curve
Another alternative for frequency-based line charts is the density curve, or kernel density estimate (KDE). While a line chart aggregates frequency counts by bins into single points, the KDE aggregates the contribution of each point in a continuous way. In a KDE, each point contributes a small lump of volume centered around its true value (the titular kernel); the sum of all volumes gives the final density curve. Since there are so many options for the shape of the kernel, kernel density estimation is usually reserved for programmatic approaches to data visualization.
Area chart
An extension to the line chart involves the addition of shading between the line and a zero-baseline, called an area chart. The area chart can be considered a hybrid of the line chart with the bar chart, since values can be read from not just their vertical positions, but also the size of the shaded area between each point and the baseline.
Connected scatter plot
If you have two series of values that you want to plot using a line chart, an alternative chart type you could use is the connected scatter plot. In a standard scatter plot, the two axes represent two variables of interest, and points plotted on the axes indicate values on those variables. If we connected points in an order specified by a third variable like time, we get a connected scatter plot. A connected scatter plot is good for looking at not just the relationship between two variables, but also how they change across time or values of a third variable.
The line chart is a versatile and useful chart type, and so should be available in pretty much any data visualization tool you choose. Basic line charts where one or more lines are plotted on a single axis should be common, but advanced options like dual axes may not be present or require additional data work to set up. The ridgeline variant is not a common built-in, and usually requires custom programming or a custom package to create. Sparklines too are not common on their own, and are more often seen as built in as part of other reporting tools.
The line chart is one of many different chart types that can be used for visualizing data. Learn more from our articles on essential chart types, how to choose a type of data visualization, or by browsing the full collection of articles in the charts category.