What should line graphs be used for

In this case each line would have a different color, identified in a legend. The line graph is a powerful visual tool for marketing, finance, and other areas.

It is also useful in laboratory research, weather monitoring, or any other function involving a correlation between two numerical values. If two or more lines are on the chart, it can be used as a comparison between them.

Once imported, you can easily change the title, legend placement and even the quickly change the graph type using the Edit Graph options or just double-click on your imported chart. Learn More. Line Graph What is a Line Graph? Data points are plotted and connected by a line in a "dot-to-dot" fashion.

How to Create a Line Graph Step one is making sure you have data formatted the correct way for a line graph. 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.

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.

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. 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. 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.

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.

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.

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.

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.

Funnel charts are specialized charts for showing the flow of users through a process. Line graphs are used to track changes over short and long periods of time. When smaller changes exist, line graphs are better to use than bar graphs. Line graphs can also be used to compare changes over the same period of time for more than one group.

Pie charts are best to use when you are trying to compare parts of a whole. They do not show changes over time. Bar graphs are used to compare things between different groups or to track changes over time.