You can put markers on all data points in a series or on only selected data points. Grid lines are horizontal or vertical lines that extend from the axis ticks. Drop lines are lines leading from a data point to the category axis. On large or complex charts, drop lines help show which category a data point belongs to. Drop lines are only available on line chart types. Data labels identify individual data points.
Data labels are a good way to emphasize or explain a particular piece of data on the chart. Data labels can display the data point's category, its value, or text you enter yourself. Ticks are short lines that mark off an axis into segments of equal size. On value axes, axis labels are displayed on ticks. On category axes, axis labels are displayed between ticks.
High-low lines are used on stock charts to show the range of prices the stock commanded over a period of time. High-low lines are available only on line chart types. In this discussion, we'll look at some of the subtleties surrounding the seemingly straightforward issue of how to choose the range and scale for the axes of a graph. We begin with a well-known issue: drawing bar charts with a measurement dependent variable axis that does not go to zero. The bar chart was created by the German economic development agency GTAI, and comes from a webpage about the German labor market.
In the accompanying text, the agency boasts that German workers are more motivated and work more hours than do workers in other EU nations. It looks like Germany has a big edge over other nations such as Sweden, let alone France, right? The size of this gap is an illusion. The graph is misleading because the horizontal axis representing working hours does not go to zero, but rather cuts off at Below, we've redrawn the graph with an axis going all the way to zero.
Now the differences between countries seem negligible. You might notice that in the redrawn graph we've removed the horizontal gridlines separating the countries.
These were not particularly misleading, but they add visual clutter without serving any purpose whatsoever. While the bars in a bar chart should almost always extend to zero, a line graph does not need to include zero on the dependent variable axis. For example, we consider the line graph below from the California Budget and Policy Center to be perfectly fine, despite the fact that the y-axis does not include zero.
What is the difference? Why does a bar graph need to include 0 on the dependent axis whereas a line graph need not do so? Our view is that the two types of graphs are telling different stories. By its design bar graph emphasizes the absolute magnitude of values associated with each category, whereas a line graph emphasizes the change in the dependent variable usually the y value as the independent variable usually the x value changes.
For a bar graph to provide a representative impression of the values being plotted, the visual weight of each bar — the amount of ink on the page, if you will — must be proportional to the value of that bar. Setting the axis above zero interferes with this. A line graph doesn't draw the attention to the absolute magnitudes of the values, because there is little visual density — i.
The exception is line graph in which the area under the curve is filled; we believe these line graphs need to have a zero axis in the vast majority of cases.
As a result, the line graph is freed from the constraint of including 0 as the axis, and thus can zoom into the relevant range to better reveal changes in the dependent variable as the independent variable changes. Thus while people may gripe about line graphs that don't include zero on the dependent axis, we are unconcerned by this display decision. To reduce any opportunity for confusion, we are fans of a recent suggestion : line graphs that do not include zero should include a generous proportion of white space between the lowest point shown and the x-axis.
Indeed, line graphs can obscure important patterns if their axes that do go to zero. One notorious example, reproduced below, was created by bloggers at Powerline and was widely shared after it was tweeted by the National Review in late Philip Bump does a nice job of taking this graph apart in a Washington Post article. He points out that the purpose of considering climate change, the proper representation of these data would look something like the following:.
Bloomberg's Business Week opted for direct and devastating satire, plotting year A. Why can't you add axis labels? Like with all design decisions in the charts you can create with Datawrapper, we have our reasons to not include axis labels: If axis labels are placed inside the chart, they might overlap with the data. That sometimes happens in scatterplots, where we do offer axis labels for both x and y-axis.
But it would happen more often in bar charts, column charts or line charts. If axis labels are placed outside the chart e. In some charts out there, a solution for that is to rotate the axis labels by 90 degree. But then readers will need to tilt their heads or phones to read the axis labels , and we don't like the look of that.
And often, axis labels are not necessary anyway. The next part will explain: Alternative 1: Explanation in title, description, notes or color key We strongly recommend to always put a precise explanation of which data you show at least in the description, if not the title of your chart. Alternative 3: Explanation in annotations In area charts and line charts, you can use annotations to create axis labels. In the following chart, the text "sold cigarettes per day per adult" is a text annotation: Note how even this text annotation is not truly necessary since readers can find the same explanation in the description above.
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