Look At The Bar Graph. What Is The Most Accurate Conclusion Someone Can Draw From This Graph?
1 of the about central chart types is the bar chart, and 1 of your virtually useful tools when it comes to exploring and understanding your information.
What is a bar chart?
A bar chart (aka bar graph, column chart) plots numeric values for levels of a categorical feature every bit bars. Levels are plotted on one chart axis, and values are plotted on the other centrality. Each categorical value claims one bar, and the length of each bar corresponds to the bar's value. Bars are plotted on a common baseline to let for easy comparison of values.
This case bar nautical chart depicts the number of purchases fabricated on a site by different types of users. The categorical feature, user type, is plotted on the horizontal axis, and each bar's height corresponds to the number of purchases made under each user blazon. We tin see from this chart that while at that place are nearly iii times as many purchases from new users who create user accounts than those that exercise not create user accounts (guests), both are dwarfed by the number of purchases fabricated past repeating users.
When you should use a bar chart
A bar chart is used when you want to show a distribution of data points or perform a comparison of metric values across different subgroups of your data. From a bar chart, we can come across which groups are highest or most common, and how other groups compare against the others. Since this is a fairly mutual task, bar charts are a adequately ubiquitous chart type.
The primary variable of a bar chart is its categorical variable. A categorical variable takes discrete values, which can exist thought of as labels. Examples include land or country, manufacture blazon, website access method (desktop, mobile), and visitor type (free, basic, premium). Some categorical variables have ordered values, like dividing objects by size (pocket-sized, medium, large). In addition, some non-chiselled variables can be converted into groups, like aggregating temporal data based on date (eg. dividing by quarter into 20XX-Q1, 20XX-Q2, 20XX-Q3, 20XX-Q4, etc.) The important point for this primary variable is that the groups are singled-out.
In dissimilarity, the secondary variable will be numeric in nature. The secondary variable's values determine the length of each bar. These values can come from a nifty variety of sources. In its simplest form, the values may be a simple frequency count or proportion for how much of the data is divided into each category – not an actual data characteristic at all. For example, the following plot counts pageviews over a period of vi months. You can come across from this visualization that there was a pocket-sized peak in June and July before returning to the previous baseline.
Other times, the values may be an average, full, or some other summary measure computed separately for each group. In the following case, the height of each bar depicts the average transaction size past method of payment. Note that while the boilerplate payments are highest with checks, it would have a different plot to show how oft customers actually use them.
Example of data structure
| Payment Type | Average Transaction |
|---|---|
| Check | 46.861 |
| Credit Carte du jour | 36.681 |
| Debit Card | 28.860 |
| Digital Wallet | eighteen.900 |
| Greenbacks | iv.802 |
Data rendered as a bar nautical chart might come in a meaty course like the in a higher place table, with one column for the categories and the second column for their values. Other times, data may come in its unaggregated form similar the below table snippet, with the visualization tool automatically performing the aggregation at the time of visualization cosmos.
Best practices for using bar charts
Utilise a common cipher-valued baseline
Kickoff and foremost, brand certain that all of your bars are existence plotted confronting a zero-value baseline. Not simply does that baseline make information technology easier for readers to compare bar lengths, it also maintains the truthfulness of your data visualization. A bar chart with a non-zero baseline or some other gap in the axis scale can easily misrepresent the comparing between groups since the ratio in bar lengths will non friction match the ratio in actual bar values.
Maintain rectangular forms for your bars
Another major no-no is to mess with the shape of the bars to be plotted. Some tools will let for the rounding of the bar caps, rather than only take straight edges. This rounding means that information technology'due south hard for the reader to tell where to read the actual value: from the top of the semicircle, or somewhere in the heart? A lilliputian bit of rounding of the corners can be okay, just brand sure each bar is flat enough to discern its true value and provide an piece of cake comparison between bars.
Similarly, you lot should avoid including iii-d furnishings on your bars. Every bit with heavy rounding, this tin make it harder to know how to measure bar lengths, and equally a bonus, might crusade baselines to not exist aligned (see the above point).
Consider the ordering of category levels
One consideration y'all should accept when putting together a bar chart is what lodge in which y'all will plot the bars. A standard convention to accept is to sort the confined from longest to shortest: while information technology is always possible to compare the bar lengths no matter the order, this can reduce the burden on the reader to make those comparisons themselves. The major exception to this is if the category labels are inherently ordered in some way. In cases like that, the inherent ordering usually takes precedence.
Apply color wisely
Another consideration is on how you should use color in your bar charts. Certain tools will color each bar differently by default, but this can distract the reader by implying additional meaning where none exists. Instead, colour should be used with purpose. For example, y'all might use color to highlight specific columns for storytelling. Colors can likewise be used if they are meaningful for the categories posted (e.1000. to match company or team colors).
Common misuses
Replacing bars with images
It may be tempting to replace confined with pictures that depict what is existence measured (e.g. bags of money for money amounts), exist careful that you practice not misrepresent your data in this way. If your choice of symbol scales both width and height with value, differences will look much larger than they actually are, since people will finish up comparing the areas of the bars rather than merely their widths or heights. In the instance below, in that location is a 58% growth in downloads from 2022 to 2022. However, this growth is exaggerated with the icon-based representation, since the area of the 2022 icon is more than 2.5 times the size of the 2022 icon.
If you feel the need to utilize icons to depict value, so a better – though still not great – option is to utilise the pictogram chart type instead. In a pictogram chart, each category'south value is indicated past a series of icons, with each icon representing a certain quantity. In a certain sense, this is like changing the texture of its corresponding bar to a repeating prototype. 1 major caution with this chart type is that information technology tin make values harder to read, since the reader needs to perform some mental mathematics to gauge the relative values of each category.
Common bar chart options
Horizontal bars vs. vertical bars
A mutual bar chart variation is whether or not the bar chart should be oriented vertically (with categories on the horizontal axis) or horizontally (with categories on the vertical axis). While the vertical bar chart is commonly the default, it's a good idea to employ a horizontal bar nautical chart when you are faced with long category labels. In a vertical chart, these labels might overlap, and would demand to exist rotated or shifted to remain legible; the horizontal orientation avoids this issue.
Include value annotations
A common addition to bar charts are value annotations. While it is adequately easy for readers to compare bar lengths and gauge approximate values from a bar chart, exact values aren't necessarily like shooting fish in a barrel to state. Annotations tin study these values where they are important, and are usually placed in the eye of the bar or at their ends.
Include variability whiskers
When the numeric values are a summary measure, a frequent consideration is whether or not to include error confined in the plot. Fault bars are additional whiskers added to the end of each bar to indicate variability in the individual information points that contributed to the summary measure. Since in that location are many choices for doubt measure out (e.g. standard departure, confidence interval, interquartile range) information technology is important that when you display error bars, that yous notation in an notation or comment what the error bars correspond.
Alternatively, you may wish to describe variance within each category with a different nautical chart type such as the box plot or violin plot. While these plots will have more elements for a reader to parse, they provide a deeper agreement of the distribution of values within each group.
Lollipop chart
One variation of the bar nautical chart is the lollipop nautical chart. It presents exactly the same information every bit a bar chart, merely with different aesthetics. Instead of bars, we have lines topped past dots at their endpoints. A lollipop chart is virtually useful when there are a lot of categories and their values are fairly close together. By changing the aesthetic class of the plotted values, information technology can make the chart much easier to read.
Pie chart
If the values in a bar chart stand for parts of a whole (the sum of bar lengths totals the number of data points or 100%), and so an alternative nautical chart type y'all could use is the pie chart. While the pie chart is much-maligned, information technology still fills a niche when in that location are few categories to plot, and the parts-to-whole division needs to be put front and heart. Withal, in full general you are most likely to utilize a bar chart in general usage, as it'south easier to make comparisons betwixt categories.
Histogram
Histograms are a close cousin to bar charts that depict frequency values. While a bar nautical chart'due south primary variable is categorical in nature, a histogram'southward primary variable is continuous and numeric. The bars in a histogram are typically placed right next to each other to emphasize this continuous nature: bar charts usually take some infinite between bars to emphasize the chiselled nature of the primary variable.
Line chart
For bar charts that depict summary statistics, the line chart is the closest relative. Similar the human relationship from the bar chart to a histogram, a line chart's primary variable is typically continuous and numeric, emphasized by the continuous line between points. Shading the region between the line and a aught baseline generates an expanse chart, which tin be thought of as a combination of the bar chart and line chart.
Dot plot
Alternatively, when we have summary statistics over a chiselled principal variable, we might choose a dot plot, or Cleveland dot plot, instead of a bar chart. A dot plot is essentially a line plot without line segments connecting each point. This frees it upwards to be used with chiselled levels, rather than a continuous progression. The biggest advantage a dot plot has over a bar chart is that values are indicated by position rather than length, then we don't necessarily need a zero-baseline. When the necessary baseline on a bar chart interferes with perception of changes or differences between confined, then a line nautical chart or dot plot tin be a adept culling pick.
Stacked bar chart and grouped bar chart
Bar charts can exist extended when we introduce a second chiselled variable to divide each of the groups in the original chiselled variable. If the bar values depict group frequencies, the second categorical variable can carve up each bar's count into subgroups. Applied to the original bars, this results in a stacked bar nautical chart, seen on the left in the effigy beneath. Alternatively, if nosotros move the dissimilar subgroups' bars to the baseline, the resulting chart blazon is the grouped bar chart, seen on the right. We likewise use the grouped bar chart when we compute statistical summary measures across levels of two categorical variables.
Most tools that tin can create visualizations, whether they be spreadsheets, programming libraries, or business intelligence tools, should be capable of creating basic vertical bar charts. Sometimes, options need to exist checked or modified in club to follow all-time practices. However, for basic data exploration needs, any tool should be sufficient. Other variations like horizontal bars, mistake bars, and annotations may non always be possible. In particular, the lollipop nautical chart variation is not usually considered a default nautical chart type, and volition normally require specialized tweaking with programmatic tools instead.
The bar nautical chart is one of many dissimilar chart types that tin 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.
Source: https://chartio.com/learn/charts/bar-chart-complete-guide/
Posted by: terrellsuaing.blogspot.com
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