Datasets need to be charted to turn information into in-depth perspectives
Diagrams give you a way to condense large data sets for comparison. They also allow you to create data views that are easy to understand even for someone who is looking at that data for the first time.
There are different types of charts that you can use to compare two or more datasets. Each of these charts comes with its own use cases, depending on your data sets. This makes getting the best data comparison chart the hardest part of comparing data.
The truth is that the type of chart you choose to use is dictated by different factors. Some of these factors include data values, variables and characteristics, your audience, and the type of conclusion you want the audience to draw.
Here are some types of charts that you can use when comparing two datasets;
Stemplots back to back
If you are looking for a graphical option to compare data from two sets, then you can use back-to-back stemplots. This type of chart gives you a stem column and vertical lines on both sides.
One of the data sets is the leaves that extend from the left. The other data set is also represented by leaves, but extends to the right. For example, suppose you want to compare two sets of data that show the amount of money carried by a random sample of men and women.
You can have both men and women on each side of the stem. The rod will be used to represent the money. This way, you can see how much money both sexes carry and you can do various calculations on the chart.
Combined diagrams can be defined as two-axis diagrams that come with a secondary Y-axis to allow users to compare two different data sets with a single data set. The only data set is represented on the X axis, but also on the same diagram.
For example, you might compare your fuel consumption to the number of runways your company operates per week or month. The graph of the fuel consumption range and the number of runways on the same axis could be complicated.
Fortunately, using the Power BI secondary axis, for example, you can leave the secondary axis for the vertical or Y axis. The X axis can be shared between data from different columns. This way, you can easily compare two datasets with a single dataset.
Using the same measurement scale, you can position two dotplots - one on top of the other - to compare two different data sets. Let’s take an example in which you want to compare the ownership of a car in two different blocks.
You can come up with two dotplots, with dots in the diagram used to represent a household that owns a car. The total number of car households in each block can be deducted from the total number of points.
The X-axis is used to represent the number of cars that each household has. This way, you can easily compare the ownership of the car in the two blocks. You can also get more information such as multi-car households, car-free households and the average number of cars in each block.
Parallel boxplots are also known as side-by-side boxplots and are important in viewing data. Using the same measurement scale, you can display two different data sets on the same chart. This diagram is called parallel boxplots.
For example, you can compare the rate of power consumption between two different sets of mobile devices. The devices are 100% charged at the same time and then parallel boxplots are used to compare how the two sets of devices consume their batteries.
Double bar charts
Double bar charts can be confused with regular bar charts. However, these two are different because a double bar chart contains two different pieces of information for all categories, while a regular bar chart has only one.
These charts use different color codes, each color representing a different piece of information. For example, you can use double-bar charts to show how satisfied customers are with different mobile phone operating systems, and then break them down by gender.
Companies can use these charts to view data and compare data sets. This is important to help them with data analysis for decision making and to see future trends in business requirements and operations.
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