![]() ![]() Let’s do this and also exclude the outlier product that sits in the top right corner of the chart and its presence compresses the differences between the other marks as the axes have to span too wide to incorporate it. ![]() The view can be enriched by bringing in another dimension (Category) onto the Color button on the Marks card. This looks right, we can observe the allocation of products in the sales-profit space, for all the time periods cumulated in the data set (as we do not filter for date or use a date field on Columns). (I did not include it on the image, this one contains 1841 marks.) The number of marks (shapes now) on the chart equals the number of different Product Names in the data set. The dimension on level of detail just splits your data – shows it at that level of detail, now Product Name level – but does not alter the colour, label or size of the marks. One way to split the total sums of sales and profit is to bring in Product Name to the level of detail. Let’s say that we want to follow sales vs profit by Product Name. As the next step it needs to be split by a relevant dimension. Our first scatter plot is not yet a scatter of plots. It shows the sum of all sales in the data set paired to the sum of all the profit. For the examples we rely on the Superstore Sales data set that is shipped with Tableau Desktop. Notice that the automatic mark type of the scatter plot is ‘shape’, though they work fine also with circles or squares, too. As the values are aggregates, the result is only one dot. The starter scatter plot with two measures and no dimension practically draws a Cartesian coordinate system with an X and Y axis. This graph type is commonly used to visualise correlation between two paired sets of numeric values, for example the sales value and profit content of sales instances.Īs always, Tableau’s Show Me panel is again a helpful guide for the building blocks of scatter plots. We will see how this chart type is created and used as well as some tips, tricks and pitfalls along the way. You can assign different colors or markers to the levels of these variables.We have arrived to scatter plots within the Show Me How series. You can use categorical or nominal variables to customize a scatter plot. Either way, you are simply naming the different groups of data. You can use the country abbreviation, or you can use numbers to code the country name. Country of residence is an example of a nominal variable. For example, in a survey where you are asked to give your opinion on a scale from “Strongly Disagree” to “Strongly Agree,” your responses are categorical.įor nominal data, the sample is also divided into groups but there is no particular order. With categorical data, the sample is divided into groups and the responses might have a defined order. Scatter plots are not a good option for categorical or nominal data, since these data are measured on a scale with specific values. Some examples of continuous data are:Ĭategorical or nominal data: use bar charts Scatter plots make sense for continuous data since these data are measured on a scale with many possible values. ![]() Scatter plots and types of data Continuous data: appropriate for scatter plots Annotations explaining the colors and markers could further enhance the matrix.įor your data, you can use a scatter plot matrix to explore many variables at the same time. The colors reveal that all these points are from cars made in the US, while the markers reveal that the cars are either sporty, medium, or large. There are several points outside the ellipse at the right side of the scatter plot. From the density ellipse for the Displacement by Horsepower scatter plot, the reason for the possible outliers appear in the histogram for Displacement. In the Displacement by Horsepower plot, this point is highlighted in the middle of the density ellipse.īy deselecting the point, all points will appear with the same brightness, as shown in Figure 17. This point is also an outlier in some of the other scatter plots but not all of them. In Figure 16, the single blue circle that is an outlier in the Weight by Turning Circle scatter plot has been selected. It's possible to explore the points outside the circles to see if they are multivariate outliers. The red circles contain about 95% of the data. The scatter plot matrix in Figure 16 shows density ellipses in each individual scatter plot. ![]()
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