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Let\u2019s start with the basic function \u2013 RANK. The first argument is the measure according to which you want to structure your ranking. It also must have aggregation (for example, sum or average). The other optional argument is the ranking calculation method \u2013 ascending or descending. Descending order is set by default: <\/p>\n\n\n\n <\/p>\n\n\n\n To better understand the way ranking is calculated, I will use the list of cities in Sweden grouped according to the number of orders placed (from the Sample Superstore database in Tableau): <\/p>\n\n\n\n <\/p>\n\n\n\n The beginning of the list is quite clear, then there are several cities with the same number of order. After adding a field with ranking, we get the following result: <\/p>\n\n\n\n As you can see in the table, ranks are allocated according to the applied measure (Count of City). It\u2019s good to pay attention to the positions with the same value \u2013 they receive the same rank. For example, Karlstad and Huddinge are ranked 5, and the next city is ranked 7. A position number 6 doesn\u2019t exist.<\/p>\n\n\n\n Another important thing is that RANK functions are array functions. In the aforementioned example, the ranking is calculated along the Table (down). You need to bear that in mind when modifying tables and visualizations to ensure that ranking is calculated correctly.<\/p>\n\n\n\n As I already mentioned, the basic difference between these functions is how positions with the same values are treated: <\/p>\n\n\n\n <\/p>\n\n\n\n It is best illustrated by our example. There are 14 cities, and depending on the calculation method, cities with the same value can be positioned differently: <\/p>\n\n\n\n <\/p>\n\n\n\n The situation is slightly different for RANK_PERCENTILE, which also is the least intuitive. The function returns a ranking percentile for a given position. In general, this type of function is not suitable for calculating rankings.<\/p>\n\n\n\n It\u2019s best to visualize ranking in the single point of time in the table. Why? Because we know the leap (or difference between levels), so adding information in the form of bar chart contributes very little: <\/p>\n\n\n\n Ranking can also be placed as the additional element in the label on the bar chart with the main measure (a bracketed value): <\/p>\n\n\n\n <\/p>\n\n\n\n Or as the element of the row header in a discrete form: <\/p>\n\n\n\n <\/p>\n\n\n\n In the case of diverse types of score cards or KPI cards, you can use the highlight table \u2013 it will work best. In this context, ranking can be treated rather as a discrete value than a continuous value, and a highlight table will be perfect here. Below is the example calculation of ranking for product subcategories for four measures: <\/p>\n\n\n\n <\/p>\n\n\n\n Based on this table, you can try to spot certain dependencies. For example, the \u201cTables\u201d position is in the second part of the table, when it comes to sales, and at the very end when it comes to quantity and profit. This category also has the highest discount. This raises a question whether the least profitable category with the highest discount should be optimized in some way.<\/p>\n\n\n\n The change of ranking over time can provide a little more input. The Context is particularly important in presenting the data, therefore it\u2019s vital to understand the dynamics over time. We use a line chart for this purpose, adding time dimensions in columns, and the rank field in rows: <\/p>\n\n\n\n <\/p>\n\n\n\n It\u2019s worth remembering at this point that RANK functions are array functions. When you add another dimension (time), you also need to configure this function correctly to properly calculate ranking. You also need to pay attention to filtering and order of Operations in Tableau.<\/p>\n\n\n\n The ranking chart can also be enhanced by adding another graph \u2013 a circle chart in the dual axis, for example, to emphasize the labels. As a result, you will get the Bump chart: <\/p>\n\n\n\n <\/p>\n\n\n\n Bump Charts<\/strong><\/p>\n\n\n\n When creating bump charts, you need to pay special attention to the readability of the chart. When you have more categories, the chart becomes unclear and useless for recipients. For example, when you change the above chart from three to ten cities, this will impact its transparency. You can enhance its usability with the highlight option \u2013 to track changes in a given position over time: <\/p>\n\n\n\n <\/p>\n\n\n\n A slope char is a specific type of bump chart \u2013 it\u2019s when you have two points in time. The chart becomes more transparent; however, it transmits less information. <\/p>\n\n\n\n <\/p>\n\n\n\n In short \u2013 whenever order is important. Let\u2019s assume that we supervise sales teams, and we want to introduce competition (or gamification) for our sales representatives. Every week or month \u2013 TOP3 get an extra bonus. Ranking will be perfect here, and the bump chart will provide the historical context. Rankings are a key element in sports \u2013 league tables, striker tables, tennis players rankings (WTA), FIFA rankings or boxing association rankings. It\u2019s important to bear in mind that ranking is just one of the elements of communication, and it doesn\u2019t show the value of a key KPI on its own. In our example of sales representative ranking, a bump chart will show us who\u2019s number one, number two and so on, but it doesn\u2019t present the differences between them. Similarly, in sports rankings the different between the first and second place can be minimal or huge. Therefore, when visualizing rankings in Tableau, it\u2019s good to remember about complementing the picture with the values or indicators. As a rule, it should be the other way round \u2013 ranking can be an additional element of the visualization of the KPI performance.<\/p>\n\n\n\n Mateusz Karmalski,<\/p>\n\n\n\n Tableau Author<\/p>\n","protected":false},"excerpt":{"rendered":" In business, it is often necessary to build rankings. For example, it\u2019s important to know the top selling products. And in the case of managing sales teams \u2013 rankings of sales assistants\/representatives. A ranking shows order, but it ignores value \u2013 it\u2019s worth to keep this in mind, as some places may differ by 1%, […]<\/p>\n","protected":false},"author":1,"featured_media":2791,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"content-sidebar","footnotes":""},"categories":[9,5],"tags":[],"class_list":{"0":"post-2790","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-blog","8":"category-tableau","9":"entry"},"yoast_head":"\n![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/2-2.png\")
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<\/figure>\n\n\n\nRankings in Tableau- RANK DENSE, MODIFIED, UNIQUE AND PERCENTILE<\/h2>\n\n\n\n
![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/5-1.png\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/6-1.png\")
<\/figure>\n\n\n\nVisualizing rankings<\/h2>\n\n\n\n
![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/7-2.png\")
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<\/figure>\n\n\n\n![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/10-1.png\")
<\/figure>\n\n\n\nAdding time dimensions<\/h2>\n\n\n\n
![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/11-1.png\")
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<\/figure>\n\n\n\n![\"\"](\"https:\/\/newdatalabs.com\/en\/wp-content\/uploads\/2023\/12\/14.png\")
<\/figure>\n\n\n\nIn what situations are rankings helpful in Tableau?<\/h2>\n\n\n\n