![]() ![]() It also provides a visual analysis of the join results, showing how many rows have been matched from each table and any that have been excluded. Tableau Prep goes several steps further though. As in the data source window in Tableau Desktop, Prep illustrates the type of join with a Venn diagram and gives a preview of the resulting table. Tableau Prep makes the join process easy as well. ![]() For example, a checkbox lets me show only the mismatched fields, so I can identify any fields that should be combined in the union. If not, several functions allow me to troubleshoot and correct the connection. To union them, I simply drag the newly added table onto the Clean step from the other data source and select “Union.” Tableau Prep’s profile pane summarizes the results, so I can quickly see whether the union worked the way I expected. I have two data sets from SporTrac, one containing rookie contracts and another with veteran contract information. Once my data sources are cleaned and grouped, Tableau Prep also makes it easy to union or join them together. By using the Aggregate step, I was able to group and aggregate the data to get stats like total wins and losses, yards gained, and yards allowed for each team. In this case, I gathered a data source from Pro Football Reference that contained game-by-game results for the 2017 NFL season, but I wanted to use the data at the total team level. The Aggregate step can be useful for analyzing data at a higher level. The Aggregate step in Tableau Prep allows the user to aggregate and group fields by simply dragging the desired columns to the “Grouped Fields” or “Aggregated Fields” bins within the Tableau Prep profile pane. I was able to split the “Player” field from Pro Football Reference, which was comprised of the player’s name and a player ID, into two fields. The clean step also works like the transformation functions that can be performed on a data source on Tableau Desktop’s Data Source page. For example, some players were listed as “LB” (linebacker) when they should have been listed as “ILB” (Inside Linebacker), which is a more specific position. In my flow, I used the clean step to group up mismatched positions in SporTrac’s 2018 player salary data. The resulting diagram shows the workflow I’m creating to transform and join my data. Tableau Prep’s Clean step allows me to change field names, filter out or replace values, split columns, and group data by selecting a column in the profile pane, then selecting the action I want to perform from the list at the top of the pane.Īs you can see at the top of the workspace above, the cleaning function is represented by a new step connected to my data source. Enter Tableau Prep.Īfter opening Tableau Prep, I connect to the Excel workbook containing my data and drag the first table onto the canvas to get started. Correcting them all manually would take hours. Though small, these discrepancies would generate mismatches if we simply joined the data in Desktop. ![]() Some of the dimensions in the data sources contain slight differences (e.g. The draft data is drawn from a few different sources, including Pro Football Reference and SporTrac. Given each team’s set of picks, where could they potentially replace a player on a market-priced contract with a similarly productive player on a rookie contract? By comparing the value of rookie contracts to those of existing players I hope to see the positions where each team might create value with their draft selection. This means that a team with the number one overall pick will pay the same salary to any player they select, whether he is a quarterback (typically thought of as the most important position on the field) or a punter (a position that gets on the field about five plays per game). ![]() In the NFL, draft position determines the value of first year contracts. With so much talk about the NFL Draft this year, I decided to look at the money being paid to each player being drafted. When the NFL Draft season arrived, I couldn’t help but to look at some NFL Draft data to analyze the incoming draft class against active NFL players. As a self-proclaimed sports nerd, when I have free time, I like to dive into sports data sets. Prep makes it easy to clean your data and to complete many data transformations that would be tedious or even impossible in Desktop or in the data source. Tableau Prep is a new visual data preparation tool that integrates with Tableau Desktop. ![]()
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