![]() And filter the total plays from 50 to 100 Add other fields like, to Tooltip under both the Marks card.Add to detail shelf under both the Marks card.Right click on the COUNTD() in Rows and add a Running Sum table calculation and compute it across.Right click on the axis, click Edit axis and uncheck the option “Include Zero” Change the color for line graph to ( #C0C0C0) and color for Scatter Plot (MAX(])) to ( #3DDE3C).Right click on Path and change the line type to Step.Change the Mark card for MAX(]) to Circle.Make them dual axis and right click on one of the axis and click on synchronise axis. IF THEN ELSE NULL END STEP 2 – BUILD THE SCATTER PLOT AND LINE GRAPH Build the combined scatter plot and line graph Change the datatype of from string to date and time datatype.Connect to the downloaded datasource (My Streaming Activity).We will be following the below steps to solve the challenge: WOW2021 S05 E21 – REQUIREMENTS STEP 1 – CONNECT TO THE DATASOURCE AND BUILD THE CALCULATIONS Check out the full list on our Tableau Essentials blog channel.Īs always, let us know if you have any questions or comments about this post or Tableau in general.If you have a last.fm account and interested in visualising your own listening data, you can download your own data here and swap data sources!īefore moving on to solve this challenge check out the solution on how to do Market Basket Analysis using Tableau WOW2021 S05 E21 – REQUIREMENTS Want to learn more about Tableau? We have several posts outlining all of Tableau’s fantastic features. Be sure to check back often as we continue to release new articles in each chart type in this sub-series. Here is the complete list of chart types from the Show Me menu. The validity of the body mass metric aside, we can see that it cuts almost right down the middle of this data set with a steep incline of weight gain for each inch in height for the typical football player.Įxamining the athlete details from the NFL draft gives us a representation of the strengths of a scatter plot, using granular data points to build the impression of a pattern. Lastly, let’s add a trend line-right-click in the white space in the visualization and select Show Trend Line.įigure 3: The scatter plot with finishing touches. Next, we’ll include a quick filter by positional group (by right-clicking on the positional group dimension and selecting Show Quick Filter) and filter out everything except Receivers & Secondary and the Linemen. We’ll edit the color legend to show anything over 30 shaded in red. First, we’ll color code the marks with a BMI score adjusted for athletes by dragging and dropping the BMI score field onto the Color shelf in the marks card. Let’s add a few finishing touches to our visualization. ![]() On the other side of the scatter plot are the speed positions, particularly the receivers, corners and safeties, mixed in among kickers and punters. Bridging the gap between the offensive and defensive line to the rest of the pack are the tight ends. The biggest players on the field are the linemen and overwhelmingly so. Let’s change the mark type to Shape and drop a dimension that lists positional groupings (backfield, QBs, LBs, etc.) onto the Shape shelf and see if we can identify a pattern within these two clusters. There seems to be two different types of physical types that excel at the elite level in football. Just from a casual glance, we can see that the scatter plot seems to form two main clusters. In Figure 1 below, we’re examining the height and weight measurements from the 2014 NFL draft ( Weight is on the Column shelf and Height on the Row shelf). The big difference with a scatter plot is that both axes in the chart are measures rather than dimensions (one measure on the Column shelf and another measure on the Row shelf). Like the circle view and the side-by-side circle chart, the scatter plot also uses symbols to visualize data. The scatter plot, also known as a scatter diagram, scatter chart, scattergram or scatter graph, is useful to compare two different measures for patterns. ![]() Since there are so many cool features to cover in Tableau, the series will include several different posts. The series is intended to be an easy-to-read reference on the basics of using Tableau Software, particularly Tableau Desktop. To help Tableau rookies, we’re starting from square one with the Tableau Essentials blog series. Not everyone is a Tableau guru, at least not yet. ![]()
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