Tell me more about End Topics in Data Maps
This is probably the trickiest aspect of data maps. Even though it was explained in the help topic on data maps, a second explanation might be useful.
Decide the purpose of a data map
Recall that the purpose of a data map is to break down data by categories, where the categories are those in the columns designated as grouped topics. The example used here is of Premier League soccer players. (This is one of the BigPicture example spreadsheets found from the Help dropdown list.)
Show the map with grouped topics only
The columns selected for grouped topics in this example are Team Rank and then Team. The section of the map showing only the grouped topics shows the various team ranks and the teams in any particular rank, here the highest rank group. However, this doesn't provide much information without end topics. (The numbers in the topics shown here actually result from end topic settings, as explained below.)
Fill in the End Topics tab
This is where you specify the information you want to show in the end table. You can choose any combination of columns for the end topics: categorical, numeric, or distinct-value text such as Player. In the dialog, a + or # sign is often shown next to a row. For example, the + sign next to Position means that the end table will include averages over players in each position. The # signs next to the numeric columns indicate that they are candidates for summary measures, including rollups to the grouped topics. However, as the Value end topic row indicates, rollups aren't required, even if they make sense.
To understand these choices better, the following substeps show typical results from the map.
Summarize by a categorical end topic column
You can do this for any end topic with a plus sign next to it. The following section of the map results from choosing Position in the View box. The end table for the Everton team shows averages for each position, and the team topic to its left shows the rollup calculations (averages over all positions) for Everton.
You can always do this to show a table row for each row in the data set corresponding to that grouped topic joint category. The following section of the map results from choosing Detail in the View box. The end table now has a row for each Everton player, so the numeric values in the table are original data values, not averages. (The left two columns are requested markers.)
Tables versus Individual Map Topics
In the "Display End Topics in" box, you can choose Tables or Individual Map Topics. If you choose the latter, you get the following warning. If you go ahead anyway, you get a separate topic for each player instead of a compact table. In an example like this one, with many players on each team, a table is almost certainly preferable.
Understand the concepts
Each data set provides a new challenge for creating a data map. Again, however, the conceptual steps are straightforward and always the same:
1. Decide which columns you want to use for categorical breakdowns. This determines the grouped topics.
2. Decide the information you want for each combination of grouped topic categories. This determines the end topic settings.