Demonstrating Editorial Focus and Learning About Your Data - Data Visualization: A Successful Design Process - page 66

We can see that the event order goes from left to right in reverse chronological order
and the vertical sorting is organized by the most successful nations as at 2008. In
addition to the medal winning totals for the selected countries, we also have the
aggregate of all medals across all countries.
We now continue our examination by noting some of the dataset's descriptive and
statistical properties to develop an increased level of familiarity:
• Two variables: Country and event year
• Country is a categorical nominal variable with nine values (each country
and the aggregate)
• Event year is a quantitative (interval-scale) variable with five values
• The maximum country medal count value is 110 medals, the minimum is 15
• The maximum aggregate value is 951 and the minimum is 815 (but that
includes the Russian Federation contribution)
• Each event year is spaced 4 years apart
• The longest country name is People's Republic of China, the shortest is France
This gives us a sense of the physicality of the data and the potential influencing
attributes that might shape our visualization architecture.
What other data preparation tasks might we undertake?
We have no real transformation activities to undertake in terms of addressing data
quality aside from already deciding to ignore the Russian Federation total.
For transforming the data for its use in analysis we may decide to create some
calculations to show the percentage of medals won out of each event total. You may
also decide to abbreviate some of the county values to potentially help accommodate
the space required for labeling.
We also need to consider data consolidation. For the purpose of this demonstration, we
are going to stick to our original dataset on its own but there could be many different
options to enhance and contextualize this subject matter, including the following:
• The details behind the medal totals of how many golds, silvers, and bronzes
each country has won
• The full dataset of medal statistics for all the other countries who have
competed, not just the recent top eight
• The full dataset of medal statistics for every Olympic games
• The number of competitors who were taking part in the games for each
country, in order to understand the percentage of success of each team
• The split of performances between the different sporting events