Demonstrating Editorial Focus and Learning About Your Data - Data Visualization: A Successful Design Process - page 54

Data Types Range

Result Quantitative (ratio-scale) Race results (9.59s > 4:02:59)

Country Categorical nominal 96 different country names

Transforming for quality : This task is naturally about tidying and cleaning your
data in response to the examination stage above. We are looking to resolve any of the
errors we discovered in order to transform the condition of the data we're going to
be working with for our design. Plugging the gaps caused by missing data, removing
duplicates, cleaning up erroneous values, and handling uncommon characters are
just some of the treatments we may be required to apply.

Transforming for analysis : In contrast to transforming for quality, we move away
from cleaning data and focus more on preparing and refining it in anticipation of its
intended use for analysis and presentation. Here, we consider actions such as:

• Parsing (split up) any variables, such as extracting year from a date value
• Merging variables to form new ones, such as creating a whole name out of
title , forename , and surname
• Converting qualitative data/free-text into coded values or keywords
• Deriving new values out of others, such as gender from title or a sentiment out
of some qualitative data
• Creating calculations for use in analysis, such as percentage proportions
• Removing redundant data for which you have no planned use
(be careful though!)

Another important consideration is to determine what level of resolution you
might wish to, or indeed need to, present your data. The decision you take about
this may require you to aggregate or disaggregate your data to achieve get the
right level of detail.

Design agency Periscopic were faced with some intricate resolution decisions in their
preparatory work for this near real-time visualization developed about the Yahoo!
Mail network. The objective was to show the huge volumes of e-mails being sent and
processed around the world at any given point, and the efforts Yahoo! is taking to
help reduce and intercept spam e-mails. This is shown in the following screenshot: