Data visualization Research 2022

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Contents
Introduction.................................................................................................................... 3
Part A: Criteria................................................................................................................ 3
Common Criteria.......................................................................................................... 3
Graphs and Charts Criteria.............................................................................................. 4
Tables Criteria............................................................................................................. 5
Part B: Evaluation............................................................................................................ 6
Conclusion................................................................................................................... 15
References................................................................................................................... 16
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Introduction
Data visualization can be referred to the use of graphs and tables in the representation of aspects
of a dataset or the results of the data analysis processes (O'Neil & Schutt, 2013; Vicenc,
2017). This paper focuses on identifying possible criteria that can be used for guiding the
preparation and presentation of data visualizations using Schmitz, Kebber & Grieve (2019) as a
case study for the Full Year report for the German based RWE AG company.
Part A: Criteria
Common Criteria
1. Data visualizations should have captions. The captions are important in presenting a brief
summarization about what the visualizations present (Hastie, Tibshir & Jerome 2009).
2. Data visualizations should have consistency in terms of the figures with respect to
significant figures and/or decimal points. This consistency in the nature of the figures
enhances the comparative ability of data visualizations (Howitt & Cramer, 2010).
3. Data visualizations should have the source(s) indicated. The sources in the case of
visualizations can either imply another report being referenced to or the data used for the
analysis that produced visualizations (Vicenc, 2017).
4. The data visualization should also have consistency in terms of the measurement units.
This is however a very specific criterion, for data visualizations, that applies to instances
where a single aspect is being observed for different categories. The value of the aspect in
all the categories should be in the same measurement unit. Similarly, this consistency
enhances the comparative ability of data visualizations (Howitt & Cramer, 2010).
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Graphs and Charts Criteria
5. Graphs and charts should have titles. The title provides the end-user with information on
what the graph or chart is about and is expected to be detailed enough yet not too detailed
so as to retain the end-user’s interest (Hastie, Tibshir & Jerome 2009).
6. The axes for the graphs and charts should be scaled. The scales are important in the
indication of the magnitude and hence informs the interpretation of the chart (Roles,
Baeten & Signer 2016).
7. The axes for the graphs and charts should be labelled. The labelling of the axes provides
information on the variable being placed on the given axis (Horova, Kolacek & Zelinka,
2012).
8. The dependent variable(s) in a research should be placed on the y-axis while the
independent variable should be placed on the x-axis (O'Neil & Schutt, 2013).
9. In trend analysis and time series analysis in general, the time is placed on the x-axis of
the graph with the variable of interest (such as share prices or asset returns) placed on the
y-axis (Kabacoff, 2017).
10. In instances where the multiple variables are observed and plotted on the plane of a line
graph, the lines representing the variables should use different colors or different shades
(Horova, Kolacek & Zelinka, 2012).
11. The grouped variables plotted using cumulative bar graphs should have different colors
for each of the groups (Roles, Baeten & Signer 2016).
12. Charts and graphs on categorical or multiple variables should have legends. The legends
would give information on which color represent which group (Martinez, Martinez &
Solka 2010).
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