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Interactive graphics: exemplified with real data applications.

Malik WA, Unlü A - Front Psychol (2011)

Bottom Line: Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively.Static plots do not allow interacting with graphics.The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Augsburg Augsburg, Germany.

ABSTRACT
Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively. Static plots do not allow interacting with graphics. User interaction, on the other hand, is crucial in exploring data. It gives flexibility and control. One can experiment with the data and the displays. One can investigate the data from different perspectives to produce views that are easily interpretable and informative. In this paper, we try to explain interactive graphics and advocate their use as a practical tool. The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

No MeSH data available.


Scatterplots of arrival time by departure time. The departure and arrival times in “hhmm” format in scatterplot on the left show consistent gaps in the data which make a regular square pattern. The scatterplot on the right is drawn after converting times into numeric format, which removes the fake pattern that is visible in the left plot.
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Figure 1: Scatterplots of arrival time by departure time. The departure and arrival times in “hhmm” format in scatterplot on the left show consistent gaps in the data which make a regular square pattern. The scatterplot on the right is drawn after converting times into numeric format, which removes the fake pattern that is visible in the left plot.

Mentions: The format of date/time type variables in which they are recorded and stored is very critical and highly influential on analysis and can give wrong analysis results if they are not properly handled. The arrival and departure times in the air flight delay dataset are stored as character in “hhmm” local time format. Because of this format a regular pattern is visible in Figure 1 (left), which is misleading and fake. This artifact can be overcome by converting the arrival and departure times into numeric format. This fake regular square pattern disappears in Figure 1 (right) when these times are converted into numeric type.


Interactive graphics: exemplified with real data applications.

Malik WA, Unlü A - Front Psychol (2011)

Scatterplots of arrival time by departure time. The departure and arrival times in “hhmm” format in scatterplot on the left show consistent gaps in the data which make a regular square pattern. The scatterplot on the right is drawn after converting times into numeric format, which removes the fake pattern that is visible in the left plot.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3111423&req=5

Figure 1: Scatterplots of arrival time by departure time. The departure and arrival times in “hhmm” format in scatterplot on the left show consistent gaps in the data which make a regular square pattern. The scatterplot on the right is drawn after converting times into numeric format, which removes the fake pattern that is visible in the left plot.
Mentions: The format of date/time type variables in which they are recorded and stored is very critical and highly influential on analysis and can give wrong analysis results if they are not properly handled. The arrival and departure times in the air flight delay dataset are stored as character in “hhmm” local time format. Because of this format a regular pattern is visible in Figure 1 (left), which is misleading and fake. This artifact can be overcome by converting the arrival and departure times into numeric format. This fake regular square pattern disappears in Figure 1 (right) when these times are converted into numeric type.

Bottom Line: Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively.Static plots do not allow interacting with graphics.The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of Augsburg Augsburg, Germany.

ABSTRACT
Graphics are widely used in modern applied statistics because they are easy to create, convenient to use, and they can present information effectively. Static plots do not allow interacting with graphics. User interaction, on the other hand, is crucial in exploring data. It gives flexibility and control. One can experiment with the data and the displays. One can investigate the data from different perspectives to produce views that are easily interpretable and informative. In this paper, we try to explain interactive graphics and advocate their use as a practical tool. The benefits and strengths of interactive graphics for data exploration and data quality analyses are illustrated systematically with three complex real datasets.

No MeSH data available.