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. ABSTRACTGraphics 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. © Copyright Policy - open-access Related In: Results  -  Collection License getmorefigures.php?uid=PMC3111423&req=5 .flowplayer { width: px; height: px; } Figure 19: Parallel coordinate plots of seven related variables. Signals of duration between 117 and 122 s are highlighted, and rest of the cases are filtered out. Mentions: In scatterplots of different variables, it has been observed that variables are associated with each other. If the counts are high then the signal duration must be long, if signals have high absolute energy then amplitude should be high, and so on. A parallel coordinate plot visualizes multivariate continuous variables. High dimensional plots are especially helpful in identifying anomalies. A parallel coordinate plot of seven variables is shown in Figure 19 (top). Signals of duration between 117 and 122 have been selected and the rest of the cases filtered out in Figure 19 (bottom). An outlier case is shown in it. The outlier case in AverageFrequency is also an outlier in Counts. This is obvious since average frequency is derived from counts and duration.

Interactive graphics: exemplified with real data applications.

Malik WA, Unlü A - Front Psychol (2011)

Related In: Results  -  Collection

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Figure 19: Parallel coordinate plots of seven related variables. Signals of duration between 117 and 122 s are highlighted, and rest of the cases are filtered out.
Mentions: In scatterplots of different variables, it has been observed that variables are associated with each other. If the counts are high then the signal duration must be long, if signals have high absolute energy then amplitude should be high, and so on. A parallel coordinate plot visualizes multivariate continuous variables. High dimensional plots are especially helpful in identifying anomalies. A parallel coordinate plot of seven variables is shown in Figure 19 (top). Signals of duration between 117 and 122 have been selected and the rest of the cases filtered out in Figure 19 (bottom). An outlier case is shown in it. The outlier case in AverageFrequency is also an outlier in Counts. This is obvious since average frequency is derived from counts and duration.

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.