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Visualization of Categorical Data Using Extracat Package in R

Visualization of Categorical Data Using Extracat Package in R AbstractVisualization in research process plays a crucial role. There are several advanced plots for visualizing categorical data, such as mosaic, association, double-decker, sieve or fourfold plot that are based on the graphical presentation of residuals in a contingency table. In this paper we present new methods for visualizing categorical data such as rmb, fluctile and scpcp plot available in extracat package in R. This package provides a well-structured representation of categorical data and allows for a detailed presentation of the relationship between categories in terms of proportions. We describe rmb, fluctile and cpcp. Those plots are based on the concept of multiple bar charts, a fluctuation diagram from a multidimensional table and parallel coordinates respectively. Such plots are mostly used for a visualization of a contingency table or a data frame; they can also be used for exploratory analysis and allows for a graphical presentation even for a high number of variables [Pilhöfer, Unwin 2013]. All the calculations and plots are obtained using R software. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Econometrics de Gruyter

Visualization of Categorical Data Using Extracat Package in R

Econometrics , Volume 22 (2): 11 – Jun 1, 2018

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Publisher
de Gruyter
Copyright
© 2018 Justyna Brzezińska, published by Sciendo
eISSN
2449-9994
DOI
10.15611/eada.2018.2.01
Publisher site
See Article on Publisher Site

Abstract

AbstractVisualization in research process plays a crucial role. There are several advanced plots for visualizing categorical data, such as mosaic, association, double-decker, sieve or fourfold plot that are based on the graphical presentation of residuals in a contingency table. In this paper we present new methods for visualizing categorical data such as rmb, fluctile and scpcp plot available in extracat package in R. This package provides a well-structured representation of categorical data and allows for a detailed presentation of the relationship between categories in terms of proportions. We describe rmb, fluctile and cpcp. Those plots are based on the concept of multiple bar charts, a fluctuation diagram from a multidimensional table and parallel coordinates respectively. Such plots are mostly used for a visualization of a contingency table or a data frame; they can also be used for exploratory analysis and allows for a graphical presentation even for a high number of variables [Pilhöfer, Unwin 2013]. All the calculations and plots are obtained using R software.

Journal

Econometricsde Gruyter

Published: Jun 1, 2018

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