<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Visualization 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 <monospace>rmb</monospace>, <monospace>fluctile</monospace> and <monospace>scpcp</monospace> plot available in <monospace>extracat</monospace> 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 <monospace>rmb, fluctile</monospace> and <monospace>cpcp</monospace>. 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 [<xref ref-type="bibr" rid="j_eada.2018.2.01_ref_008_w2aab3b7ab1b6b1ab1ab8Aa">Pilhöfer, Unwin 2013</xref>]. All the calculations and plots are obtained using R software.</p></abstract>

Visualization 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.

Visualization of Categorical Data Using Extracat Package in R

Econometrics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Visualization of Categorical Data Using Extracat Package in R"}

Visualization in research process plays a crucial role. There are several advanced plots for visualizing categorical data, such as mosaic, association,...

Visualization of Categorical Data Using Extracat Package in R

Published Online: May 26, 2018

Page range: 9 - 19

DOI: https://doi.org/10.15611/eada.2018.2.01

Keywords
categorical data, plot, R software

© 2018 Justyna Brzezińska, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Visualization of Categorical Data Using Extracat Package in R

Published Online: May 26, 2018

Page range: 9 - 19

DOI: https://doi.org/10.15611/eada.2018.2.01

Keywordscategorical data, plot, R software

© 2018 Justyna Brzezińska, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
categorical data, plot, R software