"Any conclusion drawn from an analysis of a transformed variable must be retranslated into the original domain - which is usually not an easy task. A special handling of outliers, be it a complete removal, or just visual suppression such as hot-selection or shadowing, must have a cogent motivation. At any rate, transformations of data are usually part of a data preprocessing step that might precede a data analysis. Also it can be motivated by initial findings in a data analysis which revealed yet undiscovered problems in the dataset." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Basically, one can distinguish three motivations for weighted data. The first is a technical motivation. Whenever we look at purely categorical data, it is not necessary to supply a dataset case by case. A breakdown summary can capture the dataset without loss of any information. […] The second situation in which weights are introduced is when sampling unequally from a population. Statistics and graphics must then account for the weights. A third reason to use weights is a change of the sampling population." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Choropleth maps are most effective when the range of the color-shading is fully used, i.e., the visual discrimination is maximized. A skewed distribution [...] will shrink the chosen colors to just a fraction of the possible color range. Using a continuously differentiable transformation function [...] is one way to expand the range of colors used. A more effective way to maximize the visual discrimination in a choropleth map is to transform the data to match a target distribution. One option is to force all colors to have the same frequency, i.e., to force the target distribution to be uniform. Another option is to force a normal target distribution. Obviously, the transfer function needed for this transformation is data dependent and piecewise linear." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Due to their recursive definition, switching the order of variables in a mosaic plot has a strong impact on what can be read from the plot. For instance, exchanging the two variables in a two-dimensional mosaic plot results in a completely new plot rather than in a mere graphically transposed version of the original plot." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Histograms are powerful in cases where meaningful class breaks can be defined and classes are used to select intervals and groups in the data. However, they often perform poorly when it comes to the visualization of a distribution." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Mosaic plots are defined recursively, i.e., each variable that is introduced in a mosaic plot is plotted conditioned on the groups already established in the plot. As with barcharts, the area of bars or tiles is proportional to the number of observations (or the sum of the observation weights of a class). The direction along which bars are divided by a newly introduced variable is usually alternating, starting with the x-direction." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Mosaic plots become more difficult to read for variables with more than two or three categories. One way out is to assign a constant space for all possible crossings of categories. This way, the data from the r×c table are plotted in a table-like layout. Whereas this regular layout makes it much easier to compare values across rows and columns, the plot space is used less efficiently than in a mosaic plot." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"No other statistical graphic can hold so much information at a time than the parallel coordinate plot. Thus this plot is ideal to get an initial overview of a dataset, or at the very least a large subgroup of the variables." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"One big advantage of parallel coordinate plots over scatterplot matrices. (i.e., the matrix of scatterplots of all variable pairs) is that parallel coordinate plots need less space to plot the same amount of data. On the other hand, parallel coordinate plots with p variables show only p − 1 adjacencies. However, adjacent variables reveal most of the information in a parallel coordinate plot. Reordering variables in a parallel coordinate plot is therefore essential." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Parallel coordinate plots are often overrated concerning their ability to depict multivariate features. Scatterplots are clearly superior in investigating the relationship between two continuous variables and multivariate outliers do not necessarily stick out in a parallel coordinate plot. Nonetheless, parallel coordinate plots can help to find and understand features such as groups/clusters, outliers and multivariate structures in their multivariate context. The key feature is the ability to select and highlight individual cases or groups in the data, and compare them to other groups or the rest of the data." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Presentation graphics face the challenge to depict a key message in - usually a single - graphic which needs to fit very many observers at a time, without the chance to give further explanations or context. Exploration graphics, in contrast, are mostly created and used only by a single researcher, who can use as many graphics as necessary to explore particular questions. In most cases none of these graphics alone gives a comprehensive answer to those questions, but must be seen as a whole in the context of the analysis." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Shingling is the process of dividing a continuous variable into - possibly overlapping - intervals in order to convert a continuous variable into a discrete variable. Shingling is quite different from conditioning on categorical variables. Overlapping shingles/intervals lead to multiple representation of data within a trellis display, which is not the case for categorical variables. Furthermore, it is challenging to judge which intervals/cases have been chosen to build a shingle. Trellis displays represent the shingle interval visually by an interval of the strip label. Although no plotting space is wasted, the information on the intervals is difficult to read from the strip label. Despite these drawbacks, there is a valid motivation for shingling […]." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Spineplots have the nice property that highlighted proportions can be compared directly. However, it must be noted that the x axis in a spinogram is no longer linear. It is only piecewise linear within the bars. Although this might be confusing at first sight, it yields two interesting characteristics. Areas where only very few cases have been observed are squeezed together and thus get less visual weight. [...] Spineplots use normalized bar lengths while the bar widths are proportional to the number of cases in the category" (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Sorting data is one of the most efficient actions to derive different views of data in order to see the variables from many angles. Sorting is usually not applied to the data itself, but to statistical objects of a plot. We might want to sort the bars in a barchart, the variables in a parallel boxplot or the categories in a boxplot y by x." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"The problem of overplotting can be as severe that (smaller) groups can disappear completely, which will not only lead to quantitatively biased inferences, but even to qualitatively inappropriate conclusions." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"There are many reasons for the existence of missing values: the failure of a sensor, different recording standards for different parts of a sample, or structural differences of the objects observed that make it impossible to record all attributes for all observed instances." (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
"Trellis displays introduce the concept of shingling. Shingling is the process of dividing a continuous variable into - possibly overlapping - intervals in order to convert a continuous variable into a discrete variable. Shingling is quite different from conditioning on categorical variables. Overlapping shingles/intervals lead to multiple representation of data within a trellis display, which is not the case for categorical variables. Furthermore, it is challenging to judge which intervals/cases have been chosen to build a shingle. Trellis displays represent the shingle interval visually by an interval of the strip label. Although no plotting space is wasted, the information on the intervals is difficult to read from the strip label. Despite these drawbacks, there is a valid motivation for shingling," (Martin Theus & Simon Urbanek, "Interactive Graphics for Data Analysis: Principles and Examples", 2009)
