![]() Random: set missing values to value of randomly chosen observation on that variable Min10: set missing values to 10% below the minimum of the variable Median: set missing values to the median of the variable Mean: set missing values to the mean of the variable Missing is a character string that denotes how to handle missingĮxclude: remove all cases with missing values Globalminmax: no scaling is done the range of the graphs is definedīy the global minimum and the global maximumĬenter: use uniminmax to standardize vertical height, thenĬenter each variable at a value specified by the scaleSummary paramĬenterObs: use uniminmax to standardize vertical height, thenĬenter each variable at the value of the observation specified by the centerObsID param Uniminmax: univariately, scale so the minimum of the variable is zero, and the maximum is one Robust: univariately, subtract median and divide by median absolute deviation Std: univariately, subtract mean and divide by standard deviation Scale is a character string that denotes how to scale the variables Max for each variable (no box is plotted if shadeBox = NULL) mappingĪes string to pass to ggplot object titleĬharacter string denoting the title of the plot Underlay the distribution of each variable shadeBoxĬolor of underlying box which extends from the min to the Logical operator indicating whether or not boxplots should Value of alpha scaler for the lines of the parcoord plot or a column name of the data boxplot Numeric values will multiplied by the number of columns, TRUE will default to cubic interpolation, AsIs to set the knot count directly and 0, FALSE, or non-numeric values will not use spline interpolation. Logical or numeric operator indicating whether spline interpolation should be used. Logical operator indicating whether points should be ![]() Method used to order the axes (see Details) showPoints Method used to handle missing values (see Details) order If scale="centerObs", row number of case plot should If scale="center", summary statistic to univariately Method used to scale the variables (see Details) scaleSummary This is how parallel sets are beneficial.A vector of variables (either names or indices) to be axes in the plot groupColumnĪ single variable to group (color) by scale It further divides the ribbon into an adult or a child, which are in the third dimension, and so on if the dimensions continue. The ribbons from both these categories move and deviate into male and female categories of the second dimension. What kind of data you can visualize with it?Īssume you have a dataset in which we have categories of survived and dead people due to a ship sink that falls into the first dimension. ![]() It looks attractive as well as saves time at the same time. Every deviation of that ribbon from a dimension to further dimensions is easy to understand insight. Parallel sets are useful as they make ribbons of connection which are easy to spot when their width is high. Each category from the first dimension goes to the categories of the second dimension, and so on. The width of the bar denotes the higher value of that category. Each dimension has a horizontal line, which divides into the number of categories of that dimension. A parallel sets plot is a representation of various dimensions with relationships.
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