![]() The resulting slopes formed between the two ends of each line provide an indication of the local trends between points in time. Quantitative values are plotted using joined-up lines that effectively connect consecutive points positioned along a y-axis. They are typically structured around a temporal x-axis with equal intervals from the earliest to latest point in time. The bivariate scatter plots can be found on the lower part of the plot and contain a fitted line by default.Ī line chart can help show how quantitative values for different categories have changed over time. In the diagonal part of the plot are histograms for every variable and show you the distribution of the variable. There is a range from zero to three stars and the higher the number of stars, the higher is the significance of the results for the test. The red stars show you the results of the implemented correlation test. The upper part consists of the correlation coefficients for the different variables. It splits the plot into an upper, lower and diagonal part. The scatter plot matrix from this package is already very nice by default. # Now calling the chart.Correlation() function and defining a few parameters.Ĭhart.Correlation(data, histogram = TRUE) After doing that, you can start to select the variables which will be displayed in the plot. To access the scatter plot matrix from this package, you have to install the package and import the library. A convenient way to visualize multiple variables in a scatter plot matrix is offered by the PerformanceAnalytics package. The normal scatter plot is only useful if you want to know the relationship between two variables, but often you are interested in more than two variables.
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