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This article introduces the relevant knowledge of "what are the R language related visualization functions". In the operation of actual cases, many people will encounter such a dilemma. Next, let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

1. Pairs {graphics}

1. Referenc

(1) "Modern Statistical Graph" 5.17 scatter plot matrix

(2) "R data Visualization Manual" 5.13 drawing scatter plot matrix

(3) pairs {graphics} help documentation

two。 Interpretation of main parameters

3. Graphic example

3.1 basic graphics

Pairs (iris [1:4], main = "Anderson's Iris Data-- 3 species", pch = 21, bg = c ("red", "green3", "blue") [unclass (iris$Species)]) # is equivalent to pairs (~ Sepal.Length+Sepal.Width+Petal.Length+Petal.Width, data=iris,main = "Anderson's Iris Data-- 3 species", pch = 21, bg = c ("red", "green3", "blue") [unclass (iris$Species)])

In the above example, the first four columns of Sepal.Length, Sepal.Width, Petal.Length and Petal.Width of iris Iris data set are selected to draw, and bg is set to make different colors represent different varieties of Iris. It can be seen that the diagonal of the scatter matrix generated by default is the name of the variable, and each pane of the upper triangle and the lower triangle panel is the scatter chart between the row and column, and the amount of information transmission is relatively small and repetitive. To this end, we can further modify and optimize through custom functions.

3.2 Custom Drawin

# 1. Custom function pannel.cor: displays the correlation coefficient between two variables, the larger the correlation coefficient, the larger the font size. Panel.cor

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