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This article mainly explains "how to use corrplot in R language to draw the heat map of correlation coefficient matrix". The content of the explanation in this article is simple and clear, and it is easy to learn and understand. please follow the editor's train of thought to study and learn "how to use corrplot in R language to draw correlation coefficient matrix heat map".

R language is also one of the commonly used data analysis programming languages, and its function is also powerful after the development of users and scientists. This paper introduces the use of corrplot in R language to draw the heat map of correlation coefficient matrix.

Take the house price data of Boston in Boston as an example, which is commonly used in data mining and machine learning, and can be downloaded from UCI's machine learning database: archive.ics.uci.edu/ml/

The dataset has 506 observation samples and 14 variables:

First, import the data into the R software:

> mydata summary (mydata)

Then calculate the correlation coefficient and download the correlation coefficient matrix corrplot package:

> res install.packages ("corrplot") # download the correlation coefficient matrix corrplot package

> corrplot (res, method = "shade", shade.col = NA, tl.col = "black", tl.srt = 45, order = "AOE") # draw the matrix diagram of correlation coefficient

Get the graphics:

The darker the color in the correlation coefficient diagram, the closer the correlation coefficient between the two variables is. As can be seen in the chart, the median house price (MEDV) column of Boston has the greatest correlation with variables such as LSTAT, PTRATIO, RM, etc.

Let's focus on the function corrplot:

In the command corrplot (res, method = "shade", shade.col = NA, tl.col = "black", tl.srt = 45, order = "AOE")

Res: represents the correlation coefficient matrix that needs to be visualized

Method = "shade" represents the specified visualization method, which can be circular, square, oval, numeric, shadow, color, or pie figure, method = c ("circle", "square", "ellipse", "number", "shade", "color", "pie")

Shade.col = NA: specifies the color of the graphic display, which is displayed in a uniform color by default

Tl.col: specifies the color of the text label, that is, the color of the text label of the graphic. The default is black.

Tl.srt = 45, indicating the size of the text label

Order = "AOE": specify the sorting method of correlation coefficient, which can be original order (original), feature vector angular order (AOE), first principal component order (FPC), hierarchical clustering order (hclust) and alphabetical order. Generally, the sorting result of "AOE" is better than that of "FPC". Order = c ("original", "AOE", "FPC", "hclust", "alphabet").

According to the different attributes of the corrplot function, you can transform the attributes and draw different correlation coefficient matrix heat maps:

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