How to create a Correlation plot in R Studio using the Boston Housing dataset

In this video we discuss the correlation matrix using the Corr plot function:
The corrplot function is part of the corrplot package in R and is used to create a graphical representation of a correlation matrix.
Here are some common arguments you can use with the corrplot function:
1. corr: This argument specifies the correlation matrix that you want to plot. It can be a numeric matrix or a correlation object returned by a function such as cor().
2. method: This argument specifies the method for computing the correlation coefficients. The default method is "pearson". Other options include "spearman" and "kendall".
3. type: This argument specifies the type of correlation plot you want to create. Common options include "upper", "lower", "full", and "number". The default is "upper", which shows only the upper triangle of the correlation matrix.
4. tl.col: This argument specifies the color of the text labels for the rows and columns of the correlation matrix.
5. tl.srt: This argument specifies the rotation angle of the text labels for the rows and columns of the correlation matrix.
6. diag: This argument specifies whether to include the diagonal elements of the correlation matrix in the plot. The default is "FALSE".
7. cl.pos: This argument specifies the position of the color legend. Common options include "top", "bottom", "left", and "right".
8. addColorbar: This argument specifies whether to add a color bar to the plot. The default is "TRUE".
9. col: This argument specifies the color palette to use for the correlation plot. Common options include "RdBu", "YlOrRd", and "Blues".
10. title: This argument specifies the title of the plot. The default is no title.
These are just some of the arguments that you can use with the corrplot function. You can find more information about the corrplot function and its arguments in the documentation.

[@alexandraprokhorova932]

Fantastic video. Thank you so much for such thorough explanation.

[@theoutlier7395]

i am glad you liked it!

[@nishithnaveen4854]

Super explanation

[@theoutlier7395]

Thanks Nishit, glad you liked it!!