How to visualise correlation in R

Visualizing correlation in R is an important step in understanding the relationship between different variables in a dataset. Correlation can be positive, negative or zero. Positive correlation means that as one variable increases, the other variable also increases. Negative correlation means that as one variable increases, the other variable decreases. Zero correlation means that there is no relationship between the variables.

There are various ways to visualize correlation in R, such as scatter plots, heatmaps, and correlation matrices.

Scatter plots: Scatter plots are a simple way to visualize the relationship between two variables. They show the relationship between two variables by plotting each data point as a dot on a graph. The dots are plotted according to the values of the two variables. A scatter plot can help to visualize the correlation between two variables by showing if the dots are moving in a positive or negative direction.

Heatmaps: Heatmaps are a way to visualize the relationship between multiple variables. They are useful for visualizing the relationship between multiple variables by plotting the variables in a matrix format. The cells of the matrix are colored according to the correlation between the variables.

Correlation matrices: Correlation matrices are a way to visualize the relationship between multiple variables. They are useful for visualizing the relationship between multiple variables by plotting the correlation coefficients in a matrix format. The cells of the matrix show the correlation coefficient between the variables.

It’s important to note that correlation does not imply causation, it means that two variables are related to each other. Also, it’s important to use the appropriate visualization for the type of data

In this Data Science Recipe, you will learn: How to visualise correlation in R.