Creating an Interactive Correlation Heatmap
An interactive plot shows detailed information of each data point when the user hovers on the plot. Let’s check out how to plot an interactive correlation heatmap using the correlation matrix and p-value matrix. ggplotly( ) function takes in a correlation matrix of the data and gives an interactive heatmap plot and the details can be viewed on hovering on the map.
Function: ggplotly( p = ggplot2::last_plot(), width = NULL, height = NULL … )
Arguments:
- p – a ggplot object
- width – Width of the plot in pixels (optional, defaults to automatic sizing)
- height – Height of the plot in pixels (optional, defaults to automatic sizing)
R
# install and load the plotly packageinstall.packages("plotly")library(plotly)library(ggcorrplot)# create corr matrix and# corresponding p-value matrixcorr_mat <- round(cor(data),2)p_mat <- cor_pmat(data)# plotting the interactive corr heatmapcorr_mat <- ggcorrplot( corr_mat, hc.order = TRUE, type = "lower", outline.col = "white", p.mat = p_mat)ggplotly(corr_mat) |
Output:
How to Create Correlation Heatmap in R
In this article let’s check out how to plot a Correlation Heatmap in R Programming Language.
Analyzing data usually involves a detailed analysis of each feature and how it’s correlated with each other. It’s essential to find the strength of the relationship between each feature or in other words how two variables move in association to each other. If the variables grow together in the same direction it’s a positive correlation otherwise a negative correlation. This correlation can be visualized via various graphs such as scatter plots etc.