library(plotly)
library(magrittr)

# compute a correlation matrix
correlation <- round(cor(mtcars), 3)
nms <- names(mtcars)


colorlength <- 100

null_value <- (0 - min(correlation)) / (max(correlation) - min(correlation))        
border <- as.integer(null_value * colorlength)
colorscale <- as.list(1:colorlength)

#colorscale below zero
s <- scales::seq_gradient_pal("blue","white","Lab")(seq(0,1,length.out=border))
for (i in 1:border) {
  colorscale[[i]] <- c((i - 1) / colorlength, s[i])
}

#colorscale above zero
s <- scales::seq_gradient_pal("white","red","Lab")(seq(0,1,length.out=colorlength - border))
for (i in 1:(colorlength - border)) {
  colorscale[[i + border]] <- c((i + border) / colorlength, s[i])
}



plot_ly(x = nms, y = nms, z = correlation,
            key = correlation, type ="heatmap", source ="heatplot",color = ~correlation,
            colorscale = colorscale,
            colorbar = list(len=1)) %>%
      layout(xaxis = list(title =""),
             yaxis = list(title =""))

col3 <- colorRamp(c("red","white","blue"))

plot_ly(x = nms, y = nms, z = correlation,
        key = correlation, type ="heatmap", source ="heatplot",color = col3,
        colorscale = colorscale,
        colorbar = list(len=1, limits = c(-1, 1))) %>%
    layout(xaxis = list(title =""),
           yaxis = list(title =""))

library(tidyverse);
gg <- correlation %>%
    as.data.frame() %>%
    rownames_to_column("x") %>%
    gather(y, val, -x) %>%
    ggplot(aes(x, y, fill = val)) +
    geom_tile() +
    scale_fill_gradientn(
        colours = c("blue","white","red"),
        limits = c(-1, 1))

# Create plotly object
library(plotly);
ggplotly(gg);