Contour Plots in R

How to make a contour plot in R. Two examples of contour plots of matrices and 2D distributions.

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Basic Contour

library(plotly)

fig <- plot_ly(z = ~volcano, type = "contour")

fig

Set X and Y Coordinates

library(plotly)

fig <- plot_ly(
  x = c(-9, -6, -5, -3, -1), 
  y = c(0, 1, 4, 5, 7), 
  z = matrix(c(10, 10.625, 12.5, 15.625, 20, 5.625, 6.25, 8.125, 11.25, 15.625, 2.5, 3.125, 5, 8.125, 12.5, 0.625, 1.25, 3.125,
        6.25, 10.625, 0, 0.625, 2.5, 5.625, 10), nrow = 5, ncol = 5), 
  type = "contour" 
)

fig

Set Size and Range of a Contours

library(plotly)

fig <- plot_ly(
  type = 'contour',
  z = matrix(c(10, 10.625, 12.5, 15.625, 20, 5.625, 6.25, 8.125, 
               11.25, 15.625, 2.5, 3.125, 5, 8.125, 12.5, 0.625, 
               1.25, 3.125, 6.25, 10.625, 0, 0.625, 2.5, 5.625, 
               10), nrow=5, ncol=5),
  colorscale = 'Jet',
  autocontour = F,
  contours = list(
    start = 0,
    end = 8,
    size = 2
  )
)

fig

Smoothing Contour Lines

library(plotly)

fig1 <- plot_ly(
  type = "contour", 
  z = matrix(c(2, 4, 7, 12, 13, 14, 15, 16, 3, 1, 6, 11, 12, 13, 
               16, 17, 4, 2, 7, 7, 11, 14, 17, 18, 5, 3, 8, 8, 13, 
               15, 18, 19, 7, 4, 10, 9, 16, 18, 20, 19, 9, 10, 5, 27, 
               23, 21, 21, 21, 11, 14, 17, 26, 25, 24, 23, 22), 
             nrow=7, ncol=8), 
  autocontour = TRUE, 
  contours = list(
    end = 26, 
    size = 2, 
    start = 2
  ), 
  line = list(smoothing = 0)
)

fig2 <- plot_ly(
  type = "contour", 
  z = matrix(c(2, 4, 7, 12, 13, 14, 15, 16, 3, 1, 6, 11, 12, 13, 
               16, 17, 4, 2, 7, 7, 11, 14, 17, 18, 5, 3, 8, 8, 13, 
               15, 18, 19, 7, 4, 10, 9, 16, 18, 20, 19, 9, 10, 5, 27, 
               23, 21, 21, 21, 11, 14, 17, 26, 25, 24, 23, 22), 
             nrow=7, ncol=8),
  autocontour = TRUE, 
  contours = list(
    end = 26, 
    size = 2, 
    start = 2
  ), 
  line = list(smoothing = 0.85)
) 

fig <- subplot(fig1,fig2)

fig

Smoothing Contour Coloring

library(plotly)

fig <- plot_ly(
  type = 'contour',
  z = matrix(c(10, 10.625, 12.5, 15.625, 20, 5.625, 6.25, 8.125, 
               11.25, 15.625, 2.5, 3.125, 5, 8.125, 12.5, 0.625, 
               1.25, 3.125, 6.25, 10.625, 0, 0.625, 2.5, 5.625, 
               10), nrow=5, ncol=5),
  contours = list(
    coloring = 'heatmap'
  )
)

fig

Add Contour Labels

library(plotly)

fig <- plot_ly(z = volcano, type = "contour", contours = list(showlabels = TRUE))
fig <- fig %>% colorbar(title = "Elevation \n in meters")

fig

Create Matrix and Plot Contour

This example is based on (this)[https://www.r-statistics.com/2016/07/using-2d-contour-plots-within-ggplot2-to-visualize-relationships-between-three-variables/] r-statistics post.

library(plotly)
library(stringr)
library(reshape2)

data.loess <- loess(qsec ~ wt * hp, data = mtcars)

# Create a sequence of incrementally increasing (by 0.3 units) values for both wt and hp
xgrid <-  seq(min(mtcars$wt), max(mtcars$wt), 0.3)
ygrid <-  seq(min(mtcars$hp), max(mtcars$hp), 0.3)
# Generate a dataframe with every possible combination of wt and hp
data.fit <-  expand.grid(wt = xgrid, hp = ygrid)
# Feed the dataframe into the loess model and receive a matrix output with estimates of
# acceleration for each combination of wt and hp
mtrx3d <-  predict(data.loess, newdata = data.fit)
# Abbreviated display of final matrix
mtrx3d[1:4, 1:4]

##           hp
## wt         hp= 52.0 hp= 52.3 hp= 52.6 hp= 52.9
##   wt=1.513 19.04237 19.03263 19.02285 19.01302
##   wt=1.813 19.25566 19.24637 19.23703 19.22764
##   wt=2.113 19.55298 19.54418 19.53534 19.52645
##   wt=2.413 20.06436 20.05761 20.05077 20.04383

# Transform data to long form
mtrx.melt <- melt(mtrx3d, id.vars = c('wt', 'hp'), measure.vars = 'qsec')
names(mtrx.melt) <- c('wt', 'hp', 'qsec')
# Return data to numeric form
mtrx.melt$wt <- as.numeric(str_sub(mtrx.melt$wt, str_locate(mtrx.melt$wt, '=')[1,1] + 1))
mtrx.melt$hp <-  as.numeric(str_sub(mtrx.melt$hp, str_locate(mtrx.melt$hp, '=')[1,1] + 1))

fig <- plot_ly(mtrx.melt, x = ~wt, y = ~hp, z = ~qsec, type = "contour",
             width = 600, height = 500)

fig

2D Density Contour Plot

x <- rnorm(200)
y <- rnorm(200)
s <- subplot(
  plot_ly(x = x, type = "histogram"),
  plotly_empty(),
  plot_ly(x = x, y = y, type = "histogram2dcontour"),
  plot_ly(y = y, type = "histogram"),
  nrows = 2, heights = c(0.2, 0.8), widths = c(0.8, 0.2), margin = 0,
  shareX = TRUE, shareY = TRUE, titleX = FALSE, titleY = FALSE
)
fig <- layout(s, showlegend = FALSE)

fig

Contour Colorscales

See here for more examples concerning colorscales!

Reference

See https://plotly.com/r/reference/#contour for more information and chart attribute options!

What About Dash?

Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash for R at https://dashr.plot.ly/installation.

Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this:

library(plotly)

fig <- plot_ly() 
# fig <- fig %>% add_trace( ... )
# fig <- fig %>% layout( ... ) 

library(dash)
library(dashCoreComponents)
library(dashHtmlComponents)

app <- Dash$new()
app$layout(
    htmlDiv(
        list(
            dccGraph(figure=fig) 
        )
     )
)

app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE)