Creating a continuous heat map in R

Question

I have a series of x and y coordinates that each have a distance attached to them. I would like to create a heat map that displays the average distance for every point within the x and y ranges as a heat map. Since the points are not spaced evenly from each other in a lattice-like shape, the method would require some kind of smoothing function that clusters data and calculates the average for each point the vicinity and then representing that average with a color.

So far, using ggplot2, I can only find methods like stat_density2d and geom_tile, which only work for displaying point density and representing evenly spaced points (as far as I can tell).

Ideally it would follow the same principle as this image:

in which colors were assigned based on the given points in the vicinity even though the density and placement of the points was not uniform.

I do not want to create a heat map in matrix form like this image:

in which a table is color-coded. Instead, I would like to create a continuous heat map using non-uniformly distributed x and y coordinates that, in effect, displays the limit in which the data is broken into infinitely many rectangles. This may not be the actual method used by the function, but it provides a general idea as to what I'm looking for.

Here is some sample data:

data=data.frame(x=c(1,1,2,2,3,4,5,6,7,7,8,9),
  y=c(2,4,5,1,3,8,4,8,1,1,6,9),
  distance=c(66,84,93,76,104,29,70,19,60,50,46,36))

How can I make a heat map with distance as the color scale that covers the entire range of numbers, like the plot in the first link provided?

Any help is greatly appreciated!

Primine

Have a look on this link. – Outman

In that example, you have to manually cut up the data into discrete rectangles. I would like the function to cut up the data as needed and to create a continuous heat map. – Primine

I think image2D can help you achieve what you want. rforscience.com/portfolio/volcano And it does the interpolation for you. – Counterstroke

Answer 1

In order to generate a continuous map with irregularly-spaced coordinates you need first to intrapolate a regular grid (here using function interp of package akima):

require(akima)
data <- data.frame(x=c(1,1,2,2,3,4,5,6,7,7,8,9),
                   y=c(2,4,5,1,3,8,4,8,1,1,6,9),
                   distance=c(66,84,93,76,104,29,70,19,60,50,46,36))
resolution <- 0.1 # you can increase the resolution by decreasing this number (warning: the resulting dataframe size increase very quickly)
a <- interp(x=data$x, y=data$y, z=data$distance, 
            xo=seq(min(data$x),max(data$x),by=resolution), 
            yo=seq(min(data$y),max(data$y),by=resolution), duplicate="mean")
image(a) #you can of course modify the color palette and the color categories. See ?image for more explanation

Or you can use, for the plotting itself, function filled.contour:

filled.contour(a, color.palette=heat.colors)

Homograft

Answer 2

There is a user-written function here that produces heatmaps using ggplot2:

http://www.r-bloggers.com/ggheat-a-ggplot2-style-heatmap-function/

And their example image:

If what you want is a topo map as in your example, there are plenty of tools for that (just search under "topo map".

And finally, there's the isarithmic map, which just goes to show that you need to make clear exactly what you want done if you want some smoothing incorporated: http://dsparks.wordpress.com/2011/10/24/isarithmic-maps-of-public-opinion-data/

Ictinus

Aw man. Your answer showed up in "Low Quality Posts" review. Perhaps you could add a little description about what the article says? meta.stackexchange.com/questions/8231/…. – Didymous

relevant, concise, to the point, and self-explanatory; imho this answer is perfectly fine. The question could use some minimal self-contained example however. – Inflationism

@Inflationism A brief summary of what could be found at the link is all we ask, just to guard against link rot. – Flicker

Noted and fixed. Thanks for the heads-up. – Ictinus

I do not want to create a color-coded table, as my data consists of non-uniformly distributed x and y coordinates. – Primine

Please follow the guidelines for asking a reproducible question so that we have some hope of answering: #5963769 – Ictinus

This answer is for discrete data, while the questions is on continuous data? – Penman

Answer 3

using the akima::interp solution suggested by @plannapus, you can convert it to a ggplot2 heatmap.

Advantage of this ggplot2 solution is that you can easily add initial points with geom_point() or density curves with geom_density2d() (although here density will be unreliable with the 12 points you have).

library(akima)
library(tidyverse)
data <- data.frame(x=c(1,1,2,2,3,4,5,6,7,7,8,9),
                   y=c(2,4,5,1,3,8,4,8,1,1,6,9),
                   distance=c(66,84,93,76,104,29,70,19,60,50,46,36))
resolution <- 0.1 # you can increase the resolution by decreasing this number (warning: the resulting dataframe size increase very quickly)
a <- interp(x=data$x, y=data$y, z=data$distance, 
            xo=seq(min(data$x),max(data$x),by=resolution), 
            yo=seq(min(data$y),max(data$y),by=resolution), duplicate="mean")


res <- a$z %>% 
  magrittr::set_colnames(a$y) %>% 
  as_tibble() %>% 
  mutate(x=a$x) %>% 
  gather(y, z, -x, convert=TRUE)

res %>% 
  ggplot(aes(x, y)) +
  geom_tile(aes(fill=z)) +
  geom_point(data=data) +
  scale_fill_viridis_c()

^{Created on 2020-01-29 by the reprex package (v0.3.0.9001)}

Penman

Answer 4

ggplot2::ggfluctuation(data, type="colour")

I can't give out all this data but the head is below in the dput structure.

structure(list(X1 = 236:241, HomeTeam = structure(c(8L, 19L, 
37L, 4L, 6L, 15L), .Label = c("Arizona Cardinals", "Atlanta Falcons", 
"Baltimore Ravens", "Buffalo Bills", "Carolina Panthers", "Chicago Bears", 
"Cincinnati Bengals", "Cleveland Browns", "Dallas Cowboys", "Denver Broncos", 
"Detroit Lions", "Green Bay Packers", "Houston Oilers", "Houston Texans", 
"Indianapolis Colts", "Jacksonville Jaguars", "Kansas City Chiefs", 
"Los Angeles Raiders", "Los Angeles Rams", "Miami Dolphins", 
"Minnesota Vikings", "New England Patriots", "New Orleans Saints", 
"New York Giants", "New York Jets", "Oakland Raiders", "Philadelphia Eagles", 
"Phoenix Cardinals", "Pittsburgh Steelers", "San Diego Chargers", 
"San Francisco 49ers", "Seattle Seahawks", "St. Louis Rams", 
"Tampa Bay Buccaneers", "Tennessee Oilers", "Tennessee Titans", 
"Washington Redskins"), class = "factor"), AwayTeam = structure(c(9L, 
28L, 11L, 20L, 21L, 22L), .Label = c("Arizona Cardinals", "Atlanta Falcons", 
"Baltimore Ravens", "Buffalo Bills", "Carolina Panthers", "Chicago Bears", 
"Cincinnati Bengals", "Cleveland Browns", "Dallas Cowboys", "Denver Broncos", 
"Detroit Lions", "Green Bay Packers", "Houston Oilers", "Houston Texans", 
"Indianapolis Colts", "Jacksonville Jaguars", "Kansas City Chiefs", 
"Los Angeles Raiders", "Los Angeles Rams", "Miami Dolphins", 
"Minnesota Vikings", "New England Patriots", "New Orleans Saints", 
"New York Giants", "New York Jets", "Oakland Raiders", "Philadelphia Eagles", 
"Phoenix Cardinals", "Pittsburgh Steelers", "San Diego Chargers", 
"San Francisco 49ers", "Seattle Seahawks", "St. Louis Rams", 
"Tampa Bay Buccaneers", "Tennessee Oilers", "Tennessee Titans", 
"Washington Redskins"), class = "factor"), Date = structure(c(45L, 
45L, 45L, 45L, 45L, 45L), .Label = c("1990-09-09", "1990-09-10", 
"1990-09-16", "1990-09-17", "1990-09-23", "1990-09-24", "1990-09-30", 
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"2007-10-07", "2007-10-08", "2007-10-14", "2007-10-15", "2007-10-21", 
"2007-10-22", "2007-10-28", "2007-10-29", "2007-11-04", "2007-11-05", 
"2007-11-11", "2007-11-12", "2007-11-18", "2007-11-19", "2007-11-22", 
"2007-11-25", "2007-11-26", "2007-11-29", "2007-12-02", "2007-12-03", 
"2007-12-06", "2007-12-09", "2007-12-10", "2007-12-13", "2007-12-15", 
"2007-12-16", "2007-12-17", "2007-12-20", "2007-12-22", "2007-12-23", 
"2007-12-24", "2007-12-29", "2007-12-30", "2008-01-05", "2008-01-06", 
"2008-01-12", "2008-01-13", "2008-01-20", "2008-02-03", "2008-09-04", 
"2008-09-07", "2008-09-08", "2008-09-14", "2008-09-15", "2008-09-21", 
"2008-09-22", "2008-09-28", "2008-09-29", "2008-10-05", "2008-10-06", 
"2008-10-12", "2008-10-13", "2008-10-19", "2008-10-20", "2008-10-26", 
"2008-10-27", "2008-11-02", "2008-11-03", "2008-11-06", "2008-11-09", 
"2008-11-10", "2008-11-13", "2008-11-16", "2008-11-17", "2008-11-20", 
"2008-11-23", "2008-11-24", "2008-11-27", "2008-11-30", "2008-12-01", 
"2008-12-04", "2008-12-07", "2008-12-08", "2008-12-11", "2008-12-14", 
"2008-12-15", "2008-12-18", "2008-12-20", "2008-12-21", "2008-12-22", 
"2008-12-28", "2009-01-03", "2009-01-04", "2009-01-10", "2009-01-11", 
"2009-01-18", "2009-02-01", "2009-09-10", "2009-09-13", "2009-09-14", 
"2009-09-20", "2009-09-21", "2009-09-27", "2009-09-28", "2009-10-04", 
"2009-10-05", "2009-10-11", "2009-10-12", "2009-10-18", "2009-10-19", 
"2009-10-25", "2009-10-26", "2009-11-01", "2009-11-02", "2009-11-08", 
"2009-11-09", "2009-11-12", "2009-11-15", "2009-11-16", "2009-11-19", 
"2009-11-22", "2009-11-23", "2009-11-26", "2009-11-29", "2009-11-30", 
"2009-12-03", "2009-12-06", "2009-12-07", "2009-12-10", "2009-12-13", 
"2009-12-14", "2009-12-17", "2009-12-19", "2009-12-20", "2009-12-21", 
"2009-12-25", "2009-12-27", "2009-12-28", "2010-01-03", "2010-01-09", 
"2010-01-10", "2010-01-16", "2010-01-17", "2010-01-24", "2010-02-07", 
"2010-09-09", "2010-09-12", "2010-09-13", "2010-09-19", "2010-09-20", 
"2010-09-26", "2010-09-27", "2010-10-03", "2010-10-04", "2010-10-10", 
"2010-10-11", "2010-10-17", "2010-10-18", "2010-10-24", "2010-10-25", 
"2010-10-31", "2010-11-01", "2010-11-07", "2010-11-08", "2010-11-11", 
"2010-11-14", "2010-11-15", "2010-11-18", "2010-11-21", "2010-11-22", 
"2010-11-25", "2010-11-28", "2010-11-29", "2010-12-02", "2010-12-05", 
"2010-12-06", "2010-12-09", "2010-12-12", "2010-12-13", "2010-12-16", 
"2010-12-19", "2010-12-20", "2010-12-23", "2010-12-25", "2010-12-26", 
"2010-12-27", "2010-12-28", "2011-01-02", "2011-01-08", "2011-01-09", 
"2011-01-15", "2011-01-16", "2011-01-23", "2011-02-06"), class = "factor"), 
    Season = c(1991, 1991, 1991, 1991, 1991, 1991), HomeRecord = structure(c(1L, 
    1L, 17L, 17L, 17L, 1L), .Label = c("(0-1-0)", "(0-10-0)", 
    "(0-11-0)", "(0-12-0)", "(0-13-0)", "(0-14-0)", "(0-15-0)", 
    "(0-16-0)", "(0-2-0)", "(0-3-0)", "(0-4-0)", "(0-5-0)", "(0-6-0)", 
    "(0-7-0)", "(0-8-0)", "(0-9-0)", "(1-0-0)", "(1-1-0)", "(1-10-0)", 
    "(1-10-1)", "(1-11-0)", "(1-11-1)", "(1-12-0)", "(1-13-0)", 
    "(1-14-0)", "(1-15-0)", "(1-2-0)", "(1-3-0)", "(1-4-0)", 
    "(1-5-0)", "(1-6-0)", "(1-7-0)", "(1-8-0)", "(1-8-1)", "(1-9-0)", 
    "(1-9-1)", "(10-0-0)", "(10-1-0)", "(10-2-0)", "(10-3-0)", 
    "(10-4-0)", "(10-5-0)", "(10-5-1)", "(10-6-0)", "(10-6-1)", 
    "(10-7-0)", "(10-7-1)", "(10-8-0)", "(11-0-0)", "(11-1-0)", 
    "(11-2-0)", "(11-3-0)", "(11-4-0)", "(11-5-0)", "(11-5-1)", 
    "(11-6-0)", "(11-6-1)", "(11-7-0)", "(11-7-1)", "(11-8-0)", 
    "(12-0-0)", "(12-1-0)", "(12-2-0)", "(12-3-0)", "(12-4-0)", 
    "(12-5-0)", "(12-6-0)", "(12-7-0)", "(12-8-0)", "(13-0-0)", 
    "(13-1-0)", "(13-2-0)", "(13-3-0)", "(13-4-0)", "(13-5-0)", 
    "(13-6-0)", "(14-0-0)", "(14-1-0)", "(14-2-0)", "(14-3-0)", 
    "(14-4-0)", "(14-5-0)", "(14-6-0)", "(15-0-0)", "(15-1-0)", 
    "(15-2-0)", "(15-3-0)", "(15-4-0)", "(15-5-0)", "(16-0-0)", 
    "(16-1-0)", "(16-2-0)", "(16-3-0)", "(16-4-0)", "(17-0-0)", 
    "(17-2-0)", "(18-0-0)", "(18-1-0)", "(2-0-0)", "(2-1-0)", 
    "(2-10-0)", "(2-11-0)", "(2-11-1)", "(2-12-0)", "(2-13-0)", 
    "(2-14-0)", "(2-2-0)", "(2-3-0)", "(2-4-0)", "(2-5-0)", "(2-6-0)", 
    "(2-7-0)", "(2-8-0)", "(2-9-0)", "(3-0-0)", "(3-1-0)", "(3-10-0)", 
    "(3-11-0)", "(3-11-1)", "(3-12-0)", "(3-13-0)", "(3-2-0)", 
    "(3-3-0)", "(3-4-0)", "(3-5-0)", "(3-6-0)", "(3-7-0)", "(3-8-0)", 
    "(3-9-0)", "(4-0-0)", "(4-1-0)", "(4-10-0)", "(4-11-0)", 
    "(4-11-1)", "(4-12-0)", "(4-2-0)", "(4-3-0)", "(4-4-0)", 
    "(4-5-0)", "(4-6-0)", "(4-6-1)", "(4-7-0)", "(4-7-1)", "(4-8-0)", 
    "(4-8-1)", "(4-9-0)", "(5-0-0)", "(5-1-0)", "(5-10-0)", "(5-11-0)", 
    "(5-2-0)", "(5-3-0)", "(5-3-1)", "(5-4-0)", "(5-4-1)", "(5-5-0)", 
    "(5-5-1)", "(5-6-0)", "(5-6-1)", "(5-7-0)", "(5-8-0)", "(5-8-1)", 
    "(5-9-0)", "(6-0-0)", "(6-1-0)", "(6-10-0)", "(6-2-0)", "(6-3-0)", 
    "(6-3-1)", "(6-4-0)", "(6-4-1)", "(6-5-0)", "(6-5-1)", "(6-6-0)", 
    "(6-6-1)", "(6-7-0)", "(6-7-1)", "(6-8-0)", "(6-8-1)", "(6-9-0)", 
    "(6-9-1)", "(7-0-0)", "(7-1-0)", "(7-2-0)", "(7-3-0)", "(7-3-1)", 
    "(7-4-0)", "(7-4-1)", "(7-5-0)", "(7-5-1)", "(7-6-0)", "(7-6-1)", 
    "(7-7-0)", "(7-7-1)", "(7-8-0)", "(7-9-0)", "(8-0-0)", "(8-1-0)", 
    "(8-10-0)", "(8-2-0)", "(8-3-0)", "(8-3-1)", "(8-4-0)", "(8-4-1)", 
    "(8-5-0)", "(8-5-1)", "(8-6-0)", "(8-6-1)", "(8-7-0)", "(8-7-1)", 
    "(8-8-0)", "(8-9-0)", "(9-0-0)", "(9-1-0)", "(9-2-0)", "(9-3-0)", 
    "(9-4-0)", "(9-5-0)", "(9-5-1)", "(9-6-0)", "(9-6-1)", "(9-7-0)", 
    "(9-8-0)", "(9-9-0)"), class = "factor"), AwayRecord = structure(c(17L, 
    17L, 1L, 1L, 1L, 17L), .Label = c("(0-1-0)", "(0-10-0)", 
    "(0-11-0)", "(0-12-0)", "(0-13-0)", "(0-14-0)", "(0-15-0)", 
    "(0-16-0)", "(0-2-0)", "(0-3-0)", "(0-4-0)", "(0-5-0)", "(0-6-0)", 
    "(0-7-0)", "(0-8-0)", "(0-9-0)", "(1-0-0)", "(1-1-0)", "(1-10-0)", 
    "(1-10-1)", "(1-11-0)", "(1-11-1)", "(1-12-0)", "(1-13-0)", 
    "(1-14-0)", "(1-15-0)", "(1-2-0)", "(1-3-0)", "(1-4-0)", 
    "(1-5-0)", "(1-6-0)", "(1-7-0)", "(1-8-0)", "(1-8-1)", "(1-9-0)", 
    "(1-9-1)", "(10-0-0)", "(10-1-0)", "(10-2-0)", "(10-3-0)", 
    "(10-4-0)", "(10-5-0)", "(10-5-1)", "(10-6-0)", "(10-6-1)", 
    "(10-7-0)", "(10-7-1)", "(10-8-0)", "(11-0-0)", "(11-1-0)", 
    "(11-2-0)", "(11-3-0)", "(11-4-0)", "(11-5-0)", "(11-5-1)", 
    "(11-6-0)", "(11-6-1)", "(11-7-0)", "(11-7-1)", "(11-8-0)", 
    "(12-0-0)", "(12-1-0)", "(12-2-0)", "(12-3-0)", "(12-4-0)", 
    "(12-5-0)", "(12-6-0)", "(12-7-0)", "(12-8-0)", "(13-0-0)", 
    "(13-1-0)", "(13-2-0)", "(13-3-0)", "(13-4-0)", "(13-5-0)", 
    "(13-6-0)", "(14-0-0)", "(14-1-0)", "(14-2-0)", "(14-3-0)", 
    "(14-4-0)", "(14-5-0)", "(14-6-0)", "(15-0-0)", "(15-1-0)", 
    "(15-2-0)", "(15-3-0)", "(15-4-0)", "(15-5-0)", "(16-0-0)", 
    "(16-1-0)", "(16-2-0)", "(16-3-0)", "(16-4-0)", "(17-0-0)", 
    "(17-2-0)", "(18-0-0)", "(18-1-0)", "(2-0-0)", "(2-1-0)", 
    "(2-10-0)", "(2-11-0)", "(2-11-1)", "(2-12-0)", "(2-13-0)", 
    "(2-14-0)", "(2-2-0)", "(2-3-0)", "(2-4-0)", "(2-5-0)", "(2-6-0)", 
    "(2-7-0)", "(2-8-0)", "(2-9-0)", "(3-0-0)", "(3-1-0)", "(3-10-0)", 
    "(3-11-0)", "(3-11-1)", "(3-12-0)", "(3-13-0)", "(3-2-0)", 
    "(3-3-0)", "(3-4-0)", "(3-5-0)", "(3-6-0)", "(3-7-0)", "(3-8-0)", 
    "(3-9-0)", "(4-0-0)", "(4-1-0)", "(4-10-0)", "(4-11-0)", 
    "(4-11-1)", "(4-12-0)", "(4-2-0)", "(4-3-0)", "(4-4-0)", 
    "(4-5-0)", "(4-6-0)", "(4-6-1)", "(4-7-0)", "(4-7-1)", "(4-8-0)", 
    "(4-8-1)", "(4-9-0)", "(5-0-0)", "(5-1-0)", "(5-10-0)", "(5-11-0)", 
    "(5-2-0)", "(5-3-0)", "(5-3-1)", "(5-4-0)", "(5-4-1)", "(5-5-0)", 
    "(5-5-1)", "(5-6-0)", "(5-6-1)", "(5-7-0)", "(5-8-0)", "(5-8-1)", 
    "(5-9-0)", "(6-0-0)", "(6-1-0)", "(6-10-0)", "(6-2-0)", "(6-3-0)", 
    "(6-3-1)", "(6-4-0)", "(6-4-1)", "(6-5-0)", "(6-5-1)", "(6-6-0)", 
    "(6-6-1)", "(6-7-0)", "(6-7-1)", "(6-8-0)", "(6-8-1)", "(6-9-0)", 
    "(6-9-1)", "(7-0-0)", "(7-1-0)", "(7-2-0)", "(7-3-0)", "(7-3-1)", 
    "(7-4-0)", "(7-4-1)", "(7-5-0)", "(7-5-1)", "(7-6-0)", "(7-6-1)", 
    "(7-7-0)", "(7-7-1)", "(7-8-0)", "(7-9-0)", "(8-0-0)", "(8-1-0)", 
    "(8-10-0)", "(8-2-0)", "(8-3-0)", "(8-3-1)", "(8-4-0)", "(8-4-1)", 
    "(8-5-0)", "(8-5-1)", "(8-6-0)", "(8-6-1)", "(8-7-0)", "(8-7-1)", 
    "(8-8-0)", "(8-9-0)", "(9-0-0)", "(9-1-0)", "(9-2-0)", "(9-3-0)", 
    "(9-4-0)", "(9-5-0)", "(9-5-1)", "(9-6-0)", "(9-6-1)", "(9-7-0)", 
    "(9-8-0)", "(9-9-0)"), class = "factor"), HomeFinal = c(14L, 
    14L, 45L, 35L, 10L, 7L), AwayFinal = c(26L, 24L, 0L, 31L, 
    6L, 16L), HomeLast = c(4, 4, 5, 5, 0, 7), AwayLast = c(6, 
    4, 0, 1, 6, 6), Winner = c("Away", "Away", "Home", "Home", 
    "Home", "Away")), .Names = c("X1", "HomeTeam", "AwayTeam", 
"Date", "Season", "HomeRecord", "AwayRecord", "HomeFinal", "AwayFinal", 
"HomeLast", "AwayLast", "Winner"), row.names = c(NA, 6L), class = "data.frame")

Then you would do.

ggfluctuation(table(gamesWide$HomeLast, gamesWide$AwayLast), type="colour") + labs(x="Away", y="Home") + opts(title="Distribution of Last Digit of Score")

To get

Of course, that image was generated using the full dataset. This should be further extensible to data that isn't so symmetric and rectangular.

Dirtcheap

Incidentally, this answer just showed up in "Low Quality Posts" review also. I ran all the examples in ?ggfluctuation and none of them look like a heat-map to me. Could you please provide an example? – Didymous

FYI: ggfluctuation was depricated (last in version 0.9.1) and is replaced by geom_tile in ggplot2 – Angie