Line density heatmap in R

Looking closely, one can see that the graph to which you are linking consists of many, many, many points rather than lines.

The ggpointdensity package does a similar visualisation. Note with so many data points, there are quite some performance issues. I am using the developer version, because it contains the method argument which allows to use different smoothing estimators and apparently helps deal better with larger numbers. There is a CRAN version too.

You can adjust the smoothing with the adjust argument.

I have increased the x interval density of your code, to make it look more like lines. Have slightly reduced the number of 'lines' in the plot though.

library(tidyverse)
#devtools::install_github("LKremer/ggpointdensity")
library(ggpointdensity)

set.seed(1)
gen.dat <- function(key) {
  c <- sample(seq(0.1,1, by = 0.1), 1)
  time <- seq(c*pi,length.out=500)
  val <- sin(time)
  time = seq(0.02,100,0.1)
  data.frame(time,val,key)
}
dat <- lapply(seq(1, 1000), gen.dat) %>% bind_rows()

ggplot(dat, aes(time, val)) + 
  geom_pointdensity(size = 0.1, adjust = 10) 
#> geom_pointdensity using method='kde2d' due to large number of points (>20k)

^{Created on 2020-03-19 by the reprex package (v0.3.0)}

update Thanks user Robert Gertenbach for creating some more interesting sample data. Here the suggested use of ggpointdensity on this data:

library(tidyverse)
library(ggpointdensity)

gen.dat <- function(key) {
  has_offset <- runif(1) > 0.5
  time <- seq(1, 1000, length.out = 1000)
  val <- sin(time / 100 + rnorm(1, sd = 0.2) + (has_offset * 1.5)) * 
    rgamma(1, 20, 20)
  data.frame(time,val,key)
}

dat <- lapply(seq(1,1000), gen.dat) %>% bind_rows()
ggplot(dat, aes(time, val, group=key)) +stat_pointdensity(geom = "line", size = 0.05, adjust = 10) + scale_color_gradientn(colors = c("blue", "yellow", "red"))

^{Created on 2020-03-24 by the reprex package (v0.3.0)}

Your data will result in a quite uniform polkadot density.

I generated some slightly more interesting data like this:

gen.dat <- function(key) {
  has_offset <- runif(1) > 0.5
  time <- seq(1, 1000, length.out = 1000)
  val <- sin(time / 100 + rnorm(1, sd = 0.2) + (has_offset * 1.5)) * 
    rgamma(1, 20, 20)
  data.frame(time,val,key)
}
dat <- lapply(seq(1,1000), gen.dat) %>% bind_rows()

We then get a 2d density estimate. kde2d doesn't have a predict function so we model it with a LOESS

dens <- MASS::kde2d(dat$time, dat$val, n = 400)
dens_df <- data.frame(with(dens, expand_grid( y, x)), z = as.vector(dens$z))
fit <- loess(z ~ y * x, data = dens_df, span = 0.02)
dat$z <- predict(fit, with(dat, data.frame(x=time, y=val)))

Plotting it then gets this result:

ggplot(dat, aes(time, val, group = key, color = z)) +
  geom_line(size = 0.05) +
  theme_minimal() +
  scale_color_gradientn(colors = c("blue", "yellow", "red"))

This is all highly reliant on:

• The number of series
• The resolution of series
• The density of kde2d
• The span of loess

so your mileage may vary