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I have some codes to generate a plot,the only problem I have is there're many overlapping colors.
When two colors overlap, how do I specify the dominant color?
For example, there're 4 black points when indicator = threshold. They are at 4 x-axis correspondingly. However, the black points at "Wire" and "ACH" scales do not show up because it is overlap with blue points. The black point at "RDFI" scale barely shows up. How can I make black as the dominant color when two colors overlap? Thanks ahead!
ggplot(df, aes(a-axis, y-axis), color=indicator)) +
geom_quasirandom(groupOnX=TRUE, na.rm = TRUE) +
labs(title= 'chart', x='x-axis', y= 'y-axis') +
scale_color_manual(name = 'indicator', values=c("#99ccff","#000000" ))
for specify the dominant color you should use the function new_scale () and its aliases new_scale_color () and new_scale_fill ().
As an example, lets overlay some measurements over a contour map of topography using the beloed volcano
library(ggplot2)
library(ggnewscale)
# Equivalent to melt(volcano)
topography <- expand.grid(x = 1:nrow(volcano),
y = 1:ncol(volcano))
topography$z <- c(volcano)
# point measurements of something at a few locations
set.seed(42)
measurements <- data.frame(x = runif(30, 1, 80),
y = runif(30, 1, 60),
thing = rnorm(30))
dominant point:
ggplot(mapping = aes(x, y)) +
geom_contour(data = topography, aes(z = z, color = stat(level))) +
# Color scale for topography
scale_color_viridis_c(option = "D") +
# geoms below will use another color scale
new_scale_color() +
geom_point(data = measurements, size = 3, aes(color = thing)) +
# Color scale applied to geoms added after new_scale_color()
scale_color_viridis_c(option = "A")
dominant contour:
ggplot(mapping = aes(x, y)) +
geom_point(data = measurements, size = 3, aes(color = thing)) +
scale_color_viridis_c(option = "A")+
new_scale_color() +
geom_contour(data = topography, aes(z = z, color = stat(level))) +
scale_color_viridis_c(option = "D")
Your problem may not lie with what color is dominant. You have selected colors that will show up often. You may be losing the bottom of your Y axis. The code you have in your example can not have possibly produced that plot it has errors.
Here is a simple example that show's one way to overcome your problem by simply overplottting the threshold points after you have plotted the beeswarm.
library(dplyr)
library(ggbeeswarm)
distro <- data.frame(
'variable'=rep(c('runif','rnorm'),each=1000),
'value'=c(runif(2000, min=-3, max=3))
)
distro$indicator <- "NA"
distro[3,3] <- "Threshhold"
distro[163,3] <- "Threshhold"
ggplot2::ggplot(distro,aes(variable, value, color=indicator)) +
geom_quasirandom(groupOnX=TRUE, na.rm = TRUE, width=0.1) +
scale_color_manual(name = 'indicator', values=c("#99ccff","#000000")) +
geom_point(data = distro %>% filter(indicator == "Threshhold"))
You sort your data based on the color variable (your indicator).
Basically you want your black dots to be plotted last = on top of the other ones.
df$indicator <- sort(df$indicator, decreasing=T)
#Tidyverse solution
df <- df %>% arrange(desc(indicator))
Dependent on your levels you may have to reverse sort or not.
Then you just plot.
pd <- tibble(x=rnorm(1000), y=1, indicator=sample(c("A","B"), replace=T, size = 1000))
ggplot(pd, aes(x=x,y=y,color=indicator)) + geom_point()
pd <- pd %>% arrange(indicator)
ggplot(pd, aes(x=x,y=y,color=indicator)) + geom_point()
pd <- pd %>% arrange(desc(indicator))
ggplot(pd, aes(x=x,y=y,color=indicator)) + geom_point()
I am trying to plot two variables where N=700K. The problem is that there is too much overlap, so that the plot becomes mostly a solid block of black. Is there any way of having a grayscale "cloud" where the darkness of the plot is a function of the number of points in an region? In other words, instead of showing individual points, I want the plot to be a "cloud", with the more the number of points in a region, the darker that region.
One way to deal with this is with alpha blending, which makes each point slightly transparent. So regions appear darker that have more point plotted on them.
This is easy to do in ggplot2:
df <- data.frame(x = rnorm(5000),y=rnorm(5000))
ggplot(df,aes(x=x,y=y)) + geom_point(alpha = 0.3)
Another convenient way to deal with this is (and probably more appropriate for the number of points you have) is hexagonal binning:
ggplot(df,aes(x=x,y=y)) + stat_binhex()
And there is also regular old rectangular binning (image omitted), which is more like your traditional heatmap:
ggplot(df,aes(x=x,y=y)) + geom_bin2d()
An overview of several good options in ggplot2:
library(ggplot2)
x <- rnorm(n = 10000)
y <- rnorm(n = 10000, sd=2) + x
df <- data.frame(x, y)
Option A: transparent points
o1 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.05)
Option B: add density contours
o2 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.05) +
geom_density_2d()
Option C: add filled density contours
(Note that the points distort the perception of the colors underneath, may be better without points.)
o3 <- ggplot(df, aes(x, y)) +
stat_density_2d(aes(fill = stat(level)), geom = 'polygon') +
scale_fill_viridis_c(name = "density") +
geom_point(shape = '.')
Option D: density heatmap
(Same note as C.)
o4 <- ggplot(df, aes(x, y)) +
stat_density_2d(aes(fill = stat(density)), geom = 'raster', contour = FALSE) +
scale_fill_viridis_c() +
coord_cartesian(expand = FALSE) +
geom_point(shape = '.', col = 'white')
Option E: hexbins
(Same note as C.)
o5 <- ggplot(df, aes(x, y)) +
geom_hex() +
scale_fill_viridis_c() +
geom_point(shape = '.', col = 'white')
Option F: rugs
Possibly my favorite option. Not quite as flashy, but visually simple and simple to understand. Very effective in many cases.
o6 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.1) +
geom_rug(alpha = 0.01)
Combine in one figure:
cowplot::plot_grid(
o1, o2, o3, o4, o5, o6,
ncol = 2, labels = 'AUTO', align = 'v', axis = 'lr'
)
You can also have a look at the ggsubplot package. This package implements features which were presented by Hadley Wickham back in 2011 (http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html).
(In the following, I include the "points"-layer for illustration purposes.)
library(ggplot2)
library(ggsubplot)
# Make up some data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=5000),
xvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)),
yvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)))
# Scatterplot with subplots (simple)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) +
geom_subplot2d(aes(xvar, yvar,
subplot = geom_bar(aes(rep("dummy", length(xvar)), ..count..))), bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
However, this features rocks if you have a third variable to control for.
# Scatterplot with subplots (including a third variable)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1, aes(color = factor(cond))) +
geom_subplot2d(aes(xvar, yvar,
subplot = geom_bar(aes(cond, ..count.., fill = cond))),
bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
Or another approach would be to use smoothScatter():
smoothScatter(dat[2:3])
Alpha blending is easy to do with base graphics as well.
df <- data.frame(x = rnorm(5000),y=rnorm(5000))
with(df, plot(x, y, col="#00000033"))
The first six numbers after the # are the color in RGB hex and the last two are the opacity, again in hex, so 33 ~ 3/16th opaque.
You can also use density contour lines (ggplot2):
df <- data.frame(x = rnorm(15000),y=rnorm(15000))
ggplot(df,aes(x=x,y=y)) + geom_point() + geom_density2d()
Or combine density contours with alpha blending:
ggplot(df,aes(x=x,y=y)) +
geom_point(colour="blue", alpha=0.2) +
geom_density2d(colour="black")
You may find useful the hexbin package. From the help page of hexbinplot:
library(hexbin)
mixdata <- data.frame(x = c(rnorm(5000),rnorm(5000,4,1.5)),
y = c(rnorm(5000),rnorm(5000,2,3)),
a = gl(2, 5000))
hexbinplot(y ~ x | a, mixdata)
geom_pointdenisty from the ggpointdensity package (recently developed by Lukas Kremer and Simon Anders (2019)) allows you visualize density and individual data points at the same time:
library(ggplot2)
# install.packages("ggpointdensity")
library(ggpointdensity)
df <- data.frame(x = rnorm(5000), y = rnorm(5000))
ggplot(df, aes(x=x, y=y)) + geom_pointdensity() + scale_color_viridis_c()
My favorite method for plotting this type of data is the one described in this question - a scatter-density plot. The idea is to do a scatter-plot but to colour the points by their density (roughly speaking, the amount of overlap in that area).
It simultaneously:
clearly shows the location of outliers, and
reveals any structure in the dense area of the plot.
Here is the result from the top answer to the linked question:
I'm trying to produce a scatter plot with geom_point where the points are circumscribed by a smoothed polygon, with geom_polygon.
Here's my point data:
set.seed(1)
df <- data.frame(x=c(rnorm(30,-0.1,0.1),rnorm(30,0,0.1),rnorm(30,0.1,0.1)),y=c(rnorm(30,-1,0.1),rnorm(30,0,0.1),rnorm(30,1,0.1)),val=rnorm(90),cluster=c(rep(1,30),rep(2,30),rep(3,30)),stringsAsFactors=F)
I color each point according the an interval that df$val is in. Here's the interval data:
intervals.df <- data.frame(interval=c("(-3,-2]","(-2,-0.999]","(-0.999,0]","(0,1.96]","(1.96,3.91]","(3.91,5.87]","not expressed"),
start=c(-3,-2,-0.999,0,1.96,3.91,NA),end=c(-2,-0.999,0,1.96,3.91,5.87,NA),
col=c("#2f3b61","#436CE8","#E0E0FF","#7d4343","#C74747","#EBCCD6","#D3D3D3"),stringsAsFactors=F)
Assigning colors and intervals to the points:
df <- cbind(df,do.call(rbind,lapply(df$val,function(x){
if(is.na(x)){
return(data.frame(col=intervals.df$col[nrow(intervals.df)],interval=intervals.df$interval[nrow(intervals.df)],stringsAsFactors=F))
} else{
idx <- which(intervals.df$start <= x & intervals.df$end >= x)
return(data.frame(col=intervals.df$col[idx],interval=intervals.df$interval[idx],stringsAsFactors=F))
}
})))
Preparing the colors for the leged which will show each interval:
df$interval <- factor(df$interval,levels=intervals.df$interval)
colors <- intervals.df$col
names(colors) <- intervals.df$interval
Here's where I constructed the smoothed polygons (using a function courtesy of this link):
clusters <- sort(unique(df$cluster))
cluster.cols <- c("#ff00ff","#088163","#ccbfa5")
splinePolygon <- function(xy,vertices,k=3, ...)
{
# Assert: xy is an n by 2 matrix with n >= k.
# Wrap k vertices around each end.
n <- dim(xy)[1]
if (k >= 1) {
data <- rbind(xy[(n-k+1):n,], xy, xy[1:k, ])
} else {
data <- xy
}
# Spline the x and y coordinates.
data.spline <- spline(1:(n+2*k), data[,1], n=vertices, ...)
x <- data.spline$x
x1 <- data.spline$y
x2 <- spline(1:(n+2*k), data[,2], n=vertices, ...)$y
# Retain only the middle part.
cbind(x1, x2)[k < x & x <= n+k, ]
}
library(data.table)
hulls.df <- do.call(rbind,lapply(1:length(clusters),function(l){
dt <- data.table(df[which(df$cluster==clusters[l]),])
hull <- dt[, .SD[chull(x,y)]]
spline.hull <- splinePolygon(cbind(hull$x,hull$y),100)
return(data.frame(x=spline.hull[,1],y=spline.hull[,2],val=NA,cluster=clusters[l],col=cluster.cols[l],interval=NA,stringsAsFactors=F))
}))
hulls.df$cluster <- factor(hulls.df$cluster,levels=clusters)
And here's my ggplot command:
library(ggplot2)
p <- ggplot(df,aes(x=x,y=y,colour=interval))+geom_point(cex=2,shape=1,stroke=1)+labs(x="X", y="Y")+theme_bw()+theme(legend.key=element_blank(),panel.border=element_blank(),strip.background=element_blank())+scale_color_manual(drop=FALSE,values=colors,name="DE")
p <- p+geom_polygon(data=hulls.df,aes(x=x,y=y,group=cluster),color=hulls.df$col,fill=NA)
which produces:
My question is how do I add a legend for the polygon under the legend for the points? I want it to a legend with 3 lines colored according to the cluster colors and the corresponding cluster number beside each line?
Slightly different output, only changing the last line of your code, it may solve your purpose:
p+geom_polygon(data=hulls.df,aes(x=x,y=y,group=cluster, fill=cluster),alpha=0.1)
Say, you want to add a legend of the_factor. My basic idea is,
(1) put the_factor into mapping by using unused aes arguments; aes(xx = the_factor)
(2) if (1) affects something, delete the effect by using scale_xx_manual()
(3) modify the legend by using guides(xx = guide_legend(override.aes = list()))
In your case, aes(fill) and aes(alpha) are unused. The former is better to do it because of no effect. So I used aes(fill=as.factor(cluster)).
p <- ggplot(df,aes(x=x,y=y,colour=interval, fill=as.factor(cluster))) + # add aes(fill=...)
geom_point(cex=2, shape=1, stroke=1) +
labs(x="X", y="Y",fill="cluster") + # add fill="cluster"
theme_bw() + theme(legend.key=element_blank(),panel.border=element_blank(),strip.background=element_blank()) + scale_color_manual(drop=FALSE,values=colors,name="DE") +
guides(fill = guide_legend(override.aes = list(colour = cluster.cols, pch=0))) # add
p <- p+geom_polygon(data=hulls.df,aes(x=x,y=y,group=cluster), color=hulls.df$col,fill=NA)
Of course, you can make the same graph by using aes(alpha = the_factor)). Because it has influence, you need to control it by using scale_alpha_manual().
g <- ggplot(df, aes(x=x,y=y,colour=interval)) +
geom_point(cex=2, shape=1, stroke=1, aes(alpha=as.factor(cluster))) + # add aes(alpha)
labs(x="X", y="Y",alpha="cluster") + # add alpha="cluster"
theme_bw() + theme(legend.key=element_blank(),panel.border=element_blank(),strip.background=element_blank()) + scale_color_manual(drop=FALSE,values=colors,name="DE") +
scale_alpha_manual(values=c(1,1,1)) + # add
guides(alpha = guide_legend(override.aes = list(colour = cluster.cols, pch=0))) # add
g <- p+geom_polygon(data=hulls.df,aes(x=x,y=y,group=cluster), color=hulls.df$col,fill=NA)
What you are asking for is two colour scales. My understanding is that this is not possible. But you can give the impression of having two colour scales with a bit of a cheat and using the filled symbols (shapes 21 to 25).
p <- ggplot(df, aes(x = x, y = y, fill = interval)) +
geom_point(cex = 2, shape = 21, stroke = 1, colour = NA)+
labs(x = "X", y = "Y") +
theme_bw() +
theme(legend.key = element_blank(), panel.border = element_blank(), strip.background = element_blank()) +
scale_fill_manual(drop=FALSE, values=colors, name="DE") +
geom_polygon(data = hulls.df, aes(x = x, y = y, colour = cluster), fill = NA) +
scale_colour_manual(values = cluster.cols)
p
Alternatively, use a filled polygon with a low alpha
p <- ggplot(df,aes(x=x,y=y,colour=interval))+
geom_point(cex=2,shape=1,stroke=1)+
labs(x="X", y="Y")+
theme_bw() +
theme(legend.key = element_blank(),panel.border=element_blank(), strip.background=element_blank()) +
scale_color_manual(drop=FALSE,values=colors,name="DE", guide = guide_legend(override.aes = list(fill = NA))) +
geom_polygon(data=hulls.df,aes(x=x,y=y,group=cluster, fill = cluster), alpha = 0.2, show.legend = TRUE) +
scale_fill_manual(values = cluster.cols)
p
But this might make the point colours difficult to see.
I am trying to plot two variables where N=700K. The problem is that there is too much overlap, so that the plot becomes mostly a solid block of black. Is there any way of having a grayscale "cloud" where the darkness of the plot is a function of the number of points in an region? In other words, instead of showing individual points, I want the plot to be a "cloud", with the more the number of points in a region, the darker that region.
One way to deal with this is with alpha blending, which makes each point slightly transparent. So regions appear darker that have more point plotted on them.
This is easy to do in ggplot2:
df <- data.frame(x = rnorm(5000),y=rnorm(5000))
ggplot(df,aes(x=x,y=y)) + geom_point(alpha = 0.3)
Another convenient way to deal with this is (and probably more appropriate for the number of points you have) is hexagonal binning:
ggplot(df,aes(x=x,y=y)) + stat_binhex()
And there is also regular old rectangular binning (image omitted), which is more like your traditional heatmap:
ggplot(df,aes(x=x,y=y)) + geom_bin2d()
An overview of several good options in ggplot2:
library(ggplot2)
x <- rnorm(n = 10000)
y <- rnorm(n = 10000, sd=2) + x
df <- data.frame(x, y)
Option A: transparent points
o1 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.05)
Option B: add density contours
o2 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.05) +
geom_density_2d()
Option C: add filled density contours
(Note that the points distort the perception of the colors underneath, may be better without points.)
o3 <- ggplot(df, aes(x, y)) +
stat_density_2d(aes(fill = stat(level)), geom = 'polygon') +
scale_fill_viridis_c(name = "density") +
geom_point(shape = '.')
Option D: density heatmap
(Same note as C.)
o4 <- ggplot(df, aes(x, y)) +
stat_density_2d(aes(fill = stat(density)), geom = 'raster', contour = FALSE) +
scale_fill_viridis_c() +
coord_cartesian(expand = FALSE) +
geom_point(shape = '.', col = 'white')
Option E: hexbins
(Same note as C.)
o5 <- ggplot(df, aes(x, y)) +
geom_hex() +
scale_fill_viridis_c() +
geom_point(shape = '.', col = 'white')
Option F: rugs
Possibly my favorite option. Not quite as flashy, but visually simple and simple to understand. Very effective in many cases.
o6 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.1) +
geom_rug(alpha = 0.01)
Combine in one figure:
cowplot::plot_grid(
o1, o2, o3, o4, o5, o6,
ncol = 2, labels = 'AUTO', align = 'v', axis = 'lr'
)
You can also have a look at the ggsubplot package. This package implements features which were presented by Hadley Wickham back in 2011 (http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html).
(In the following, I include the "points"-layer for illustration purposes.)
library(ggplot2)
library(ggsubplot)
# Make up some data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=5000),
xvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)),
yvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)))
# Scatterplot with subplots (simple)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) +
geom_subplot2d(aes(xvar, yvar,
subplot = geom_bar(aes(rep("dummy", length(xvar)), ..count..))), bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
However, this features rocks if you have a third variable to control for.
# Scatterplot with subplots (including a third variable)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1, aes(color = factor(cond))) +
geom_subplot2d(aes(xvar, yvar,
subplot = geom_bar(aes(cond, ..count.., fill = cond))),
bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
Or another approach would be to use smoothScatter():
smoothScatter(dat[2:3])
Alpha blending is easy to do with base graphics as well.
df <- data.frame(x = rnorm(5000),y=rnorm(5000))
with(df, plot(x, y, col="#00000033"))
The first six numbers after the # are the color in RGB hex and the last two are the opacity, again in hex, so 33 ~ 3/16th opaque.
You can also use density contour lines (ggplot2):
df <- data.frame(x = rnorm(15000),y=rnorm(15000))
ggplot(df,aes(x=x,y=y)) + geom_point() + geom_density2d()
Or combine density contours with alpha blending:
ggplot(df,aes(x=x,y=y)) +
geom_point(colour="blue", alpha=0.2) +
geom_density2d(colour="black")
You may find useful the hexbin package. From the help page of hexbinplot:
library(hexbin)
mixdata <- data.frame(x = c(rnorm(5000),rnorm(5000,4,1.5)),
y = c(rnorm(5000),rnorm(5000,2,3)),
a = gl(2, 5000))
hexbinplot(y ~ x | a, mixdata)
geom_pointdenisty from the ggpointdensity package (recently developed by Lukas Kremer and Simon Anders (2019)) allows you visualize density and individual data points at the same time:
library(ggplot2)
# install.packages("ggpointdensity")
library(ggpointdensity)
df <- data.frame(x = rnorm(5000), y = rnorm(5000))
ggplot(df, aes(x=x, y=y)) + geom_pointdensity() + scale_color_viridis_c()
My favorite method for plotting this type of data is the one described in this question - a scatter-density plot. The idea is to do a scatter-plot but to colour the points by their density (roughly speaking, the amount of overlap in that area).
It simultaneously:
clearly shows the location of outliers, and
reveals any structure in the dense area of the plot.
Here is the result from the top answer to the linked question:
I am trying to plot two variables where N=700K. The problem is that there is too much overlap, so that the plot becomes mostly a solid block of black. Is there any way of having a grayscale "cloud" where the darkness of the plot is a function of the number of points in an region? In other words, instead of showing individual points, I want the plot to be a "cloud", with the more the number of points in a region, the darker that region.
One way to deal with this is with alpha blending, which makes each point slightly transparent. So regions appear darker that have more point plotted on them.
This is easy to do in ggplot2:
df <- data.frame(x = rnorm(5000),y=rnorm(5000))
ggplot(df,aes(x=x,y=y)) + geom_point(alpha = 0.3)
Another convenient way to deal with this is (and probably more appropriate for the number of points you have) is hexagonal binning:
ggplot(df,aes(x=x,y=y)) + stat_binhex()
And there is also regular old rectangular binning (image omitted), which is more like your traditional heatmap:
ggplot(df,aes(x=x,y=y)) + geom_bin2d()
An overview of several good options in ggplot2:
library(ggplot2)
x <- rnorm(n = 10000)
y <- rnorm(n = 10000, sd=2) + x
df <- data.frame(x, y)
Option A: transparent points
o1 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.05)
Option B: add density contours
o2 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.05) +
geom_density_2d()
Option C: add filled density contours
(Note that the points distort the perception of the colors underneath, may be better without points.)
o3 <- ggplot(df, aes(x, y)) +
stat_density_2d(aes(fill = stat(level)), geom = 'polygon') +
scale_fill_viridis_c(name = "density") +
geom_point(shape = '.')
Option D: density heatmap
(Same note as C.)
o4 <- ggplot(df, aes(x, y)) +
stat_density_2d(aes(fill = stat(density)), geom = 'raster', contour = FALSE) +
scale_fill_viridis_c() +
coord_cartesian(expand = FALSE) +
geom_point(shape = '.', col = 'white')
Option E: hexbins
(Same note as C.)
o5 <- ggplot(df, aes(x, y)) +
geom_hex() +
scale_fill_viridis_c() +
geom_point(shape = '.', col = 'white')
Option F: rugs
Possibly my favorite option. Not quite as flashy, but visually simple and simple to understand. Very effective in many cases.
o6 <- ggplot(df, aes(x, y)) +
geom_point(alpha = 0.1) +
geom_rug(alpha = 0.01)
Combine in one figure:
cowplot::plot_grid(
o1, o2, o3, o4, o5, o6,
ncol = 2, labels = 'AUTO', align = 'v', axis = 'lr'
)
You can also have a look at the ggsubplot package. This package implements features which were presented by Hadley Wickham back in 2011 (http://blog.revolutionanalytics.com/2011/10/ggplot2-for-big-data.html).
(In the following, I include the "points"-layer for illustration purposes.)
library(ggplot2)
library(ggsubplot)
# Make up some data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=5000),
xvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)),
yvar = c(rep(1:20,250) + rnorm(5000,sd=5),rep(16:35,250) + rnorm(5000,sd=5)))
# Scatterplot with subplots (simple)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) +
geom_subplot2d(aes(xvar, yvar,
subplot = geom_bar(aes(rep("dummy", length(xvar)), ..count..))), bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
However, this features rocks if you have a third variable to control for.
# Scatterplot with subplots (including a third variable)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1, aes(color = factor(cond))) +
geom_subplot2d(aes(xvar, yvar,
subplot = geom_bar(aes(cond, ..count.., fill = cond))),
bins = c(15,15), ref = NULL, width = rel(0.8), ply.aes = FALSE)
Or another approach would be to use smoothScatter():
smoothScatter(dat[2:3])
Alpha blending is easy to do with base graphics as well.
df <- data.frame(x = rnorm(5000),y=rnorm(5000))
with(df, plot(x, y, col="#00000033"))
The first six numbers after the # are the color in RGB hex and the last two are the opacity, again in hex, so 33 ~ 3/16th opaque.
You can also use density contour lines (ggplot2):
df <- data.frame(x = rnorm(15000),y=rnorm(15000))
ggplot(df,aes(x=x,y=y)) + geom_point() + geom_density2d()
Or combine density contours with alpha blending:
ggplot(df,aes(x=x,y=y)) +
geom_point(colour="blue", alpha=0.2) +
geom_density2d(colour="black")
You may find useful the hexbin package. From the help page of hexbinplot:
library(hexbin)
mixdata <- data.frame(x = c(rnorm(5000),rnorm(5000,4,1.5)),
y = c(rnorm(5000),rnorm(5000,2,3)),
a = gl(2, 5000))
hexbinplot(y ~ x | a, mixdata)
geom_pointdenisty from the ggpointdensity package (recently developed by Lukas Kremer and Simon Anders (2019)) allows you visualize density and individual data points at the same time:
library(ggplot2)
# install.packages("ggpointdensity")
library(ggpointdensity)
df <- data.frame(x = rnorm(5000), y = rnorm(5000))
ggplot(df, aes(x=x, y=y)) + geom_pointdensity() + scale_color_viridis_c()
My favorite method for plotting this type of data is the one described in this question - a scatter-density plot. The idea is to do a scatter-plot but to colour the points by their density (roughly speaking, the amount of overlap in that area).
It simultaneously:
clearly shows the location of outliers, and
reveals any structure in the dense area of the plot.
Here is the result from the top answer to the linked question: