Here is a code snippet from the docs site:
# Generate data
library(reshape2) # for melt
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
# Basic plot
v <- ggplot(volcano3d, aes(x, y, z = z))
v + stat_contour(binwidth = 10)
Output:
What if I want to draw contour lines at custom levels? For example, in the volcano3d data set, I want these levels to be indicate: z == 120, 140, 160.
Replace binwidth= with argument breaks= and provide breakpoint you need.
ggplot(volcano3d, aes(x, y, z = z)) +
stat_contour(breaks=c(120,140,160))
Related
I have been trying to find method to add a loess regression line on a hexbin plot. So far I do not have any success... Any suggestions?
My code is as follow:
bin<-hexbin(Dataset$a, Dataset$b, xbins=40)
plot(bin, main="Hexagonal Binning",
xlab = "a", ylab = "b",
type="l")
I would suggest using ggplot2 to build the plot.
Since you didn't include any example data, I've used the palmerpenguins package dataset for the example below.
library(palmerpenguins) # For the data
library(ggplot2) # ggplot2 for plotting
ggplot(penguins, aes(x = body_mass_g,
y = bill_length_mm)) +
geom_hex(bins = 40) +
geom_smooth(method = 'loess', se = F, color = 'red')
Created on 2021-01-05 by the reprex package (v0.3.0)
I don't have a solution for base, but it's possible to do this with ggplot. It should be possible with base too, but if you look at the documentation for ?hexbin, you can see the quote:
Note that when plotting a hexbin object, the grid package is used. You must use its graphics (or those from package lattice if you know how) to add to such plots.
I'm not familiar with how to modify these. I did try ggplotify to convert the base to ggplot and edit that way, but couldn't get the loess line added to the plot window properly.
So here is a solution with ggplot with some fake data that you can try on your Datasets:
library(hexbin)
library(ggplot2)
# fake data with a random walk, replace with your data
set.seed(100)
N <- 1000
x <- rnorm(N)
x <- sort(x)
y <- vector("numeric", length=N)
for(i in 2:N){
y[i] <- y[i-1] + rnorm(1, sd=0.1)
}
# current method
# In documentation for ?hexbin it says:
# "You must use its graphics (or those from package lattice if you know how) to add to such plots."
(bin <- hexbin(x, y, xbins=40))
plot(bin)
# ggplot option. Can play around with scale_fill_gradient to
# get the colour scale similar or use other ggplot options
df <- data.frame(x=x, y=y)
d <- ggplot(df, aes(x, y)) +
geom_hex(bins=40) +
scale_fill_gradient(low = "grey90", high = "black") +
theme_bw()
d
# easy to add a loess fit to the data
# span controls the degree of smoothing, decrease to make the line
# more "wiggly"
model <- loess(y~x, span=0.2)
fit <- predict(model)
loess_data <- data.frame(x=x, y=fit)
d + geom_line(data=loess_data, aes(x=x, y=y), col="darkorange",
size=1.5)
Here are two options; you will need to decide if you want to smooth over the raw data or the binned data.
library(hexbin)
library(grid)
# Some data
set.seed(101)
d <- data.frame(x=rnorm(1000))
d$y <- with(d, 2*x^3 + rnorm(1000))
Method A - binned data
# plot hexbin & smoother : need to grab plot viewport
# From ?hexVP.loess : "Fit a loess line using the hexagon centers of mass
# as the x and y coordinates and the cell counts as weights."
bin <- hexbin(d$x, d$y)
p <- plot(bin)
hexVP.loess(bin, hvp = p$plot.vp, span = 0.4, col = "red", n = 200)
Method B - raw data
# calculate loess predictions outside plot on raw data
l = loess(y ~ x, data=d, span=0.4)
xp = with(d, seq(min(x), max(x), length=200))
yp = predict(l, xp)
# plot hexbin
bin <- hexbin(d$x, d$y)
p <- plot(bin)
# add loess line
pushHexport(p$plot.vp)
grid.lines(xp, yp, gp=gpar(col="red"), default.units = "native")
upViewport()
I wonder how to get data labels on lines in ggplot2 for contours. Thanks
require(grDevices) # for colours
x <- seq(-4*pi, 4*pi, len = 27)
y <- seq(-4*pi, 4*pi, len = 27)
r <- sqrt(outer(x^2, y^2, "+"))
rx <- range(x <- 10*1:nrow(volcano))
ry <- range(y <- 10*1:ncol(volcano))
ry <- ry + c(-1, 1) * (diff(rx) - diff(ry))/2
plot(
x = 0
, y = 0
, type = "n"
, xlim = rx
, ylim = ry
, xlab = ""
, ylab = ""
)
contour(
x = x
, y = y
, z = volcano
, add = TRUE
)
library(ggplot2)
library(reshape2)
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
# Basic plot
v <- ggplot(volcano3d, aes(x, y, z = z))
v + stat_contour()
using directlabels package and picking solution from this
# Basic plot
v <- ggplot(volcano3d, aes(x, y, z = z))
library(directlabels)
v2 <- v + stat_contour(aes(colour = ..level..))
direct.label(v2, method="bottom.pieces")
This is an old question already answered, but I do a lot of contour plots and I think that there is an easier and more versatile way to do this using the package metR (https://rdrr.io/github/eliocamp/metR/f/vignettes/Visualization-tools.Rmd). This package has the function geom_label_contour() that provides an easy way to plot labels of contours. Also provides a lot of functions to plot maps.
library(ggplot2)
library(reshape2)
library(metR)
volcano3d <- melt(volcano)
colnames(volcano3d) <- c('x','y','z')
ggplot(data = volcano3d, aes(x=x,y=y,z=z)) + geom_contour() +
geom_label_contour()
When we take the following example from ggplot2 docs
df <- data.frame(x = c(rnorm(100, 0, 3), rnorm(100, 0, 10)),
g = gl(2, 100))
library(ggplot2)
p <- ggplot(df, aes(x, colour = g)) +
stat_ecdf(geom = "step", na.rm = T) + # interchange point and step
theme_bw()
p
We can create a standard cdf plot. Now if we want to play with the plot in plotly, I obtain a very confusing image when I use the step command. See below. However, when I use the point command plotly behaves like it should. What is happening with the step command? Why can't I recreate the image from using ggplot only?
library(plotly)
ggplotly(p)
I found the solution here https://community.plotly.com/t/bug-with-ggplot2-stat-ecdf-function/1187/3.
You should reorder the dataframe along x.
df <- dplyr::arrange(df, x)
library(ggplot2)
p <- ggplot(df, aes(x, colour = g)) +
stat_ecdf(geom = "step", na.rm = T) +
theme_bw()
p
library(plotly)
ggplotly(p)
This can be solved using ecdf() function.
## ecdf function to get y and 1-y
rcdf <- function (x) {
cdf <- ecdf(x)
y1 <- cdf(x)
y <- unique(y1)
# xrcdf <- 1-y ## to get reverse cdf
xrcdf <- y ## to get cdf
}
ug <- unique(df$g)
ng <- length(ug)
xll <- min(df$x)
xul <- max(df$x)
adr <- data.frame(myxx=c(), myyy=c(), mygg=c())
lapply(1:ng, function(i){
ad2r <- subset(df, g==ug[i])
myx1 <- unique(ad2r$x)
myxx <- c(xll,myx1,xul) ## add lowest value - dummy to assign 100%
myy1 <- rcdf(ad2r$x)
# myyy <- c(1.0,myy1,0.0) ## add 100% to get reverse cdf
myyy <- c(0.0,myy1,1.0) ## add 0% to get cdf
mygg <- ug[i]
ad2rf <- data.frame(myxx,myyy,mygg)
adr <<- rbind(adr,ad2rf)
})
adf <- adr[order(adr$myxx),]
pp <- ggplot(data=adf,
aes_(x=adf$myxx, y=100*adf$myyy, col=adf$mygg, group=adf$mygg)) +
geom_step() +
labs(title="CDF", y = "Y", x = "X", col=NULL)
ppp <- ggplotly(pp, tooltip=c("x","y"))
ppp
This gives the following output:
CDF
What I'm trying to do is to have a variable binwidth in a ggplot2 contour plot, that is the distance between the different "equi-level lines".
Here is a minimal working example from the documentation, with a constant binwidth:
# Generate data
library(reshape2) # for melt
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
# Basic plot
v <- ggplot(volcano3d, aes(x, y, z = z))
v + stat_contour(aes(colour=..level..), binwidth = 2)
Use the breaks argument, for example:
v <- ggplot(volcano3d, aes(x, y, z = z))
v + stat_contour(aes(colour= ..level..), breaks = c(100, 101, 102, 105, 110, 150, 175))
I am trying to plot some extra points onto an existing contour plot with ggplot2:
require(ggplot2)
library(reshape2) # for melt
volcano3d <- melt(volcano)
names(volcano3d) <- c("x", "y", "z")
v <- ggplot(volcano3d, aes(x, y, z = z))
v <- v + stat_contour()
print(v)
newdata <- data.frame(x = runif(7)*60, y = runif(7)*60)
v <- v + geom_point(data=newdata, aes(x, y))
print(v)
The first print is ok, but the second is just blank. Why?