If my X axis is time, and my Y is numeric data, how can I add a point at an arbitrary Y value (Say 500) whenever a point exists?
I am overlaying using lines on top of other plots.
Just add more points, with the same x values as your previous data, and a fixed y value.
So you have something like:
dfr <- data.frame(x = sample(100, 10, replace = TRUE), y = runif(10))
with(dfr, plot(x, y))
and you want to add
points(dfr$x, rep.int(0.5, 10), col = "blue")
Having time for x values shouldn't affect anything.
Related
How can I highlight the ten minimum value grid points of a 385*373 levelplot as black points?
I have the indexes as well as the coordinates of the ten minimum grid points. Preferably I would use the idexes...
I have the following levelplot displaying Europe's air temperature (Z), with X and Y being longitude and latitude respectively.
levelplot(Z ~ X*Y, data=data , xlab="X" , col.regions = heat.colors(100))
One further question: how can I add the country contours with the same projection type as the base data? I tried that before within another function
image(x,y,data,...)
data(wrdl_simpl)
plot(wrld_simpl, add = TRUE)
where the country contours plot seemed to have a totally different projection. However, I want to do this for levelplot() now.
I am very thankful for any help!
lattice plots differ to base plots. Therefore using points does not work. But there are replacement functions. Here is a way to do it:
x <- seq(-10, 10, length.out = 100)
y <- seq(-10, 10, length.out = 100)
z <- as.vector(sqrt(outer(x^2, y^2, "+")))
grid <- cbind(expand.grid(x=x, y=y), z)
minimum <- grid[which.min(grid$z),]
levelplot(z ~ x * y, grid, panel = function(...) {
panel.levelplot(...)
panel.points(x = minimum$x, y = minimum$y, pch = "x", cex =2)
})
We are basically building up the plot inside the panel argument.
I have the following dataframe that I would like to plot. I was wondering if it is possible to color portions of the lines connecting my outcome variable(stackOne$y) in a different color, depending on whether it is less than a certain value or not. For example, I would like portions of the lines falling below 2.2 to be red in color.
set.seed(123)
stackOne = data.frame(id = rep(c(1, 2, 3), each = 3),
y = rnorm(9, 2, 1),
x = rep(c(1, 2, 3), 3))
ggplot(stackOne, aes(x = x, y = y)) +
geom_point() +
geom_line(aes(group = id))
Thanks!
You have at least a couple of options here. The first is quite simple, general (in that it's not limited to straight-line segments) and precise, but uses base plot rather than ggplot. The second uses ggplot, but is slightly more complicated, and colour transition will not be 100% precise (but near enough, as long as you specify an appropriate resolution... read on).
base:
If you're willing to use base plotting functions rather than ggplot, you could clip the plotting region to above the threshold (2.2), then plot the segments in your preferred colour, and subsequently clip to the region below the threshold, and plot again in red. While the first clip is strictly unnecessary, it prevents overplotting different colours, which can look a bit dud.
threshold <- 2.2
set.seed(123)
stackOne=data.frame(id=rep(c(1,2,3),each=3),
y=rnorm(9,2,1),
x=rep(c(1,2,3),3))
# create a second df to hold segment data
d <- stackOne
d$y2 <- c(d$y[-1], NA)
d$x2 <- c(d$x[-1], NA)
d <- d[-findInterval(unique(d$id), d$id), ] # remove last row for each group
plot(stackOne[, 3:2], pch=20)
# clip to region above the threshold
clip(min(stackOne$x), max(stackOne$x), threshold, max(stackOne$y))
segments(d$x, d$y, d$x2, d$y2, lwd=2)
# clip to region below the threshold
clip(min(stackOne$x), max(stackOne$x), min(stackOne$y), threshold)
segments(d$x, d$y, d$x2, d$y2, lwd=2, col='red')
points(stackOne[, 3:2], pch=20) # plot points again so they lie over lines
ggplot:
If you want or need to use ggplot, you can consider the following...
One solution is to use geom_line(aes(group=id, color = y < 2.2)), however this will assign colours based on the y-value of the point at the beginning of each segment. I believe you want to have the colour change not just at the nodes, but wherever a line crosses your given threshold of 2.2. I'm not all that familiar with ggplot, but one way to achieve this is to make a higher-resolution version of your data by creating new points along the lines that connect your existing points, and then use the color = y < 2.2 argument to achieve the desired effect.
For example:
threshold <- 2.2 # set colour-transition threshold
yres <- 0.01 # y-resolution (accuracy of colour change location)
d <- stackOne # for code simplification
# new cols for point coordinates of line end
d$y2 <- c(d$y[-1], NA)
d$x2 <- c(d$x[-1], NA)
d <- d[-findInterval(unique(d$id), d$id), ] # remove last row for each group
# new high-resolution y coordinates between each pair within each group
y.new <- apply(d, 1, function(x) {
seq(x['y'], x['y2'], yres*sign(x['y2'] - x['y']))
})
d$len <- sapply(y.new, length) # length of each series of points
# new high-resolution x coordinates corresponding with new y-coords
x.new <- apply(d, 1, function(x) {
seq(x['x'], x['x2'], length.out=x['len'])
})
id <- rep(seq_along(y.new), d$len) # new group id vector
y.new <- unlist(y.new)
x.new <- unlist(x.new)
d.new <- data.frame(id=id, x=x.new, y=y.new)
p <- ggplot(d.new, aes(x=x,y=y)) +
geom_line(aes(group=d.new$id, color=d.new$y < threshold))+
geom_point(data=stackOne)+
scale_color_discrete(sprintf('Below %s', threshold))
p
There may well be a way to do this through ggplot functions, but in the meantime I hope this helps. I couldn't work out how to draw a ggplotGrob into a clipped viewport (rather it seems to just scale the plot). If you want colour to be conditional on some x-value threshold instead, this would obviously need some tweaking.
Encouraged by people in my answer to a newer but related question, I'll also share a easier to use approximation to the problem here.
Instead of interpolating the correct values exactly, one can use ggforce::geom_link2() to interpolate lines and use after_stat() to assign the correct colours after interpolation. If you want more precision you can increase the n of that function.
library(ggplot2)
library(ggforce)
#> Warning: package 'ggforce' was built under R version 4.0.3
set.seed(123)
stackOne = data.frame(id = rep(c(1, 2, 3), each = 3),
y = rnorm(9, 2, 1),
x = rep(c(1, 2, 3), 3))
ggplot(stackOne, aes(x = x, y = y)) +
geom_point() +
geom_link2(
aes(group = id,
colour = after_stat(y < 2.2))
) +
scale_colour_manual(
values = c("black", "red")
)
Created on 2021-03-26 by the reprex package (v1.0.0)
Problem: I am trying to reproduce a round filled 2d contour plot in R using plotly (have tried ggplot2 also but plotly seemed to be easier).
Data: Sample data download link -
https://drive.google.com/file/d/10Mr5yWVReQckPI6TKLY_vzPT8zWiijKl/view?usp=sharing
The data to be plotted for contour is in a column format and typically called z variable, there is x and y data also available for all values of z. A simple dataframe would look like this:
Please ignore the repeat common x and y as I have truncated decimals. The data has about 25000 rows.
Approach: I first use akima package to interpolate z variable values for given x and y to map z in 2d. This makes the z column data fit in a xy grid for 2d plotting and show contours.
Expected outcome:
Code used:
dens <- akima::interp(x = dt$`Xvalue(mm)`,
y = dt$`Yvalue(mm)`,
z = dt$Values,
duplicate = "mean",
xo=seq(min(dt$`Xvalue(mm)`), max(dt$`Xvalue(mm)`), length = 10),
yo=seq(min(dt$`Yvalue(mm)`), max(dt$`Yvalue(mm)`), length = 10))
plot_ly(x = dens$x,
y = dens$y,
z = dens$z,
colors = c("blue","grey","red"),
type = "contour")
Actual outcome:
Help Needed:
To refine edges of the actual outcome plot to something of a close match to the expected outcome image.
Many thanks in advance for your comments and help.
I found that I could increase the grid output z matrix from akima::interp() from default 40x40 to custom using nx and ny input in function.
And then in plot_ly() add contours = list(coloring = 'fill', showlines = FALSE) to hide contour lines to get output close to my expected outcome.
So working code is like this:
dens <- akima::interp(x = dt$`Xvalue(mm)`,
y = dt$`Yvalue(mm)`,
z = dt$Values,
nx = 50,
ny = 50,
duplicate = "mean",
xo=seq(min(dt$`Xvalue(mm)`), max(dt$`Xvalue(mm)`), length = 50),
yo=seq(min(dt$`Yvalue(mm)`), max(dt$`Yvalue(mm)`), length = 50))
plot_ly(x = dens$x,
y = dens$y,
z = dens$z,
colors = c("blue","grey","red"),
type = "contour",
contours = list(coloring = 'fill', showlines = FALSE))
Plotly contour plot reference was very helpful in this case:
https://plot.ly/r/reference/#contour
I want to plot a matrix of z values with x rows and y columns as a surface similar to this graph from MATLAB.
Surface plot:
Code to generate matrix:
# Parameters
shape<-1.849241
scale<-38.87986
x<-seq(from = -241.440, to = 241.440, by = 0.240)# 2013 length
y<-seq(from = -241.440, to = 241.440, by = 0.240)
matrix_fun<-matrix(data = 0, nrow = length(x), ncol = length(y))
# Generate two dimensional travel distance probability density function
for (i in 1:length(x)) {
for (j in 1:length(y)){
dxy<-sqrt(x[i]^2+y[j]^2)
prob<-1/(scale^(shape)*gamma(shape))*dxy^(shape-1)*exp(-(dxy/scale))
matrix_fun[i,j]<-prob
}}
# Rescale 2-d pdf to sum to 1
a<-sum(matrix_fun)
matrix_scale<-matrix_fun/a
I am able to generate surface plots using a couple methods (persp(), persp3d(), surface3d()) but the colors aren't displaying the z values (the probabilities held within the matrix). The z values only seem to display as heights not as differentiated colors as in the MATLAB figure.
Example of graph code and graphs:
library(rgl)
persp3d(x=x, y=y, z=matrix_scale, color=rainbow(25, start=min(matrix_scale), end=max(matrix_scale)))
surface3d(x=x, y=y, z=matrix_scale, color=rainbow(25, start=min(matrix_scale), end=max(matrix_scale)))
persp(x=x, y=y, z=matrix_scale, theta=30, phi=30, col=rainbow(25, start=min(matrix_scale), end=max(matrix_scale)), border=NA)
Image of the last graph
Any other tips to recreate the image in R would be most appreciated (i.e. legend bar, axis tick marks, etc.)
So here's a ggplot solution which seems to come a little bit closer to the MATLAB plot
# Parameters
shape<-1.849241
scale<-38.87986
x<-seq(from = -241.440, to = 241.440, by = 2.40)
y<-seq(from = -241.440, to = 241.440, by = 2.40)
df <- expand.grid(x=x,y=y)
df$dxy <- with(df,sqrt(x^2+y^2))
df$prob <- dgamma(df$dxy,shape=shape,scale=scale)
df$prob <- df$prob/sum(df$prob)
library(ggplot2)
library(colorRamps) # for matlab.like(...)
library(scales) # for labels=scientific
ggplot(df, aes(x,y))+
geom_tile(aes(fill=prob))+
scale_fill_gradientn(colours=matlab.like(10), labels=scientific)
BTW: You can generate your data frame of probabilities much more efficiently using the built-in dgamma(...) function, rather than calculating it yourself.
In line with alexis_laz's comment, here is an example using filled.contour. You might want to increase your by to 2.40 since the finer granularity increases the time it takes to generate the plot by a lot but doesn't improve quality.
filled.contour(x = x, y = y, z = matrix_scale, color = terrain.colors)
# terrain.colors is in the base grDevices package
If you want something closer to your color scheme above, you can fiddle with the rainbow function:
filled.contour(x = x, y = y, z = matrix_scale,
color = (function(n, ...) rep(rev(rainbow(n/2, ...)[1:9]), each = 3)))
Finer granularity:
filled.contour(x = x, y = y, z = matrix_scale, nlevels = 150,
color = (function(n, ...)
rev(rep(rainbow(50, start = 0, end = 0.75, ...), each = 3))[5:150]))
I have 4 columns of data in R that looks like this
x y z group
the group columns has categorical values, so it is a discrete set of values, whereas the other three columns are continuous.
I want to make a 3d plot in R with x, y, and z, and where the color of the dot is given by "group". I also want to have a legend to this plot. How can I do this? I don't have a particular preference on the actual colors. I suppose rainbow(length(unique(group)) should do fine.
Here is an example using scatterplot3d and based on the example in the vignette
library(scatterplot3d)
# some basic dummy data
DF <- data.frame(x = runif(10),
y = runif(10),
z = runif(10),
group = sample(letters[1:3],10, replace = TRUE))
# create the plot, you can be more adventurous with colour if you wish
s3d <- with(DF, scatterplot3d(x, y, z, color = as.numeric(group), pch = 19))
# add the legend using `xyz.convert` to locate it
# juggle the coordinates to get something that works.
legend(s3d$xyz.convert(0.5, 0.7, 0.5), pch = 19, yjust=0,
legend = levels(DF$group), col = seq_along(levels(DF$group)))
Or, you could use lattice and cloud, in which case you can construct the key using key
cloud(z~x+y, data = DF, pch= 19, col.point = DF$group,
key = list(points = list(pch = 19, col = seq_along(levels(DF$group))),
text = list(levels(DF$group)), space = 'top', columns = nlevels(DF$group)))