Related
I would like manually adjust the scales of two contour plots such that each have the same scale even though they contain different ranges of values in the z-direction.
For instance, lets say that I want to make contour plots of z1 and z2:
x = 1:15
y = 1:15
z1 = x %*% t(y)
z2 = 50+1.5*(x %*% t(y))
data <- data.frame(
x = as.vector(col(z1)),
y = as.vector(row(z1)),
z1 = as.vector(z1),
z2 = as.vector(z2)
)
ggplot(data, aes(x, y, z = z1)) +
geom_contour_filled(bins = 8)
ggplot(data, aes(x, y, z = z2)) +
geom_contour_filled(bins = 8)
Is there a way I can manually adjust the scale of each plot such that each contain the same number of levels (in this case bins = 8), the minimum is the same for both (in this case min(z1)), and the max is the same for both (max(z2))?
One can manually define a vector of desired breaks points and then pass the vector to the "breaks" option in the geom_contour_filled() function.
In the below script, finds 8 break intervals between the grand minimum and the grand maximum of the dataset.
Also there are 2 functions defined to create the palette and label names for the legend.
#establish the min and max of scale
grandmin <- min(z1, z2)-1
grandmax <- max(z2, z2)
#define the number of breaks. In this case 8 +1
mybreaks <- seq(grandmin, ceiling(round(grandmax, 0)), length.out = 9)
#Function to return the dersired number of colors
mycolors<- function(x) {
colors<-colorRampPalette(c("darkblue", "yellow"))( 8 )
colors[1:x]
}
#Function to create labels for legend
breaklabel <- function(x){
labels<- paste0(mybreaks[1:8], "-", mybreaks[2:9])
labels[1:x]
}
ggplot(data, aes(x, y, z = z1)) +
geom_contour_filled(breaks= mybreaks, show.legend = TRUE) +
scale_fill_manual(palette=mycolors, values=breaklabel(8), name="Value", drop=FALSE) +
theme(legend.position = "right")
ggplot(data, aes(x, y, z = z2)) +
geom_contour_filled(breaks= mybreaks, show.legend = TRUE) +
scale_fill_manual(palette=mycolors, values=breaklabel(8), name="Value", drop=FALSE)
I would like to plot multiple Poisson (with different lambdas (1:10))
I found the following function to draw a plot
plot_pois = function(lambda = 5)
{
plot(0:20, dpois( x=0:20, lambda=lambda ), xlim=c(-2,20))
normden <- function(x){dnorm(x, mean= lambda, sd=sqrt(lambda))}
curve(normden, from=-4, to=20, add=TRUE, col=lambda)
}
plot.new()
plot_pois(2)
But I can't plot another Poisson over it. I tried to change plot to points or lines but it totally changes the plot. I would also like to add a legends containing different colors for different values of lambda.
If I could plot it using ggplot, it would be a better option.
Another possible tidyverse solution:
library(tidyverse)
# Build Poisson distributions
p_dat <- map_df(1:10, ~ tibble(
l = paste(.),
x = 0:20,
y = dpois(0:20, .)
))
# Build Normal distributions
n_dat <- map_df(1:10, ~ tibble(
l = paste(.),
x = seq(0, 20, by = 0.001),
y = dnorm(seq(0, 20, by = 0.001), ., sqrt(.))
))
# Use ggplot2 to plot
ggplot(n_dat, aes(x, y, color = factor(l, levels = 1:10))) +
geom_line() +
geom_point(data = p_dat, aes(x, y, color = factor(l, levels = 1:10))) +
labs(color = "Lambda:") +
theme_minimal()
Created on 2019-05-06 by the reprex package (v0.2.1)
In ggplot2 you can use lapply to loop over different lambdas:
library(ggplot2)
lambdas <- c(5, 2)
ggplot(data = data.frame(x = 0:20)) +
lapply(lambdas, function(l) geom_point(aes(x = x, y = dpois(x, lambda = l), col = factor(l)))) +
lapply(lambdas, function(l) stat_function(fun = dnorm, args = list(mean = l, sd = sqrt(l)),
aes(x = x, col = factor(l))))
Axes titles and limits, the legend title etc. can then be customized as usual in ggplot2.
I have a large dataset of gene expression from ~10,000 patient samples (TCGA), and I'm plotting a predicted expression value (x) and the actual observed value (y) of a certain gene signature. For my downstream analysis, I need to draw a precise line through the plot and calculate different parameters in samples above/below the line.
No matter how I draw a line through the data (geom_smooth(method = 'lm', 'glm', 'gam', or 'loess')), the line always seems imperfect - it doesn't cut through the data to my liking (red line is lm in figure).
After playing around for a while, I realized that the 2d kernel density lines (geom_density2d) actually do a good job of showing the slope/trends of my data, so I manually drew a line that kind of cuts through the density lines (black line in figure).
My question: how can I automatically draw a line that cuts through the kernel density lines, as for the black line in the figure? (Rather than manually playing with different intercepts and slopes till something looks good).
The best approach I can think of is to somehow calculate intercept and slope of the longest diameter for each of the kernel lines, take an average of all those intercepts and slopes and plot that line, but that's a bit out of my league. Maybe someone here has experience with this and can help?
A more hacky approach may be getting the x,y coords of each kernel density line from ggplot_build, and going from there, but it feels too hacky (and is also out of my league).
Thanks!
EDIT: Changed a few details to make the figure/analysis easier. (Density lines are smoother now).
Reprex:
library(MASS)
set.seed(123)
samples <- 10000
r <- 0.9
data <- mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x <- data[, 1] # standard normal (mu=0, sd=1)
y <- data[, 2] # standard normal (mu=0, sd=1)
test.df <- data.frame(x = x, y = y)
lm(y ~ x, test.df)
ggplot(test.df, aes(x, y)) +
geom_point(color = 'grey') +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = c(2,2)) + ### EDIT: h = c(2,2)
geom_smooth(method = "glm", se = F, lwd = 1, color = 'red') +
geom_abline(intercept = 0, slope = 0.7, lwd = 1, col = 'black') ## EDIT: slope to 0.7
Figure:
I generally agree with #Hack-R.
However, it was kind of a fun problem and looking into ggplot_build is not such a big deal.
require(dplyr)
require(ggplot2)
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = c(2,2))
#basic version of your plot
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),] %>%
select(x,y) # extracts the x/y coordinates of the points on the largest ellipse from your 2d-density contour
Now this answer helped me to find the points on this ellipse which are furthest apart.
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2)) #extra column specifying the distance of each point to the mean of those points
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
# gives the coordinates of the point farthest away from the mean point
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
#now this looks which of the points is the farthest from the point farthest from the mean point :D
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = c(2,2)) +
# geom_segment using the coordinates of the points farthest apart
geom_segment((aes(x = coord_farthest['x'], y = coord_farthest['y'],
xend = coord_fff['x'], yend = coord_fff['y']))) +
geom_smooth(method = "glm", se = F, lwd = 1, color = 'red') +
# as per your request with your geom_smooth line
coord_equal()
coord_equal is super important, because otherwise you will get super weird results - it messed up my brain too. Because if the coordinates are not set equal, the line will seemingly not pass through the point furthest apart from the mean...
I leave it to you to build this into a function in order to automate it. Also, I'll leave it to you to calculate the y-intercept and slope from the two points
Tjebo's approach was kind of good initially, but after a close look, I found that it found the longest distance between two points on an ellipse. While this is close to what I wanted, it failed with either an irregular shape of the ellipse, or the sparsity of points in the ellipse. This is because it measured the longest distance between two points; whereas what I really wanted is the longest diameter of an ellipse; i.e.: the semi-major axis. See image below for examples/details.
Briefly:
To find/draw density contours of specific density/percentage:
R - How to find points within specific Contour
To get the longest diameter ("semi-major axis") of an ellipse:
https://stackoverflow.com/a/18278767/3579613
For function that returns intercept and slope (as in OP), see last piece of code.
The two pieces of code and images below compare two Tjebo's approach vs. my new approach based on the above posts.
#### Reprex from OP
require(dplyr)
require(ggplot2)
require(MASS)
set.seed(123)
samples <- 10000
r <- 0.9
data <- mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x <- data[, 1] # standard normal (mu=0, sd=1)
y <- data[, 2] # standard normal (mu=0, sd=1)
test.df <- data.frame(x = x, y = y)
#### From Tjebo
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = 2)
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),][,2:3]
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2)) #extra column specifying the distance of each point to the mean of those points
p_maxring = p_maxring[round(seq(1, nrow(p_maxring), nrow(p_maxring)/23)),] #### Make a small ellipse to illustrate flaws of approach
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
# gives the coordinates of the point farthest away from the mean point
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
#now this looks which of the points is the farthest from the point farthest from the mean point :D
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
farthest_2_points = data.frame(t(cbind(coord_farthest, coord_fff)))
plot(p_maxring[,1:2], asp=1)
lines(farthest_2_points, col = 'blue', lwd = 2)
#### From answer in another post
d = cbind(p_maxring[,1], p_maxring[,2])
r = ellipsoidhull(d)
exy = predict(r) ## the ellipsoid boundary
lines(exy)
me = colMeans((exy))
dist2center = sqrt(rowSums((t(t(exy)-me))^2))
max(dist2center) ## major axis
lines(exy[dist2center == max(dist2center),], col = 'red', lwd = 2)
#### The plot here is made from the data in the reprex in OP, but with h = 0.5
library(MASS)
set.seed(123)
samples <- 10000
r <- 0.9
data <- mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x <- data[, 1] # standard normal (mu=0, sd=1)
y <- data[, 2] # standard normal (mu=0, sd=1)
test.df <- data.frame(x = x, y = y)
## MAKE BLUE LINE
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = 0.5) ## NOTE h = 0.5
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),][,2:3]
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2))
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
## MAKE RED LINE
## h = 0.5
## Given the highly irregular shape of the contours, I will use only the largest contour line (0.95) for draing the line.
## Thus, average = 1. See function below for details.
ln = long.diam("x", "y", test.df, h = 0.5, average = 1) ## NOTE h = 0.5
## PLOT
ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 0.5, contour = T, h = 0.5) + ## NOTE h = 0.5
geom_segment((aes(x = coord_farthest['x'], y = coord_farthest['y'],
xend = coord_fff['x'], yend = coord_fff['y'])), col = 'blue', lwd = 2) +
geom_abline(intercept = ln[1], slope = ln[2], color = 'red', lwd = 2) +
coord_equal()
Finally, I came up with the following function to deal with all this. Sorry for the lack of comments/clarity
#### This will return the intercept and slope of the longest diameter (semi-major axis).
####If Average = TRUE, it will average the int and slope across different density contours.
long.diam = function(x, y, df, probs = c(0.95, 0.5, 0.1), average = T, h = 2) {
fun.df = data.frame(cbind(df[,x], df[,y]))
colnames(fun.df) = c("x", "y")
dens = kde2d(fun.df$x, fun.df$y, n = 200, h = h)
dx <- diff(dens$x[1:2])
dy <- diff(dens$y[1:2])
sz <- sort(dens$z)
c1 <- cumsum(sz) * dx * dy
levels <- sapply(probs, function(x) {
approx(c1, sz, xout = 1 - x)$y
})
names(levels) = paste0("L", str_sub(formatC(probs, 2, format = 'f'), -2))
#plot(fun.df$x,fun.df$y, asp = 1)
#contour(dens, levels = levels, labels=probs, add=T, col = c('red', 'blue', 'green'), lwd = 2)
#contour(dens, add = T, col = 'red', lwd = 2)
#abline(lm(fun.df$y~fun.df$x))
ls <- contourLines(dens, levels = levels)
names(ls) = names(levels)
lines.info = list()
for (i in 1:length(ls)) {
d = cbind(ls[[i]]$x, ls[[i]]$y)
exy = predict(ellipsoidhull(d))## the ellipsoid boundary
colnames(exy) = c("x", "y")
me = colMeans((exy)) ## center of the ellipse
dist2center = sqrt(rowSums((t(t(exy)-me))^2))
#plot(exy,type='l',asp=1)
#points(d,col='blue')
#lines(exy[order(dist2center)[1:2],])
#lines(exy[rev(order(dist2center))[1:2],])
max.dist = data.frame(exy[rev(order(dist2center))[1:2],])
line.fit = lm(max.dist$y ~ max.dist$x)
lines.info[[i]] = c(as.numeric(line.fit$coefficients[1]), as.numeric(line.fit$coefficients[2]))
}
names(lines.info) = names(ls)
#plot(fun.df$x,fun.df$y, asp = 1)
#contour(dens, levels = levels, labels=probs, add=T, col = c('red', 'blue', 'green'), lwd = 2)
#abline(lines.info[[1]], col = 'red', lwd = 2)
#abline(lines.info[[2]], col = 'blue', lwd = 2)
#abline(lines.info[[3]], col = 'green', lwd = 2)
#abline(apply(simplify2array(lines.info), 1, mean), col = 'black', lwd = 4)
if (isTRUE(average)) {
apply(simplify2array(lines.info), 1, mean)
} else {
lines.info[[average]]
}
}
Finally, here's the final implementation of the different answers:
library(MASS)
set.seed(123)
samples = 10000
r = 0.9
data = mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(2, r, r, 2), nrow=2))
x = data[, 1] # standard normal (mu=0, sd=1)
y = data[, 2] # standard normal (mu=0, sd=1)
#plot(x, y)
test.df = data.frame(x = x, y = y)
#### Find furthest two points of contour
## BLUE
p <- ggplot(test.df, aes(x, y)) +
geom_density2d(color = 'red', lwd = 2, contour = T, h = 2)
p_built <- ggplot_build(p)
p_data <- p_built$data[[1]]
p_maxring <- p_data[p_data[['level']] == min(p_data[['level']]),][,2:3]
coord_mean <- c(x = mean(p_maxring$x), y = mean(p_maxring$y))
p_maxring <- p_maxring %>%
mutate (mean_dev = sqrt((x - mean(x))^2 + (y - mean(y))^2))
coord_farthest <- c('x' = p_maxring$x[which.max(p_maxring$mean_dev)], 'y' = p_maxring$y[which.max(p_maxring$mean_dev)])
farthest_from_farthest <- sqrt((p_maxring$x - coord_farthest['x'])^2 + (p_maxring$y - coord_farthest['y'])^2)
coord_fff <- c('x' = p_maxring$x[which.max(farthest_from_farthest)], 'y' = p_maxring$y[which.max(farthest_from_farthest)])
#### Find the average intercept and slope of 3 contour lines (0.95, 0.5, 0.1), as in my long.diam function above.
## RED
ln = long.diam("x", "y", test.df)
#### Plot everything. Black line is GLM
ggplot(test.df, aes(x, y)) +
geom_point(color = 'grey') +
geom_density2d(color = 'red', lwd = 1, contour = T, h = 2) +
geom_smooth(method = "glm", se = F, lwd = 1, color = 'black') +
geom_abline(intercept = ln[1], slope = ln[2], col = 'red', lwd = 1) +
geom_segment((aes(x = coord_farthest['x'], y = coord_farthest['y'],
xend = coord_fff['x'], yend = coord_fff['y'])), col = 'blue', lwd = 1) +
coord_equal()
I have made a plot of a polynomial function: y = x^2 - 6*x + 9
with a series of several points in a sequence + minor standard error in y. I used these points to construct a spline model for that function from the raw data points, and then I calculated the derivative from the spline model with R's predict() function and then I added both of the spline curves to the plot.
By the way, the expected derivative function is this: dy / dx = 2*x - 6
The original function I colored blue and the 1st derivative function I colored red. I wish to add legends to these plots, but I'm finding that difficult since I did not assign any points to the plots, as I declared the data-frames within the geom_smooth() functions.
The code I'm using is this:
library(ggplot2)
# Plot the function: f(x) = x^2 - 6x + 9
# with a smooth spline:
# And then the deriviative of that function from predicted values of the
# smoothed spline: f ' (x) = 2*x - 6
# Get a large sequence of x-values:
x <- seq(from = -10, to = 10, by = 0.01)
# The y-values are a function of each x value.
y <- x^2 - 6*x + 9 + rnorm(length(x), 0, 0.5)
# Fit the curve to a model which is a smoothed spine.
model <- smooth.spline(x = x, y = y)
# Predict the 1st derivative of this smoothed spline.
f_x <- predict(model, x = seq(from = min(x), to = max(x), by = 1), deriv = 1)
# Plot the smoothed spline of the original function and the derivative with respect to x.
p <- ggplot() + theme_bw() + geom_smooth(data = data.frame(x,y), aes(x = x, y = y), method = "loess", col = "blue", se = TRUE) + geom_smooth(data = data.frame(f_x$x, f_x$y), aes(x = f_x$x, y = f_x$y), method = "loess", col = "red", se = TRUE)
# Set the bounds of the plot.
p <- p + scale_x_continuous(breaks = scales::pretty_breaks(n = 20), limits = c(-5, 10)) + scale_y_continuous(breaks = scales::pretty_breaks(n = 20), limits = c(-10, 10))
# Add some axis labels
p <- p + labs(x = "x-axis", y = "y-axis", title = "Original Function and predicted derivative function")
p <- p + scale_fill_manual(values = c("blue", "red"), labels = c("Original Function", "Derivative Function with respect to x"))
print(p)
I was hoping that I could add the legend with scale_fill_manual(), but my attempt does not add a legend to the plot. Essentially, the plot I get generally looks like this, minus the messy legend that I added in paint. I would like that legend, thank you.
I did this because I want to show to my chemistry instructor that I can accurately measure the heat capacity just from the points from differential scanning calorimetry data for which I believe the heat capacity is just the first derivative plot of heat flow vs Temperature differentiated with respect to temperature.
So I tried to make a plot showing the original function overlayed with the 1st derivative function with respect to x, showing that the plot of the first derivative made only from a spline curve fitted to raw data points reliably produces the expected line dy / dx = 2 * x - 6, which it does.
I just want to add that legend.
Creating a data frame with you data and use color within aesthetics is the most common way of doing this.
df <- rbind(
data.frame(data='f(x)', x=x, y=y),
data.frame(data='f`(x)', x=f_x$x, y=f_x$y))
p <- ggplot(df, aes(x,y, color=data)) + geom_smooth(method = 'loess')
p <- p + scale_x_continuous(breaks = scales::pretty_breaks(n = 20), limits = c(-5, 10)) + scale_y_continuous(breaks = scales::pretty_breaks(n = 20), limits = c(-10, 10))
p <- p + labs(x = "x-axis", y = "y-axis", title = "Original Function and predicted derivative function")
p <- p + scale_color_manual(name = "Functions", values = c("blue", "red"), labels = c("Original Function", "Derivative Function with respect to x"))
print(p)
The polygon function in R seems rather simple...however I can't get it to work.
It easily works with this code:
x <- seq(-3,3,0.01)
y1 <- dnorm(x,0,1)
y2 <- 0.5*dnorm(x,0,1)
plot(x,y1,type="l",bty="L",xlab="X",ylab="dnorm(X)")
points(x,y2,type="l",col="red")
polygon(c(x,rev(x)),c(y2,rev(y1)),col="skyblue")
When adopting this to something else, it doesn't work. Here some stuff to reproduce the issue:
lowerbound = c(0.05522914,0.06567045,0.07429926,0.08108482,0.08624472,0.09008050,0.09288837,0.09492226)
upperbound = c(0.1743657,0.1494058,0.1333106,0.1227383,0.1156714,0.1108787,0.1075915,0.1053178)
lim = c(100,200,400,800,1600,3200,6400,12800)
plot(upperbound, ylim=c(0, 0.2), type="b", axes=FALSE)
lines(lowerbound, type="b", col="red")
atvalues <- seq(1:8)
axis(side=1, at=atvalues, labels=lim)
axis(side=2, at=c(0,0.05,0.1,0.15,0.2), labels=c(0,0.05,0.1,0.15,0.2))
polygon(lowerbound,upperbound, col="skyblue")
It also doesn't work when only segmenting a subset when directly calling the coordinates:
xpoly <- c(100,200,200,100)
ypoly <- c(lowerbound[1], lowerbound[2], upperbound[2], upperbound[1])
polygon(xpoly,ypoly, col="skyblue")
What am I missing?
Plotting the whole polygon
You need to supply both x and y to polygon. Normally, you'd also do that for plot, but if you don't it will just use the Index as x, that is integers 1 to n. We can use that to make an x range. seq_along will create a 1:n vector, where n is the length of another object.
x <- c(seq_along(upperbound), rev(seq_along(lowerbound)))
y <- c(lowerbound, rev(upperbound))
plot(upperbound, ylim=c(0, 0.2), type="b", axes=FALSE)
lines(lowerbound, type="b", col="red")
atvalues <- seq(1:8)
axis(side=1, at=atvalues, labels=lim)
axis(side=2, at=c(0,0.05,0.1,0.15,0.2), labels=c(0,0.05,0.1,0.15,0.2))
polygon(x = x, y = y, col="skyblue")
Plotting a subset
For a subset, I would create the y first, and then use the old x to easily get `x values:
y2 <- c(lowerbound[1:2], upperbound[2:1])
x2 <- x[which(y2 == y)]
polygon(x2, y2, col="skyblue")
How I would do it
Creating something like this is much easier in ggplot2, where geom_ribbon does a lot of the heavy lifting. We just have to make an actual data.frame, an stop relying on indices.
Full polygon:
library(ggplot2)
ggplot(d, aes(x = x, ymin = low, ymax = up)) +
geom_ribbon(fill = 'skyblue', alpha = 0.5) +
geom_line(aes(y = low), col = 'red') +
geom_line(aes(y = up), col = 'black') +
scale_x_continuous(trans = 'log2') +
theme_bw()
Subset:
ggplot(d, aes(x = x, ymin = low, ymax = up)) +
geom_ribbon(data = d[1:2, ], fill = 'skyblue', alpha = 0.5) +
geom_line(aes(y = low), col = 'red') +
geom_line(aes(y = up), col = 'black') +
scale_x_continuous(trans = 'log2') +
theme_bw()