How do I plot contours ?
I have x, y, z. I wish to plot contour lines using V values.
# data
tbl <- tibble(x = runif(n = 1000, min = 0, max = 1),
y = runif(n = 1000, min = 0, max = 1),
V = x^2.5 + y^2)
# plots
ggplot(data = tbl,
aes(x = x,
y = y
z = V)) +
geom_contour_filled(alpha = 0.8, breaks = seq(0, 2, 0.2)) +
theme_bw()
Here is a way, solving the problem with a shameless copy&paste of the franke example in the documentation of geom_contour_filled.
The trick is to use package interp to prepare the data for plotting. In the code below the only change in the instruction to create grid is the data set being binned.
suppressPackageStartupMessages({
library(tidyverse)
library(interp)
})
set.seed(2022)
tbl <- tibble(x = runif(n = 1000, min = 0, max = 1),
y = runif(n = 1000, min = 0, max = 1),
V = x^2.5 + y^2)
grid <- with(tbl, interp::interp(x, y, V))
griddf <- subset(data.frame(x = rep(grid$x, nrow(grid$z)),
y = rep(grid$y, each = ncol(grid$z)),
z = as.numeric(grid$z)),
!is.na(z))
# plots
ggplot(data = griddf,
aes(x = x,
y = y,
z = z)) +
stat_contour_filled(alpha = 0.8, breaks = seq(0, 2, 0.2)) +
theme_bw()
Created on 2022-05-18 by the reprex package (v2.0.1)
Edit
To better control the bins, use either argument bins or argument binwidth instead of breaks. The following code has a bin width of 0.1, doubling the number of bins and now uses geom_contour_filled, like in the question.
ggplot(data = griddf,
aes(x = x,
y = y,
z = z)) +
geom_contour_filled(alpha = 0.8, binwidth = 0.1, show.legend = FALSE) +
theme_bw()
Created on 2022-05-18 by the reprex package (v2.0.1)
geom_contour_filled require binned data.
So your data should be
# data
tbl <- tibble(x = rep(seq(0,1,length.out=100),100),
y = rep(seq(0,1,length.out=100),each=100),
V = x^2.5 + y^2)
Related
Hi I have a problem changing the scale using ggparcoord
please tell me how to change y axis value to (1, 10, 100, 1000)
p<- ggparcoord(dt1, columns = c(108,111), groupColumn = 155, order = "anyClass", scale = "globalminmax", showPoints = TRUE,
alphaLines = 0.3)
Obviously, we don't have your data, so let's make a dummy example that is similar to yours:
library(GGally)
set.seed(8)
dt1 <- data.frame(BNP = rexp(500, 0.001),
BNP_Max3 = rexp(500, 0.001),
minusDeltaBNP = rep(c("A", "B"), each = 250))
p <- ggparcoord(dt1, columns = 1:2, groupColumn = 3,
scale = "globalminmax", showPoints = TRUE, alphaLines = 0.3) +
theme_minimal(base_size = 16)
To change the y axis to a log scale, we can simply do:
p + scale_y_log10()
Created on 2022-09-04 with reprex v2.0.2
How do I pre-define bins for the histogram? For e.g.
predefine_bin_edges <- seq(0, 10, 1)
Can someone please use this predefine bin edges to update the example.
Edited later -
Also, Is there a way to include the extreme points, that are outside the binrange, in the corner bins for a complete picture of data distribution.
library(tidyverse)
# data
x <- rnorm(n = 1000, mean = 5, sd = 3)
tbl <- tibble(x)
# geom_histogram()
ggplot(data = tbl,
aes(x = x)) +
geom_histogram()
Do you mean like this?
library(tidyverse)
# data
x <- rnorm(n = 1000, mean = 5, sd = 3)
tbl <- tibble(x)
# geom_histogram()
ggplot(data = tbl,
aes(x = x)) +
geom_histogram(breaks = seq(0, 10, 1))
I don't think there is an argument to geom histogram that can include the values outside the range in the corner bins. However, you can squish the data at the aes() level.
ggplot(data = tbl,
aes(x = pmax(pmin(x, 10), 0))) +
geom_histogram(breaks = seq(0, 10, 1))
Or if you're uncomfortable with that, you can set the limits + oob arguments in the scale.
ggplot(data = tbl,
aes(x = x)) +
geom_histogram(breaks = seq(0, 10, 1)) +
scale_x_continuous(limits = c(0, 10),
oob = scales::oob_squish)
I have two dataframes, one which I want to make a stat_density_2d plot using a 'raster' geom and one in which I want to use a 'point' geom. For the point geom I want to remove any point where there is no data though, as measured by a point size of 0.
The following is my code:
library(tidyverse)
set.seed(1)
#tibble for raster density plot
df <- tibble(x = runif(1000000, min = -7, max = 5),
y = runif(1000000, min = 0, max = 1000))
#tibble for point density plot
df2 <- tibble(x = runif(20000, min = -2, max = 2),
y = runif(20000, min = 0, max = 500))
#create the density plot
p1 <- ggplot(NULL, aes(x=x, y=y) ) +
stat_density_2d(data = df, aes(fill = stat(density)), geom = "raster", contour = FALSE) +
scale_fill_gradient(low="transparent", high="red") +
stat_density_2d(data = df2, geom = "point", aes(size = ..density..), n = 40, contour = FALSE) +
theme_bw() +
theme(text=element_text(size=18)) +
ylim(0, 1000) + xlim(-7, 5)
p1
which returns:
But where the points are smallest (outside the bounds specified in the df2 tibble) I don't want any density points to be shown. Is there anyway to remove these?
Here's a hack, though I don't know how robust it is to differences in data.
BLUF: add scale_radius(range=c(-1,6)).
I reduced your data a lot so that it doesn't take 5 minutes to render.
set.seed(1)
df <- tibble(x = runif(1000, min = -7, max = 5),
y = runif(1000, min = 0, max = 1000))
df2 <- tibble(x = runif(20, min = -2, max = 2),
y = runif(20, min = 0, max = 500))
Four plots:
Your code (my data), no other change;
scale_radius();
scale_radius(range = c(-0.332088004, 6)); and
scale_radius(range = c(-1, 6)).
This is surely a hack, and I don't know how to find a more precise way of filtering out specific levels.
The modified code:
p1 <- ggplot(NULL, aes(x=x, y=y) ) +
stat_density_2d(data = df, aes(fill = stat(density)), geom = "raster", contour = FALSE) +
scale_fill_gradient(low="transparent", high="red") +
stat_density_2d(data = df2, geom = "point", aes(size = ..density..), n = 40, contour = FALSE) +
theme_bw() +
# scale_radius() +
# scale_radius(range = c(-0.332088004, 6)) +
scale_radius(range = c(-1, 6)) +
theme(text=element_text(size=18)) +
ylim(0, 1000) + xlim(-7, 5)
I am trying to replicate the following picture in R, in particular with ggplot2
I was able to draw the red rss contour lines but I've no idea how to draw a diamond (like the one in the left picture). The "expected Output" should be a way to draw a diamond with a given side length.
EDIT: Here is a short reproducible example to add the diamond randomly inside the following plot:
mlb<- read.table('https://umich.instructure.com/files/330381/download?download_frd=1', as.is=T, header=T)
str(mlb)
fit<-lm(Height~Weight+Age-1, data = as.data.frame(scale(mlb[,4:6])))
points = data.frame(x=c(0,fit$coefficients[1]),y=c(0,fit$coefficients[2]),z=c("(0,0)","OLS Coef"))
Y=scale(mlb$Height)
X = scale(mlb[,c(5,6)])
beta1=seq(-0.556, 1.556, length.out = 100)
beta2=seq(-0.661, 0.3386, length.out = 100)
df <- expand.grid(beta1 = beta1, beta2 = beta2)
b = as.matrix(df)
df$sse <- rep(t(Y)%*%Y,100*100) - 2*b%*%t(X)%*%Y + diag(b%*%t(X)%*%X%*%t(b))
base <- ggplot() +
stat_contour(data=df, aes(beta1, beta2, z = sse),breaks = round(quantile(df$sse, seq(0, 0.2, 0.03)), 0),
size = 0.5,color="darkorchid2",alpha=0.8) +
scale_x_continuous(limits = c(-0.4,1))+
scale_y_continuous(limits = c(-0.55,0.4))+
geom_point(data = points,aes(x,y))+
geom_text(data = points,aes(x,y,label=z),vjust = 2,size=3.5)
base
You can draw shapes with geom_polygon.
library(ggplot2)
df <- data.frame(x = c(1, 0, -1, 0), y = c(0, 1, 0, -1))
ggplot(df) + geom_polygon(aes(x = x, y = y))
If you want to generate the coordinates from a center and a side length, you can transform a base matrix. You can also combine this with an existing plot by supplying the coordinates to the data argument of the geom instead of to ggplot() as shown. Change the sqrt2 scaling if you want the corner-to-center as the argument instead of the side length.
diamond <- function(side_length, center) {
base <- matrix(c(1, 0, 0, 1, -1, 0, 0, -1), nrow = 2) * sqrt(2) / 2
trans <- (base * side_length) + center
as.data.frame(t(trans))
}
ggplot() + geom_polygon(data = diamond(2, c(1, 2)), mapping = aes(x = V1, y = V2))
Here's an example of adding it in to your provided data. Note that I put it before (underneath) the text, and named the arguments to be clear (probably the source of that object coercible by fortify error.
mlb <- read.table("https://umich.instructure.com/files/330381/download?download_frd=1", as.is = T, header = T)
fit <- lm(Height ~ Weight + Age - 1, data = as.data.frame(scale(mlb[, 4:6])))
points <- data.frame(x = c(0, fit$coefficients[1]), y = c(0, fit$coefficients[2]), z = c("(0,0)", "OLS Coef"))
Y <- scale(mlb$Height)
X <- scale(mlb[, c(5, 6)])
beta1 <- seq(-0.556, 1.556, length.out = 100)
beta2 <- seq(-0.661, 0.3386, length.out = 100)
df <- expand.grid(beta1 = beta1, beta2 = beta2)
b <- as.matrix(df)
df$sse <- rep(t(Y) %*% Y, 100 * 100) - 2 * b %*% t(X) %*% Y + diag(b %*% t(X) %*% X %*% t(b))
ggplot(df) +
stat_contour(aes(beta1, beta2, z = sse),
breaks = round(quantile(df$sse, seq(0, 0.2, 0.03)), 0),
size = 0.5, color = "darkorchid2", alpha = 0.8
) +
geom_polygon(data = diamond(0.1, c(0, 0)), mapping = aes(x = V1, y = V2), fill = "cadetblue1") +
scale_x_continuous(limits = c(-0.4, 1)) +
scale_y_continuous(limits = c(-0.55, 0.4)) +
geom_point(data = points, aes(x, y)) +
geom_text(data = points, aes(x, y, label = z), vjust = 2, size = 3.5)
#> Warning: Removed 4215 rows containing non-finite values (stat_contour).
Created on 2018-08-01 by the reprex package (v0.2.0).
I have two populations A and B distributed spatially with one character Z, I want to be able to make an hexbin substracting the proportion of the character in each hexbin. Here I have the code for two theoretical populations A and B
library(hexbin)
library(ggplot2)
set.seed(2)
xA <- rnorm(1000)
set.seed(3)
yA <- rnorm(1000)
set.seed(4)
zA <- sample(c(1, 0), 20, replace = TRUE, prob = c(0.2, 0.8))
hbinA <- hexbin(xA, yA, xbins = 40, IDs = TRUE)
A <- data.frame(x = xA, y = yA, z = zA)
set.seed(5)
xB <- rnorm(1000)
set.seed(6)
yB <- rnorm(1000)
set.seed(7)
zB <- sample(c(1, 0), 20, replace = TRUE, prob = c(0.4, 0.6))
hbinB <- hexbin(xB, yB, xbins = 40, IDs = TRUE)
B <- data.frame(x = xB, y = yB, z = zB)
ggplot(A, aes(x, y, z = z)) + stat_summary_hex(fun = function(z) sum(z)/length(z), alpha = 0.8) +
scale_fill_gradientn(colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
ggplot(B, aes(x, y, z = z)) + stat_summary_hex(fun = function(z) sum(z)/length(z), alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
here is the two resulting graphs
My goal is to make a third graph with hexbins with the values of the difference between hexbins at the same coordinates but I don't even know how to start to do it, I have done something similar in the raster Package, but I need it as hexbins
Thanks a lot
You need to make sure that both plots use the exact same binning. In order to achieve this, I think it is best to do the binning beforehand and then plot the results with stat_identity / geom_hex. With the variables from your code sample you ca do:
## find the bounds for the complete data
xbnds <- range(c(A$x, B$x))
ybnds <- range(c(A$y, B$y))
nbins <- 30
# function to make a data.frame for geom_hex that can be used with stat_identity
makeHexData <- function(df) {
h <- hexbin(df$x, df$y, nbins, xbnds = xbnds, ybnds = ybnds, IDs = TRUE)
data.frame(hcell2xy(h),
z = tapply(df$z, h#cID, FUN = function(z) sum(z)/length(z)),
cid = h#cell)
}
Ahex <- makeHexData(A)
Bhex <- makeHexData(B)
## not all cells are present in each binning, we need to merge by cellID
byCell <- merge(Ahex, Bhex, by = "cid", all = T)
## when calculating the difference empty cells should count as 0
byCell$z.x[is.na(byCell$z.x)] <- 0
byCell$z.y[is.na(byCell$z.y)] <- 0
## make a "difference" data.frame
Diff <- data.frame(x = ifelse(is.na(byCell$x.x), byCell$x.y, byCell$x.x),
y = ifelse(is.na(byCell$y.x), byCell$y.y, byCell$y.x),
z = byCell$z.x - byCell$z.y)
## plot the results
ggplot(Ahex) +
geom_hex(aes(x = x, y = y, fill = z),
stat = "identity", alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
ggplot(Bhex) +
geom_hex(aes(x = x, y = y, fill = z),
stat = "identity", alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
ggplot(Diff) +
geom_hex(aes(x = x, y = y, fill = z),
stat = "identity", alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)