I have the following code:
boxplot(c(Scatt_nocoop, Scatt_coop),
xlab="Scattered", col=c("red","red"),
names=c("Non-cooperative"," Cooperative "),
ylim = c(0,2.5))
I am trying to add tick marks in the Y axis every 0.1, to then add a grid.
Also, I would like to get the Y axis in percentage rather than with numbers.
Thank you!
not sure what your data looks like, but I guess you want something like this:
x1 <- rnorm(100) + 2
x2 <- rnorm(100) + 2
df <- data.frame(x = c(x1, x2), g = rep(1:2,each=100))
boxplot(df$x~df$g,
xlab="Scattered", col=c("red","red"),
names=c("Non-cooperative"," Cooperative "),
ylim = c(0,5),
yaxt = "n")
add ticks and (manual) grid lines
axis(2, at = seq(0,5,0.1))
lapply(seq(0,5,0.1), function(x) abline(a = x,b = 0))
Related
I am planning to reproduce the attached figure, but I have no clue how to do so:
Let´s say I would be using the CO2 example dataset, and I would like to plot the relative change of the Uptake according to the Treatment. Instead of having the three variables in the example figure, I would like to show the different Plants grouped for each day/Type.
So far, I managed only to get this bit of code, but this is far away from what it should look like.
aov1 <- aov(CO2$uptake~CO2$Type+CO2$Treatment+CO2$Plant)
plot(TukeyHSD(aov1, conf.level=.95))
Axes should be switched, and I would like to add statistical significant changes indicated with letters or stars.
You can do this by building it in base R - this should get you started. See comments in code for each step, and I suggest running it line by line to see what's being done to customize for your specifications:
Set up data
# Run model
aov1 <- aov(CO2$uptake ~ CO2$Type + CO2$Treatment + CO2$Plant)
# Organize plot data
aov_plotdata <- data.frame(coef(aov1), confint(aov1))[-1,] # remove intercept
aov_plotdata$coef_label <- LETTERS[1:nrow(aov_plotdata)] # Example labels
Build plot
#set up plot elements
xvals <- 1:nrow(aov_plotdata)
yvals <- range(aov_plotdata[,2:3])
# Build plot
plot(x = range(xvals), y = yvals, type = 'n', axes = FALSE, xlab = '', ylab = '') # set up blank plot
points(x = xvals, y = aov_plotdata[,1], pch = 19, col = xvals) # add in point estimate
segments(x0 = xvals, y0 = aov_plotdata[,2], y1 = aov_plotdata[,3], lty = 1, col = xvals) # add in 95% CI lines
axis(1, at = xvals, label = aov_plotdata$coef_label) # add in x axis
axis(2, at = seq(floor(min(yvals)), ceiling(max(yvals)), 10)) # add in y axis
segments(x0=min(xvals), x1 = max(xvals), y0=0, lty = 2) #add in midline
legend(x = max(xvals)-2, y = max(yvals), aov_plotdata$coef_label, bty = "n", # add in legend
pch = 19,col = xvals, ncol = 2)
Does any one know how do you apply this
set.seed(101)
x <- 1:10
y <- rnorm(10)
## second data set on a very different scale
z <- runif(10, min=1000, max=10000)
par(mar = c(5, 4, 4, 4) + 0.3) # Leave space for z axis
plot(x, y) # first plot
par(new = TRUE)
plot(x, z, type = "l", axes = FALSE, bty = "n", xlab = "", ylab = "")
axis(side=4, at = pretty(range(z)))
mtext("z", side=4, line=3)
but using ggplot.
In ggplot you can only create sec.axis() or dup.axis() using a transformation of y axis. What about a whole new independent y axis which will be applied only for z variable and the simple y axis to be applied for the y variable.
ggplot2::sec_axis provides only one mechanism for providing a second axis, and it took a lot of convincing to get that into the codebase. You are responsible for coming up with the transformation. This transform must be linear in some way, so if either axis needs to be non-linear (e.g., exponential, logarithmic, etc), then your expert math skills will be put to the test.
If you can use scales, then this process becomes trivial:
dat <- data.frame(x, y, z)
ggplot(dat, aes(x, y)) +
geom_point() +
geom_line(
aes(y = zmod),
data = ~ transform(., zmod = scales::rescale(z, range(y), range(z)))
) +
scale_y_continuous(
sec.axis = sec_axis(~ scales::rescale(., range(dat$z), range(dat$y)),
breaks = c(2000,4000,6000,8000))
)
Unless I've missed something (I just checked ggplot2-3.3.5's NEWS.md file), this has not changed.
I'm hoping to keep in the image below the ticks on the vertical z axis, but remove ticks and numbers from the x and y axes. I would like to be able to label my x and y axes with a label for each condition in my matrix, but have not figured out how to do this with text3D. For some reason (because I'm on a mac?) I can't download axes3D, which is one potential solution I've seen in other responses.
Here is my code:
x = c(0,1)
y = c(0,1)
zval = c(104.1861, 108.529, 110.3675, 110.4112)
z = matrix (zval, nrow=2, ncol=2, byrow=TRUE)
hist3D(x,y,z, zlim=c(101,111), colvar = NULL, d=2, col = "lightblue", NAcol = "white", breaks = NULL, colkey = NULL, theta=-60, phi=20, nticks=10, axes=TRUE, ticktype="detailed", space=0.5, lighting=TRUE, light="diffuse", shade=.5, ltheta = 50, bty = "g")
My output

Ultimately, I'd like something more along the lines of this:
I'm very new to R.
stackoverflow.com/questions/26794236/ggplot2-3d-bar-plot
^ this seems like it might be what I need, but I couldn't replicate the code without an error. When I tried to run this piece I got an error because my x and z (in this case) axes aren't numerical:
cloud(y~x+z, d, panel.3d.cloud=panel.3dbars, col.facet='grey', xbase=0.4, ybase=0.4, scales=list(arrows=FALSE, col=1), par.settings = list(axis.line = list(col = "transparent")))
Maybe this might be helpful (with the caveat that 3D plots can sometimes make interpretation more challenging).
First, I recreated a data frame d based on something similar to what you started with:
x = c(0, 0, 1, 1)
y = c(0, 1, 0, 1)
z = c(104.1861, 108.529, 110.3675, 110.4112)
d <- data.frame(
x = factor(as.logical(x)),
y = factor(as.logical(y)),
z = z
)
Note that for x and y I converted the 0 and 1 to FALSE and TRUE with as.logical, then made them factors.
Then for the plot:
library(latticeExtra)
cloud(z ~ x + y, data = d, panel.3d.cloud=panel.3dbars, col.facet='grey',
xbase=0.4, ybase=0.4, scales=list(arrows=FALSE, col=1),
par.settings = list(axis.line = list(col = "transparent")))
You will want the formula as z ~ x + y where z is a numeric response.
Edit: If you wish to customize the axis labels, you can set the factor labels as follows (for example):
d <- data.frame(
x = factor(as.logical(x), labels = c("Hi", "Lo")),
y = factor(as.logical(y), labels = c("Label1", "Label2")),
z = z
)
Plot
I have two Poisson processes:
n <- 100
x <- seq(0, 10, length = 1000)
y1 <- cumsum(rpois(1000, 1 / n))
y2 <- -cumsum(rpois(1000, 1 / n))
I would like to plot them in one plot and expect that y1 lies above x-axis and y2 lies below x-axis. I tried the following code:
plot(x, y1)
par(new = TRUE)
plot(x, y2, col = "red",
axes = FALSE,
xlab = '', ylab = '',
xlim = c(0, 10), ylim = c(min(y2), max(y1)))
but it did not work. Can someone please tell me how to fix this? (I am working with R for my code)
Many thanks in advance
How about
plot(x,y1, ylim=range(y1,y2), type="l")
lines(x, y2, col="red")
I would suggest trying to avoid multiple calls to plot with par(new=TRUE). That is usually very messy. Here we use lines() to add to an existing plot. The only catch is that the x and y limits won't change based on the new data, so we use ylim in the first plot() call to set a range appropriate for all the data.
Or if you don't want to worry about limits (like MrFlick mentioned) or the number of lines, you could also tide up your data and using melt and ggplot
df <- data.frame(x, y1, y2)
library(reshape2)
library(ggplot2)
mdf <- melt(df, "x")
ggplot(mdf, aes(x, value, color = variable)) +
geom_line()
I have the following data
test<-data.frame(group=1:10, var.a=rnorm(n=10,mean=500,sd=20), var.b=runif(10))
I would like a barplot with 2 y axis (one for var.a, one for var.2). Each group (x axis, 1:10) should have 2 bars next to each other, one for var.a and one for var.b.
I cannot use one y-axis because of the difference morder of magnitude of var.a and var.b
Is this possible with base R?
Thank you
To use the graphics package in R, one could create new variables as the values in var.a and var.b converted into proportions of the maximum values in the respective variable:
test <- data.frame(group = 1:10, var.a = rnorm(n = 10, mean = 500, sd = 20),
var.b = runif(10))
funProp <- function(testCol) {
test[, testCol]/max(test[, testCol])
}
test$var.a.prop <- funProp("var.a")
test$var.b.prop <- funProp("var.b")
Then draw the plot using barplot() without the axes:
barplot(t(as.matrix(test[, c("var.a.prop", "var.b.prop")])), beside = TRUE,
yaxt = "n", names.arg = test$group)
Then add the axes on the left and the right using the original value ranges for the labels (the labels argument) and the proportional value ranges to place the labels on the axes (the at argument) (this part is not pretty, but it gets the job done):
axis(2, at = seq(0, max(test$var.a.prop), length.out = 10),
labels = round(seq(0, max(test$var.a), length.out = 10)))
axis(4, at = seq(0, max(test$var.b.prop), length.out = 10),
labels = round(seq(0, max(test$var.b), length.out = 10), 2))
(Sorry for the lack of an image)
EDIT:
To get the axes a bit prettyer,
myLeftAxisLabs <- pretty(seq(0, max(test$var.a), length.out = 10))
myRightAxisLabs <- pretty(seq(0, max(test$var.b), length.out = 10))
myLeftAxisAt <- myLeftAxisLabs/max(test$var.a)
myRightAxisAt <- myRightAxisLabs/max(test$var.b)
barplot(t(as.matrix(test[, c("var.a.prop", "var.b.prop")])),
beside = TRUE, yaxt = "n", names.arg = test$group,
ylim=c(0, max(c(myLeftAxisAt, myRightAxisAt))))
axis(2, at = myLeftAxisAt, labels = myLeftAxisLabs)
axis(4, at = myRightAxisAt, labels = myRightAxisLabs)