I want to plot a line graph, with multiple lines, coloured depending on a grouping variable. Now I want to set the legend labels via scale-command:
scale_color_manual(values = colors_values, labels = ...)
The legend labels are as following: "x^2", "x^3", "x^4" etc., where the range is dynamically created. I would now like to dynamically create the expression as label text, i.e.
"x^2" should become x2
"x^3" should become x3
etc.
The amount of legend labels varies, so I thought about something like as.expression(sprintf("x^%i", number)), which does of course not work as label parameter for the scale function.
I have searched google and stack overflow, however, I haven't found a working solution yet, so I hope someone can help me here.
Here's a reproducible example:
poly.term <- runif(100, 1, 60)
resp <- rnorm(100, 40, 5)
poly.degree <- 2:4
geom.colors <- scales::brewer_pal(palette = "Set1")(length(poly.degree))
plot.df <- data.frame()
for (i in poly.degree) {
mydat <- na.omit(data.frame(x = poly.term, y = resp))
fit <- lm(mydat$y ~ poly(mydat$x, i, raw = TRUE))
plot.df <- rbind(plot.df, cbind(mydat, predict(fit), sprintf("x^%i", i)))
}
colnames(plot.df) <- c("x","y", "pred", "grp")
ggplot(plot.df, aes(x, y, colour = grp)) +
stat_smooth(method = "loess", se = F) +
geom_line(aes(y = pred))
scale_color_manual(values = geom.colors
# here I want to change legend labels
# lables = expresion???
)
I would like to have the legend labels to be x2, x3 and x4.
ggplot(plot.df, aes(x, y, colour = grp)) +
stat_smooth(method = "loess", se = F) +
geom_line(aes(y = pred)) +
scale_color_manual(values = setNames(geom.colors,
paste0("x^",poly.degree)),
labels = setNames(lapply(poly.degree, function(i) bquote(x^.(i))),
paste0("x^",poly.degree)))
It's important to ensure correct mapping if you change values or labels in the scale. Thus, you should always use named vectors.
Related
I am trying to figure out a way to color my point on a geom_point plot based upon the type of transmission, but in the mpg dataset, the trans column has different names for auto and manual trans. How can I rename the values in the trans column to be either Auto for automatic and Manual for manual transmissions? I also attached a picture of the desired graph for reference.
Here is my main plot code:
data <- mpg
n <- nrow(mpg)
mpg_auto <- subset(mpg, substring(trans[1:n],1,1)=="a")
mpg_manual <- subset(mpg, substring(trans[1:n],1,1)=="m")
mpg$trans <- factor(mpg$trans, levels=c(mpg_auto,mpg_manual),
labels = c("0","1"))
mpg_select <- subset(mpg, mpg$hwy > 30 & mpg$displ < 3)
mpg_select <- as.data.frame(mpg_select)
gg<- ggplot(mpg) + aes(x = displ, y = hwy) +
geom_point(aes(col = trans))+
geom_encircle(aes(x = displ, y = hwy),
data = mpg_select,
color= "darkgreen",
expand = .05,
size = 2) +
annotate_textp(x = .2, y = .9, size = 15,
label = "Efficient Vehicle", color = "darkgreen")+
labs(y = "Hwy MPG",
x = "Displacement")
ggMarginal(gg, type= "density", alpha = 0.5,
groupColour = TRUE, groupFill = TRUE)
Picture of the plot with the above code: https://ibb.co/fGMSXdn
Here's a good way to relabel the transmission (I create a new column named transmission, but you could just as easily overwrite the existing column).
mpg$transmission = ifelse(substring(mpg$trans, 1, 1) == "a", "automatic", "manual")
Now that's done, coloring is easy:
gg <- ggplot(mpg, aes(x = displ, y = hwy) +
geom_point(aes(color = transmission))+
labs(y = "Hwy MPG",
x = "Displacement")
I've left out all your non-standard ggplot stuff because I'm not sure what package(s) it's from. It doesn't seem related to your issue anyway, so you should be able to just add it back in.
I have recently came across a problem with ggplot2::geom_density that I am not able to solve. I am trying to visualise a density of some variable and compare it to a constant. To plot the density, I am using the ggplot2::geom_density. The variable for which I am plotting the density, however, happens to be a constant (this time):
df <- data.frame(matrix(1,ncol = 1, nrow = 100))
colnames(df) <- "dummy"
dfV <- data.frame(matrix(5,ncol = 1, nrow = 1))
colnames(dfV) <- "latent"
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.2, position = "identity") +
geom_vline(data = dfV, aes(xintercept = latent, color = 'ls'), size = 2)
This is OK and something I would expect. But, when I shift this distribution to the far right, I get a plot like this:
df <- data.frame(matrix(71,ncol = 1, nrow = 100))
colnames(df) <- "dummy"
dfV <- data.frame(matrix(75,ncol = 1, nrow = 1))
colnames(dfV) <- "latent"
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.2, position = "identity") +
geom_vline(data = dfV, aes(xintercept = latent, color = 'ls'), size = 2)
which probably means that the kernel estimation is still taking 0 as the centre of the distribution (right?).
Is there any way to circumvent this? I would like to see a plot like the one above, only the centre of the kerner density would be in 71 and the vline in 75.
Thanks
Well I am not sure what the code does, but I suspect the geom_density primitive was not designed for a case where the values are all the same, and it is making some assumptions about the distribution that are not what you expect. Here is some code and a plot that sheds some light:
# Generate 10 data sets with 100 constant values from 0 to 90
# and then merge them into a single dataframe
dfs <- list()
for (i in 1:10){
v <- 10*(i-1)
dfs[[i]] <- data.frame(dummy=rep(v,100),facet=v)
}
df <- do.call(rbind,dfs)
# facet plot them
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.5, position = "identity") +
facet_wrap( ~ facet,ncol=5 )
Yielding:
So it is not doing what you thought it was, but it is also probably not doing what you want. You could of course make it "translation-invariant" (almost) by adding some noise like this for example:
set.seed(1234)
noise <- +rnorm(100,0,1e-3)
dfs <- list()
for (i in 1:10){
v <- 10*(i-1)
dfs[[i]] <- data.frame(dummy=rep(v,100)+noise,facet=v)
}
df <- do.call(rbind,dfs)
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.5, position = "identity") +
facet_wrap( ~ facet,ncol=5 )
Yielding:
Note that there is apparently a random component to the geom_density function, and I can't see how to set the seed before each instance, so the estimated density is a bit different each time.
I'm trying to plot 2 sets of data points and a single line in R using ggplot.
The issue I'm having is with the legend.
As can be seen in the attached image, the legend applies the lines to all 3 data sets even though only one of them is plotted with a line.
I have melted the data into one long frame, but this still requires me to filter the data sets for each individual call to geom_line() and geom_path().
I want to graph the melted data, plotting a line based on one data set, and points on the remaining two, with a complete legend.
Here is the sample script I wrote to produce the plot:
xseq <- 1:100
x <- rnorm(n = 100, mean = 0.5, sd = 2)
x2 <- rnorm(n = 100, mean = 1, sd = 0.5)
x.lm <- lm(formula = x ~ xseq)
x.fit <- predict(x.lm, newdata = data.frame(xseq = 1:100), type = "response", se.fit = TRUE)
my_data <- data.frame(x = xseq, ypoints = x, ylines = x.fit$fit, ypoints2 = x2)
## Now try and plot it
melted_data <- melt(data = my_data, id.vars = "x")
p <- ggplot(data = melted_data, aes(x = x, y = value, color = variable, shape = variable, linetype = variable)) +
geom_point(data = filter(melted_data, variable == "ypoints")) +
geom_point(data = filter(melted_data, variable == "ypoints2")) +
geom_path(data = filter(melted_data, variable == "ylines"))
pushViewport(viewport(layout = grid.layout(1, 1))) # One on top of the other
print(p, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
You can set them manually like this:
We set linetype = "solid" for the first item and "blank" for others (no line).
Similarly for first item we set no shape (NA) and for others we will set whatever shape we need (I just put 7 and 8 there for an example). See e.g. http://www.r-bloggers.com/how-to-remember-point-shape-codes-in-r/ to help you to choose correct shapes for your needs.
If you are happy with dots then you can use my_shapes = c(NA,16,16) and scale_shape_manual(...) is not needed.
my_shapes = c(NA,7,8)
ggplot(data = melted_data, aes(x = x, y = value, color=variable, shape=variable )) +
geom_path(data = filter(melted_data, variable == "ylines") ) +
geom_point(data = filter(melted_data, variable %in% c("ypoints", "ypoints2"))) +
scale_colour_manual(values = c("red", "green", "blue"),
guide = guide_legend(override.aes = list(
linetype = c("solid", "blank","blank"),
shape = my_shapes))) +
scale_shape_manual(values = my_shapes)
But I am very curious if there is some more automated way. Hopefully someone can post better answer.
This post relied quite heavily on this answer: ggplot2: Different legend symbols for points and lines
this is my first stack overflow post and I am a relatively new R user, so please go gently!
I have a data frame with three columns, a participant identifier, a condition (factor with 2 levels either Placebo or Experimental), and an outcome score.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
I would like to construct a bar plot with two bars with the mean outcome score for each condition and the standard deviation as an error bar. I would like to then overlay lines connecting points for each participant's score in each condition. So the plot displays the individual response as well as the group mean.If it is also possible I would like to include an axis break.
I don't seem to be able to find any advice in other threads, apologies if I am repeating a question.
Many Thanks.
p.s. I realise that presenting data in this way will not be to everyones tastes. It is for a specific requirement!
This ought to work:
library(ggplot2)
library(dplyr)
dat.summ <- dat %>% group_by(Condition) %>%
summarize(mean.outcome = mean(Outcome),
sd.outcome = sd(Outcome))
ggplot(dat.summ, aes(x = Condition, y = mean.outcome)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = mean.outcome - sd.outcome,
ymax = mean.outcome + sd.outcome),
color = "dodgerblue", width = 0.3) +
geom_point(data = dat, aes(x = Condition, y = Outcome),
color = "firebrick", size = 1.2) +
geom_line(data = dat, aes(x = Condition, y = Outcome, group = ID),
color = "firebrick", size = 1.2, alpha = 0.5) +
scale_y_continuous(limits = c(0, max(dat$Outcome)))
Some people are better with ggplot's stat functions and arguments than I am and might do it differently. I prefer to just transform my data first.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
dat.w <- reshape(dat, direction = 'wide', idvar = 'ID', timevar = 'Condition')
means <- colMeans(dat.w[, 2:3])
sds <- apply(dat.w[, 2:3], 2, sd)
ci.l <- means - sds
ci.u <- means + sds
ci.width <- .25
bp <- barplot(means, ylim = c(0,20))
segments(bp, ci.l, bp, ci.u)
segments(bp - ci.width, ci.u, bp + ci.width, ci.u)
segments(bp - ci.width, ci.l, bp + ci.width, ci.l)
segments(x0 = bp[1], x1 = bp[2], y0 = dat.w[, 2], y1 = dat.w[, 3], col = 1:10)
points(c(rep(bp[1], 10), rep(bp[2], 10)), dat$Outcome, col = 1:10, pch = 19)
Here is a method using the transfomations inside ggplot2
ggplot(dat) +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.y="mean", geom="bar") +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.data="mean_se", geom="errorbar", col="green", width=.8, size=2) +
geom_line(aes(x=Condition, y=Outcome, group=ID), col="red")
I need to add a legend of the two lines (best fit line and 45 degree line) on TOP of my two plots. Sorry I don't know how to add plots! Please please please help me, I really appreciate it!!!!
Here is an example
type=factor(rep(c("A","B","C"),5))
xvariable=seq(1,15)
yvariable=2*xvariable+rnorm(15,0,2)
newdata=data.frame(type,xvariable,yvariable)
p = ggplot(newdata,aes(x=xvariable,y=yvariable))
p+geom_point(size=3)+ facet_wrap(~ type) +
geom_abline(intercept =0, slope =1,color="red",size=1)+
stat_smooth(method="lm", se=FALSE,size=1)
Here is another approach which uses aesthetic mapping to string constants to identify different groups and create a legend.
First an alternate way to create your test data (and naming it DF instead of newdata)
DF <- data.frame(type = factor(rep(c("A", "B", "C"), 5)),
xvariable = 1:15,
yvariable = 2 * (1:15) + rnorm(15, 0, 2))
Now the ggplot code. Note that for both geom_abline and stat_smooth, the colour is set inside and aes call which means each of the two values used will be mapped to a different color and a guide (legend) will be created for that mapping.
ggplot(DF, aes(x = xvariable, y = yvariable)) +
geom_point(size = 3) +
geom_abline(aes(colour="one-to-one"), intercept =0, slope = 1, size = 1) +
stat_smooth(aes(colour="best fit"), method = "lm", se = FALSE, size = 1) +
facet_wrap(~ type) +
scale_colour_discrete("")
Try this:
# original data
type <- factor(rep(c("A", "B", "C"), 5))
x <- 1:15
y <- 2 * x + rnorm(15, 0, 2)
df <- data.frame(type, x, y)
# create a copy of original data, but set y = x
# this data will be used for the one-to-one line
df2 <- data.frame(type, x, y = x)
# bind original and 'one-to-one data' together
df3 <- rbind.data.frame(df, df2)
# create a grouping variable to separate stat_smoothers based on original and one-to-one data
df3$grp <- as.factor(rep(1:2, each = nrow(df)))
# plot
# use original data for points
# use 'double data' for abline and one-to-one line, set colours by group
ggplot(df, aes(x = x, y = y)) +
geom_point(size = 3) +
facet_wrap(~ type) +
stat_smooth(data = df3, aes(colour = grp), method = "lm", se = FALSE, size = 1) +
scale_colour_manual(values = c("red","blue"),
labels = c("abline", "one-to-one"),
name = "") +
theme(legend.position = "top")
# If you rather want to stack the two keys in the legend you can add:
# guide = guide_legend(direction = "vertical")
#...as argument in scale_colour_manual
Please note that this solution does not extrapolate the one-to-one line outside the range of your data, which seemed to be the case for the original geom_abline.