I do a Multiple Linear Regression in R, where I want to add a simple legend to a graph (ggplot). The legend should show the points and fitted lines with their corresponding colors. So far it works fine (without legend):
ggplot() +
geom_point(aes(x = training_set$R.D.Spend, y = training_set$Profit),
col = 'red') +
geom_line(aes(x = training_set$R.D.Spend, y = predict(regressor, newdata = training_set)),
col = 'blue') +
geom_line(aes(x = training_set$R.D.Spend, y = predict(regressor_sig, newdata = training_set)),
col = 'green') +
ggtitle('Multiple Linear Regression (Training set)') +
xlab('R.D.Spend [k$]') +
ylab('Profit of Venture [k$]')
How can I add a legend here most easily?
I tried the solutions from similar question, but did not succeed (add legend to ggplot2 | Add legend for multiple regression lines from different datasets to ggplot)
So, I appended my original model like this:
ggplot() +
geom_point(aes(x = training_set$R.D.Spend, y = training_set$Profit),
col = 'p1') +
geom_line(aes(x = training_set$R.D.Spend, y = predict(regressor, newdata = training_set)),
col = 'p2') +
geom_line(aes(x = training_set$R.D.Spend, y = predict(regressor_sig, newdata = training_set)),
col = 'p3') +
scale_color_manual(
name='My lines',
values=c('blue', 'orangered', 'green')) +
ggtitle('Multiple Linear Regression (Training set)') +
xlab('R.D.Spend [k$]') +
ylab('Profit of Venture [k$]')
But here I am getting the error of "Unknown colour name: p1". which makes somewhat sense, as I do not define p1 above. How can I make the ggplot recognise my intended legend?
Move col into the aes and then you can set the color using scale_color_manual:
library(ggplot2)
set.seed(1)
x <- 1:30
y <- rnorm(30) + x
fit <- lm(y ~ x)
ggplot2::ggplot(data.frame(x, y)) +
geom_point(aes(x = x, y = y)) +
geom_line(aes(x = x, y = predict(fit), col = "Regression")) +
scale_color_manual(name = "My Lines",
values = c("blue"))
Related
Problem
I have some data points stored in data.frame with three variables, x, y, and gender. My goal is to draw several generally fitted lines and also lines specifically fitted for male/female over the scatter plot, with points coloured by gender. It sounds easy but some issues just persist.
What I currently do is to use a new set of x's and predict y's for every model, combine the fitted lines together in a data.frame, and then convert wide to long, with their model name as the third var (from this post: ggplot2: how to add the legend for a line added to a scatter plot? and this: Add legend to ggplot2 line plot I learnt that mapping should be used instead of setting colours/legends separately). However, while I can get a multicolor line plot, the points come without specific colour for gender (already a factor) as I expected from the posts I referenced.
I also know it might be possible to use aes=(y=predict(model)), but I met other problems for this. I also tried to colour the points directly in aes, and assign colours separately for each line, but the legend cannot be generated unless I use lty, which makes legend in the same colour.
Would appreciate any idea, and also welcome to change the whole method.
Code
Note that two pairs of lines overlap. So it only appeared to be two lines. I guess adding some jitter in the data might make it look differently.
slrmen<-lm(tc~x+I(x^2),data=data[data['gender']==0,])
slrwomen<-lm(tc~x+I(x^2),data=data[data['gender']==1,])
prdf <- data.frame(x = seq(from = range(data$x)[1],
to = range(data$x)[2], length.out = 100),
gender = as.factor(rep(1,100)))
prdm <- data.frame(x = seq(from = range(data$x)[1],
to = range(data$x)[2], length.out = 100),
gender = as.factor(rep(0,100)))
prdf$fit <- predict(fullmodel, newdata = prdf)
prdm$fit <- predict(fullmodel, newdata = prdm)
rawplotdata<-data.frame(x=prdf$x, fullf=prdf$fit, fullm=prdm$fit,
linf=predict(slrwomen, newdata = prdf),
linm=predict(slrmen, newdata = prdm))
plotdata<-reshape2::melt(rawplotdata,id.vars="x",
measure.vars=c("fullf","fullm","linf","linm"),
variable.name="fitmethod", value.name="y")
plotdata$fitmethod<-as.factor(plotdata$fitmethod)
plt <- ggplot() +
geom_line(data = plotdata, aes(x = x, y = y, group = fitmethod,
colour=fitmethod)) +
scale_colour_manual(name = "Fit Methods",
values = c("fullf" = "lightskyblue",
"linf" = "cornflowerblue",
"fullm"="darkseagreen", "linm" = "olivedrab")) +
geom_point(data = data, aes(x = x, y = y, fill = gender)) +
scale_fill_manual(values=c("blue","green")) ## This does not work as I expected...
show(plt)
Code for another method (omitted two lines), which generates same-colour legend and multi-color plot:
ggplot(data = prdf, aes(x = x, y = fit)) + # prdf and prdm are just data frames containing the x's and fitted values for different models
geom_line(aes(lty="Female"),colour = "chocolate") +
geom_line(data = prdm, aes(x = x, y = fit, lty="Male"), colour = "darkblue") +
geom_point(data = data, aes(x = x, y = y, colour = gender)) +
scale_colour_discrete(name="Gender", breaks=c(0,1),
labels=c("Male","Female"))
This is related to using the colour aesthetic for lines and the fill aesthetics for points in your own (first) example. In the second example, it works because the colour aesthetic is used for lines and points.
By default, geom_point can not map a variable to fill, because the default point shape (19) doesn't have a fill.
For fill to work on points, you have to specify shape = 21:25 in geom_point(), outside of aes().
Perhaps this small reproducible example helps to illustrate the point:
Simulate data
set.seed(4821)
x1 <- rnorm(100, mean = 5)
set.seed(4821)
x2 <- rnorm(100, mean = 6)
data <- data.frame(x = rep(seq(20,80,length.out = 100),2),
tc = c(x1, x2),
gender = factor(c(rep("Female", 100), rep("Male", 100))))
Fit models
slrmen <-lm(tc~x+I(x^2), data = data[data["gender"]=="Male",])
slrwomen <-lm(tc~x+I(x^2),data = data[data["gender"]=="Female",])
newdat <- data.frame(x = seq(20,80,length.out = 200))
fitted.male <- data.frame(x = newdat,
gender = "Male",
tc = predict(object = slrmen, newdata = newdat))
fitted.female <- data.frame(x = newdat,
gender = "Female",
tc = predict(object = slrwomen, newdata = newdat))
Plot using colour aesthetics
Use the colour aesthetics for both points and lines (specify in ggplot such that it gets inherited throughout). By default, geom_point can map a variable to colour.
library(ggplot2)
ggplot(data, aes(x = x, y = tc, colour = gender)) +
geom_point() +
geom_line(data = fitted.male) +
geom_line(data = fitted.female) +
scale_colour_manual(values = c("tomato","blue")) +
theme_bw()
Plot using colour and fill aesthetics
Use the fill aesthetics for points and the colour aesthetics for lines (specify aesthetics in geom_* to prevent them being inherited). This will reproduce the problem.
ggplot(data, aes(x = x, y = tc)) +
geom_point(aes(fill = gender)) +
geom_line(data = fitted.male, aes(colour = gender)) +
geom_line(data = fitted.female, aes(colour = gender)) +
scale_colour_manual(values = c("tomato","blue")) +
scale_fill_manual(values = c("tomato","blue")) +
theme_bw()
To fix this, change the shape argument in geom_point to a point shape that can be filled (21:25).
ggplot(data, aes(x = x, y = tc)) +
geom_point(aes(fill = gender), shape = 21) +
geom_line(data = fitted.male, aes(colour = gender)) +
geom_line(data = fitted.female, aes(colour = gender)) +
scale_colour_manual(values = c("tomato","blue")) +
scale_fill_manual(values = c("tomato","blue")) +
theme_bw()
Created on 2021-09-19 by the reprex package (v2.0.1)
Note that the scales for colour and fill get merged automatically if the same variable is mapped to both aesthetics.
It seems to me that what you really want to do is use ggplot2::stat_smooth instead of trying to predict yourself.
Borrowing the data from #scrameri:
ggplot(data, aes(x = x, y = tc, color = gender)) +
geom_point() +
stat_smooth(aes(linetype = "X^2"), method = 'lm',formula = y~x + I(x^2)) +
stat_smooth(aes(linetype = "X^3"), method = 'lm',formula = y~x + I(x^2) + I(x^3)) +
scale_color_manual(values = c("darkseagreen","lightskyblue"))
I'm having some issue overlaying 2 point graphs on a box plot. The code seems to work well when i added only one point graph. Here is the code below:
ggplot(data1, aes(x= reorder(DMU,order), y = Efficiency)) +
geom_boxplot() +
geom_point(data = data2, aes(x = dmu, y = eff, color = "eff")) +
scale_color_manual("", breaks = c("eff"), values = c("blue")) +
geom_point(data = data3, aes(x = DMU, y = eff2, color = "eff2")) +
scale_color_manual("", breaks = c("eff2"), values = c("red"))
I keep getting the error below:
Scale for 'colour' is already present. Adding another scale for
'colour', which will replace the existing scale.
Error: Insufficient values in manual scale. 2 needed but only 1 provided.
You cannot add scale_color_manual() twice.
Build a single dataframe for the colon:
df_points <- data.frame(x = c(data2$dmu, data3$DMU),
y = c(data2$eff, data3$eff2),
data = c("data2", "data3")
)
And then:
ggplot(data1, aes(x = reorder(DMU,order), y = Efficiency)) +
geom_boxplot() +
geom_point(data = df_points, aes(x = x, y = y, color = data)) +
scale_colour_manual(values = c("red", "blue") +
theme(legend.position = "none")
Not having the data available I could have made a mistake
I am plotting 2 sets of data on the same plot using ggplot. I have specified the colour for each data set, but there is no legend that comes out when the dot plot is generated.
What can i do to manually add a legend?
# Create an index to hold values of m from 1 to 100
m_index <- (1:100)
data_frame_50 <- data(prob_max_abs_cor_50)
data_frame_20 <- data.frame(prob_max_abs_cor_20)
library(ggplot2)
plot1 <- ggplot(data_frame_50, mapping = aes(x = m_index,
y = prob_max_abs_cor_50),
colour = 'red') +
geom_point() +
ggplot(data_frame_20, mapping = aes(x = m_index,
y = prob_max_abs_cor_20),
colour = 'blue') +
geom_point()
plot1 + labs(x = " Values of m ",
y = " Maximum Absolute Correlation ",
title = "Dot plot of probability")
First, I would suggest neatening your ggplot code a little. This is equivalent to your posted code;
ggplot() +
geom_point(data = data_frame_50, aes(x = m_index, y = prob_max_abs_cor_50,
colour = 'red')) +
geom_point(data = data_frame_20, aes(x = m_index, y = prob_max_abs_cor_20,
colour = 'blue')) +
labs(x = " Values of m ", y = " Maximum Absolute Correlation ",
title = "Dot plot of probability")
You won't get a legend here, because you are plotting different datasets with only one category in each. You need to have a single dataset with a column grouping your data (i.e. 20 or 50). So using some example data, this is the equivalent of what you are plotting and ggplot won't provide a legend;
ggplot() +
geom_point(data = iris, aes(x = Sepal.Length, y = Petal.Width), colour = 'red') +
geom_point(data = iris, aes(x = Sepal.Length, y = Petal.Length), colour = 'blue')
If you want to colour by category, include a colour argument inside the aes call;
ggplot() +
geom_point(data = iris, aes(x = Sepal.Length, y = Petal.Width,
colour = factor(Species)))
Have a look at the iris dataset to get a sense of how you need to shape your data. It's hard to give precise advice, because you haven't provided an idea of what your data look like, but something like this might work;
df.20 <- data.frame("m" = 1:100, "Group" = 20, "Numbers" = prob_max_abs_cor_20)
df.50 <- data.frame("m" = 1:100, "Group" = 50, "Numbers" = prob_max_abs_cor_50)
df.All <- rbind(df.20, df.50)
I want to plot a data set where the size of the points are proportional to the x-variable and have a regression line with a 95% prediction interval. The "sample" code I have written is as follows:
# Create random data and run regression
x <- rnorm(40)
y <- 0.5 * x + rnorm(40)
plot.dta <- data.frame(y, x)
mod <- lm(y ~ x, data = plot.dta)
# Create values for prediction interval
x.new <- data.frame(x = seq(-2.5, 2.5, length = 1000))
pred <- predict(mod,, newdata = x.new, interval = "prediction")
pred <- data.frame(cbind(x.new, pred))
# plot the data w/ regression line and prediction interval
p <- ggplot(pred, aes(x = x, y = upr)) +
geom_line(aes(y = lwr), color = "#666666", linetype = "dashed") +
geom_line(aes(y = upr), color = "#666666", linetype = "dashed") +
geom_line(aes(y = fit)) +
geom_point(data = plot.dta, aes(y = y, size = x))
p
This produces the following plot:
Obviously, the legend is not too helpful here. I would like to have one entry in the legend for the points, say, labeled "data", one grey, dashed line labeled "95% PI" and one entry with a black line labeled "Regression line."
As Hack-R alluded in the provided link, you can set the breaks and labels for scale_size() to make that legend more meaningful.
You can also construct a legend for all your geom_line() calls by adding linetype into your aes() and use a scale_linetype_manual() to set the values, breaks and labels.
ggplot(pred, aes(x = x, y = upr)) +
geom_line(aes(y = lwr, linetype = "dashed"), color = "#666666") +
geom_line(aes(y = upr, linetype = "dashed"), color = "#666666") +
geom_line(aes(y = fit, linetype = "solid")) +
geom_point(data = plot.dta, aes(y = y, size = x)) +
scale_size(labels = c("Eensy-weensy", "Teeny", "Small", "Medium", "Large")) +
scale_linetype_manual(values = c("dashed" = 2, "solid" = 1), labels = c("95% PI", "Regression Line"))
I plotted two ggplots from two different datasets in one single plot. plots are simple linear regression. I want to add legend both for lines and dots in the plot with different colours. How can I do that? The code I used for plot is as below. But, I failed to add a desirable legend to that.
ggplot() +
geom_point(aes(x = Time_1, y = value1)) +
geom_point(aes(x = Time_2, y = value2)) +
geom_line(aes(x = Time_1, y = predict(reg, newdata = dataset)))+
geom_line(aes(x = Time_Month.x, y = predict(regressor, newdata = training_set)))+
ggtitle('Two plots in a single plot')
ggplot2 adds legends automatically if it has groups within the data. Your original code provides the minimum amount of information to ggplot(), basically enough for it to work but not enough to create a legend.
Since your data comes from two different objects due to the two different regressions, then it looks like all you need in this case is to add the 'color = "INSERT COLOR NAME"' argument to each geom_point() and each geom_line(). Using R's built-in mtcars data set for example, what you have is similar to
ggplot(mtcars) + geom_point(aes(x = cyl, y = mpg)) + geom_point(aes(x = cyl, y = wt)) + ggtitle("Example Graph")
Graph without Legend
And what you want can be obtained by using something similar to,
ggplot(mtcars) + geom_point(aes(x = cyl, y = mpg, color = "blue")) + geom_point(aes(x = cyl, y = wt, color = "green")) + ggtitle("Example Graph")
Graph with Legend
Which would seem to translate to
ggplot() +
geom_point(aes(x = Time_1, y = value1, color = "blue")) +
geom_point(aes(x = Time_2, y = value2, color = "green")) +
geom_line(aes(x = Time_1, y = predict(reg, newdata = dataset), color = "red"))+
geom_line(aes(x = Time_Month.x, y = predict(regressor, newdata = training_set), color = "yellow"))+
ggtitle('Two plots in a single plot')
You could also use the size, shape, or alpha arguments inside of aes() to differentiate the different series.