I'm new to R and I'm trying to create a single plot with data from 2 melted dataframes.
Ideally I would have a legend for each of the dataframes with their respective titles; however, I get a only a single legend with the title of the first aesthetic.
My starting point is:
aerobic_melt <- melt(aerobic, id.vars = 'Distance', variable.name = 'Aerobic')
anaerobic_melt <- melt(anaerobic, id.vars = 'Distance', variable.name = 'Anaerobic')
plot <- ggplot() +
geom_line(data = aerobic_melt, aes(Distance, value, col=Aerobic)) +
geom_line(data = anaerobic_melt, aes(Distance, value, col= Anaerobic)) +
xlim(0, 125) +
ylab('Energy (J/kg )') +
xlab('Distance (m)')
Which results in
I've searched, but with my limited ability I haven't been able to find a way to do it.
My question is:
How do I create separate legends with titles 'Aerobic' and 'Anaerobic' which should respectively refer to A,B,C,F,G,L and E,H,I,J,K?
Any help is appreciated.
Obviously we don't have your data, but I have created some sample data that should have the same names and structure as your own data frames, since it works with your own plot code. See the end of the answer for the data used here.
You can use the package ggnewscale if you want two color scales on the same plot. Just add in a new_scale_color() call between your geom_line calls. I have left the rest of your code as-is.
library(ggplot2)
library(ggnewscale)
plot <- ggplot() +
geom_line(data = aerobic_melt, aes(Distance, value, col=Aerobic)) +
new_scale_color() +
geom_line(data = anaerobic_melt, aes(Distance, value, col= Anaerobic)) +
xlim(0, 125) +
ylab('Energy (J/kg )') +
xlab('Distance (m)')
plot
Data
set.seed(1)
aerobic_melt <- data.frame(
Aerobic = rep(c("A", "B", "C", "F", "G", "L"), each = 120),
value = as.numeric(replicate(6, cumsum(rnorm(120)))),
Distance = rep(1:120, 6))
anaerobic_melt <- data.frame(
Anaerobic = rep(c("E", "H", "I", "J", "K"), each = 120),
value = as.numeric(replicate(5, cumsum(rnorm(120)))),
Distance = rep(1:120, 5))
Related
I coloured my data by "Full Bath", however it is a similar shade & unclear.
I am trying to change the colour by variable of "Full Bath" to be more clear.
Could anyone help :)
This is what I have:
data %>%
ggplot(aes(Sale.Price, Total.Bsmt.SF)) +
geom_point(aes(colour = Full.Bath)) + geom_smooth()
To manually change the colors, you can use scale_color_manual, as follows:
### Simulation of data
set.seed(1)
data <- data.frame(Sale.Price=rnorm(100, 100, 10),
Total.Bsmt.SF=rnorm(100, 10, 1),
Full.Bath=rep(c("a", "b", "c", "d", "e"), 20))
### Plot data
data %>%
ggplot(aes(x = Sale.Price, y = Total.Bsmt.SF)) +
geom_point(aes(colour = Full.Bath)) +
geom_smooth() +
scale_color_manual(values=c("#84d58d", "#84c1d5", "#9e84d5", "#d584b0", "#d5be84"))
Link for colors selection
https://htmlcolorcodes.com/fr/
Why are the pies flat?
df<- data.frame(
Day=(1:6),
Var1=c(172,186,191,201,205,208),
Var2= c(109,483,64010,161992,801775,2505264), A=c(10,2,3,4.5,16.5,39.6), B=c(10,3,0,1.4,4.8,11.9), C=c(2,5,2,0.1,0.5,1.2), D=c(0,0,0,0,0.1,0.2))
ggplot() +
geom_scatterpie(data = df, aes(x = Var1 , y = Var2, group = Var1), cols = c("A", "B", "C", "D"))
I have tried using coord_fixed() and does not work either.
The problem seems to be the scales of the x- and y-axes. If you rescaled them to both to have zero mean and unit variance, the plot works. So, one thing you could do is plot the rescaled values, but transform the labels back into the original scale. To do this, you would have to do the following:
Make the data:
df<- data.frame(
Day=(1:6),
Var1=c(172,186,191,201,205,208),
Var2= c(109,483,64010,161992,801775,2505264), A=c(10,2,3,4.5,16.5,39.6), B=c(10,3,0,1.4,4.8,11.9), C=c(2,5,2,0.1,0.5,1.2), D=c(0,0,0,0,0.1,0.2))
Rescale the variables
df <- df %>%
mutate(x = c(scale(Var1)),
y = c(scale(Var2)))
Find the linear map that transforms the rescaled values back into their original values. Then, you can use the coefficients from the model to make a function that will transform the rescaled values back into the original ones.
m1 <- lm(Var1 ~ x, data=df)
m2 <- lm(Var2 ~ y, data=df)
trans_x <- function(x)round(coef(m1)[1] + coef(m1)[2]*x)
trans_y <- function(x)round(coef(m2)[1] + coef(m2)[2]*x)
Make the plot, using the transformation functions as the call to labels in the scale_[xy]_continuous() functions
ggplot() +
geom_scatterpie(data=df, aes(x = x, y=y), cols = c("A", "B", "C", "D")) +
scale_x_continuous(labels = trans_x) +
scale_y_continuous(labels = trans_y) +
coord_fixed()
There may be an easier way than this, but it wasn't apparent to me.
The range on the y-axis is so large it's compressing the disks to lines. Change the y-axis to a log scale, and you can see the shapes. Adding coord_fixed() to keep the pies circular:
ggplot() +
geom_scatterpie(data = df, aes(x = Var1 , y = Var2, group = Var1), cols = c("A", "B", "C", "D")) +
scale_y_log10() +
coord_fixed()
I want to use facet_wrap in R to split my plots based on a certain column. Here is a working example I reproduced from here:
set.seed(1)
df <- data.frame(age = runif(500, min = 10, max = 100),
group = rep(c("a", "b", "c", "d", "e"), 100))
#Plotting
ggplot(df, aes(age)) +
geom_histogram(aes(y = (..count..)), binwidth = 5) +
facet_wrap(~group, ncol = 3)
This produces plots, all in grey color (shown below). However, I want each plot be in a specific color. That is, they have the following color in order c("green","orange","blue","black", "red"). All bars in plot (a) be green, all in (b) be orange, and so on. These colors match my other plots and preserve consistency.
How can I achieve this task?
Thanks.
ggplot(df, aes(age)) +
geom_histogram(aes(y = (..count..), fill=group), binwidth = 5) +
facet_wrap(~group, ncol = 3) +
scale_fill_manual(values=c("green","orange","blue","black", "red"))
I am creating a matrix of plots similar to
ggplot(mpg, aes(displ, hwy)) + geom_point() + facet_grid(rows = vars(cyl), cols = vars(drv))
Now, I would like to have some way to highlight some of the individual plots, say the ones where cyl is 5 or 6, and drv is f. So, ideally, this might look like this:
But I would also be happy with those panels having a different look by setting ggtheme to classic or similar.
However, it is very unclear to me how I can modify individually selected plots within a matrix of plots generated via facet_grid
From #joran answer found here, this is what I get :
[EDIT] code edited to select multiple facets
if(!require(tidyverse)){install.packages("tidyverse")}
library(tidyverse)
#dummy dataset
df = data.frame(type = as.character(c("a", "b", "c", "d")),
id = as.character(c("M5", "G5", "A7", "S3")),
val = runif(4, min = 1, max = 10),
temp = runif(4))
# use a rectangle to individually select plots
ggplot(data = df, aes(x = val, y = temp)) +
geom_point() +
geom_rect(data = subset(df, type %in% c("b", "c") & id %in% c("A7","G5")),
fill = NA, colour = "red", xmin = -Inf,xmax = Inf,
ymin = -Inf,ymax = Inf) +
facet_grid(type~id)
It does not use theme() but it seems simple enough to highlight some facets.
I have a dataset, named “data”:
df=ddply(data,c("Treatment","Concentration"),summarise,mean=mean(Inhibition),sd=sd(Inhibition),n=length(Inhibition),se=sd/sqrt(n))
p <- ggplot(df, aes(x=Treatment, y=Inhibition))
p1 <- p + geom_bar(stat="identity", position="dodge") +
geom_errorbar(aes(ymin=Inhibition-se,ymax=Inhibition+se), position="dodge",width=0.2)
and I got the following graph:
I want x-axis to be like the picture below:
How woud I do this??
This is best achieved using a facet within ggplot. As you haven’t included a reusable dataset, I have made one here:
df <- data.frame(Group = c("A", "A", "A", "A", "B"),
SubGroup = c(letters[1:5]),
value = 1:5
)
See below the facet_grid line which has a few additional options specified. You can read more about the added arguments here
library(ggplot2)
ggplot(df, aes(x = SubGroup, value)) +
geom_bar(stat="identity", position="dodge") +
facet_grid(.~Group, scales = "free_x", space = "free", switch = "x") +
theme(strip.placement = "outside")
For your data, you will need to split the drug and dose into two separate columns first, like my example.