I plot data with ggplot, and I wanted to see the smoothed lines using stat_smooth.
But now I would like to only plot the smoothed lines (somehow extract it), without the original ggplot.
Do you think it's possible?
Here is my code :
Graph <- ggplot(data=Forecasttemp, aes(x=Price.date, y=Price, colour=Group)) + geom_line() + scale_colour_hue(guide = "none")
Graph <- Graph + stat_smooth(se = FALSE, aes(fill = Group)) + scale_colour_hue(guide = "none")
If you want to plot only the smoothed lines without original sample points, you can simply omit geom_line(), thus resulting in:
Graph <- ggplot(data=Forecasttemp, aes(x=Price.date, y=Price, colour=Group)) +
stat_smooth(se = FALSE, aes(fill = Group)) +
scale_colour_hue(guide = "none")
Unfortunately I can not try this due to the lack of a reproducible example, but I make a try with an R base dataset and it worked:
library(ggplot2)
data(iris)
g1 <- ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length, colour=Species)) +
scale_colour_hue(guide = "none") + geom_smooth()
g1
Related
I start to study R. I'm starting with Iris dataset in the package datasets. To draw som graph I need to use the ggplot2 package. How can I split the Plots window and draw two graphs?
I try with the following code, but only one graph is showed.
iris=datasets::iris
par(mfrow=c(2,1))
ggplot(iris, aes(x=Sepal.Length,y=Sepal.Width,color=Species))+ geom_point(size=3)
ggplot(iris, aes(x=Petal.Length,y=Petal.Width,color=Species))+ geom_point(size=3)
use win.graph() to split the window into two.
Since you have not provided dataset, if you want to create a side by side plot try based on my example below
Try this:
library(cowplot)
iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
geom_boxplot() + theme_bw()
iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
geom_density(alpha = 0.7) + theme_bw() +
theme(legend.position = c(0.8, 0.8))
plot_grid(iris1, iris2, labels = "AUTO")
As ggplot2 is based on grid graphics system instead of base plot, par does not effective in adjusting ggplot2 plots, and the latest version of ggplot2 has already supported the arrangement of different plots, and you can set tags for each of them:
iris=datasets::iris
ggplot(iris, aes(x=Sepal.Length,y=Sepal.Width,color=Species))+ geom_point(size=3) + labs(tag = "A") -> p1
ggplot(iris, aes(x=Petal.Length,y=Petal.Width,color=Species))+ geom_point(size=3) + labs(tag = "B") -> p2
p1 + p2
For more sophisticated arrangement, you can use patchwork package to arrange them
How I can modify the following code to have a plot between x and y (scatter plot) and the fitted values X0.025 and y=X0.975 as the curves (lines) on the plot. (please run plot(m6) to see the plot which I am looking for to make by ggplot)
library(quantregGrowth)
data(growthData)
m6<-gcrq(y~ps(x, lambda=seq(0,100,l=20)), tau=c(0.025,0.975), n.boot=10,
data=growthData)
plot(m6)
I tried to make this plot by ggplot and here is the code:
library(ggplot2)
library(plotly)
temp <- data.frame(m6$fitted)
growthData_b <- cbind(growthData, temp)
a <- ggplot(data=growthData_b, aes(x, y=X0.025)) + geom_line() +
geom_line(data=growthData_b, aes(x, y=X0.975), color = "red") + theme_bw()
Are you looking for this?
ggplot(data=growthData_b, aes(x, y=X0.025)) +
geom_line() +
geom_line(data=growthData_b, aes(x, y=X0.975), color = "red", linetype = 2) +
theme_bw() +
geom_point(aes(x=x, y=y), shape = 1)
I am trying to make an extremely single heatmap of percentages using ggplot2 which ideally will just be two single thin columns. I tried the following code, believing that the width option in aes would solve the problem.
p_prev_tg <- ggplot(tg_melt, aes(x = variable , y = OTU, fill = value,
width=.3)) + geom_tile() +
scale_fill_gradientn(colours = hm.palette2(10)) +
xlab(NULL) + ylab(NULL) +
theme(axis.text=element_text(size=7))
p_prev_tg
Unfortunately, this returns a plot with lots of empty space as shown. The plot I would like is those two bars side by side, how can I do this in ggplot?
thanks
What about this solution ?
set.seed(1234)
tg_melt <- data.frame(variable=rep(c("Prevalence_T","Prevalence_NT"), each=10),
OTU=rep(paste0("OTU_",1:10),2),
value=rnorm(20))
library(RColorBrewer)
library(ggplot2)
hm.palette2 <- colorRampPalette(rev(brewer.pal(11, 'Spectral')))
p_prev_tg <- ggplot(tg_melt, aes(x = as.numeric(variable), y = OTU, fill = value)) +
geom_tile() +
scale_fill_gradientn(colours = hm.palette2(10)) +
xlab(NULL) + ylab(NULL) +
theme(axis.text=element_text(size=7)) +
scale_x_continuous(breaks=c(1,2),
limits=c(0,3),
labels=levels(tg_melt$variable))+
theme_bw()
p_prev_tg
I am using ggplot2 to produce a plot that has 3 facets. Because I am comparing two different data sets, I would like to then be able to plot a second data set using the same y scale for the facets as in the first plot. However, I cannot find a simple way to save the settings of the first plot to then re-use them with the second plot. Since each facet has its own y scale, it will be a pain to specify them by hand for the second plot. Does anyone know of a quick way of re-using scales? To make this concrete, here is how I am generating first my plot:
p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + facet_wrap(~ cyl, scales = "free_y")
EDIT
When applying one of the suggestions below, I found out that my problem was more specific than described in the original post, and it had to do specifically with scaling of the error bars. Concretely, the error bars look weird when I rescale the second plot as suggested. Does anyone have any suggestions on how to keep the same scale for both plots and dtill display the error bars correctly? I am attaching example below for concreteness:
#Create sample data
d1 <- data.frame(fixtype=c('ff','ff','fp','fp'), detype=c('det','pro','det','pro'),
diffscore=c(-1,-15,3,-17),se=c(2,3,1,2))
d2 <- data.frame(fixtype=c('ff','ff','fp','fp'), detype=c('det','pro','det','pro'),
diffscore=c(-1,-3,-2,-1),se=c(4,3,5,3))
#Plot for data frame 1, this is the scale I want to keep
lim_d1 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d1, aes(colour=detype, y=diffscore, x=detype)) +
geom_point(aes(size=1), shape=15) +
geom_errorbar(lim_d1, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
#Plot for data frame 2 original scale
lim_d2 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d2, aes(colour=detype, y=diffscore, x=detype)) +
geom_point(aes(size=1), shape=15) +
geom_errorbar(lim_d2, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
#Plot for data frame 2 adjusted scale. This is where things go wrong!
#As suggested below, first I plot the first plot, then I draw a blank screen and try
#to plot the second data frame on top.
lim_d2 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d1, aes(colour=detype, y=diffscore, x=detype)) +
geom_blank() +
geom_point(data=d2, aes(size=1), shape=15) +
geom_errorbar(lim_d2, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
#If the error bars are fixed, by adding data=d2 to geom_errorbar(), then
#the error bars are displayed correctly but the scale gets distorted again
lim_d2 <- aes(ymax = diffscore + se, ymin=diffscore - se)
ggplot(d1, aes(colour=detype, y=diffscore, x=detype)) +
geom_blank() +
geom_point(data=d2, aes(size=1), shape=15) +
geom_errorbar(data=d2,lim_d2, width=0.2,size=1) +
facet_wrap(~fixtype, nrow=2, ncol=2, scales = "free_y")
You may first call ggplot on your original data where you add a geom_blank as a first layer. This sets up a plot area, with axes and legends based on the data provided in ggplot.
Then add geoms which use data other than the original data. In the example, I use a simple subset of the original data.
From ?geom_blank: "The blank geom draws nothing, but can be a useful way of ensuring common scales between different plots.".
ggplot(data = mtcars, aes(mpg, wt)) +
geom_blank() +
geom_point(data = subset(mtcars, wt < 3)) +
facet_wrap(~ cyl, scales = "free_y")
Here is an ugly hack that assumes you have an identical facetting layout in both plots.
It replaces the panel element of the ggplot build.
p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p1 <- p + facet_wrap(~ cyl, scales = "free_y") + labs(title = 'original')
# create "other" data.frame
n <- nrow(mtcars)
set.seed(201405)
mtcars2 <- mtcars[sample(seq_len(n ),n-15),]
# create this second plot
p2 <- p1 %+% mtcars2 + labs(title = 'new data')
# and a copy so we can attempt to fix
p3 <- p2 + labs(title = 'new data original scale')
# use ggplot_build to construct the plots for rendering
p1b <- ggplot_build(p1)
p3b <- ggplot_build(p3)
# replace the 'panel' information in plot 2 with that
# from plot 1
p3b[['panel']] <- p1b[['panel']]
# render the revised plot
# for comparison
library(gridExtra)
grid.arrange(p1 , p2, ggplot_gtable(p3b))
I have this kind of data frame:
df<-data.frame(x=c(1,2,3,4,5,6,7,8,9,10),y=c(2,11,24,30,45,65,90,110,126,145), a=c(0.2,0.2,0.3,0.4,0.1,0.8,0.7,0.6,0.8,0.9))
Using ggplot, I would like to plot on the same figure two regression lines, calculated for a subset of my data frame under condition (a > or < 0.5).
Visually, I would like that both regression lines:
df_a<-subset(df, df$a<0.5)
ggplot(df_a,aes(x,y))+
geom_point(aes(color = a), size=3.5) +
geom_smooth(method="lm", size=1, color="black") +
ylim(-5,155) +
xlim(0,11)
df_b<-subset(df, df$a>0.5)
ggplot(df_b,aes(x,y)) +
geom_point(aes(color = a), size=3.5) +
geom_smooth(method="lm", size=1, color="black") +
ylim(-5,155) +
xlim(0,11)
Appear on this figure:
ggplot(df,aes(x,y))+ geom_point(aes(color = a), size=3.5)
I've tried with par(new=TRUE) without success.
Make a flag variable, and use group:
df$small=df$a<0.5
ggplot(df,aes(x,y,group=small))+geom_point() + stat_smooth(method="lm")
and have yourself pretty colours and a legend if you want:
ggplot(df,aes(x,y,group=small,colour=small))+geom_point() + stat_smooth(method="lm")
Or maybe you want to colour the dots:
ggplot(df,aes(x,y,group=small)) +
stat_smooth(method="lm")+geom_point(aes(colour=a))