I have 12 variables, M1, M2, ..., M12, for which I compute a certain statistic x.
df = data.frame(model = factor(paste("M", 1:28, sep = ""), levels=paste("M", 1:28, sep = "")), x = runif(28, 1, 1.05))
levels = seq(0.8, 1.2, 0.05)
I would like to plot this data as follows:
Each circle (contour) represents the a level of that statistic "x". The three blue lines simply represent three different scenarios.
The dataframe included in this example represents one scenario. The blue line would simply join the values of all the models M1 to M28 for that specific scenario.
I tried the following:
ggplot(data=df, aes(x=model, y=x, group=1)) +
geom_line() + coord_polar() +
scale_y_continuous(limits=range(levels), breaks=levels, labels=levels) +
theme(axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank())
However, I get a disconnected path (between M28 and M1)
Then, I replicated the first row and placed it at the bottom of the dataframe (see below), and then used geom_path() instead of geom_line(), but I didn't get the result I was looking for:
## Replicating the first row (model1) and placing it at end of dataframe
df = rbind(df, df[1,])
## using geom_path()
ggplot(data=df, aes(x=model, y=lg, group=1)) +
geom_path() + coord_polar() +
scale_y_continuous(limits=range(levels), breaks=levels, labels=levels) +
theme(axis.text.y = element_blank(), axis.ticks = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank())
Could any please help me achieve the result that I am looking for? Any help would be appreciated. Thanks!
You have to use geom_polygon for closed paths:
library(ggplot2)
ggplot(data=df, aes(x=model, y=x, group=1)) +
geom_polygon(fill = NA, colour = "black") +
coord_polar() +
scale_y_continuous(limits=range(levels), breaks=levels, labels=levels) +
theme(axis.text.y = element_blank(), axis.ticks = element_blank(),
axis.title.x = element_blank(), axis.title.y = element_blank())
Related
I have the following data consisting of two data.frames of differing lengths.
df1 <- data.frame(cbind(rnorm(20,0.4,0.2), seq(0,200,by=10)))
df2 <- data.frame(cbind(rnorm(30,0.6,0.25), seq(0,270,by=9)))
I would like to plot them on the same plot and have them distinguished from each other by different types of dashed lines. I can't seem to get this to work and have rather extensively searched for a solution. The trouble is that my two sets of data are of different lengths, therefore I cannot simply melt the data and stick into ggplot.
ggplot() +
geom_path(data = df1, aes(x = X1, y = X2)) +
geom_path(data = df2, aes(x = X1, y = X2)) +
theme_bw() + geom_point() + geom_line() +
scale_y_reverse()+
scale_x_continuous(position="top") +
scale_linetype_manual(values=c("twodash", "dotted")) +
theme(axis.line=element_line(),
axis.line.y = element_line(),
panel.background= element_blank(),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank())
Any help will be appreciated!
The fact that the data have different lengths doesn't really matter here, you just need to create a new column that identifies which dataset is which and you can stack them on top of each other with rbind():
df1$Source = "df1"
df2$Source = "df2"
df_combined = rbind(df1, df2)
ggplot(df_combined, aes(x = X1, y = X2, linetype = Source)) +
geom_path() +
theme_bw() +
scale_y_reverse()+
scale_x_continuous(position="top") +
scale_linetype_manual(values=c("twodash", "dotted")) +
theme(axis.line=element_line(),
axis.line.y = element_line(),
panel.background= element_blank(),
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank())
Hi I have the plot below and the marginal density plots are slightly off. They do not line up to the x and y axis of the scatter plot so interpretation can be a bit misleading.
I can sort of play with these lines of code to try and get the margins to align for rthe marginal plots but it is very manual and frustrating.
theme0(plot.margin = unit(c(1,0,0,2.2),"lines"))
theme0(plot.margin = unit(c(0,1,1.2,0),"lines"))
Is there a way to automatically find the right margins to pass to theme0(plot.margin = unit(c(0,1,1.2,0),"lines") so that no manual work needs to be done to line up the margins? Thank you.
library(ggplot2)
library(gridExtra)
set.seed(42)
DF <- data.frame(x=rnorm(100,mean=c(1,5)),y=rlnorm(100,meanlog=c(8,6)),group=1:2)
DF
## Scatter plot
p1 <- ggplot(DF,aes(x=x,y=y)) + geom_point() +
scale_x_continuous(expand=c(0.02,0)) +
scale_y_continuous(expand=c(0.02,0)) +
theme_bw() +
theme(legend.position="none",plot.margin=unit(c(0,0,0,0),"points")) # ggplot(DF,aes(x=x,y=y,colour=factor(group))) color the gorup
theme0 <- function(...) theme( legend.position = "none",
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.margin = unit(0,"null"),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.length = unit(0,"null"),
axis.ticks.margin = unit(0,"null"),
panel.border=element_rect(color=NA),...)
### DENSITY OF X
p2 <- ggplot(DF,aes(x=x, fill="blue")) +
geom_density(alpha=0.5) +
scale_x_continuous(breaks=NULL,expand=c(0.02,0)) +
scale_y_continuous(breaks=NULL,expand=c(0.02,0)) +
theme_bw() +
theme0(plot.margin = unit(c(1,0,0,2.2),"lines")) # to color group ggplot(DF,aes(x=x,colour=factor(group),fill=factor(group)))
### DENSITY OF Y
p3 <- ggplot(DF,aes(x=y, fill = "red")) +
geom_density(alpha=0.5) +
coord_flip() +
scale_x_continuous(labels = NULL,breaks=NULL,expand=c(0.02,0)) +
scale_y_continuous(labels = NULL,breaks=NULL,expand=c(0.02,0)) +
theme_bw() +
theme0(plot.margin = unit(c(0,1,1.2,0),"lines")) # color group ggplot(DF,aes(x=y,colour=factor(group),fill=factor(group)))
grid.arrange(arrangeGrob(p2,ncol=2,widths=c(3,1)),
arrangeGrob(p1,p3,ncol=2,widths=c(3,1)),
heights=c(1,3))
I am pretty new to R and am trying to create a composite plot using ggplot. I have searched how to do this and have seen I can use the facet function, however, it seems that this is for plotting data which can be split by type e.g. male/female. I have a data frame and I want to plot recovery against concentration, and recovery against equilibrium time on separate plots but as a composite plot. For this I have the following code:
p1 <- ggplot(dat2, aes(x = EqmTime, y = Recovery))
limits <- aes(ymax = Recovery + RecoveryError, ymin=Recovery - RecoveryError)
p1 + geom_point(size = 4) + geom_errorbar(limits, width=4) + geom_smooth(method = "lm", se = FALSE, colour="gray", size=1.5, linetype="dashed") +
labs(x='Equilibrium Time (hrs)', y='Nitrate Recovery (%)') + theme_bw() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
p2 <- ggplot(dat2, aes(x = StockConc, y = Recovery))
limits <- aes(ymax = Recovery + RecoveryError, ymin=Recovery - RecoveryError)
p2 + geom_point(size = 4) + geom_errorbar(limits, width=0.1) + geom_smooth(method = "lm", se = FALSE, colour="gray", size=1.5, linetype="dashed") +
labs(x='Concentration (g L-1)', y='Nitrate Recovery (%)') + theme_bw() +
theme(axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
Additionally, I also have a problem that I cannot get the '-1' in the x axis label of plot 2 as a superscript, and am having trouble setting axis limits. When I set, for example, xlim=20-180, the axis doesn't start and finish at these, but makes these the major tick marks.
I would greatly appreciate any help with this! I know some of these issues have been addressed in other posts but I cannot seem to use this advise to sort the issue here.
From your question, I understand that you want to plot both the ggplots in single plot window. You can do this using gridextra package as:
library(gridExtra)
grid.arrange(p1, p2, nrow=2)
I need to gather two facet columns into one column with ggplot2.
In the following example, I need to overlay the content of the two columns DEG and RAN into one, while giving different colours to DEG and RAN data (small points and smooth line) and provide the corresponding legend (so I can distinguish them as they are overlayed).
I feel my code is not too, too far from what I need, but the relative complexity of the dataset blocks me. How to go about achieving this in ggplot2?
Here's my code so far:
require(reshape2)
library(ggplot2)
library(RColorBrewer)
fileName = paste("./4.csv", sep = "") # csv file available here: https://www.dropbox.com/s/bm9hd0t5ak74k89/4.csv?dl=0
mydata = read.csv(fileName,sep=",", header=TRUE)
dataM = melt(mydata,c("id"))
dataM = cbind(dataM,colsplit(dataM$variable,pattern = "_",names = c("NM", "ORD", "CAT")))
dataM$variable <- NULL
dataM <- dcast(dataM, ... ~ CAT, value.var = "value")
my_palette <- colorRampPalette(rev(brewer.pal(11, "Spectral")))
ggplot(dataM, aes(x=NR ,y= ASPL)) +
geom_point(size = .4,alpha = .5) +
stat_smooth(se = FALSE, size = .5) +
theme_bw() +
theme(plot.background = element_blank(),
axis.line = element_blank(),
legend.key = element_blank(),
legend.title = element_blank()) +
scale_y_continuous("ASPL", expand=c(0,0), limits = c(1, 7)) +
scale_x_continuous("NR", expand=c(0,0), limits = c(0, 100)) +
theme(legend.position="bottom") +
theme(axis.title.x = element_text(vjust=-0.3, face="bold", size=12)) +
theme(axis.title.y = element_text(vjust=1.5, face="bold", size=12)) +
ggtitle("Title") + theme(plot.title = element_text(lineheight=.8, face="bold")) +
theme(title = element_text(vjust=2)) +
facet_grid(NM ~ ORD)
Here's what it gives me right now:
Extra question: how come DEG/SF doesn't show a smooth line?
You can use the group aesthetic to define that data points with the same value of ORD belong together. You can also map aesthetics shape and color to this variable. You can also use . to specify that the facets are not split along a specific dimension.
I have made the changes to your code below after transforming NR and ASPL to numeric variables:
dataM$NR <- as.integer(dataM$NR)
dataM$ASPL <- as.numeric(dataM$ASPL)
ggplot(dataM, aes(x=NR ,y= ASPL, group=ORD, color=ORD)) +
geom_point(size = .7,alpha = .5, aes(shape=ORD)) + ## increased size
stat_smooth(se = FALSE, size = .5) +
theme_bw() +
theme(plot.background = element_blank(),
axis.line = element_blank(),
legend.key = element_blank(),
legend.title = element_blank()) +
scale_y_continuous("ASPL", expand=c(0,0), limits = c(1, 7)) +
scale_x_continuous("NR", expand=c(0,0), limits = c(0, 100)) +
theme(legend.position="bottom") +
theme(axis.title.x = element_text(vjust=-0.3, face="bold", size=12)) +
theme(axis.title.y = element_text(vjust=1.5, face="bold", size=12)) +
ggtitle("Title") + theme(plot.title = element_text(lineheight=.8, face="bold")) +
theme(title = element_text(vjust=2)) +
facet_grid(NM ~.)
How can scatter plots with alpha transparent, scale-less histograms can be made in R, like this figure?
looks like it's not made in ggplot2.
does anyone know what command is used?
library(ggplot2)
library(gridExtra)
set.seed(42)
DF <- data.frame(x=rnorm(100,mean=c(1,5)),y=rlnorm(100,meanlog=c(8,6)),group=1:2)
p1 <- ggplot(DF,aes(x=x,y=y,colour=factor(group))) + geom_point() +
scale_x_continuous(expand=c(0.02,0)) +
scale_y_continuous(expand=c(0.02,0)) +
theme_bw() +
theme(legend.position="none",plot.margin=unit(c(0,0,0,0),"points"))
theme0 <- function(...) theme( legend.position = "none",
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.margin = unit(0,"null"),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks.length = unit(0,"null"),
axis.ticks.margin = unit(0,"null"),
panel.border=element_rect(color=NA),...)
p2 <- ggplot(DF,aes(x=x,colour=factor(group),fill=factor(group))) +
geom_density(alpha=0.5) +
scale_x_continuous(breaks=NULL,expand=c(0.02,0)) +
scale_y_continuous(breaks=NULL,expand=c(0.02,0)) +
theme_bw() +
theme0(plot.margin = unit(c(1,0,0,2.2),"lines"))
p3 <- ggplot(DF,aes(x=y,colour=factor(group),fill=factor(group))) +
geom_density(alpha=0.5) +
coord_flip() +
scale_x_continuous(labels = NULL,breaks=NULL,expand=c(0.02,0)) +
scale_y_continuous(labels = NULL,breaks=NULL,expand=c(0.02,0)) +
theme_bw() +
theme0(plot.margin = unit(c(0,1,1.2,0),"lines"))
grid.arrange(arrangeGrob(p2,ncol=2,widths=c(3,1)),
arrangeGrob(p1,p3,ncol=2,widths=c(3,1)),
heights=c(1,3))
Edit:
I couldn't find out what causes the space below the densities geoms. You can fiddle with the plot margins to avoid it, but I don't really like that.
p2 <- ggplot(DF,aes(x=x,colour=factor(group),fill=factor(group))) +
geom_density(alpha=0.5) +
scale_x_continuous(breaks=NULL,expand=c(0.02,0)) +
scale_y_continuous(breaks=NULL,expand=c(0.00,0)) +
theme_bw() +
theme0(plot.margin = unit(c(1,0,-0.48,2.2),"lines"))
p3 <- ggplot(DF,aes(x=y,colour=factor(group),fill=factor(group))) +
geom_density(alpha=0.5) +
coord_flip() +
scale_x_continuous(labels = NULL,breaks=NULL,expand=c(0.02,0)) +
scale_y_continuous(labels = NULL,breaks=NULL,expand=c(0.00,0)) +
theme_bw() +
theme0(plot.margin = unit(c(0,1,1.2,-0.48),"lines"))
I have no idea whether there is a package that does that directly, but I'm sure this can be done in R. Transparency is easy: you add another two digits to the RGB specification of a color for a given transparency:
#FF0000 # red
#FF0000FF # full opacity
#FF000000 # full transparency
Combining different plots is also easy using the layout function. As for the vertical density plot, it is just the same as the horizontal plot with x and y switched. The example given here can easily be expanded to include colors, smaller margins etc. I can try to come up with a more elaborate example if this description is not sufficient.