I am using the following code to plot a stacked area graph and I get the expected plot.
P <- ggplot(DATA2, aes(x=bucket,y=volume, group=model, fill=model,label=volume)) + #ggplot initial parameters
geom_ribbon(position='fill', aes(ymin=0, ymax=1))
but then when I add lines which are reading the same data source I get misaligned results towards the right side of the graph
P + geom_line(position='fill', aes(group=model, ymax=1))
does anyone know why this may be? Both plots are reading the same data source so I can't figure out what the problem is.
Actually, if all you wanted to do was draw an outline around the areas, then you could do the same using the colour aesthetic.
ggplot(DATA2, aes(x=bucket,y=volume, group=model, fill=model,label=volume)) +
geom_ribbon(position='fill', aes(ymin=0, ymax=1), colour = "black")
I have an answer, I hope it works for you, it looks good but very different from your original graph:
library(ggplot2)
DATA2 <- read.csv("C:/Users/corcoranbarriosd/Downloads/porsche model volumes.csv", header = TRUE, stringsAsFactors = FALSE)
In my experience you want to have X as a numeric variable and you have it as a string, if that is not the case I can Change that, but this will transform your bucket into a numeric vector:
bucket.list <- strsplit(unlist(DATA2$bucket), "[^0-9]+")
x=numeric()
for (i in 1:length(bucket.list)) {
x[i] <- bucket.list[[i]][2]
}
DATA2$bucket <- as.numeric(x)
P <- ggplot(DATA2, aes(x=bucket,y=volume, group=model, fill=model,label=volume)) +
geom_ribbon(aes(ymin=0, ymax=volume))+ geom_line(aes(group=model, ymax=volume))
It gives me the area and the line tracking each other, hope that's what you needed
If you switch to using geom_path in place of geom_line, it all seems to work as expected. I don't think the ordering of geom_line is behaving the same as geom_ribbon (and suspect that geom_line -- like geom_area -- assumes a zero base y value)
ggplot(DATA2, aes(x=bucket, y=volume, ymin=0, ymax=1,
group=model, fill=model, label=volume)) +
geom_ribbon(position='fill') +
geom_path(position='fill')
Should give you
Related
I have recently been playing around with various plot types using fictitious data to get my head around how I could display various pieces of information. One plot type that is gaining popularity is the so called individual differences dot plot which shows the change in each subjects score pre-post. The plot is fairly easy to produce, but my issue is that when I go to change the labels using either the labs or xlab ylab functions in ggplot, the plot itself becomes messed up. Below I have attached the fictitious data, the code used and the results.
Data
df<- data.frame(Participant<- c(rep(1:10,2)), Score<- c(rnorm(20,100,5)), Session<- c(1,1,1,1,1,1,1,1,1,1, 2,2,2,2,2,2,2,2,2,2))
colnames(df) <- c("Participant", "Score", "Session")
Code for plot
p<- ggplot(df, aes(x=df$Session, y=df$Score, colour=df$Participant))+ geom_point()+
geom_line(group=df$Participant)+
theme_classic()
Plot
Individual difference plot
My dilemma is that anytime I try to change the label names, the plot messes up as per below.
Problem
p + xlab("Session") + ylab("Score")
Plot after relabelling
The same thing happens if I try the labs function i.e, p + labs(x= "Session", y= "Score"). You can see that the labels themselves do actually change, but for some reason this messes up the actual plot. Does any have any ideas as to what could be going wrong here?
The issue appears to be the grouping is undone when the label functions are called. Instead, issue the grouping as an aesthetic mapping:
library(dplyr); library(ggplot)
df %>% mutate(across(c(Session,Participant),factor)) -> df
p <- ggplot(df, aes(x=Session, y=Score, colour=Participant))+ geom_point()+
geom_line(aes(group=Participant))+
theme_classic()
p + xlab("Session") + ylab("Score")
I suspect this is probably a bug.
I'm trying to create a stacked barchart with gene sequencing data, where for each gene there is a tRF.type and Amino.Acid value. An example data set looks like this:
tRF <- c('tRF-26-OB1690PQR3E', 'tRF-27-OB1690PQR3P', 'tRF-30-MIF91SS2P46I')
tRF.type <- c('5-tRF', 'i-tRF', '3-tRF')
Amino.Acid <- c('Ser', 'Lys', 'Ser')
tRF.data <- data.frame(tRF, tRF.type, Amino.Acid)
I would like the x-axis to represent the amino acid type, the y-axis the number of counts of each tRF type and the the fill of the bars to represent each tRF type.
My code is:
ggplot(chart_data, aes(x = Amino.Acid, y = tRF.type, fill = tRF.type)) +
geom_bar(stat="identity") +
ggtitle("LAN5 - 4 days post CNTF treatment") +
xlab("Amino Acid") +
ylab("tRF type")
However, it generates this graph, where the y-axis is labelled with the categories of tRF type. How can I change my code so that the y-axis scale is numerical and represents the counts of each tRF type?
Barchart
OP and Welcome to SO. In future questions, please, be sure to provide a minimal reproducible example - meaning provide code, an image (if possible), and at least a representative dataset that can demonstrate your question or problem clearly.
TL;DR - don't use stat="identity", just use geom_bar() without providing a stat, since default is to use the counts. This should work:
ggplot(chart_data, aes(x = Amino.Acid, fill = tRF.type)) + geom_bar()
The dataset provided doesn't adequately demonstrate your issue, so here's one that can work. The example data herein consists of 100 observations and two columns: one called Capitals for randomly-selected uppercase letters and one Lowercase for randomly-selected lowercase letters.
library(ggplot2)
set.seed(1234)
df <- data.frame(
Capitals=sample(LETTERS, 100, replace=TRUE),
Lowercase=sample(letters, 100, replace=TRUE)
)
If I plot similar to your code, you can see the result:
ggplot(df, aes(x=Capitals, y=Lowercase, fill=Lowercase)) +
geom_bar(stat="identity")
You can see, the bars are stacked, but the y axis is all smooshed down. The reason is related to understanding the difference between geom_bar() and geom_col(). Checking the documentation for these functions, you can see that the main difference is that geom_col() will plot bars with heights equal to the y aesthetic, whereas geom_bar() plots by default according to stat="count". In fact, using geom_bar(stat="identity") is really just a complicated way of saying geom_col().
Since your y aesthetic is not numeric, ggplot still tries to treat the discrete levels numerically. It doesn't really work out well, and it's the reason why your axis gets smooshed down like that. What you want, is geom_bar(stat="count").... which is the same as just using geom_bar() without providing a stat=.
The one problem is that geom_bar() only accepts an x or a y aesthetic. This means you should only give it one of them. This fixes the issue and now you get the proper chart:
ggplot(df, aes(x=Capitals, fill=Lowercase)) + geom_bar()
You want your y-axis to be a count, not tRF.type. This code should give you the correct plot: I've removed the y = tRF.type from ggplot(), and stat = "identity from geom_bar() (it is using the default value of stat = "count instead).
ggplot(tRF.data, aes(x = Amino.Acid, fill = tRF.type)) +
geom_bar() +
ggtitle("LAN5 - 4 days post CNTF treatment") +
xlab("Amino Acid") +
ylab("tRF type")
I'm using ggplot as described here
Smoothed density estimates
and entered in the R console
m <- ggplot(movies, aes(x = rating))
m + geom_density()
This works but is there some way to remove the connection between the x-axis and the density plot (the vertical lines which connect the density plot to the x-axis)
The most consistent way to do so is (thanks to #baptiste):
m + stat_density(geom="line")
My original proposal was to use geom_line with an appropriate stat:
m + geom_line(stat="density")
but it is no longer recommended since I'm receiving reports it's not universally working for every case in newer versions of ggplot.
The suggested answers dont provide exactly the same results as geom_density. Why not draw a white line over the baseline?
+ geom_hline(yintercept=0, colour="white", size=1)
This worked for me.
Another way would be to calculate the density separately and then draw it. Something like this:
a <- density(movies$rating)
b <- data.frame(a$x, a$y)
ggplot(b, aes(x=a.x, y=a.y)) + geom_line()
It's not exactly the same, but pretty close.
Summary: I want to choose the colors for a ggplot2() density distribution plot without losing the automatically generated legend.
Details: I have a dataframe created with the following code (I realize it is not elegant but I am only learning R):
cands<-scan("human.i.cands.degnums")
non<-scan("human.i.non.degnums")
df<-data.frame(grp=factor(c(rep("1. Candidates", each=length(cands)),
rep("2. NonCands",each=length(non)))), val=c(cands,non))
I then plot their density distribution like so:
library(ggplot2)
ggplot(df, aes(x=val,color=grp)) + geom_density()
This produces the following output:
I would like to choose the colors the lines appear in and cannot for the life of me figure out how. I have read various other posts on the site but to no avail. The most relevant are:
Changing color of density plots in ggplot2
Overlapped density plots in ggplot2
After searching around for a while I have tried:
## This one gives an error
ggplot(df, aes(x=val,colour=c("red","blue"))) + geom_density()
Error: Aesthetics must either be length one, or the same length as the dataProblems:c("red", "blue")
## This one produces a single, black line
ggplot(df, aes(x=val),colour=c("red","green")) + geom_density()
The best I've come up with is this:
ggplot() + geom_density(aes(x=cands),colour="blue") + geom_density(aes(x=non),colour="red")
As you can see in the image above, that last command correctly changes the colors of the lines but it removes the legend. I like ggplot2's legend system. It is nice and simple, I don't want to have to fiddle about with recreating something that ggplot is clearly capable of doing. On top of which, the syntax is very very ugly. My actual data frame consists of 7 different groups of data. I cannot believe that writing + geom_density(aes(x=FOO),colour="BAR") 7 times is the most elegant way of coding this.
So, if all else fails I will accept with an answer that tells me how to get the legend back on to the 2nd plot. However, if someone can tell me how to do it properly I will be very happy.
set.seed(45)
df <- data.frame(x=c(rnorm(100), rnorm(100, mean=2, sd=2)), grp=rep(1:2, each=100))
ggplot(data = df, aes(x=x, color=factor(grp))) + geom_density() +
scale_color_brewer(palette = "Set1")
ggplot(data = df, aes(x=x, color=factor(grp))) + geom_density() +
scale_color_brewer(palette = "Set3")
gives me same plots with different sets of colors.
Provide vector containing colours for the "values" argument to map discrete values to manually chosen visual ones:
ggplot(df, aes(x=val,color=grp)) +
geom_density() +
scale_color_manual(values=c("red", "blue"))
To choose any colour you wish, enter the hex code for it instead:
ggplot(df, aes(x=val,color=grp)) +
geom_density() +
scale_color_manual(values=c("#f5d142", "#2bd63f")) # yellow/green
I'm trying to plot a scatter-plot with two layers. The reason is I want to represent the size of the points by its number of answers. Then I need to have a smooth-curve layed over it. So I use two datasets to achieve this.
The problem is, when I lay the second layer with the smoother using the original dataset, then the smoother is shifted by one point on the x-scale to the left.
Does anyone know, how to correct this in the R code? Is there maybe something wrong in it?
I thought about to add 1 to the x variable, but I don't want to have to go this far.
library(ggplot2)
q.tab <- xtabs(~x + y, mydata)
q.df <- as.data.frame(q.tab)
pointsize <- q.df$Freq
qplot(x, y, data=q.df) + geom_point(aes(size=as.factor(pointsize)))
+ geom_smooth(data=mydata, method="loess", span=1))
With ggplot2 , when you think in terms of layer it is better to use ggplot function and not qplot.
I generate your data (sample function is very convenient to generate data)
mydata$x <- sample(1:10,100,replace=TRUE)
mydata$y <- sample(1:10,100,replace=TRUE)
q.tab <- xtabs(~x + y, mydata)
q.df <- as.data.frame(q.tab)
ggplot version:
library(ggplot2)
ggplot(data=mydata,aes(x,y,size=Freq)) +
geom_point() +
geom_smooth( method="loess", span=1)
qplot version:
qplot(data=mydata,x=x,y=y,size=Freq,geom='point')+
geom_smooth( method="loess", span=1)