Let me introduce my data-set and my preliminary result first for better understanding my question. my dataset looks like:
Place Species Size Conc.
A BT 24 0.2
A ST 76 1.4
...
B BT 45 1.2
B ST 21 0.7
...
I want to make scatterplot of Size against Conc. for each Species at each Place. What I have done uses ggplot2 to make a graph as below:
scatterplot <- ggplot(mydata, aes(x = Size, y = Conc, color = Species)) +
geom_point(shape = 1)
Though this graph plots by the species group in different color, it summarizes all data in the dataset and fails to plot for different places.
I think the code below
scatterplot <- ggplot(mydata[mydata$place == "A"], aes(x = Size, y = Conc, color = Species)) + geom_point(shape = 1)
works for plotting just place A and I can do this for different places one by one. However, in my real dataset, the place variable has tons of different places, and I can't type them all out one by one manually. Thus my question actually is how to let R make those plots for different places automatically at one time?
Try:
ggplot(ddf)+geom_point(aes(Size, Conc.))+facet_grid(Place~Species)
If there are too many places:
ggplot(ddf)+geom_point(aes(Size, Conc., color=Place))+facet_grid(.~Species)
Or, in one graph:
ggplot(ddf)+geom_point(aes(Size, Conc., color=Place,shape=Species), size=5)
Related
So my first ggplot2 box plot was just one big stretched out box plot, the second one was correct but I don't understand what changed and why the second one worked. I'm new to R and ggplot2, let me know if you can, thanks.
#----------------------------------------------------------
# This is the original ggplot that didn't work:
#----------------------------------------------------------
zSepalFrame <- data.frame(zSepalLength, zSepalWdth)
zPetalFrame <- data.frame(zPetalLength, zPetalWdth)
p1 <- ggplot(data = zSepalFrame, mapping = aes(x=zSepalWdth, y=zSepalLength, group = 4)) + #fill = zSepalLength
geom_boxplot(notch=TRUE) +
stat_boxplot(geom = 'errorbar', width = 0.2) +
theme_classic() +
labs(title = "Iris Data Box Plot") +
labs(subtitle ="Z Values of Sepals From Iris.R")
p1
#----------------------------------------------------------
# This is the new ggplot box plot line that worked:
#----------------------------------------------------------
bp = ggplot(zSepalFrame, aes(x=factor(zSepalWdth), y=zSepalLength, color = zSepalWdth)) + geom_boxplot() + theme(legend.position = "none")
bp
This is what the ggplot box plot looked like
I don't have your precise dataset, OP, but it seems to stem from assigning a continuous variable to your x axis, when boxplots require a discrete variable.
A continuous variable is something like a numeric column in a dataframe. So something like this:
x <- c(4,4,4,8,8,8,8)
Even though the variable x only contains 4's and 8's, R assigns this as a numeric type of variable, which is continuous. It means that if you plot this on the x axis, ggplot will have no issue with something falling anywhere in-between 4 or 8, and will be positioned accordingly.
The other type of variable is called discrete, which would be something like this:
y <- c("Green", "Green", "Flags", "Flags", "Cars")
The variable y contains only characters. It must be discrete, since there is no such thing as something between "Green" and "Cars". If plotted on an x axis, ggplot will group things as either being "Green", "Flags", or "Cars".
The cool thing is that you can change a continuous variable into a discrete one. One way to do that is to factorize or force R to consider a variable as a factor. If you typed factor(x), you get this:
[1] 4 4 4 8 8 8 8
Levels: 4 8
The values in x are the same, but now there is no such thing as a number between 4 and 8 when x is a factor - it would just add another level.
That is in short why your box plot changes. Let's demonstrate with the iris dataset. First, an example like yours. Notice that I'm assigning x=Sepal.Length. In the iris dataset, Sepal.Length is numeric, so continuous.
ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width)) +
geom_boxplot()
This is similar to yours. The reason is that the boxplot is drawn by grouping according to x and then calculating statistics on those groups. If a variable is continuous, there are no "groups", even if data is replicated (like as in x above). One way to make groups is to force the data to be discrete, as in factor(Sepal.Length). Here's what it looks like when you do that:
ggplot(iris, aes(x=factor(Sepal.Length), y=Sepal.Width)) +
geom_boxplot()
The other way to have this same effect would be to use the group= aesthetic, which does what you might think: it groups according to that column in the dataset.
ggplot(iris, aes(x=Sepal.Length), y=Sepal.Width, group=Sepal.Length)) +
geom_boxplot()
I'm having trouble with position_dodge, when using colours and shapes.
I want to graph results from an experiment in which two treatments are replicated at many sites and I would like to emphasize certain data points graphically.
As the x-axis is a factor, I'd used position_dodge, to separate the treatments. So far so good, see graph 1 below.
However, if I want to emphasize a particular data point by changing the shape, see graph 2. The data points have now been split into three columns, not the two.
Any suggestions on how I would make a graph as pictured in the third panel below.
site <- rep(c("site1"),times=6)
treatment <- rep(c("one","two"),times=2,each=3)
set.seed(21)
response <- c(rnorm(3,mean=4),
rnorm(3,mean=5))
special <- as.factor(c(0,1,0,0,0,0))
mydata <- data.frame(site,treatment,response,special)
#graph 1
ggplot()+
geom_point(data=mydata,
aes(x = site,
y = response,
colour=treatment),
size=4,
position=position_dodge(1))
#graph 2
ggplot()+
geom_point(data=mydata,
aes(x = site,
y = response,
colour=treatment,
shape=special),
size=4,
position=position_dodge(0.5))
I want to compare the distribution of several variables (here X1 and X2) with a single value (here bm). The issue is that these variables are too many (about a dozen) to use a single boxplot.
Additionaly the levels are too different to use one plot. I need to use facets to make things more organised:
However with this plot my benchmark category (bm), which is a single value in X1 and X2, does not appear in X1 and seems to have several values in X2. I want it to be only this green line, which it is in the first plot. Any ideas why it changes? Is there any good workaround? I tried the options of facet_wrap/facet_grid, but nothing there delivered the right result.
I also tried combining a bar plot with bm and three empty categories with the boxplot. But firstly it looked terrible and secondly it got similarly screwed up in the facetting. Basically any work around would help.
Below the code to create the minimal example displayed here:
# Creating some sample data & loading libraries
library(ggplot2)
library(RColorBrewer)
set.seed(10111)
x=matrix(rnorm(40),20,2)
y=rep(c(-1,1),c(10,10))
x[y==1,]=x[y==1,]+1
x[,2]=x[,2]+20
df=data.frame(x,y)
# creating a benchmark point
benchmark=data.frame(y=rep("bm",2),key=c("X1","X2"),value=c(-0.216936,20.526312))
# melting the data frame, rbinding it with the benchmark
test_dat=rbind(tidyr::gather(df,key,value,-y),benchmark)
# Creating a plot
p_box <- ggplot(data = test_dat, aes(x=key, y=value,color=as.factor(test_dat$y))) +
geom_boxplot() + scale_color_manual(name="Cluster",values=brewer.pal(8,"Set1"))
# The first line delivers the first plot, the second line the second plot
p_box
p_box + facet_wrap(~key,scales = "free",drop = FALSE) + theme(legend.position = "bottom")
The problem only lies int the use of test_dat$y inside the color aes. Never use $ in aes, ggplot will mess up.
Anyway, I think you plot would improve if you use a geom_hline for the benchmark, instead of hacking in a single value boxplot:
library(ggplot2)
library(RColorBrewer)
ggplot(tidyr::gather(df,key,value,-y)) +
geom_boxplot(aes(x=key, y=value, color=as.factor(y))) +
geom_hline(data = benchmark, aes(yintercept = value), color = '#4DAF4A', size = 1) +
scale_color_manual(name="Cluster",values=brewer.pal(8,"Set1")) +
facet_wrap(~key,scales = "free",drop = FALSE) +
theme(legend.position = "bottom")
I'm an undergrad researcher and I've been teaching myself R over the past few months. I just started trying ggplot, and have run into some trouble. I've made a series of boxplots looking at the depth of fish at different acoustic receiver stations. I'd like to add a scatterplot that shows the depths of the receiver stations. This is what I have so far:
data <- read.csv(".....MPS.csv", header=TRUE)
df <- data.frame(f1=factor(data$Tagging.location), #$
f2=factor(data$Station),data$Detection.depth)
df2 <- data.frame(f2=factor(data$Station), data$depth)
df$f1f2 <- interaction(df$f1, df$f2) #$
plot1 <- ggplot(aes(y = data$Detection.depth, x = f2, fill = f1), data = df) + #$
geom_boxplot() + stat_summary(fun.data = give.n, geom = "text",
position = position_dodge(height = 0, width = 0.75), size = 3)
plot1+xlab("MPS Station") + ylab("Depth(m)") +
theme(legend.title=element_blank()) + scale_y_reverse() +
coord_cartesian(ylim=c(150, -10))
plot2 <- ggplot(aes(y=data$depth, x=f2), data=df2) + geom_point()
plot2+scale_y_reverse() + coord_cartesian(ylim=c(150,-10)) +
xlab("MPS Station") + ylab("Depth (m)")
Unfortunately, since I'm a new user in this forum, I'm not allowed to upload images of these two plots. My x-axis is "Stations" (which has 12 options) and my y-axis is "Depth" (0-150 m). The boxplots are colour-coded by tagging site (which has 2 options). The depths are coming from two different columns in my spreadsheet, and they cannot be combined into one.
My goal is to to combine those two plots, by adding "plot2" (Station depth scatterplot) to "plot1" boxplots (Detection depths). They are both looking at the same variables (depth and station), and must be the same y-axis scale.
I think I could figure out a messy workaround if I were using the R base program, but I would like to learn ggplot properly, if possible. Any help is greatly appreciated!
Update: I was confused by the language used in the original post, and wrote a slightly more complicated answer than necessary. Here is the cleaned up version.
Step 1: Setting up. Here, we make sure the depth values in both data frames have the same variable name (for readability).
df <- data.frame(f1=factor(data$Tagging.location), f2=factor(data$Station), depth=data$Detection.depth)
df2 <- data.frame(f2=factor(data$Station), depth=data$depth)
Step 2: Now you can plot this with the 'ggplot' function and split the data by using the `col=f1`` argument. We'll plot the detection data separately, since that requires a boxplot, and then we'll plot the depths of the stations with colored points (assuming each station only has one depth). We specify the two different plots by referencing the data from within the 'geom' functions, instead of specifying the data inside the main 'ggplot' function. It should look something like this:
ggplot()+geom_boxplot(data=df, aes(x=f2, y=depth, col=f1)) + geom_point(data=df2, aes(x=f2, y=depth), colour="blue") + scale_y_reverse()
In this plot example, we use boxplots to represent the detection data and color those boxplots by the site label. The stations, however, we plot separately using a specific color of points, so we will be able to see them clearly in relation to the boxplots.
You should be able to adjust the plot from here to suit your needs.
I've created some dummy data and loaded into the chart to show you what it would look like. Keep in mind that this is purely random data and doesn't really make sense.
I am trying to do the comparison of my observed and modeled data sets for two stations. One station is called station "red" and another is called "blue". I was able to create the facets but when I tried to add two series in one facet, only one facet got updated while other didn't.
This means for blue only one series is plotted and for red two series are plotted.
The code I used is as follows:
# install.packages("RCurl", dependencies = TRUE)
require(RCurl)
out <- postForm("https://dl.dropbox.com/s/ainioj2nn47sis4/watersurf1.csv?dl=1", format="csv")
watersurf <- read.csv(textConnection(out))
watersurf[1:100,]
watersurf$coupleid <- factor(rep(unlist(by(watersurf$id,watersurf$group1,
function(x) {ave(as.numeric(unique(x)),FUN=seq_along)}
)),each=6239))
p <- ggplot(data=watersurf,aes(x=time,y=data,group=id))+geom_line(aes(linetype=group1),size=1)+facet_wrap(~coupleid)
p
Is it also possible to add a third series in the graph but of unequal length (i.e not same interval)?
The output is
I followed the example on this page to create the graphs.
http://www.ats.ucla.edu/stat/r/faq/growth.htm
Is this what you are looking for,
ggplot(data = watersurf, aes( x = time, y = data))
+ geom_line(aes(linetype = group1, colour = group1), size = 0.2)
+ facet_wrap(~ id)