I am currently trying to plot some data and don't manage to obtain a nice result. I have a set of 51 individuals with each a specific value (Pn) and split within 14 groups. The closest thing I end up with is this kind of plot. I obtain it thanks to the simple code bellow, starting by ordering my values for the Individuals :
Individuals <- factor(Individuals,levels=Individuals[order(Pn)])
dotchart(Pn,label=Individuals,color=Groups)
The issue is that I only have 9 colors on this plot (so I lost information somehow) and I can't manage to find a way to apply manually one color per group.
I've also try to use the ggplot2 package by reading it could give nice looking things. In that case I can't manage to order properly the Individuals (the previous sorting doesn't seem to have any effect here), plus I end up with only different type of blue for the group representation which is not an efficient way to represent the information given by my data set. The plot I get is accessible here and I used the following code:
ggplot(data=gps)+geom_point(mapping=aes(x=Individuals, y=Pn, color=Groups))
I apologize if this question seems redundant but I couldn't figure a solution on my own, even following some answer given to others...
Thank you in advance!
EDIT: Using the RColorBrewer as suggested bellow sorted out the issue with the colors when I use the ggplot2 package.
I believe you are looking for the scale_color_manual() function within ggplot2. You didn't provide a reproducible example, but try something along the lines of this:
ggplot(data=gps, mapping=aes(x=Individuals, y=Pn, color=Groups))+
geom_point() +
scale_color_manual(values = c('GROUP1' = 'color_value_1',
'GROUP2' = 'color_value_2',
'GROUP3' = 'color_value_3'))
Replace GROUPX with the values inside your Group column, and replace color_value_x with whatever colors you want to use.
A good resource for further learning about ggplot2 is chapter 3 of R For Data Science, which you can read here: http://r4ds.had.co.nz/data-visualisation.html
I can't be sure without looking at your data, but it looks like Groups may be a numeric value. Try this:
gps$Groups <- as.factor(gps$Groups)
library(RColorBrewer)
ggplot(data=gps)+
geom_point(mapping=aes(x=Individuals, y=Pn, color=Groups))+
scale_colour_brewer(palette = "Set1")
Related
Okay so I have an assignment where I need to conduct a graph that best represents the before and after affects of two streams. The graph(s) have to contain means and standard error for each stream in each year.. I cannot figure the proper coding for the graph. I continue to get errors and bad graphs. I will attach a sample of what the data looks like too.
A sample of the data, it changes to after at 51
Try to post a reproducible error or specification of your problem.
As far as I can analyze your problem, you maybe should not create b4, because it does not seem to be an effective subset. If you want to assemble certain plots, you can use plot_grid from cowplot.
Otherwise you can add facet_wrap(~ VARIABLE_NAME) to ggplot in order to create many plots divided by deviating observations in the specified variable.
If you are not happy with the visual outcome and result of your graph, you can choose another theme, e.g. theme_bw() which can be simply added to your ggplot function. You can add and change further labels with labs() and theme().
I would like to create a ggplot2 layer that includes multiple geom_bspline(), or something similar, to point to regions on different plots after combining them into a single figure. A feature in the data seen in one plot appears in another plot after a transformation. However, it may not be clear to a non-expert they are due to the same phenomenon. The plots are to be combined into a single figure using ggarrange(), cowplot(), patchwork() or something similar.
I can get by using ggforce::geom_ellipse() on each plot but it's not as clean. Any suggestions?
Of course, after asking the question and staring at the figure in question, it came to me that I simply need to add a geom_bspline() to the combined figure. Tried that earlier but didn't give enough thought to the coordinates on the new layer. The coordinates of the spline are given in the range of 0 to 1 for both the x and y values on this new layer. Simple and obvious.
I have the following plot:
plot.ts(returns)
I have another dataframe ma_sd which contains the rolling SD from moving averages of the above returns. The df is structured exactly like returns. Is there a simple way to add each line to the corresponding plots?
lines(1:N, ma_sd) seemed intuitive, but it does not work.
Thanks
The only way I can see you doing this is to plot them separately. This code is a bit clunky but will allow you full flexibility to be able to specify labels and axis ranges. You can build on this.
par(mfrow=c(3,1),oma=c(5,4,4,2),mar=c(0,0,0,0))
time<-as.data.frame(matrix(c(1:length(returns[,1])),length(returns[,1]),3))
plot(time[,1],returns[,1],type='l',xaxt='n')
points(time[,1],ma_sd[,1],type='l',col='red')
plot(time[,2],returns[,2],type='l',xaxt='n')
points(time[,2],ma_sd[,2],type='l',col='red')
plot(time[,3],returns[,3],type='l')
points(time[,3],ma_sd[,3],type='l',col='red')
I have a dataset as follows:
(10,75)
(20,80)
(50,85)
(100,92)
How to plot a bar-graph in R? I saw many examples in the net but none of them conform to this simple circumstance. Thanks
Try this:
data1=rbind(c(10,20,50,100),c(75,80,85,92))
barplot(data1, beside=TRUE, col=c("blue", "red"))
As an alternative, you can always use the ggplot2 library. Because of the way the data is shaped, you should also use the reshape2 library to differentiate between variables. It's a bit more complicated in this case, but in general you'll get nicer-looking barplots.
library(ggplot2)
library(reshape2)
#id variable tells what row number is used
data1=as.data.frame(cbind(id=1:4,var1=c(10,20,50,100),var2=c(75,80,85,92)))
#melt will create a row for each variable of each row, except it saves the id as a separate variable that's on every row
data1=melt(data1,id.vars='id')
#ggplot tells what data set is used and which variables do what
#geom_bar tells what type of plot should be used and certain options for the plot
ggplot(data1,aes(x=id,y=value,fill=variable))+geom_bar(stat='identity',position='dodge')
I just started using ggplot2 on R and have a violin plot question.
I have a data set that can be accessed here: data.
The data comes from a study of making estimations. The variables of interest are the question.no (questions), condition, estimate.no (tr.est1 or tr.est2) and estimate.
The code below makes the plot look almost the way I want it to look at least for one question, yet the median dots generated by stat_summary() are displayed in between the "violins."
v.data<-read.csv("data.csv")
# loop through each question number
d_ply(v.data, c("question.no"), function(d.plot){
q.no <- v.data$question.no
plot.q <- ggplot(d.plot,aes(condition, estimate, fill=estimate.no)) +
geom_violin() +
stat_summary(fun.y="median", geom="point") +
scale_y_continuous('Change Scores') +
scale_x_discrete("Conditions")
ggsave(filename=paste(q.no,".png",sep=""))
})
My Question: How can I make the median dots display correctly on the "violins" rather than in between them?
I searched the previous questions asked on ggplot2 on this site and looked at the ggplot2 documentation as well as other R forums but have not been able to find anything relevant.
I would appreciate any comments and suggestions as to how I can fix it. Also, if the questions I ask are already answered somewhere else, I would appreciate the links to the threads,too. Many thanks in advance.
stat_summary is limited to the variable that determines your x-axis. One way to convey the information you want would be to replace condition in your call to aes with interaction(condition, estimate.no).
Plotluck is a library based on ggplot2 that aims at automating the choice of plot type based on characteristics of 1-3 variables. For your data set, the command plotluck(v.data, condition, estimate, question.no) generates the following plot:
Note that the library chose to scale y logarithmically. You can override this behavior with plotluck(v.data,condition,estimate,question.no,opts=plotluck.options(trans.log.thresh=1E20)) but it doesn't display well, and the median points look like they are all on the zero line.