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')
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 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")
I want to plot some data with barplot. Rather, I want to make a bar graph and barplot seemed the logical choice. I am plotting just fine but I was wondering if there is a way to intelligently scale the y axis to round up from the highest count.
For example I set the yaxis in this case to be 30, because I knew that Strand.22 had 27 counts in it: barplot(unlist(d), ylim=c(0,30), xlab="Forward Reverse", ylab="Counts")
In the future, I want this script to run on its own, so it would be optimal for the the Y-axis to choose it's own ylim. Short of pulling the information out of my 'd' variable I can't think of a good way to do this. Is there an easy way to do this with barplot? Would some other plotter work better? I have seen things about ggplots but it seemed super complex and I wasn't sure that it would do anything better.
EDIT: If I do not choose a ylim it picks automatically and this is what it decided was best.
I disagree with it's choice.
If you don't specify ylim, R will come up with something based on the data. (Sounds like you don't like it's choice, which is fair.)
If you specify something based on the data like:
barplot(unlist(d), ylim=c(0,1.1*max(unlist(d)))
R will draw you a plot that reflects the maximum value of data. That example just takes the maximum of your values and multiplies that by 1.1 (this could be any number) to give it a little extra height. R does something similar to this when you make a scatterplot but it handles barplots slightly differently.
I'm trying to create an actuarial survival analysis in R (I'm following some worked examples). I think the best way to do this is using the survival package. So something like:
library(survival)
surv.test <- survfit(Surv(TIME,STATUS), data=test)
However, to get the correct answer I will need to divide the TIME variable into 365 day intervals and I can't quite work out how to do this so it matches the given result.
As far as I can make out, there is no option within the survfit function that will do this. I went through several document examples and none of them were trying to create a stairstep type of plot (there is a type='interval' option, but seems to do something different). So I guess I need to regroup my data before I apply the survival function?
Any ideas?
P.S: In SPSS this would be INTERVAL = THRU 10000 BY 365; in Stata intervals(365) ... connect(stairsteps)
I am guessing that you want to divide the TIME variable into intervals because you want to plot a Kaplan-Meier curve. In R, that isn't necessary, you can just call plot on the survfit object. For example,
s=survfit(Surv(futime, fustat)~rx, data=ovarian)
plot(s)
I think I understand your question a little better. The reason why you are getting a thick black line is because you have a lot of censoring, and a + is being plotted at every single point where there is censoring, you can turn this off with mark.time=F. (You can see other options in ?survival:::plot.survfit)
However, if you still want to aggregate by year, simply divide your follow up time by 365, and round up. ceiling is used to round up. Here is an example of aggregating at different time levels without censoring.
par(mfrow=c(1,3))
plot(survfit(Surv(ceiling(futime), fustat)~rx, data=ovarian),col=c('blue','red'),main='Day',mark.time=F)
plot(survfit(Surv(ceiling(futime/30), fustat)~rx, data=ovarian),col=c('blue','red'),main='Month',mark.time=F)
plot(survfit(Surv(ceiling(futime/365), fustat)~rx, data=ovarian),col=c('blue','red'),main='Year',mark.time=F)
par(mfrow=c(1,1))
But I think that plotting the Kaplan-Meier without the censoring symbols will look very nice, and provide more insight.
Hurray, I should be able to post the images now:
1) this is how the R basic survival plot looks like at the moment
2) and this is how it should look like (SPSS example)
That was exactly what I was missing! Thanks!
Solution:
vas.surv <- survfit(Surv(ceiling(TIME/365), STATUS)~1, conf.type="none", data=vasectomy)
plot(vas.surv, ylim=c(0.975,1), mark.time=F, xlab="Years", ylab="Cumulative Survival")
A nice touch would be to displays the days on the x-axis instead of the years (as in SPSS) example, but I'm not too bothered about this.