I have read the Cufflinks examples. The only subplots examples are generated from a single DataFrame with a subplots=True parameter and an optional shape parameter (i.e. df.iplot(..., subplots=True, shape=(...), ...). As I understand it, the mechanism is that when subplots=True is provided, each column of the DataFrame is plotted as a subplot.
Now, about heatmaps in Cufflinks. The example in the same link shows that the DataFrame of a heatmap of N * M is simply an N * M DataFrame where the column names and indexes tells the x and y coordiates and the values are the "heat" of each cell of the grid.
Combining the two, it seems that if I have two heatmaps (thus two DataFrames), I cannot plot both in a subplot-fashion, because subplots require a single DataFrame and I cannot combine two heatmap DataFrames into one.
Anyone has any idea how it might work?
BTW, I also tried plotly.offline.iplot(..., subplots=True, ...) and the parameter is not supported.
EDIT
There is another question (from me, too) asking about doing the same in plotly, which got answered. So if you are working w/ plotly directly then that's the answer you might want to take a look.
This question is about using Cufflinks to achieve the same. It still seems impossible (or at least very difficult) to me.
You can use the following:
import cufflinks as cf
df1=cf.datagen.heatmap()
df2=cf.datagen.heatmap()
cf.subplots([df1.figure(kind='heatmap'),df2.figure(kind='heatmap')]).iplot()
You can do this with as many heatmaps, and you can also use the shape parameters.
Related
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 preparing a figure for a paper presenting data for 2 different experiments in one plot. For that reason I don't need a legend for every plot, so I try to combine them with ggdraw from cowplot.
My code
should generate a reproducible example
and gives this output:
It seems like the two figures get the same slot (A) and the legend gets slot (B). Typically, I would probably use facet wrap to plot them together (which should also guarantee that the scaling/legend is consistent across the two plots.), but that will probably not work in this case, as I am trying to add an additional figure type to C and D.
The problem is that this figure type is ordinal so I have used a somewhat “hacky” approach to plot it, giving me this figure looking essentially as I want it to:
I so far have not been able to extract to another element that ggdraw can use.
Ideally the final plot should roughly look like this (of course with different labels):
How would you go about plotting these different types together?
Thank you for taking time to read my question and I hope that you can help me. I now it is quite a mouth full, but I was not sure how I meaningfully could reduce it to smaller chunks.
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 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 am trying to illustrate a histogram of 33 different variables. Due to the number of variables I think "beside" different Colors I need to label each bar in a clear way, even using an arrow, if its doable.
I was wondering about
1) How can I define 33 distinct color in R
2) How can I label them, say vertical below X axis with a certain distance from each other to make my figure more clear.
I am using multhist function from Plotrix package, and for data you can image just 33 random vector with different length !
Thanks
As Chris mentioned, trying to distinguish 33 colours doesn't work for humans. You need to find a different plot type that doesn't rely on only colour.
Without a reproducible example, it is not possible to say what this plot should be, but here's some generic colour advice.
Use HCL colours rather the RGB or HSV. Read Escaping RGBland by Achim Zeileis for an explanation. There are some useful functions for generating palettes in the colorspace package.
If your variables are unordered categories (i.e., encoded as factors) then your colours should have different hues. (Use rainbow_hcl.)
If your variables are in some sort of order (ranges or ordered factors) then your colours should have different lightness or chroma. (Use sequential_hcl.) A variation on this is if they differ about some midpoint, in which case you need diverge_hcl.
You can define colors in R in any number of ways; try ?rainbow or ?greyscale for some suggestions
You could also look at all the colors here and just create a vector of your desired colors that you call inside your plot function.
Your problem though is that the human eye and the printing process has trouble distinguishing and reproducing that many distinct colors. See the documentation at the colorbrewer site for more information (and advice on picking colors).
Not sure I understand what your trying to do with the labels, but you can re-label an axis with a call to axis. See the documentation in ?axis.