I'm new to Octave, so there are many confusing things for me, and I've never done computer programming before so most of the language is also confusing.
I have sets of data c_o, m_o, y_o, k_o as 144 x 1 matrices (column vectors?)
Box plots
Using examples I found online, I wrote this:
axis ([0,5]);
boxplot (c_o, m_o, y_o, k_o);
set(gca (), "xtick", [1,2,3,4], "xticklabel", {"cyan", "magenta", "yellow", "key"});
However, it results in an error
Boxplot.m: grouping vector may only be passed as second arg
I have no idea what this means.
Plots
I'm trying to figure out how to plot multiple data sets with different colors.
For example,
figure (1); plot (c_o , "c");
works perfectly fine.
However, I'd like to remove the horizontal axis, change the horizontal axis from [0,200] to [0,150] , and plot multiple sets of data on the same plot (not multiple plots in the same figure, but the different data on the same set of axis). I haven't been able to find out how, though.
For the record, I do know that there are probably other programming languages more suited for statistical analysis; it just so happens that my first use of this happened to be statistical in nature.
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.
My head is getting sore from me banging it so much.
I have a time-series that I've converted into an xts object w/ 7 variables. Now I'm trying to plot 4 of them, all price indices, on the same graph. I used autoplot (from the ggfortify package) to initialize the graph, and this is where the trouble begins.
Autoplot doesn't seem to work unless I give it at least one variable to plot. That's fine, but the two customizations I want for the variable -- its color and line type -- seem to have no effect.
But once I create the plot this way, I have little trouble adding the other 3 variables by adding geom_lines. Here's sort of what the code looks like:
p <- autoplot(foo.xts,xlab="Year",
ylab="Price Index",
columns="Variable1",linetype=4) # the linetype accomplishes nothing
p <- p + geom_line(aes(y="Variable2", color="green", linetype="solid"
# etc. for the other 2 variables
p # The 3 added variables do get the selected colors & line types.
But how can I customize the line for the first variable?
Then there's another problem in that I can't get a legend to appear. Here's how I'm trying to do that:
p <- p + scale_color_discrete(
name="Price Indices",
breaks=c("Variable1", "Variable2", "Variable3", "Variable4"),
labels=c("Index 1", "Index 2", "Index 3", "Index 4"))
This seems to accomplish nothing.
One thing I'd add is that in my various experiments trying to get the legend to work, I've sometimes gotten two sets of keys: one for colors and one for line types. This is obviously not what I'm after.
If someone could help me with this, I'd be forever in your debt!
I spent yesterday away from the computer, and when I returned in the evening fixed the problems. Here's how:
Stopped using autoplot. It's a classic case of hand-holding that throws you over the cliff. In other words, it automatically formats the plot in ways that are difficult (impossible?) to customize. Instead, ggplot makes the initial plot.
Since I'm making a series of plots, moved all the shared features to a separate, preamble section. This section creates a base plot, sets the x-axis variable (the date of the observation), labels the x-axis, and formats its tick marks. It also sets up standardized colors, line styles, and shapes to be used by all the "production" plots.
To set up the standardized elements, it uses scale_color_manual, etc. Each one has to be identical in all respects except those that are unique to its specific aesthetic attribute. E.g., scale_color_manual uses values like "red" whereas scale_linetype_manual uses values like "solid." Each manual setting includes the following elements: legend.title*, values, labels, and guide = guide_legend()*. (Items marked with * must be identical, otherwise you'll get different legends for each one.) For each plot, the actual legend title is first stored in a variable, legend.title, and then used in all the manual scale setting. This way the manual settings can be moved to the common section, but each plot has is own unique title for its legend.
3A. Actually, I was wrong about this. I was thinking LaTeX, where most things are evaluated where they appear at execution time. So a scale_color_manual statement at the start could change later on just by changing the value of legend.title. But in R, things are evaluated sequentially, and changing legend.title after the scale_color_manual statement is executed will have no effect. I worked around this by defining several variables in the preamble (e.g., one with the colors I'm using) and then using these variables in the various source_x_manual statements. This way, the only thing that change is the legend title.
Then each production plot starts by copying the base plot, labeling the y-axis, and then adds the geometric objects that it needs.
This approach has several advantages. 1) It modularizes the plotting so that problems are easier to isolate and solve, and most solved problems in the preamble section are solved for all plots. 2) It standardizes the plots, ensuring that their common features are formatted identically. 3) It reduces each production plot to a few statements; since this is the unique part for each plot, creating a new style of plot becomes relatively easy. 4) The value added by autoplot becomes minimal because this approach, separating shared elements in a preamble, compensates by isolating reusable code and the preamble, once debugged, allows much more fine-grain customization.
If you have any questions, please feel free to ask.
I would like to draw $3$ dimensional scatter plots, or more precisely I have a program that gives me the mass distribution in the unit cube with respect to a 3 dimensional equidistant grid. You can interpret this as a continuous relaxation of a $3$ dimensional assignment problem if you want.
Anyway this is just to give you a very brief background since my actual problem is not really concerned with the maths behind the procedure but with the visualization. I have:
$n$ points in the unit cube $[0,1]^3$
each of the $n$ points is assigned a "weight" between $0$ and $\frac1n$ (typically a lot of the weights coincide, if there are too many different values, i use the cut command to reduce the range to, say $60$ different values)
And I'd like to plot the $n$ points in a color which corresponds to their weight.
Now I found the rgl Package in R which allows me to do exactly that and also provides a very nice interactive plot window but it doesn't seem to allow a "col key" parameter, i.e. I cannot add a continuous color legend to my plot.
On the other hand the package plot3D provides a function to do a $3$ dimensional scatterplot and easily allows me to add the col key. However plot3D does not work with interactive plots but merely gives me the option to specify the angle at which I want to look at the cube. In a $3$D setting I strongly prefer the interactive alternative.
Now is there a way to automatically add a continuous color legend to an rgl plot? If not, do you know why this hasn't been implemented? Or would you solve my problem completely different altogether?
P.S. sorry for the formatting, I'm new to SO and the math environment "$" doesn't seem to work here.
The reason this hasn't been implemented is because until fairly recently it wasn't easy to have a static legend and a dynamic plot in the same window.
Now it's easy; there's a legend3d() function that might do what you want, but I think you probably want a different sort of legend than it will draw. If you know how to draw what you want in 2D, you can use the bgplot3d() function to put it in the background of your plot.
Both of those options give bitmapped legends. It would also be possible to do vector-based legends, but that would be quite a bit more work.
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.