So I've been running a DCA analysis on a species/site count spreadsheet (DCA file made using Vegan and decorana command). I'm having a bit of overlap with my points, so I'm trying to extend DCA 1 axis. I keep trying to use the xlim value to narrow it down to -2,2, but it just won't do it. For some reason, it seems tied to the ylim value. If I drop the ylim to -1,1, that will force the xlim to -2,2, but I can't actually have the ylim that small.
> plot(DCA, type = "n", xlim = c(-2,2))1
First plot shows result of this command. Trying to include a ylim of (-2,2) didn't change it either. Second plot shows result of this command:
> plot(DCA, type = "n", xlim = c(-2,2)), ylim = c(-2,2)2
I'm not exactly an expert at this, and I feel like I might be making a stupid mistake. Anyone got any ideas?
The plot is enforcing equal aspect ratio, and if you insist on having full range on y-axis (you do not set ylim), the x-axis will be set to accommodate the range you want to show on y-axis. You must either change the shape of your graphics display for a shorter x-axis, or then you also must set the range on y-axis with ylim Your choice. If you draw on square graphics window, the output will be square and it will be taken care that both the x and y scale (xlim, ylim) will fit the square. Changing the shape of the graphics window or setting both limits will help. Function locator() can be used to query coordinates in an existing plot, and these can be used to set up the new limits.
It is better to start again from a clean table. My intention was not to be rude, but trying to complement the answer in comments leads into terse messages where details and special cases are hard to handle. So I try to explain here how you can control the display and the axis limits in vegan ordination graphics. This answer has two main parts: (1) why we insist on equal aspect ratio, and (2) how to survive with equal aspect ratio. The latter can indeed be tricky, but we have not made it tricky because we are evil, but it is necessarily tricky.
First about the equal aspect ratio. This means that the numeric scales is equal on vertical (y) and horizontal (x) axes. In ordination the "importance" of an axis is normally reflected by its length: important axes are long, minor axes are short. In eigenvector methods this is determined by eigenvalues (which actually define the scatter of points along axes). In DCA (decorana) it should be the SD scaling so that one unit is equal to average width of species responses. We pay great attention in scaling axes accordingly, and we want to show this in the plot so that long x axis remains long and short y axis remains short even when the graph is designed for a portrait printer page shape. In this way the axis scaling (tic values) are equally spaced on both axes, and distances in the graph are equal horizontally, vertically or diagonally. Also if you draw a circle in the plot, it will remain a circle and not flattened or elongated to an ellipse. So this is something that we insist as a necessary feature of an ordination graph.
Insistence on equal scaling of equal axes comes with a price. One obvious price is that the ordination plot may not fill the graph area, but there can be a plenty of empty space in the graph for shorter axes. You can get rid of this adjusting graph shape – typically making it flatter. Another price that must be paid is the one that bit you: setting axis limits is tricky. However, this is not a vegan invention, but we use base R plot command with option asp = 1 for equal aspect ratio of axes.
To see how we can set axis limits with equal ratio, let us generate a regular rectangular grid and plot it:
x <- seq(-2, 2, by=0.2)
xy <- expand.grid(x, x)
plot(xy, asp=1)
This is a square grid on a square plot and nothing very special. However, if we plot the same grid on rectangle the aspect ratio remains equal, numeric scales are equal and the points remain equidistant on a square, but there is a lot of empty space and x-axis has longer numeric scale (but the points have unchanged numeric scale). If we try reduce only the x-scale, we face a disappointment:
plot(xy, asp=1, xlim=c(-1,1), main="xlim=c(-1,1)")
The graph is essentially similar as the first unlimited case with unchanged x. Setting xlim does not remove any points, but it only tells plot that do not reserve space but for that range on x-axis. However, y-scale is still longer and with equal aspect ratio it will also set the scale for x-axis and since there is empty space in the graph, the points are plotted there (this is analogous to having empty space even when there is nothing to plot there). To really limit the x-axis, you must simultaneously limit the y-axis accordingly:
plot(xy, asp=1, xlim=c(-1,1), ylim=c(-1,1), main="xlim=c(-1,1), ylim=c(-1,1)")
This give the desired x-limits .
Like I wrote, we did not invent that nastiness, but this is base R plot(..., asp=1) behaviour. I know this can be tricky (I have used that myself and sometimes I get irritated). I have been thinking could we be more user-friendly and by-pass base R. It is pretty easy to do this in a way that works in many normal use cases, but it is much harder to do this so that it works in most cases, and I don't know how to do this in all possible cases. If anybody knows, pull requests are welcome in vegan.
Finally, there is one tool that may help: vegan has an interactive dynamic plotting tool orditkplot where you can zoom into a plot by selecting a rectangle with left mouse button. However, this function may not work in all R systems, but if it works it gives an easy way of studying details of the plot (but if you have Mac with one-button mouse, don't ask me how it works: I don't know). You can start this with
orditkplot(mod, display="sites") # or "species", but only one
Even without orditkplot you can use base R function locator(): click the diagonally opposite corners of the rectangle you want to focus on, and this give you the xlim and ylim you need to set to zoom into this rectangle.
Basically I'm building an area graph with Chart.js, the data that I'm using in order to build the graph usually contains a peak that is much higher than the rest of the points and the y-axis range of values will be to high, to notice the diference between the lower points and it wil seem almost as a parallel line to the x-axis as we can see in this image:
Graph with problems
The solution I want to try is to skip the values from the y-axis between the lower points and the peak of the graph, and accomplish a graph presentation similar to this one:
Solution graph sketch
As we can see at this sketch the y-axis has a normal scale until 300 but then as the next point is to far away from the other ones the y-axis values are skiped.
So what I want to know is if this jump on the values of the y-axis is possible to achieve with this library (Chart.js) and if so where can I find documentation about it, because I already looked everywhere and couldn't find a thing. If not I would ask you for recommendations of any other librarys where I could achieve this.
This is for viewing purposes. The actual scales are the same.
A short version of my question would be "Force grids to be square in plots".
As can be seen in the screenshot taken from the plot in Atom, while the axis are the same increments numerically, the grids are rectangular rather than square. I am not sure how to fix this. because the plot is wider than it is high, it skews the plot when I look at it.
if you do a quick estimate of the height of the Z and length of the X axis just using your fingers, you can tell that the X axis is considerably longer.
I think you want ratio=1, at least in Plots.jl:
julia> using Plots
julia> plot(rand(20,3), randn(20,3); ratio=1,
xticks=[-1,0,1], yticks=[-1,0,1], size=(600,300))
I've come up with a graph (a scatterplot) of the log(1+inf) (inf = number of people infected with a given disease on the y-axis against one of the explanatory variables, in this case, the populational density (pop./km²; x-axis) on my model. The log transformation was used merely for visualization, because it spreads the distribution of the data and allows for more aesthetically appealing plots. Basically, what I want is both axis to show the value of that same variable before the log transformation. The dots need to be plotted like plot(log(1+inf),log(populational_density), but the number on the axis should refer to plot(inf,populational_density). I've provided a picture of my graph with some manual editing on the y-axis to show you the idea of what I want.
The numbers in red would be the 'inf' values equivalent to log(inf);
Please, bear in mind that those values in red do not correspond to reality.
I understand the whole concept of y = f(x), but i've been asked to provide it. Is this possible? I'm using the ggplot2package for plotting.
I'm trying to create a dot chart in Stata, splitting it into two categories
Running a chunk of code:
sysuse nlsw88, clear
drop if race == 3
graph dot (mean) wage, over(occ) by(race)
Creates such output:
So far so good but I'd like to remove labels of Y axis from the right graph to give the data some more space.
The only way I've been able to do that was to manually edit graph and hide the axis label object:
Is there a way to do it programmatically? I do know I could use one more over() but in some graphs of mine that is already taken.
I believe the solution is buried in help bystyle and help by_option. However, I can't get it to work with your example (I'm on Stata 12). But the description is clear. For example:
A bystyle determines the overall look of the combined graphs,
including
whether the individual graphs have their own axes and labels or if instead the axes and labels are shared across graphs arrayed in the
same row and/or in the same column;
...
There are options that let you control each of the above attributes --
see [G-3] by_option --
And also
iyaxes and ixaxes (and noiyaxes and noixaxes) specify whether the y axes and x axes are
to be displayed with each graph. The default
with most styles and
schemes is to place y axes on the leftmost graph of each row and to place x axes on
the bottommost graph of each column. The y and
x axes include the
default ticks and labels but exclude the axes titles.
If for some reason that doesn't work out, something like
sysuse nlsw88, clear
drop if race == 3
graph dot (mean) wage, over(occ) by(race)
gr_edit .plotregion1.grpaxis[2].draw_view.setstyle, style(no)
does (but I don't really like the approach). You can mess with at least the axis number [#] to do a bit of customization. I guess recording changes in the graphical editor and then recycling the corresponding code, may be one way out of difficult situations.