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.
Although it might sound easy at first, I do not have a scatterplot. And I think that is what make this question challenging. I am having this plot, which comes from this question.
Summing up, each axis represents a variable that is not connected to the other. It is not an XY scatterplot, as you see.
I wonder to know if there is any possibility to trace the 95% confidence interval for the mean in both variables, and draw a square in the middle of the plot representing the overlapping area among both datasets.
The result might be something similar to this, bearing in mind that 95CL represented do not correspond to reality (just for the sake of illustrating how it might appear):
Here is a another question which deals with this situation, but not using ggplot.
I have a sample dataset
d=data.frame(n=rep(c(1,1,1,1,1,1,2,2,2,3),2),group=rep(c("A","B"),each=20),stringsAsFactors = F)
And I want to draw two separate histograms based on group variable.
I tried this method suggested by #jenesaisquoi in a separate post here
Generating Multiple Plots in ggplot by Factor
ggplot(data=d)+geom_histogram(aes(x=n,y=..count../sum(..count..)),binwidth = 1)+facet_wrap(~group)
It did the trick but if you look closely, the proportions are wrong. It didn't calculate the proportion for each group but rather a grand proportion. I want the proportion to be 0.6 for number 1 for each group, not 0.3.
Then I tried dplyr package, and it didn't even create two graphs. It ignored the group_by command. Except the proportion is right this time.
d%>%group_by(group)%>%ggplot(data=.)+geom_histogram(aes(x=n,y=..count../sum(..count..)),binwidth = 1)
Finally I tried factoring with color
ggplot(data=d)+geom_histogram(aes(x=n,y=..count../sum(..count..),color=group),binwidth = 1)
But the result is far from ideal. I was going to accept one output but with the bins side by side, not on top of each other.
In conclusion, I want to draw two separate histograms with correct proportions calculated within each group. If there is no easy way to do this, I can live with one graph but having the bins side by side, and with correct proportions for each group. In this example, number 1 should have 0.6 as its proportion.
By changing ..count../sum(..count..) to ..density.., it gives you the desired proportion
ggplot(data=d)+geom_histogram(aes(x=n,y=..density..),binwidth = 1)+facet_wrap(~group)
You actually have the separation of charts by variable correct! Especially with ggplot, you sometimes need to consider the scales of the graph separately from the shape. Facet_wrap applies a new layer to your data, regardless of scale. It will behave the same, no matter what your axes are. You could also try adding scale_y_log10() as a layer, and you'll notice that the overall shape and style of your graph is the same, you've just changed the axes.
What you actually need is a fix to your scales. Understandable - frequency plots can be confusing. ..count../sum(..count..)) treats each bin as an independent unit, regardless of its value. See a good explanation of this here: Show % instead of counts in charts of categorical variables
What you want is ..density.., which is basically the count divided by the total count. The difference is subtle in principle, but the important bit is that the value on the x-axis matters. For an extreme case of this, see here: Normalizing y-axis in histograms in R ggplot to proportion, where tiny x-axis values produced huge densities.
Your original code will still work, just substituting the aesthetics I described above.
ggplot(data=d)+geom_histogram(aes(x=n,y=..density..,)binwidth = 1)+facet_wrap(~group)
If you're still confused about density, so are lots of people. Hadley Wickham wrote a long piece about it, you can find that here: http://vita.had.co.nz/papers/density-estimation.pdf
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.
I have a very basic grasp of stats, and a very basic grasp of R so please bear with me.
I have survey data which shows the weekly expenditure of a number of respondents. I have put this into a histogram, and have plotted a density function as well. So far so good.
How do I then apply this curve to a larger population? Say that I know that the population of my town is 25000. How can I apply that to the density curve to arrive at a new histogram and the data table behind it?
I hope this is an appropriate question, thank you.
It is not exactly clear what you want to do.
If you only have data on the sample then the best estimate that you have of the histogram/density for the population is the histogram/density of the sample, the only difference would be the scale on the y-axis. Personally I think the tick marks on the y axis should be ignored (and my preference would be that the tick labels were never plotted) since it is really the shape of the histogram/density that is important and the tick labels can change based on things that don't change the meaning. If you really feel the need to have the tick labels represent population values then see the axis function.
If you want something more than this then give us a better description of what you are trying to accomplish.