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.
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 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 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.
Is there an function in R that does the same job as Matlab's "bar" function?
R does have a "barplot" function in the library graphics, however, it is not the same.
The Matlab bar(X,Y) (verbatim excerpt from MATLAB documentation) "draws a bar for each element in Y at locations specified in X, where X is a vector defining the x-axis intervals for the vertical bars." (emphasis mine)
However, the R barplot function does not allow one to specify locations.
Perhaps there is a method in ggplot2 that supports this? I am only able to find standard bar charts in ggplot2.
No, barplot is not the same as bar, but you should read the whole help. You can do many things to position the bars. The first is simply their order in Y. You could insert spaces if you wish (additional 0s). If you have X and Y then sort Y on X (Y[order(X)]) and plot it. If you need to change positions use the "space" and "width" arguments. It's not as straightforward as specifying X values I suppose but it's definitely more useful in most situations. Generally what you want to adjust is widths of bars and spaces between bars. Their position on the X-axis should be arbitrary. If the position on the X-axis is really meaningful then you should be using line plots, not bar graphs.
In R:
barplot(rbind(1:10, 2:11), beside=T, names.arg=1:10)
In MATLAB:
>> bar(1:10, [(1:10)' (2:11)'])
Read up on par . Then observe, for example:
x<-c(1,2,4,5,6)
y<-c(3,4,3,4,2)
plot(x,y,type='h',lwd=6)
Edit: yes, I know this doesn't (yet) plot multiple data sets, but I would hope you can see simple ways to make that happen, with spacings, colors, etc. specified to your exact liking :-)
Sounds vaguely like the R stepfun. On the other hand one would need to know what "draws a bar" means before saying it is not the same as barplot(..., horiz=TRUE) One would, of course, need to examine some more detailed evidence such as data and plots before arriving at a conclusion, however. #John Colby should be congratulated for adding some specificity to the discussion. The axis function is probably what Quant Guy needs education regarding.
I am in my way of finishing the graphs for a paper and decided (after a discussion on stats.stackoverflow), in order to transmit as much information as possible, to create the following graph that present both in the foreground the means and in the background the raw data:
However, one problem remains and that is overplotting. For example, the marked point looks like it reflects one data point, but in fact 5 data points exists with the same value at that place.
Therefore, I would like to know if there is a way to deal with overplotting in base graph using points as the function.
It would be ideal if e.g., the respective points get darker, or thicker or,...
Manually doing it is not an option (too many graphs and points like this). Furthermore, ggplot2 is also not what I want to learn to deal with this single problem (one reason is that I tend to like dual-axes what is not supprted in ggplot2).
Update: I wrote a function which automatically creates the above graphs and avoids overplotting by adding vertical or horizontal jitter (or both): check it out!
This function is now available as raw.means.plot and raw.means.plot2 in the plotrix package (on CRAN).
Standard approach is to add some noise to the data before plotting. R has a function jitter() which does exactly that. You could use it to add the necessary noise to the coordinates in your plot. eg:
X <- rep(1:10,10)
Z <- as.factor(sample(letters[1:10],100,replace=T))
plot(jitter(as.numeric(Z),factor=0.2),X,xaxt="n")
axis(1,at=1:10,labels=levels(Z))
Besides jittering, another good approach is alpha blending which you can obtain (on the graphics devices supporing it) as the fourth color parameter. I provided an example for 'overplotting' of two histograms in this SO question.
One additional idea for the general problem of showing the number of points is using a rug plot (rug function), this places small tick marks along the margin that can show how many points contribute (still use jittering or alpha blending for ties). This allows the actual points to show their true rather than jittered values, but the rug can then indicate which parts of the plot have more values.
For the example plot direct jittering or alpha blending is probably best, but in some other cases the rug plot can be useful.
You may also use sunflowerplot, while it would be hard to implement it here. I would use alpha-blending, as Dirk suggested.