I came across this post: Texture in barplot for 7 bars in R? which suggests that you can use hatching in R, and I've been looking for this type of thing for a long time... However, I then realised that this post is about barplot, rather than geom_bar (or any ggplot2 function), and that any attempt to use "density" as a parameter in geom_bar just results in the parameter being ignored.
So my question is, is it possible to use the hatching density in ggplot? I know there are things like geom_patterns, but I'm not able adapt it to my needs, I just want to use hatching because I have too many colours to use distinguishible shades. And if so, how do I implement it? Whenever I google density geom_bar it shows me density plots which is not what I'm after so I'm turning to this forum for help...
Thanks
EDIT: I figured out ggpattern! I'm pretty sure I tried it before but maybe I wasn't as determined (read: desperate). so thanks for the suggestion
Related
I am implementing a R shiny with a plot panel implemented by library(ggplot2). If there are 12 plots, the layout looks great. Please check below.
12-plot layout
However, if I increase the plot number to 70, then each plot looks being compressed (pls see below). Is that possible I can keep the size of each plot fixed? Thank you so much!
enter image description here
Is there another way to approach this? For instance, can you group your data by two categorical variables and use on for colouring and the other for facetting? In that way, you may be able to reduce the number of facets, and stick with the larger facet size, while still conveying all relevant information? 70 facet plots is a lot!
Is this more of a QC thing? For QC, I tend to break it into groups by condition as Paul was suggesting. The reason is that within a condition, things should be really similar. Outside a condition, all bets are off. When I do this for genomics data, I tend to use “pairs” customized to my liking.
What don’t you like about the 70 sample display? Simply the change in aspect ratio? IMO, these are the things I don’t like about ggplot. You can make these plots using base R and then place them on a page manually using par or layout. For that matter, you can do the same with ggplot and use ggarrange or a different manual layout function to place the plots. All wrapped in a for or apply of course.
The other things I like to do when I have a LOT of QCs to look through is create a movie. I can use the forward/back buttons and go through a lot quickly. I like the idea of having this in a dashboard, nice one!
you could also try coord_fixed(ratio= ), not sure if that will work with faceting or not
Finally, I have made a movie-like visualization for those 70 plots using the plot_ly function in R package "plotly".
I am trying to make the following plot (which I made in R) using ggplot2. How do I go about doing this? (The plot has a finer resolution from 0 to 10, but coarser resolution from 10 onwards.) know how to do this in R, but I am not sure how to proceed with this in the case of ggplot2. Here is the figure obtained using base R.
To make things useful, note that i don't have that much interest in hanging on to the right-hand y-axis.
Also, I would like to do a similar task on a pair of barplots (have the same scale, and only one y-axis. I am hoping that that approach is similar.
I would also be open to other suggestions that would make a similar plot conveying the information similarly or better.
Many thanks for suggestions!
I'd like to achieve what this person has achieved without using ggplot. Any ideas?
How do I create a continuous density heatmap of 2D scatter data in R?
You can see what I get when using the solution detailed in that question.
ggplot(df,aes(x=x,y=y))+
stat_density2d(aes(alpha=..level..), geom="polygon") +
scale_alpha_continuous(limits=c(0,1),breaks=seq(0,1,by=0.1))+
geom_point(colour="red",alpha=0.2)+
theme_bw()
The heatmap is so sparse. I want it to cover much more than what it is covering now. It's terribly hard to see anything about the density. Any ideas of different ways to make density heatmaps from 2D data besides this ggplot solution?
One idea I had was instead of using linear color labeling (see the black to white spectrum on the left, which is linear), using logarithmic scale for the density labeling. Any ideas how I could do this?
"The heatmap is so sparse. I want it to cover much more than what it is covering now. It's terribly hard to see anything about the density."
Please be specific: what do you want to see in areas with most or all NAs?
if you use geom_point with alpha-blending and position_jitter, the current plot is as good as it gets
if some solid color, then use geom_hex(), see http://mfcovington.github.io/r_club/solutions/2013/02/28/peer-produced-plots-solutions/ for code. Then play with the continuous color_scale... you probably want a nonlinear transform. Post us your revised attempt, if you want a critique.
I actually ended up using smoothScatter, which works well and uses classic R plotting.
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.
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.