my scatterplot that shows the relationship between my principal component and one of my questionnaire items looks strange.
The scatterplot between two of my principal components looks great (see second image).
The item is measured on a Likert scale from 1 to 5 (1 = strongly disagree, 5 = strongly agree). The histogram of this item also has many gaps between the bars, which is strange to me too.
Please let me know which specific output you still need to figure this out.
Thanks
That is because the principal components are both metric and the Likert scale is ordinal, so the values can only be integers from 1-5. In this regard the Scatterplot does not look strange but it might be the wrong type of plot. If you anyway want to use a scatterplot you can use jitter(var, 0.3) on the ordinal variable when creating the plot. 0.3 is just a suggestion, it's best if you just try what works. As you didn't provide a reproducible example I wasn't able to try it out.
For reproducibility, it is best if you provide a piece of the database you are using to make these scatterplots. It would also be great if you include the axes titles inside the graph.
To be honest, I dont see anything wrong with the first figure. Variable y (the questionare item) is a discrete variable between 1 and 5. It make sense that one result from the PC1 is match with more than one of the values of the item questionare.
Related
Okay so I have an assignment where I need to conduct a graph that best represents the before and after affects of two streams. The graph(s) have to contain means and standard error for each stream in each year.. I cannot figure the proper coding for the graph. I continue to get errors and bad graphs. I will attach a sample of what the data looks like too.
A sample of the data, it changes to after at 51
Try to post a reproducible error or specification of your problem.
As far as I can analyze your problem, you maybe should not create b4, because it does not seem to be an effective subset. If you want to assemble certain plots, you can use plot_grid from cowplot.
Otherwise you can add facet_wrap(~ VARIABLE_NAME) to ggplot in order to create many plots divided by deviating observations in the specified variable.
If you are not happy with the visual outcome and result of your graph, you can choose another theme, e.g. theme_bw() which can be simply added to your ggplot function. You can add and change further labels with labs() and theme().
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.
Hi there stackoverflow community!
I am a graduate student inquiring for some consultation on an aethetics R problem I am encountering.
The data I am working with is in the form of a VERY large matrix (49x51).
My problem is that my data ranges from very small to very large, with the bulk of my data falling within the "very large" end of the spectrum, so unless I convert my data to log10, the heatmap is rather boring and almost entirely the same color.
The spectrum of my data is totally within the range I am expecting, but I am hoping to display it in a more aesthetic way.
Proposed solution: I think I need to bin my data in a non-uniform way. If you look at the attached image, you will see that their heatmap looks nice and the color key shows the heat spectrum in a non-fixed bin format. I would like to do something like that, however, I am not sure how to declare cutoffs for each bin. I would ideally like to declare the cutoffs.
For example, bin 1 (0-1), bin 2 (2-50), bin 3 (51-5000). As you can see, my bins would not be fixed in equal increments.
I have been using heatmap.2 for this. Thanks so much in advance!
heatmap with color legend in non-uniform bins:
Hey #Punintended and #S Rivero,
I think I have reached the point that my heatmap will only improve marginally. Both of you contributed deeply to this success, so thanks! First, to condense the matrix values as much as possible, I normalized by column. I was then able to assign gradients. This turned out much better than I had hoped. As you can see, most of my data is clustered (check out the density in the key) at very low values, this is okay though, for I am interested in the higher values. I had to use custom color gradients to account for possible instances of colorblind attendees that might look at my poster. Anyways, if you guys have comments or recommendations, they will be much appreciated :). Again, thanks a bunch!
enter image description here
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.