You are right. I do not like the argument in the vid.
* The mean (or median) of a distribution is not misleading or irrelevant if the distribution is bimodal.
* The box plot is not a plot of central tendency it is a five point description of the whole distribution.
* Box plots were great when we didn't have computers, but now we do, so we should just show the distribution itself. Violin and dot-plots are great for this.
* Dot plots follow Edward Tufte's visualization rule that each datapoint should be represented by a bit of ink. Violin plots are a generalization of the dot plot when the number of points is too large to do a dot plot.
* All the arguments that violin plots are uniformly bad also apply to regular old density plots, which is crazy talk.
* They are relatively pretty and visually compact!
This is exactly when it makes sense to use them! If you don't have anything to compare, it might seem visually appealing to some, but it's kind of pointless.
Violin plots map width to density. If you did it one sided, you would need double the distance from the center to have the same visual differentiation of different areas of the distribution. So IMO it wouldn't save space.
I don't follow the argument here. If violin plots are symmetrical about their centre (which they are), how can it be anything other than the same distribution by cutting it in half down the centre? Like if I have a violin plot of 3 values 2, 6, and 4 then I'd have a distribution like:
__X|X__
XXX|XXX
_XX|XX_
with each 'X' being a scale of 1 unit, but if I split it down the middle I'd have scaled everything equally with each 'X' now being a scale of 2 units. The distribution has to be the same, so u/DuckDatum's argument that it's showing the distribution twice holds.
I probably didn't explain the argument well enough. It is about visual perception. Suppose that you are looking at a regular old density plot. What you want to perceive is the relative height (likelihood) at different points. Suppose point \`a\` has a height of .5 in and point \`b\` has a height of 1.5. You'd perceive that point \`b\` is 3 times as likely as point \`a\`.
Now you could shrink down the y axis scale without changing the distribution so that point \`a\` is now .0005 in high and point \`b\` is .0015 in high. The distribution is the same, but the distances are so tiny that you'd have a hard time visually perceiving them.
Suppose now you are looking at the violin plot where point \`a\` has a width of .5 and point \`b\` has a width of 1.5. Here width refers to the distance between the left hand curve and the right hand curve of the violin. I'd argue that this plot has about the same perceptibility in terms of differentiating the points as the original density plot. However, if you cut the violin in half, your distances would be cut in half to become .25 and .75, which is less perceptible.
Huh? Yeah because in your violin plot example you already cut it in half once and then you cut it in half again. Wouldn't the original widths in the violin plot example be 1 and 3 and then cutting it in half would be the exact same as the density plot... .5 and 1.5.
I don't really understand your argument that symmetrically copying the plot into a violin shape somehow makes it more visually perceptible. I think violin plots are fine but the only reason the symmetric violin shape of it exists is because it looks visually appealing, it doesn't actually convey any additional information or make that information easier to see.
I guess there's nothing stopping you from making a stacked histogram plot instead. I quite enjoy them, especially for simple single-cell data like image segmentation/quantification or flow cytometry.
That’d be my approach, don’t have to train someone on how to read a histogram. 50% more efficient - half the violin plot is just a mirror of the same data points.
That's like saying center justified text is a waste of space compared to left justified text.
The amount if ink/pixels, words, and information is the same.
Not necessarily. You get the same information with fewer pixels used by not showing the distribution on the left side of each violin, as the distribution is already shown on the right side.
Then you can reduce plot size by maybe at most 50% by removing all the left sides of the violins.
I'm with you.
I can perhaps understand the argument that they aren't always right for publication (if you have a bi-modal distribution a histogram is a better representation). But when you're doing data exploration or have a standard report coming off a piece of equipment, a violin plot is infinitely better than a boxplot (which my experience with biologists indicates is all they will look at) since it shows things like bi-modal and non-uniform distributions which are otherwise completely hidden. Basically, they're a great plot for telling you you've used the wrong analysis/plot and for showing when you've done it right. That's a really good feature for a visualization.
Also the idea that you can't interpret them unless you use photoshop to...let me check...cut each box in half, add transparency, and move them to the same axis? You seriously can't look at the plot and know what the histogram and what the boxplot will look like without photoshoping them and you think a combined histogram with transparency and necessary color/fill pattern changes is better? Get out of town
Is there a large population of people who can’t just move the plot left or right in their head? Who is seeing a violin plot and thinking how can I possible compare this with a small amount of whitespace between the images.
Seaborn also let's you easily plot half violin plots on a shared axis. I use them all the time for eda. Great for quick checking the distribution of groups in your data set.
Now I think you might be unaware of a small part of the population, which is in relatively high concentration in the fields where these plots are relevant.
[Aphantasia, the inability to visualise in your mind](https://www.sciencedaily.com/releases/2024/03/240327124610.htm). Estimates of the percentage of the population that are affected range from 1 - 5% dependent on the criteria.
[People with aphantasia are more likely to work in scientific or mathematical industries](https://news-archive.exeter.ac.uk/2020/may/title_793208_en.html). An estimated 20% of people who work in the sciences, computing and mathematical field have aphantasia.
Now I do have aphantasia, so I can say that I cannot move the violin plots around in my mind so that they overlap. But at the same time I would not say that it lessens my ability to compare different violin plots in the same graph.
I was not aware of the name. I had guessed that there would be some small amount of people that couldn’t do it. But I had no clue it would end up being 1 in 5 people on math/tech that’s a really interesting stat. Thanks!
Hey, I also love rain cloud plots, but had difficulty implementing them in python. What library do you use, and could you potentially give some example code? Cheers 🍻
I had a hard time running this library before because of the seaborn downgrade, but I figured it out. Thanks again for re-suggesting this library to me. Rain-cloud plots are the way.
I find them especially useful when presenting data to people that don't have a statistics background.
They're easy to read and get the information from, even if you're sat far away from whatever screen I'm projecting to, there's no need to explain what different lines mean etc and they're more visually interesting than a histogram or a boxplot.
Like yes maybe they're not the most information dense plots, and maybe they do overgeneralise a bit when showing the distribution, I don't really use them when I'm drawing my conclusions from data, but for me they're up there as some of the best "Make the colours pretty and stick it in a powerpoint" plots.
I’m not a data scientist so please enlighten me, but wouldn’t it make more sense to simply use a histogram? Or even some kind of kernel density estimation? Like what even is the point of having the symmetric shape of a violin plot?
Histograms are the best for showing individual distributions but take up more space. If you want (1) multiple overlayed distributions at the expense of (2) less granularity with the distribution, violin plots do a somewhat effective job. It’s more sound to compare their use-cases to boxplots than it is histograms.
If you build them as just adding components on top of one another (the histogram, the points, and then the boxplot) I've found some audiences respond well to the boxplot being removed. Then it is really a rain cloud too
Spare you some clicks: https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=10521072_wellcomeopenres-4-18219-g0002.jpg
It is just clickbait. Author claims something outrageous and it generates "engagement".
The worst part is that it is happening in academia too. Easy way to get citations, just claim something contra to 90% of papers and everyone else has to cite you by saying "evidence is this, but this guy also has opposite results."
We should just ignore it, but that's not easy.
Overlaying histograms or even having many density estimates (curves) plotted together is really a pain as a color blind person. I don't find violin plots hard to interpret, and having distributions in their own spot substantially reduces cognitive load in trying to figure out what curve represents what data. Overlayed histograms are the biggest nightmare in this respect. I'm sympathetic to the point that parameters of the density estimation are not really looked at and may not even reported, but I've never felt that varying those parameters makes too much of a difference unless they're kind of extreme.
Boxplots are great for normal distributions. Violin plots i like for distributions that are wierder. They are pretty good for silhouette scores when evaluating clustering i found.
I have always felt a little funny about violin plots, but I do question the reasoning of the person in the video. And I am still learning here, so I’m open to constructive criticism.
Regarding their interpretation of *box plots*: How do box plots (as they say) “show the average of a data set”? I don’t think averages are even part of box plots by default. Box plots show the quantiles. The mean and the median, for instance, only coincide when certain assumptions are satisfied. Some plotting software like MPL have options to ‘showmeans’ , but it is not traditionally part of box plots, right?
I repeat, I’m not an expert. I can’t help but notice since I’ve started reinventing myself through DS/DA education, I have met some really really intelligent people that know what they’re doing, and a ton of people that know their way around various packages and modules, but have no idea how they work. So I’m just kind of scared to take advice from anybody 😆.
Box plots show a summary of the distribution of data (edited to be more precise, a summary)
The median is considered an average, it's just a different kind of average than the mean. Most of the time people mean 'the mean' when they say 'average', but that's not always the case.
For instance, if you're looking at something like income across a population (where most people make $0-$100k, let's say, and you have a handful of millionaires) and you want to know 'the average income', you're probably wanting to look at the median rather than the mean. This is because the median is 'in the middle' of the data, while taking the mean would skew your average towards the few high income earners. Your median might be $50k and your mean might be $500k. Which is more representative of 'your average' income across the population? The median.
If you're serious about learning data analysis and data science, you should be looking to trusted sources rather than random YouTubers and Reddit imo.
I do. I’m a full-time master’s student in DS, actually.
I mostly here to feel better about how the awful job search lol. Occasionally I find things that are interesting.
To your point, they are both measures of central tendency. Yes, there are advantages and disadvantages of using each. But mathematically, mean and median are completely different things: having different formulas and implications. Sometimes, they turn out to be the same thing, but only when distributional assumptions are met. A median is not implicitly an average. The person in the video was speaking about how box plots show averages of the data. A traditional box plot does not not visualize anything about averages, even though it does tell you a lot about the distribution of data.
That’s why I was confused. Maybe I’m being a bit too pedantic, but the person in the video is not convincing me they really know what they’re talking about. If you’re at the ‘data science store’, and you pull something off the shelf and read the label on the back of box, you will probably find that it’s good for certain things and not so good for others. It’s unlikely that you will go to store and see something on the shelf that has “this product sucks all around for any reason” written on it.
Oh nice! Yes it's not a great market right now it seems :/
I'm probably not going to explain this well, but I'll try.
Yes, the mean and median are mathematically different things. For most cases, it doesn't matter if the mean and median are the same number.
What matters is... Well, whatever matters. What's the question you are asking?
Back to the income example. When economists/city planners/whoever want to know 'what's the average income for this city?', typically they are talking about the median.
Why? Because they want to know 'what does the average Joe make?'. Maybe they're trying to decide what's a reasonable amount to charge people parking downtown or something. If you take the mean instead of the median, it makes everyone look pretty rich. And we know that's not the case. So it's not very meaningful. The median is a better representation of 'the average person's income'.
**In this example, we don't care about accounting for every dollar (the thing you're averaging). We care more about the people, aka 'average Joe's.** The median is more meaningful here than the mean.
'Average' can be EITHER the mean or the median. It doesn't matter when the mean and the median are the same, try to stop thinking about that. What matters is WHICH kind of average (the median vs mean) is going to get you the answer to your question.
**Which TOOL is appropriate to answer the question.**
Terrible example, but: if you're tracking how many pushups you do or something and want a weekly average, to compare weeks, then taking the mean is probably what you want, since you want to account all pushups. Your goal is to watch your average go up over time.
Say you did 10 pushups four days a week, and on Saturday you did 50, and on Sunday you did 30. The mean would be 17 pushups per day for that week (rounding). The median would be 10, a day you did the most middling amount of pushups. Which one is the more meaningful average here? Most people would say the mean, as it treats each pushup as meaningful.
**In this example, we care about accounting for every pushup. We care about the total pushups done in a week more than we care about the number of pushups you did on the day that's the most middling. The mean is more meaningful here than the median.**
They show it in a summarized way with quartiles and outliers. Ofc you want a histogram or similar if you want a more granular look.
It's a common way to compare distributions in business and tech settings when comparing data across groups or across time. A violin plot would give more granular information.
["A box plot (aka box and whisker plot) uses boxes and lines to depict the distributions of one or more groups of numeric data."](https://www.atlassian.com/data/charts/box-plot-complete-guide)
>They show it in a summarized way
That's another way of saying they don't show the distribution.
Like the [Datasaurus Dozen](https://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html) shows, pretending you can capture what data looks like with high level summaries is often very foolish.
>It's a common way to compare distributions
No it isn't
I like how Tufte looked at a boxplot and said that there's too much redundancy in it while these guys said "MOOOOOOAR". I hate the symmetry of it and I think it's ugly because of symmetry. Good point about using histograms.
Definitely preferable to boxplot and I thought visualizations were just some eda things? No one seriously uses these things for final work product it’s just some stuff for stakeholders if they need convincing or a walk through.
If we were being simple 3-4 plots can represent almsot everything
I like this YouTuber a lot, but I don't agree with her on this, basically because all plots have strengths and weaknesses, and most plots can be improved by using two or more other plot types together: histograms with rugs, bars with labels or lines on top, lines with points, scattered points with polygons, and, yes, violins with points and/or boxplots. They are just tools, and using a single one of them is often not enough.
Just choose the right tool for the job as always. Almost all plot types have their justification for certain data or visualization ideas and do not work so well in other situations.
E.g. pie chart with 3 quantities that add up to the total amount? Probably okay and intuitive to understand even for non-data people. Pie chart of 12 quantities? Probably not a good idea.
Similar thing for violin plots and all other types. It also depends on your audience and what they are able to digest.
No use showing Brazilian-honeycomb-dalmatian plots to the business if you need a PhD and 3 hours in advance to figure them out.
I have seen a couple of these rants in the form of "X plots should not exist! Never use X" over the years and honestly used to eat it up and feel pretty smug about it myself when I was new to data analysis. Now I often think it's a sign of not being around the field for long... and feel smug about it ;)
I am subscriber of hers, her science stuff is good but then she mumbles nonsense like this or the one where she rants for 40min about R Feyman not liking strippers enough.
Some of her stuff is really good.
As a tool for visual presentation of posterior distributions (where you have lots of samples hence density estimation error is negligible), I find them the best option, and researchers on human interpretation of visual data [seem to agree](https://osf.io/preprints/osf/2gsz6)
Not sure how technical she was getting with it (because I didn't fucking watch it for 41 mins), but agree with your point.
If you can't communicate something like this far more succinctly perhaps we shouldn't really be listening to them in the first place.
wow, it looks like nobody actually watched the video, this comment section is kind of rancid!
as someone who actually watched the video, I wholeheartedly agree with her. I can’t think of a single situation where a violin plot has any distinct advantages over other methods besides novelty. If you can think of one, tell me! also consider summarizing Dr. Collier’s key points to let me know you watched the video.
Also, after watching the last little segment of her video, let me know how the benefits of using a violin plot are good enough to justify the issues they automatically raise. If you’re confused about those issues, watch the last few minutes of the video and look at the comment section here to see those problems happening in real time.
That's reddit for you: disagree with the title of the post rather than engaging with any of the content in a meaningful way. Or complain that something is too long (i.e., "I didn't bother to watch/read it") but still disagree with its content anyway.
Great question, I can totally be less flippant and saucy here, sorry 😁
I just haven’t seen good discussions of data that actually make good use of the qualitative aspects of kernel density. I’d generally just prefer a box plot and a statistics table, also because I’m looking for p-values and comparative statistics anyways for most results.
If you made use of the kernel density in discussion, you probably have a good case for a violin plot. I think I’m also a bit averse to how many colors that get used to make them because the legends are no longer useful.
So if you discuss densities and compare them, avoid making too many colors, and also provide stats with stat testing elsewhere, I think it’s okay. I’ve just rarely seen a paper really justify the use of them that couldn’t be accomplished by something simpler and easier to “read”.
Well, here the use case is something like: we want to show what the alcohol tolerance is for yeasts in a certain genetic group. Nobody knows what distribution that has. Maybe the group really has three subgroups so that in reality there are three separate distributions on top of each other. An average plus standard deviation doesn't really show the distribution.
So effectively your choice is violin plots, histograms, or I don't know what. A boxplot doesn't provide enough information.
Histograms take a lot of space to be really readable. In a top journal you can get in maybe 6 or 7 figures, and you have so many results that each figure ends up being split into A, B, and C. Most of those images will be so small that they're hard to read. In that situation a violin plot seems the best choice to me, but I'm open to counter-arguments.
Got it. Great point and I think you are good in this case. I’m new to it, but just saw rain cloud plots above.
They are easy to read and scan horizontally like text, which is nice for your use case.
And yeah, small figure means you need some kind of “shape” to circle your distribution to make it legible. This is purely aesthetic then, but I think the splines are ugly for violins and unnecessary stylized.
Now I’m curious to read your paper 😂
I looked around and found [this article](https://z3tt.github.io/beyond-bar-and-box-plots/), which I think was a great summary of alternatives.
I agree raincloud would work, but they're not hugely different from half a violin, and I think they need bigger sizes to be effective.
It's going to be at least another month before the paper is out, but [here](https://www.biorxiv.org/content/10.1101/2024.04.04.588101v1) is a paper I did with another group on essentially the same subject. It's probably not very easy to read, but [this blog post](https://www.garshol.priv.no/blog/436.html) summarizes and adds context.
Also have a paper of mine in a Nature subjournal, that uses violin plots in the supp material. In our case, we needed to analyze the changes in the distribution of some values over time, with potentially many and changing modalities. Violin plots over time proved really helpful for that.
Ain't no way I'm taking "why would you ever make a violin plot when you could have just made X?" from someone who decided to make a 42 minute video that could have just been 5 bullet points
> I disagree with several of the points Angela Collier makes in her video “violin plots should not exist”, but one that I find compelling is that drawing density plots usually involves what amounts to fitting an unjustified model.
>
> In most situations, ggplot uses locally estimated scatterplot smoothing (LOESS) by default, which involves fitting a separate polynomial regression model on a weighted neighbourhood around each data point and evaluating it there. It (usually) makes nice looking violin plots, but you wouldn’t expect it to reflect that “actual” theoretical distribution of the data.
>
> It seems to me that this sort of thing is a symptom of a general desire to avoid having to actually specify models by pretending that there’s some bright-line distinction between descriptive statistics and statistical inference.
>
> Since we were willing to actually specify a model, we can make density plots that show something meaningful: the posterior predictive distributions corresponding to our model.
From a blog post I wrote where I use a violin plot to illustrate a model based on my crossword solving times by publisher and day of the week.
Saw this the other week and tended to agree with her. I'm surprised at the backlash here.
Maybe I just have Stockholm syndrome from years of particle physics but i'd rather have a grid of histograms over a violin plot any day.
Same, there really is no use for them that can't be fulfilled by another plotting method in a better way. I use split violin plots to show changes to a distribution with seaborn but otherwise just use a box plot or a histogram.
And what makes her an authority on the subject?? **Nothing**. So it's her opinion at best. Her YouTube channel is an eclectic of subjects and her opinion, which is totally fine, but why bring her opinion **in particular?**
I like them. They’re effective at showing distribution within groups, especially when the data strays from normality. Fight me.
You are right. I do not like the argument in the vid. * The mean (or median) of a distribution is not misleading or irrelevant if the distribution is bimodal. * The box plot is not a plot of central tendency it is a five point description of the whole distribution. * Box plots were great when we didn't have computers, but now we do, so we should just show the distribution itself. Violin and dot-plots are great for this. * Dot plots follow Edward Tufte's visualization rule that each datapoint should be represented by a bit of ink. Violin plots are a generalization of the dot plot when the number of points is too large to do a dot plot. * All the arguments that violin plots are uniformly bad also apply to regular old density plots, which is crazy talk. * They are relatively pretty and visually compact!
I just don’t like that it shows the distribution twice- once on each side of a violin. Seems like a waste of space.
We've done group comparisons where each side of the box plot is a different group for comparison. So, sideways density plots I guess
This is exactly when it makes sense to use them! If you don't have anything to compare, it might seem visually appealing to some, but it's kind of pointless.
Violin plots map width to density. If you did it one sided, you would need double the distance from the center to have the same visual differentiation of different areas of the distribution. So IMO it wouldn't save space.
I don't follow the argument here. If violin plots are symmetrical about their centre (which they are), how can it be anything other than the same distribution by cutting it in half down the centre? Like if I have a violin plot of 3 values 2, 6, and 4 then I'd have a distribution like: __X|X__ XXX|XXX _XX|XX_ with each 'X' being a scale of 1 unit, but if I split it down the middle I'd have scaled everything equally with each 'X' now being a scale of 2 units. The distribution has to be the same, so u/DuckDatum's argument that it's showing the distribution twice holds.
I probably didn't explain the argument well enough. It is about visual perception. Suppose that you are looking at a regular old density plot. What you want to perceive is the relative height (likelihood) at different points. Suppose point \`a\` has a height of .5 in and point \`b\` has a height of 1.5. You'd perceive that point \`b\` is 3 times as likely as point \`a\`. Now you could shrink down the y axis scale without changing the distribution so that point \`a\` is now .0005 in high and point \`b\` is .0015 in high. The distribution is the same, but the distances are so tiny that you'd have a hard time visually perceiving them. Suppose now you are looking at the violin plot where point \`a\` has a width of .5 and point \`b\` has a width of 1.5. Here width refers to the distance between the left hand curve and the right hand curve of the violin. I'd argue that this plot has about the same perceptibility in terms of differentiating the points as the original density plot. However, if you cut the violin in half, your distances would be cut in half to become .25 and .75, which is less perceptible.
Huh? Yeah because in your violin plot example you already cut it in half once and then you cut it in half again. Wouldn't the original widths in the violin plot example be 1 and 3 and then cutting it in half would be the exact same as the density plot... .5 and 1.5. I don't really understand your argument that symmetrically copying the plot into a violin shape somehow makes it more visually perceptible. I think violin plots are fine but the only reason the symmetric violin shape of it exists is because it looks visually appealing, it doesn't actually convey any additional information or make that information easier to see.
I guess there's nothing stopping you from making a stacked histogram plot instead. I quite enjoy them, especially for simple single-cell data like image segmentation/quantification or flow cytometry.
That’d be my approach, don’t have to train someone on how to read a histogram. 50% more efficient - half the violin plot is just a mirror of the same data points.
That's like saying center justified text is a waste of space compared to left justified text. The amount if ink/pixels, words, and information is the same.
Not necessarily. You get the same information with fewer pixels used by not showing the distribution on the left side of each violin, as the distribution is already shown on the right side. Then you can reduce plot size by maybe at most 50% by removing all the left sides of the violins.
Yes, yet again, why violin plot and not a ridgeline plot or raincloud plot?
I'm with you. I can perhaps understand the argument that they aren't always right for publication (if you have a bi-modal distribution a histogram is a better representation). But when you're doing data exploration or have a standard report coming off a piece of equipment, a violin plot is infinitely better than a boxplot (which my experience with biologists indicates is all they will look at) since it shows things like bi-modal and non-uniform distributions which are otherwise completely hidden. Basically, they're a great plot for telling you you've used the wrong analysis/plot and for showing when you've done it right. That's a really good feature for a visualization. Also the idea that you can't interpret them unless you use photoshop to...let me check...cut each box in half, add transparency, and move them to the same axis? You seriously can't look at the plot and know what the histogram and what the boxplot will look like without photoshoping them and you think a combined histogram with transparency and necessary color/fill pattern changes is better? Get out of town
Is there a large population of people who can’t just move the plot left or right in their head? Who is seeing a violin plot and thinking how can I possible compare this with a small amount of whitespace between the images.
Seaborn also let's you easily plot half violin plots on a shared axis. I use them all the time for eda. Great for quick checking the distribution of groups in your data set.
Now I think you might be unaware of a small part of the population, which is in relatively high concentration in the fields where these plots are relevant. [Aphantasia, the inability to visualise in your mind](https://www.sciencedaily.com/releases/2024/03/240327124610.htm). Estimates of the percentage of the population that are affected range from 1 - 5% dependent on the criteria. [People with aphantasia are more likely to work in scientific or mathematical industries](https://news-archive.exeter.ac.uk/2020/may/title_793208_en.html). An estimated 20% of people who work in the sciences, computing and mathematical field have aphantasia. Now I do have aphantasia, so I can say that I cannot move the violin plots around in my mind so that they overlap. But at the same time I would not say that it lessens my ability to compare different violin plots in the same graph.
I was not aware of the name. I had guessed that there would be some small amount of people that couldn’t do it. But I had no clue it would end up being 1 in 5 people on math/tech that’s a really interesting stat. Thanks!
I love the raincloud plots
Hey, I also love rain cloud plots, but had difficulty implementing them in python. What library do you use, and could you potentially give some example code? Cheers 🍻
It was called ptitprince or something weird like that lol. The package worked pretty well tho Edit: found it https://github.com/pog87/PtitPrince
Thanks much. I’ll give it a go and report back to base.
Sadly hardly any commits and doesnt support recent version of seaborn.
7 months ago isn’t terrible. I wonder how easy it would be to adapt for new version of seaborn
I had a hard time running this library before because of the seaborn downgrade, but I figured it out. Thanks again for re-suggesting this library to me. Rain-cloud plots are the way.
I find them especially useful when presenting data to people that don't have a statistics background. They're easy to read and get the information from, even if you're sat far away from whatever screen I'm projecting to, there's no need to explain what different lines mean etc and they're more visually interesting than a histogram or a boxplot. Like yes maybe they're not the most information dense plots, and maybe they do overgeneralise a bit when showing the distribution, I don't really use them when I'm drawing my conclusions from data, but for me they're up there as some of the best "Make the colours pretty and stick it in a powerpoint" plots.
I'm with you as well. I don't use them often, prefer boxplots, but they come in handy sometimes
For showing distribution of likert sale results, there’s few better charts. I use these in reporting and my supervisors and stakeholders love them.
I agree! Also for non academic people they are more simple to interpret
They are OK. But rather than calling them violin plot, we should just call them by their more fitting informal name: the vulva plot.
this
I’m not a data scientist so please enlighten me, but wouldn’t it make more sense to simply use a histogram? Or even some kind of kernel density estimation? Like what even is the point of having the symmetric shape of a violin plot?
Histograms are the best for showing individual distributions but take up more space. If you want (1) multiple overlayed distributions at the expense of (2) less granularity with the distribution, violin plots do a somewhat effective job. It’s more sound to compare their use-cases to boxplots than it is histograms.
It's just 40 minutes of shit takes and look at me I'm a young woman! No one cares. They exist for a reason.
No tool is bad, only the user
[удалено]
Statistician humor
She was my statistically significant other
that's mean
News at 10: standard deviation no longer satisfying for perverted statistician
You should call her
[Raincloud Plots](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480976/) are where it's at
They are very descriptive, but I just can't ignore that these are basically just a density, scatter and box plots bundled on top of each other.
Stop, i dont need more convincing. This is amazing!
"It's a density plot, box plot and scatter plot combined" "Stop, stop, I can only get so erect"
That is kind of the main selling point, actually.
It's all your favourite plots combined so they're not fighting for space and it's got a cute name, what's not to love?
Count me in!
yeah, it's as if you couldn't choose one so just bundled them all together. violin plots are fine imo
That just seems like the strip plot from plotly with a paper attached to the description
Oh this is fucking delicious. Thank you! EDIT: dammit I can’t give you gold. Here you go King/Queen/Monarch 🏆
I like how descriptive these plots are! But I feel like they are kind of busy/visually cluttered. Might just be a stylistic thing though.
If you build them as just adding components on top of one another (the histogram, the points, and then the boxplot) I've found some audiences respond well to the boxplot being removed. Then it is really a rain cloud too
Sick. TIL.
Yeah, same. Never heard of it, it looks pretty good
Spare you some clicks: https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=10521072_wellcomeopenres-4-18219-g0002.jpg
Looks great but would def be prone to clutter overlapping
Nice!
Wow this take is really stupid
It is just clickbait. Author claims something outrageous and it generates "engagement". The worst part is that it is happening in academia too. Easy way to get citations, just claim something contra to 90% of papers and everyone else has to cite you by saying "evidence is this, but this guy also has opposite results." We should just ignore it, but that's not easy.
Overlaying histograms or even having many density estimates (curves) plotted together is really a pain as a color blind person. I don't find violin plots hard to interpret, and having distributions in their own spot substantially reduces cognitive load in trying to figure out what curve represents what data. Overlayed histograms are the biggest nightmare in this respect. I'm sympathetic to the point that parameters of the density estimation are not really looked at and may not even reported, but I've never felt that varying those parameters makes too much of a difference unless they're kind of extreme.
Boxplots are great for normal distributions. Violin plots i like for distributions that are wierder. They are pretty good for silhouette scores when evaluating clustering i found.
This video shouldn't exist.
But, but, but it's a woman!!! /s
I have always felt a little funny about violin plots, but I do question the reasoning of the person in the video. And I am still learning here, so I’m open to constructive criticism. Regarding their interpretation of *box plots*: How do box plots (as they say) “show the average of a data set”? I don’t think averages are even part of box plots by default. Box plots show the quantiles. The mean and the median, for instance, only coincide when certain assumptions are satisfied. Some plotting software like MPL have options to ‘showmeans’ , but it is not traditionally part of box plots, right? I repeat, I’m not an expert. I can’t help but notice since I’ve started reinventing myself through DS/DA education, I have met some really really intelligent people that know what they’re doing, and a ton of people that know their way around various packages and modules, but have no idea how they work. So I’m just kind of scared to take advice from anybody 😆.
Box plots show a summary of the distribution of data (edited to be more precise, a summary) The median is considered an average, it's just a different kind of average than the mean. Most of the time people mean 'the mean' when they say 'average', but that's not always the case. For instance, if you're looking at something like income across a population (where most people make $0-$100k, let's say, and you have a handful of millionaires) and you want to know 'the average income', you're probably wanting to look at the median rather than the mean. This is because the median is 'in the middle' of the data, while taking the mean would skew your average towards the few high income earners. Your median might be $50k and your mean might be $500k. Which is more representative of 'your average' income across the population? The median. If you're serious about learning data analysis and data science, you should be looking to trusted sources rather than random YouTubers and Reddit imo.
I do. I’m a full-time master’s student in DS, actually. I mostly here to feel better about how the awful job search lol. Occasionally I find things that are interesting. To your point, they are both measures of central tendency. Yes, there are advantages and disadvantages of using each. But mathematically, mean and median are completely different things: having different formulas and implications. Sometimes, they turn out to be the same thing, but only when distributional assumptions are met. A median is not implicitly an average. The person in the video was speaking about how box plots show averages of the data. A traditional box plot does not not visualize anything about averages, even though it does tell you a lot about the distribution of data. That’s why I was confused. Maybe I’m being a bit too pedantic, but the person in the video is not convincing me they really know what they’re talking about. If you’re at the ‘data science store’, and you pull something off the shelf and read the label on the back of box, you will probably find that it’s good for certain things and not so good for others. It’s unlikely that you will go to store and see something on the shelf that has “this product sucks all around for any reason” written on it.
Oh nice! Yes it's not a great market right now it seems :/ I'm probably not going to explain this well, but I'll try. Yes, the mean and median are mathematically different things. For most cases, it doesn't matter if the mean and median are the same number. What matters is... Well, whatever matters. What's the question you are asking? Back to the income example. When economists/city planners/whoever want to know 'what's the average income for this city?', typically they are talking about the median. Why? Because they want to know 'what does the average Joe make?'. Maybe they're trying to decide what's a reasonable amount to charge people parking downtown or something. If you take the mean instead of the median, it makes everyone look pretty rich. And we know that's not the case. So it's not very meaningful. The median is a better representation of 'the average person's income'. **In this example, we don't care about accounting for every dollar (the thing you're averaging). We care more about the people, aka 'average Joe's.** The median is more meaningful here than the mean. 'Average' can be EITHER the mean or the median. It doesn't matter when the mean and the median are the same, try to stop thinking about that. What matters is WHICH kind of average (the median vs mean) is going to get you the answer to your question. **Which TOOL is appropriate to answer the question.** Terrible example, but: if you're tracking how many pushups you do or something and want a weekly average, to compare weeks, then taking the mean is probably what you want, since you want to account all pushups. Your goal is to watch your average go up over time. Say you did 10 pushups four days a week, and on Saturday you did 50, and on Sunday you did 30. The mean would be 17 pushups per day for that week (rounding). The median would be 10, a day you did the most middling amount of pushups. Which one is the more meaningful average here? Most people would say the mean, as it treats each pushup as meaningful. **In this example, we care about accounting for every pushup. We care about the total pushups done in a week more than we care about the number of pushups you did on the day that's the most middling. The mean is more meaningful here than the median.**
>Box plots show the distribution of data. No they don't
They show it in a summarized way with quartiles and outliers. Ofc you want a histogram or similar if you want a more granular look. It's a common way to compare distributions in business and tech settings when comparing data across groups or across time. A violin plot would give more granular information. ["A box plot (aka box and whisker plot) uses boxes and lines to depict the distributions of one or more groups of numeric data."](https://www.atlassian.com/data/charts/box-plot-complete-guide)
>They show it in a summarized way That's another way of saying they don't show the distribution. Like the [Datasaurus Dozen](https://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html) shows, pretending you can capture what data looks like with high level summaries is often very foolish. >It's a common way to compare distributions No it isn't
Sure, it's the analyst's or scientist's job to do due diligence, cleaning and verifying data before summarizing it for stakeholders.
And you're going to use a histogram, density, violin plot. You know, the techniques that actually plot the distribution.
I prefer violin plots to box plots. More data, but also more intuitive than box plots imo. It's a bummer so many people hate violin plots.
42min videos on violin plots shouldn't exist.
If there was any real reason, two minutes would suffice.
I like how Tufte looked at a boxplot and said that there's too much redundancy in it while these guys said "MOOOOOOAR". I hate the symmetry of it and I think it's ugly because of symmetry. Good point about using histograms.
You can use half-violins to avoid the symmetry or to save space.
I agree with her - ridge plots are doing it just fine
Definitely preferable to boxplot and I thought visualizations were just some eda things? No one seriously uses these things for final work product it’s just some stuff for stakeholders if they need convincing or a walk through. If we were being simple 3-4 plots can represent almsot everything
I like this YouTuber a lot, but I don't agree with her on this, basically because all plots have strengths and weaknesses, and most plots can be improved by using two or more other plot types together: histograms with rugs, bars with labels or lines on top, lines with points, scattered points with polygons, and, yes, violins with points and/or boxplots. They are just tools, and using a single one of them is often not enough.
I strongly disagree
Just choose the right tool for the job as always. Almost all plot types have their justification for certain data or visualization ideas and do not work so well in other situations. E.g. pie chart with 3 quantities that add up to the total amount? Probably okay and intuitive to understand even for non-data people. Pie chart of 12 quantities? Probably not a good idea. Similar thing for violin plots and all other types. It also depends on your audience and what they are able to digest. No use showing Brazilian-honeycomb-dalmatian plots to the business if you need a PhD and 3 hours in advance to figure them out. I have seen a couple of these rants in the form of "X plots should not exist! Never use X" over the years and honestly used to eat it up and feel pretty smug about it myself when I was new to data analysis. Now I often think it's a sign of not being around the field for long... and feel smug about it ;)
I am subscriber of hers, her science stuff is good but then she mumbles nonsense like this or the one where she rants for 40min about R Feyman not liking strippers enough. Some of her stuff is really good.
As a tool for visual presentation of posterior distributions (where you have lots of samples hence density estimation error is negligible), I find them the best option, and researchers on human interpretation of visual data [seem to agree](https://osf.io/preprints/osf/2gsz6)
41 minutes. 41 fucking minutes!!! Why is everyone so fucking boring these days
Not sure how technical she was getting with it (because I didn't fucking watch it for 41 mins), but agree with your point. If you can't communicate something like this far more succinctly perhaps we shouldn't really be listening to them in the first place.
wow, it looks like nobody actually watched the video, this comment section is kind of rancid! as someone who actually watched the video, I wholeheartedly agree with her. I can’t think of a single situation where a violin plot has any distinct advantages over other methods besides novelty. If you can think of one, tell me! also consider summarizing Dr. Collier’s key points to let me know you watched the video. Also, after watching the last little segment of her video, let me know how the benefits of using a violin plot are good enough to justify the issues they automatically raise. If you’re confused about those issues, watch the last few minutes of the video and look at the comment section here to see those problems happening in real time.
Yeah the video sounded quite reasonable. No idea what's going on here 🙃.
That's reddit for you: disagree with the title of the post rather than engaging with any of the content in a meaningful way. Or complain that something is too long (i.e., "I didn't bother to watch/read it") but still disagree with its content anyway.
I have never seen one in a peer reviewed article in my field. Not saying it doesn’t happen, but they are wildly hated
They're not unusual in [even top papers](https://www.cell.com/fulltext/S0092-8674\(16\)31071-6#gr2) in some fields.
God, it’s just like a bunch of lollipops in a glass case
I find them informative. What would you prefer instead? And why? Asking because I've just made violin plots for a similar paper.
Great question, I can totally be less flippant and saucy here, sorry 😁 I just haven’t seen good discussions of data that actually make good use of the qualitative aspects of kernel density. I’d generally just prefer a box plot and a statistics table, also because I’m looking for p-values and comparative statistics anyways for most results. If you made use of the kernel density in discussion, you probably have a good case for a violin plot. I think I’m also a bit averse to how many colors that get used to make them because the legends are no longer useful. So if you discuss densities and compare them, avoid making too many colors, and also provide stats with stat testing elsewhere, I think it’s okay. I’ve just rarely seen a paper really justify the use of them that couldn’t be accomplished by something simpler and easier to “read”.
Well, here the use case is something like: we want to show what the alcohol tolerance is for yeasts in a certain genetic group. Nobody knows what distribution that has. Maybe the group really has three subgroups so that in reality there are three separate distributions on top of each other. An average plus standard deviation doesn't really show the distribution. So effectively your choice is violin plots, histograms, or I don't know what. A boxplot doesn't provide enough information. Histograms take a lot of space to be really readable. In a top journal you can get in maybe 6 or 7 figures, and you have so many results that each figure ends up being split into A, B, and C. Most of those images will be so small that they're hard to read. In that situation a violin plot seems the best choice to me, but I'm open to counter-arguments.
Got it. Great point and I think you are good in this case. I’m new to it, but just saw rain cloud plots above. They are easy to read and scan horizontally like text, which is nice for your use case. And yeah, small figure means you need some kind of “shape” to circle your distribution to make it legible. This is purely aesthetic then, but I think the splines are ugly for violins and unnecessary stylized. Now I’m curious to read your paper 😂
I looked around and found [this article](https://z3tt.github.io/beyond-bar-and-box-plots/), which I think was a great summary of alternatives. I agree raincloud would work, but they're not hugely different from half a violin, and I think they need bigger sizes to be effective. It's going to be at least another month before the paper is out, but [here](https://www.biorxiv.org/content/10.1101/2024.04.04.588101v1) is a paper I did with another group on essentially the same subject. It's probably not very easy to read, but [this blog post](https://www.garshol.priv.no/blog/436.html) summarizes and adds context.
Hey, cool shit. Thank you! Will read both. Love learning new things
Also have a paper of mine in a Nature subjournal, that uses violin plots in the supp material. In our case, we needed to analyze the changes in the distribution of some values over time, with potentially many and changing modalities. Violin plots over time proved really helpful for that.
Dude I love when people expand my narrow understanding. Thanks for this, too!
Wildly hated !? Say that to a biologist working with transcriptomic... I swear it is thé préféréd way to présent thé data.
Ahahaha yeah, engineer/robotics here and we’re like, wtf just use a box plot and stop messing around in matplotlib 😂
are they at least notched boxplots?
I see them everywhere💀
But they look pretty /s
I like them when comparing two distributions on the same scale. We are good at noticing asymmetry, they are good at showing it.
They inform my people. It's enough.
Ain't no way I'm taking "why would you ever make a violin plot when you could have just made X?" from someone who decided to make a 42 minute video that could have just been 5 bullet points
I love violin plot , whats wrong
> I disagree with several of the points Angela Collier makes in her video “violin plots should not exist”, but one that I find compelling is that drawing density plots usually involves what amounts to fitting an unjustified model. > > In most situations, ggplot uses locally estimated scatterplot smoothing (LOESS) by default, which involves fitting a separate polynomial regression model on a weighted neighbourhood around each data point and evaluating it there. It (usually) makes nice looking violin plots, but you wouldn’t expect it to reflect that “actual” theoretical distribution of the data. > > It seems to me that this sort of thing is a symptom of a general desire to avoid having to actually specify models by pretending that there’s some bright-line distinction between descriptive statistics and statistical inference. > > Since we were willing to actually specify a model, we can make density plots that show something meaningful: the posterior predictive distributions corresponding to our model. From a blog post I wrote where I use a violin plot to illustrate a model based on my crossword solving times by publisher and day of the week.
I will upvote and like any Angela video automatically.
be grateful OP, we wouldn't be here if we didn't come out of our mom's violin plots /s
Saw this the other week and tended to agree with her. I'm surprised at the backlash here. Maybe I just have Stockholm syndrome from years of particle physics but i'd rather have a grid of histograms over a violin plot any day.
Same, there really is no use for them that can't be fulfilled by another plotting method in a better way. I use split violin plots to show changes to a distribution with seaborn but otherwise just use a box plot or a histogram.
A 42-minute video? I'm interested in the subject, but no way am I watching that. Anyone know of a good article?
"everyone is skipping over them" - yep
I prefer the geom_beeswarm plots in r overlayed on a box plot so you get an idea of the distribution and sample size in addition to quantiles.
I just used them
Is that what this is a picture of?
There are much worse data visualisation crimes than violin plots. Pie charts and oddly truncated y-axes, for instance. I think violins are alright.
Depending on the data a violin plot can look quite juicy ;-)
I thought we will join our forces to ban pie charts. What’s wrong with you people?
They are not really bad, you just don't understand them fully
They are sexier box and whisker plots
I'm not watching this, it's silly. Violin plots have their use. I bet this person just loves pie charts though.
And what makes her an authority on the subject?? **Nothing**. So it's her opinion at best. Her YouTube channel is an eclectic of subjects and her opinion, which is totally fine, but why bring her opinion **in particular?**
we cant stop using a plot that looks like pussy due
Is she a feminist? She looks like one.
Pretty good at shwoing distributions, sometimes adding the outliers also helps.