I have a dataset of passwords that looks like this:

enter image description here

My goal is to make a graph that accurately showcases the strength of each category so my initial plan was to group the data by category and calculate the average strength like this:

enter image description here

then represent the data with a barplot like this one:

enter image description here

My problem with this is that when I looked up the cardinality of each category I noticed that the number of data points is very different as seen here below:

enter image description here

So I thought that calculating the average blindly and making a barplot isn't very faithful to the data because of two reasons. First, you can't see the number of data points in a bar plot; secondly, you can't see the outliers that would immensely affect the mean, especially when you have a very small number of data points in certain categories. So I decided to try and make a boxplot instead as seen here:

enter image description here

But my problem with this is that a boxplot is more complicated for the average person than a simple barplot, even if it's more faithful to the dataset.

What do you guys think is better? A barplot or a boxplot? Is there any alternative that is simple and faithful to the dataset I described?

EDIT: First of all here is the variables concerned in the dataset for you guys to see the data :

> dput(passwords[c(3,8)])
structure(list(category = c("password-related", "simple-alphanumeric", 
"simple-alphanumeric", "simple-alphanumeric", "simple-alphanumeric", 
"simple-alphanumeric", "animal", "sport", "sport", "password-related", 
"animal", "simple-alphanumeric", "simple-alphanumeric", "cool-macho", 
"name", "cool-macho", "cool-macho", "name", "simple-alphanumeric", 
"simple-alphanumeric", "name", "name", "cool-macho", "simple-alphanumeric", 
"cool-macho", "simple-alphanumeric", "cool-macho", "cool-macho", 
"name", "name", "name", "sport", "name", "password-related", 
"password-related", "cool-macho", "sport", "name", "name", "name", 
"name", "fluffy", "fluffy", "name", "simple-alphanumeric", "food", 
"name", "password-related", "simple-alphanumeric", "simple-alphanumeric", 
"name", "name", "nerdy-pop", "fluffy", "name", "sport", "sport", 
"simple-alphanumeric", "name", "simple-alphanumeric", "simple-alphanumeric", 
"name", "fluffy", "rebellious-rude", "rebellious-rude", "nerdy-pop", 
"cool-macho", "cool-macho", "name", "fluffy", "name", "cool-macho", 
"fluffy", "cool-macho", "name", "name", "simple-alphanumeric", 
"nerdy-pop", "name", "simple-alphanumeric", "sport", "food", 
"fluffy", "name", "name", "name", "name", "fluffy", "fluffy", 
"animal", "rebellious-rude", "simple-alphanumeric", "cool-macho", 
"cool-macho", "cool-macho", "name", "nerdy-pop", "animal", "fluffy", 
"name", "cool-macho", "name", "fluffy", "cool-macho", "animal", 
"simple-alphanumeric", "name", "animal", "cool-macho", "name", 
"cool-macho", "fluffy", "sport", "fluffy", "name", "simple-alphanumeric", 
"cool-macho", "name", "fluffy", "sport", "name", "nerdy-pop", 
"nerdy-pop", "rebellious-rude", "cool-macho", "name", "password-related", 
"name", "sport", "sport", "animal", "cool-macho", "simple-alphanumeric", 
"password-related", "name", "name", "cool-macho", "cool-macho", 
"name", "nerdy-pop", "food", "fluffy", "name", "animal", "simple-alphanumeric", 
"password-related", "name", "cool-macho", "nerdy-pop", "nerdy-pop", 
"name", "name", "cool-macho", "password-related", "name", "name", 
"sport", "food", "cool-macho", "animal", "sport", "cool-macho", 
"animal", "password-related", "name", "name", "name", "cool-macho", 
"name", "sport", "nerdy-pop", "name", "name", "animal", "cool-macho", 
"name", "sport", "nerdy-pop", "cool-macho", "name", "name", "sport", 
"rebellious-rude", "cool-macho", "name", "name", "name", "cool-macho", 
"fluffy", "sport", "name", "nerdy-pop", "simple-alphanumeric", 
"name", "rebellious-rude", "sport", "nerdy-pop", "simple-alphanumeric", 
"name", "animal", "simple-alphanumeric", "sport", "name", "simple-alphanumeric", 
"name", "name", "simple-alphanumeric", "name", "fluffy", "name", 
"cool-macho", "name", "fluffy", "rebellious-rude", "sport", "name", 
"fluffy", "name", "name", "fluffy", "name", "name", "fluffy", 
"name", "name", "name", "name", "nerdy-pop", "cool-macho", "cool-macho", 
"name", "food", "rebellious-rude", "name", "sport", "name", "cool-macho", 
"name", "nerdy-pop", "name", "name", "fluffy", "fluffy", "fluffy", 
"name", "food", "animal", "name", "name", "sport", "name", "name", 
"name", "name", "name", "sport", "sport", "name", "name", "name", 
"name", "simple-alphanumeric", "name", "fluffy", "cool-macho", 
"sport", "name", "name", "name", "password-related", "password-related", 
"fluffy", "name", "nerdy-pop", "cool-macho", "animal", "simple-alphanumeric", 
"simple-alphanumeric", "animal", "name", "name", "fluffy", "food", 
"fluffy", "fluffy", "simple-alphanumeric", "simple-alphanumeric", 
"name", "name", "name", "name", "simple-alphanumeric", "name", 
"nerdy-pop", "cool-macho", "name", "cool-macho", "cool-macho", 
"rebellious-rude", "animal", "cool-macho", "name", "food", "animal", 
"simple-alphanumeric", "fluffy", "name", "animal", "fluffy", 
"sport", "simple-alphanumeric", "name", "name", "sport", "simple-alphanumeric", 
"simple-alphanumeric", "food", "name", "animal", "password-related", 
"nerdy-pop", "cool-macho", "name", "cool-macho", "simple-alphanumeric", 
"name", "name", "cool-macho", "cool-macho", "password-related", 
"name", "name", "nerdy-pop", "name", "name", "nerdy-pop", "cool-macho", 
"food", "fluffy", "name", "name", "name", "name", "simple-alphanumeric", 
"cool-macho", "name", "name", "animal", "fluffy", "cool-macho", 
"cool-macho", "simple-alphanumeric", "simple-alphanumeric", "name", 
"name", "animal", "animal", "cool-macho", "nerdy-pop", "sport", 
"sport", "name", "sport", "animal", "simple-alphanumeric", "name", 
"nerdy-pop", "simple-alphanumeric", "fluffy", "cool-macho", "animal", 
"simple-alphanumeric", "cool-macho", "simple-alphanumeric", "name", 
"fluffy", "fluffy", "cool-macho", "name", "name", "cool-macho", 
"cool-macho", "name", "cool-macho", "name", "name", "cool-macho", 
"name", "cool-macho", "name", "sport", "name", "name", "name", 
"simple-alphanumeric", "cool-macho", "name", "name", "name", 
"fluffy", "name", "name", "simple-alphanumeric", "fluffy", "fluffy", 
"sport", "simple-alphanumeric", "name", "name", "simple-alphanumeric", 
"name", "name", "cool-macho", "simple-alphanumeric", "cool-macho", 
"fluffy", "sport", "rebellious-rude", "cool-macho", "name", "simple-alphanumeric", 
"nerdy-pop", "cool-macho", "animal", "name", "cool-macho", "cool-macho", 
"simple-alphanumeric", "name", "simple-alphanumeric", "cool-macho", 
"cool-macho", "cool-macho", "name", "name", "name", "animal", 
"fluffy", "cool-macho", "name", "nerdy-pop", "name", "name", 
"sport", "name", "nerdy-pop", "sport", "name", "name", "sport", 
"fluffy", "name", "cool-macho", "simple-alphanumeric", "cool-macho", 
"animal", "nerdy-pop", "nerdy-pop", "simple-alphanumeric", "name", 
"name", "name", "simple-alphanumeric", "cool-macho", "animal", 
"fluffy", "animal", "name", "name", "name", "name", "name", "simple-alphanumeric", 
"cool-macho", "name", "cool-macho", "name", "simple-alphanumeric", 
"fluffy", "rebellious-rude", "name", "nerdy-pop", "name", "name", 
"name", "sport", "name", "food", "name", "simple-alphanumeric", 
"name", "cool-macho", "name", "nerdy-pop", "cool-macho", "cool-macho", 
"name", "nerdy-pop", "name", "password-related"), strength = c(8, 
4, 4, 4, 8, 4, 8, 4, 7, 8, 8, 1, 32, 9, 9, 8, 8, 9, 0, 0, 8, 
10, 8, 4, 8, 25, 7, 8, 8, 6, 7, 6, 7, 4, 3, 6, 8, 6, 9, 8, 8, 
6, 9, 7, 4, 0, 8, 3, 4, 4, 8, 6, 8, 8, 7, 5, 7, 7, 8, 0, 4, 6, 
8, 7, 7, 10, 6, 9, 8, 7, 8, 6, 6, 8, 8, 8, 0, 8, 7, 1, 8, 5, 
8, 8, 8, 9, 8, 8, 6, 7, 7, 5, 8, 7, 7, 8, 8, 8, 9, 8, 8, 9, 7, 
7, 9, 0, 8, 8, 9, 8, 10, 6, 7, 8, 8, 1, 7, 8, 9, 8, 8, 6, 6, 
9, 8, 8, 8, 7, 8, 7, 8, 7, 8, 7, 7, 6, 8, 9, 7, 8, 3, 0, 6, 7, 
0, 8, 7, 8, 46, 8, 6, 9, 8, 8, 6, 8, 6, 0, 9, 8, 8, 6, 6, 6, 
8, 8, 7, 7, 6, 5, 8, 7, 7, 7, 8, 8, 8, 8, 8, 8, 7, 8, 7, 7, 7, 
8, 6, 9, 6, 8, 0, 8, 0, 8, 2, 8, 46, 6, 8, 9, 9, 8, 8, 8, 8, 
9, 0, 8, 7, 6, 6, 9, 8, 8, 7, 6, 8, 6, 8, 8, 8, 9, 7, 9, 8, 8, 
5, 8, 8, 6, 9, 6, 6, 7, 6, 8, 8, 7, 8, 6, 8, 8, 7, 4, 8, 6, 7, 
8, 8, 9, 6, 7, 6, 8, 5, 8, 9, 9, 7, 8, 1, 0, 7, 8, 9, 8, 6, 7, 
8, 7, 6, 7, 7, 19, 6, 8, 8, 0, 7, 8, 6, 4, 7, 7, 7, 0, 0, 5, 
6, 8, 9, 0, 10, 9, 8, 7, 9, 2, 5, 7, 7, 8, 6, 6, 10, 7, 4, 10, 
9, 6, 4, 6, 8, 8, 1, 4, 5, 8, 9, 8, 38, 6, 8, 0, 0, 7, 6, 5, 
6, 6, 8, 8, 9, 5, 6, 48, 3, 4, 8, 9, 6, 7, 4, 35, 7, 7, 8, 7, 
6, 6, 8, 0, 0, 5, 6, 9, 6, 7, 36, 6, 8, 3, 5, 7, 0, 7, 8, 4, 
7, 7, 7, 0, 7, 0, 7, 4, 9, 8, 7, 8, 10, 9, 6, 6, 7, 4, 3, 7, 
10, 6, 7, 6, 6, 7, 36, 6, 4, 10, 5, 5, 7, 8, 0, 6, 7, 34, 21, 
8, 8, 0, 5, 7, 3, 4, 7, 7, 8, 8, 7, 5, 0, 8, 9, 9, 8, 8, 6, 7, 
7, 4, 8, 8, 8, 9, 8, 9, 6, 4, 9, 6, 7, 8, 7, 8, 8, 7, 8, 8, 4, 
10, 8, 9, 7, 4, 8, 8, 7, 8, 0, 6, 8, 7, 32, 6, 7, 4, 6, 8, 4, 
7, 7, 9, 0, 8, 8, 8, 8, 36, 6, 8, 8, 8, 8, 6, 8, 7, 8, 6, 6, 
0, 7, 8, 7, 7, 7, 6, 9, 7, 7, 28)), row.names = c(NA, -500L), class = c("tbl_df", 
"tbl", "data.frame"), na.action = structure(501:507, .Names = c("501", 
"502", "503", "504", "505", "506", "507"), class = "omit")

here is the boxplot I made after taking some of your suggestions. What do you think I should change?

enter image description here

  • 2
    $\begingroup$ Could you elaborate on what you mean by "faithful to the dataset"? After all, the boxplots themselves hide many details. If you wish for your plot to represent the count of each group, then avail yourself of the graphical options in your software. These include varying the widths, positions, and appearances of the individual boxplots. E.g., a standard solution is to vary the widths to represent the counts. $\endgroup$
    – whuber
    Commented Feb 8, 2023 at 18:02
  • $\begingroup$ @whuber By faithful I mean that I want the observer to get as much information as possible about the dataset without knowing the dataset while keeping the graph very simple with a high data-ink ratio. I will try to vary the widths of the boxplots to see what they would look like! $\endgroup$
    – wageeh
    Commented Feb 8, 2023 at 18:06
  • 1
    $\begingroup$ That's an admirable objective. Please bear in mind, though, that the full context of Tufte's rule is to maximize the data-ink ratio within reason and, in many of his examples, this is accomplished by erasing ink rather than adding more graphical elements. A relevant application of this rule is shown at stats.stackexchange.com/questions/13875. Varying boxplot widths is salutary insofar as it doesn't really change the net amount of ink, while permitting a meaningful and clear representation of a variable not otherwise depicted. Notched boxplots are another (more complex) technique. $\endgroup$
    – whuber
    Commented Feb 8, 2023 at 18:11
  • 2
    $\begingroup$ When posting, please consider pasting in text for a sample of your data, not a screenshot of it. Potential answerers will be able to grab text data and play with them, whereas there's nothing we can do with a picture. $\endgroup$ Commented Feb 8, 2023 at 19:30
  • 1
    $\begingroup$ What about something like a stripplot or a swarmplot? (Potentially combined with an underlying boxplot, boxenplot, violinplot, or similar.) $\endgroup$
    – Eike P.
    Commented Feb 8, 2023 at 22:47

4 Answers 4


Concrete suggestions:

  1. Alphabetical order is not helpful. Order categories according to their medians.

  2. Use log scale. More than half your graph space is devoted to showing the 12 highest values out of 500 observations, which is not using space efficiently. Some of your distributions are left-skewed but they won't suffer much under log scale. (Note: If there are exact zeros in your data, something like log (value + 1) should be fine for visualization.)

  3. Show all the data as points: even if they blur together, you will see some fine structure. For example, it looks as if more than 25% of the simple-alphanumeric passwords all tie with the lowest possible strength (zero?).

You hadn't posted your data at the time of first writing, but here is a loosely comparable dataset with 11 categories and 500 observations.

The display is a quantile-box plot in the sense of Emanuel Parzen. As @Gung suggests in his answer, although perhaps for different reasons, I start with just the boxes, medians and quartiles. To a good enough approximation, calculating medians and quartiles commutes with taking logarithms, so that e.g. log(median) will almost always be close to, if not identical with, median(log) (*). But then I do not bother with the usual fal-lal about drawing whiskers to the furthest points within 1.5 IQR of the nearer quartile and plotting as individual points any values that lie beyond. That is because I plot all the data and am happy that moderate or severe outliers will separate themselves from any crowd. That is how they are defined and recognised.

In my case the data arrive on a natural logarithm scale. In your case you need to arrange that your favourite software shows vertical axis labels that make sense, such as 0 (if needed), 1, 2, 5, 10, 20 and 50.

enter image description here

(*) The small print arises whenever medians and quartiles are calculated by interpolation between data values.

EDIT Your data are quite a challenge to an occasional R user like myself. (Yes, I know I could install it and have done so many times.) Here is the graph I proposed for your data, produced using Stata. Putting the names vertically is against my best instincts, but swapping axes does not help much here. The main plus point about this graph -- other than underlining that the medians are mostly about the same -- is to show some of the fine structure. The scale is log(strength + 1) with (tick) axis labels for strength.

enter image description here


enter image description here

Here is the left-hand category (simple-alphanumeric) expanded to show how the plot works.

Individual values are sorted in order. So, we can see repeated values as such; and the horizontal scale is a rank or more precisely a sort from smallest to largest. The median and quartiles are defined as usual. The box bears this interpretation (with small print for ties): half the values are inside the box and half outside. The left-hand display uses log(strength + 1) and the right-hand display uses a plain strength scale.


Yet another attempt follows. Here I am mindful that

  • Logarithmic scale (especially the variant based on adding 1 first) often does not appeal or is not well understood.

  • There is need to show fine structure, not just the outliers but also detail such as the many zeros for some categories. Much of that is implicit in the box plots, but questions here on CV show that box plots for discrete variables with many repeated values are often found puzzling or even misinterpreted.

  • There is need to convey the different sample sizes, which I think this version does as well or better than any previously posted.

The one simplification made here is to lump several groups with similar distributions together.

I call this a quantile plot, but that is a technical term and I would explain it to lay audiences as plotting values in order. Labelling in terms of percentile rank is based on an idea that plotting position or cumulative probability are less likely to be familiar terms. (Those wanting sympathy for non-statistical audiences might not know that I am not a statistician, but a geographer.)

enter image description here

  • $\begingroup$ I took into account your suggestion, could you please check the comments and give me your input? $\endgroup$
    – wageeh
    Commented Feb 9, 2023 at 11:11
  • $\begingroup$ +1, these are good suggestions, but to me, a prominent feature of the OP's situation is that they believe boxplots will be too sophisticated to be understood by their audience. You don't seem to be addressing that aspect. In fact, I would argue your suggestion is more sophisticated / exotic that what they started with. $\endgroup$ Commented Feb 9, 2023 at 12:43
  • $\begingroup$ Fair comment, which I will address now. By reducing box plots to median and quartiles I removed most of the difficulty in explaining them. Otherwise if plotting the data in rank order is too hard to grasp, then there is a difficulty for the reader and I really don't know what to suggest. $\endgroup$
    – Nick Cox
    Commented Feb 9, 2023 at 13:59
  • $\begingroup$ I don't necessarily mean sorting the categories by order. Figures for the general public rarely use logs, b/c it is believed people won't understand (in the US, only ~1/3 get a 4 year degree, mostly at noncompetitive universities & often w/ very little math :/ ). I get the info in your plot quite readily & get more from yours than the other sources on this page, but I'm not the typical person. When there's a news story discussing test scores by state or income or something, the standard plot would be a barplot of medians & showing a plot at all would be considered very technical & advanced. $\endgroup$ Commented Feb 9, 2023 at 14:14
  • 1
    $\begingroup$ @gung I don't disagree with anything you're saying but we're getting close to there being no graph that satisfies all the criteria either that the OP may have or that anyone else may have, as a bar chart showing means and a standard box plot both seem limiting or disappointing. We're not in a fixed landscape as scatter plots were once thought too difficult outside science or statistics, and good newspapers and weeklies show log scales without explaining them fully (there was a small explosion of log scales during the pandemic). $\endgroup$
    – Nick Cox
    Commented Feb 9, 2023 at 15:47

I agree with you that a box plot alone doesn't describe the data very well. But a box plot overlaid with the data points might do. See for example the graph below. I've switched the axis around so it's easier to read, jittered the points and added tick marks so the exact values can be read, added the overall median for comparison and indicated the N on the category axis. The transformation is square root, not as extreme as log and allows you to see the low and the high end data well. The categories are sorted by median score.

enter image description here

R Code follows:

pass$medianstrength <- ave(pass$strength , pass$category, FUN=median)
pass$counts <- ave(pass$strength , pass$category, FUN=length)

ggplot(pass, aes(y=reorder(sprintf("%s (N=%d)", category, counts), -medianstrength), x=strength, col=category)) + 
 geom_boxplot(outlier.color = NA,col="black")  + 
 geom_point(position = position_jitter(height = 0.3,width=0.01),alpha=0.5) + 
 scale_x_sqrt(breaks=c(0,1,2,3,4,5,6,7,8,9,10,20,30,40,50)) + 
 theme_bw() + 
 theme(legend.position = "none") + 
 geom_vline(xintercept=median(pass$strength), lty="dashed") + 
 labs(x="Strength", y="Category") 

Edit following the suggestion from @bjorn to use geom_sina:

I've also removed the whiskers from the box, filled the box a light grey and added the median more prominently for each group. The height of the box reflects the sample size in the category.

A potential problem here is the horizontal jitter, I should have made it proportional to the square root of the value to avoid the very large jitters at the low end, and applied it to the points only not the data as a whole.

enter image description here


pass$medianstrength <- ave(pass$strength , pass$category, FUN=median)
pass$counts <- ave(pass$strength , pass$category, FUN=length)

ggplot(pass, aes(y=reorder(sprintf("%s (N=%d)", category, counts), -medianstrength), x=strength+runif(seq_along(strength),-0.2,0.2), col=category)) + 
  geom_boxplot(outlier.color = NA,col=NA,coef=1,fill=hsv(0,0,0,.2), varwidth=TRUE)  + 
  ggforce::geom_sina(alpha=0.6) + 
  geom_errorbar(aes(xmin=medianstrength, xmax=medianstrength), col="black",alpha=0.5, width=0.5) + 
  scale_x_sqrt(breaks=c(0,1,2,3,4,5,6,7,8,9,10,20,30,40,50)) + 
  theme_bw() + 
  theme(legend.position = "none") + geom_vline(xintercept=median(pass$strength), lty="dashed") + 
  labs(x="Strength", y="Category") 
  • 1
    $\begingroup$ Something like geom_sina from ggforce might look even better (it's a jittered plot, where the width of the jitter is determined by the density like in a violin plot). $\endgroup$
    – Björn
    Commented Feb 10, 2023 at 11:00
  • $\begingroup$ @Björn thanks that worked really well. $\endgroup$ Commented Feb 10, 2023 at 11:50
  • 3
    $\begingroup$ I don't want to get into "mine is better than yours" for the excellent reasons that I like many features of yours too, and in any case there is plenty of room for different approaches. As commentary rather than criticism, I too have struggled with the OP's brief that any graph must make sense to a non-statistical audience. That brief in view, square root scale (which I do agree works as well or better than my early proposal of log1p()), the rules for whiskers or not, and the use of jittering might all pose challenges to the naive or inexperienced reader. $\endgroup$
    – Nick Cox
    Commented Feb 10, 2023 at 13:39
  • 1
    $\begingroup$ To me, most of the challenge is in conveying the details. Most of the medians are very similar and the interesting and I presume important features include the frequent but varying occurrence of passwords with very low strength. $\endgroup$
    – Nick Cox
    Commented Feb 10, 2023 at 14:18
  • 2
    $\begingroup$ I agree with @NickCox. These are really impressive figures. However, as I commented on his answer, my interpretation of this Q is that the fact that the OP needs something simpler than boxplots is the defining feature here. $\endgroup$ Commented Feb 11, 2023 at 1:16

I gather there are several issues here: 1) there is a wide range of amounts of data for the categories; 2) you suspect outliers are distorting the category means; 3) you suspect boxplots are too sophisticated for your audience.

In general, I think boxplots are superior to barplots and that they convey more information. But if you think your audience will be overwhelmed by them, they're out. If you think a good figure caption would be enough to make them intelligible, I would try for that. The outliers expand the range of the plot so much as to make the bulk of the distributions hard to see, though, so I might try just plotting the 'boxes', i.e., the medians and quartiles. That would let you zoom in on the data.

If you think boxplots are not salvageable, you will need to fall back to barplots. Barplots don't have to display means, though. You could make them for the medians, or you could make them for trimmed means. A $20\%$ trimmed mean is the mean of the middle $60\%$ of your data after trimming off the top and bottom $20\%$. You would need a figure caption to explain that and, of course, you could use some other trimming percentage as you find appropriate. Again, if you think it's too complicated, just use medians (a.k.a., $50\%$ trimmed means). Either approach will minimize the distortion caused by the outliers.

Varying the width of boxplots or bars is a common way to represent different amounts of constituent data, as @whuber suggests. It is common for software to allow this. A common recommendation is to make the widths proportional to the square root of the $n$s. Once again, this will require a figure caption to explain it, so there's always still a tradeoff between the ability of the plot to represent information and the required sophistication on the part of the viewers.

  • $\begingroup$ The issues you identified are exactly right. Do you think it's better to leave the boxplots I made in the edit or try with the barplots using medians/trimmed mean? $\endgroup$
    – wageeh
    Commented Feb 9, 2023 at 11:16
  • 1
    $\begingroup$ @wageeh, it really depends on what will best suit your goals, in particular whether you think your intended audience will 'get' the plot. Using the log scale, eg, will add another layer of complexity. It's fine for me, but logs are difficult for the general public to grasp. Consider your audience in making your final choice. $\endgroup$ Commented Feb 9, 2023 at 12:48

Here is an attempt at visualizing the password strength data by following the "less is more" principle.

The idea is simple:

  • Create five strength categories: "very weak [0-4]", "weak [5-6]", "medium [7-8]", "strong [9-10]", "very strong [11+]". I came up with these groups by looking at the histogram of strength values but you might have a better idea for how to split the numeric range of strength into bins. The "very strong" passwords are the "outliers".
  • Make a bar plot for each password category.
  • Order the bar plots by increasing average strength.

For comparison (and since otherwise there is a gap in the 3x4 grid of panels by password category) I add the overall bar plot as well.

enter image description here

This visual has a lot less detail than those proposed by @NickCox and @GeorgeSavva but it's easier (at least for me) to compare the password categories qualitatively. For example:

  • "food" is the worst choice as it never results in a strong password.
  • "simple-alphanumeric" is the second worst choice because it's risky: it can generate a (very) strong password but many choices are very weak. NB: The alphanumeric password "abc123" has strength 32 but I have doubts that it's a good password choice.
  • "rebellious-rude", "name", "cool-macho" and "sport" are mostly medium strength.
  • "nerdy-pop" is the safest choice.
  • 1
    $\begingroup$ Interestingly different, but red and green should not be mixed like this, as many people cannot easily distinguish them. $\endgroup$
    – Nick Cox
    Commented Feb 11, 2023 at 22:39
  • 1
    $\begingroup$ @NickCox Thank you for pointing this out. I changed the palette to sequential green colors. It's the Greens palette from ColorBrewer: colorbrewer2.org. $\endgroup$
    – dipetkov
    Commented Feb 11, 2023 at 22:56

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