The following graph caught my eye recently in my Twitter feed:

I've been debating whether to post about it (and finally decided that I couldn't resist).

I don't want to rip it apart.

Well, that's not entirely true.

I do want to rip it apart, but it's not in an effort to be mean. The above visual breaks pretty much every best practice out there when it comes to effective graph design. It's simple data. Probably not so much is being lost in terms of being able to interpret the data through this less-than-stellar data viz. But the specifics of the design choices (or lack thereof) drive me batty. To the extent that I can't help but comment and resolve to show what it has the potential to be.

First, let me list the main components that get under my skin (and I should note that it's possible some of these are constraints in the Plot.ly tool through which the above visual was published, which I have not used directly) :

No meaningful ordering to the data (rather, the categories are shown in reverse alphabetical order... not so helpful);
Lack of axis labels (sure, we can infer, but why should we have to?);
Diagonal text on x-axis (avoid, avoid, avoid!); and
Grey background, white vertical gridlines, and black bar outlines add unnecessary clutter (eliminate!).

I think the only positives I have to say about the original visual are: 1) a horizontal bar chart is a good choice here because we're dealing with categorical data with long category names, 2) good descriptive graph title, and 3) it makes me happy to see the data source listed (both as general good graph hygiene, as well as because it allows me to get to the source data to remake the visual).

Speaking of remaking the visual, here's what it could look like when we tackle the above issues:

If we just want to show the data, we could proceed with the above. Taking a cue from the original visual - a single point is labeled with its corresponding value: Drug Offenses - perhaps there is a story here worth highlighting. If that's the case, our visual might look something like the following:

Meta-lesson: if you're going to go through the effort of visualizing data, take the time to be thoughtful about your design choices!

If you're interested in the Excel version of the above makeovers, you can download it here.