 When you're working in data science and trying to communicate your results, presentation graphics can be an enormously helpful tool. Think of it this way, you are trying to paint a picture for the benefit of your client. Now, when you're working with graphics, there can be a couple of different goals. It depends on what kind of graphics you're working with. There's the general category of exploratory graphics. These are ones that you are using as the analysts. And for exploratory graphics, you need speed and responsiveness. And so you get very simple graphics. This is a base histogram in R, and they can get a little more sophisticated. And this is done in GG plot, and then you can break it down a couple of histograms, or you can make it a different way or make them see through or split them apart into small multiples. But in each case, this is done for the benefit of you as the analysts understanding the data. These are quick, they're effective now, they're not very well labeled, and they're usually for your insight, and then you do other things as a result of that. On the other hand, presentation graphics, which are for the benefit of your client, those need clarity, and they need a narrative flow. Now, let me talk about each of those characteristics very briefly. Clarity versus distraction. There are things that can go wrong in graphics. Number one is colors. Colors can actually be a problem. Also, three dimensional or false third dimensions are nearly always a distraction. One that gets a little touchy for some people is interaction. We think of interactive graphics as really cool, great things to have. But you run the risk of people getting distracted by the interaction and start playing around with it, we're like, Oh, I press here, it does that. And that distracts from the message. So actually, it may be important to not have interaction. And then the same thing is true of animation. Flat static graphics can often be more informative because they have fewer distractions in them. Let me give you a quick example of how not to do things. Now, this is a chart that I made, I made it in Excel, and I did it based on some of the mistakes I've seen in graphics submitted to me when I teach. And I guarantee you everything in here I have seen in real life just not necessarily combined all at once. Let's zoom in on this a little bit so we can see the full badness of this graphic. And let's see what's going on here, we've got a scale here that starts at eight goes to 28% and it's tiny doesn't even cover the range of the data. We've got this bizarre picture on the wall, we have no access lines on the walls. We come down here, the labels for educational levels are an alphabetical order instead of the more logical higher levels of education. Then we've got the data represented as cones which are difficult to read and compare. And it's only made worse by the colors and the textures. You know, if you want to take an extreme this one for grad degrees doesn't even make it to the floor value of 8%. And this one for high school grad is cut off at the top at 28%. And this by the way is a picture of a sheep and people do this kind of stuff and it drives me crazy. If you want to see a better chart with the exact same data, this is it right here. It's a straight bar chart. It's flat. It's as simple. It's as clean as possible. And this is better in many ways. Most effective here is that it communicates clearly. There's no distractions. It's a logical flow. This is going to get the point across so much faster. And I can give you another example of it. Here's a chart I showed previously about salaries for incomes. I have a list here I've got data scientists in it. If I want to draw attention to it, I have the option of like putting a circle around it. And I can put a number next to it to explain it. That's one way to make it easy to see what's going on. But you don't even have to get fancy. You know, I just got out of pen and a post it note and I drew a bar chart of some real data about life expectancy. This tells the story as well that there is something terribly amiss in Sierra Leone. But now let's talk about creating narrative flow in your presentation graphics. To do this, I'm going to pull some charts from my most cited academic paper, which is called a third voice, a review of empirical research on the psychological outcomes of restorative justice. Think of that as mediation for juvenile crimes, mostly juvenile. And this paper is interesting because really, it's about 14 bar charts with just enough text to hold them together. And you can see there's a flow. The charts are very simple. This is judgments about whether the criminal justice system was fair. The two bars on the left are victims, the two bars on the right are offenders. And for each group on the left are people who participated in restorative justice or victim offender mediation or mediation for crimes. And for each set on the right are people who went through standard criminal procedures. It says court, but it usually means plea bargaining. Anyhow, it's really easy to see that in both cases restorative justice bar is higher. People were more likely to say it was fair. They also felt that they had an opportunity to tell their story. That's one reason they might think it's fair. They also felt the offender was held accountable more often. In fact, if you go to court on the offenders, that lines below 50%. And that's the offenders themselves making the judgment. Then you can go to forgiveness and apologies. And again, this is actually a simple thing to code. And you can see there's an enormous difference. And in fact, one of the reasons there's such a big difference is because in standard court proceedings, the offender very rarely meets the victim. Now it also turns out that I need to qualify this a little bit because a bunch of the studies included drunk driving with no injuries or accidents. When we take them out, we see a huge change. And then we can go to whether a person is satisfied with the outcome. Again, we see an advantage for restorative justice, whether the victim is still upset about the crime. Now the bars are a little different. And whether they're afraid of re victimization, that's over a two to one difference. And then finally, recidivism for offenders are re offending, and you see a big difference there. And so what I have here is a bunch of charts that are very, very simple to read. And they kind of flow in how they're giving the overall impression, and then detailing it a little bit more. There's nothing fancy here. There's nothing interactive. There's nothing animated. There's nothing kind of flowing in 17 different directions. It's easy, but it follows a story and it tells a narrative about the data. And that should be your major goal with presentation graphics in some presenting or the graphics that use for presenting are not the same as the graphics you use for exploring, they have different needs and different goals. But no matter what you're doing, be clear in your graphics and be focused in what you're trying to tell. And above all, create a strong narrative that gives a different level of perspective and answers questions as you go to anticipate a client's question and to give them the most reliable, solid information and the greatest confidence in your analysis.