Bringing sophistication of charts we're going to talk about how the careful attention to detail will ensure database is effective and efficient and communicating our insight. We're going to really look deeper at Dona Wong's guidelines because she has put together a tremendous point of view on that sort of visual Polish in the same port. Remembering again we are looking at the three elements of visual form that really answers for us, what does good visual form do? We have talked about clear meaning as physic ated use of contrast, we're now on to the third element, which is refined execution. When we are building charts that are in our discovery mode and we're trying to find insights, R a fantastic tool for that. R works very quickly and efficiently chugs through tons and tons of data, and it can present those patterns that will help us find the stories. But in no way should we present a chart like the one you're seeing here that comes directly from our to clients, there's two little Polish applied. Even if the story is clear to us an audience who has not been engaged in the data collection and all the analysis that that we've done, will look at this and see nothing, see nothing but a jagged line. So we need to apply some Polish to make this as Baron Otto would call everyday database or as I'm like call client ready data visualization. And R can still be play an important role in that process, but it's typically to create visuals that look a little more like this. So what you're looking at are cycle hire Journeys taken in London. This is when people are renting bikes from the city and using them to move across the city. The darker the line means the more travel through that route. That part of this graphic came from R, that was a simple output from R. R has the kind of power and the different packages to do that sort of really sophisticated analysis. Once that output is collected though, we then move it into a second platform, a separate program, to clean it up and add some other elements that make it a little more easy to understand. In this case we have added a map of London and place that underneath the R output of the cycle hire journeys. That now gives us context to where these journeys are actually happening before they just would have looked like a collection of squiggly lines. We've also added a subtitle, we've added a proper title. We've added some more information to this visual so that it does communicate a little more effectively. These elements though are certainly things we learn over time. Look Donna Wong said it best. She said we don't start out writing editorials, we start by learning the alphabet. We should take the same approach as we think about applying some of these guidelines. Because the guidelines had Dona Wong talks about that truly do separate good visuals from great visuals are complex, there are many of them. Some of them will certainly fly right in the face of habits that you have been building for your entire career in the way that you create charts. But I'm here to tell you that if you do adopt the practices that Dona Wong talks about, you will be creating great data visuals. Every single thing that Dona talks about is helping to improve the legibility of the charts we create. From the idea of font choice to the way that we present font on present text on a page. From those ideas of direct labeling that we talked about before, the idea of creating very unadorned line charts, right? Not using data markers or never ever creating a dotted line, right? Just using different elements of color to bring contrast and striving for clean lines with clear signals in our visuals. These are the things that Dona talks about. As I've said her list is long, some of it may feel a little wonky at first as you try to apply them. But the more we can build the habits that Dona talks about, the better our graphics will be. And that is the power of this refined execution in the Polish that we can place on our visuals. A couple of things to think about then. Sophisticated execution of Dataviz really does require great attention to detail. This careful attention to detail will ensure that our Dataviz is effective and efficient and communicating to our insight, which is exactly what we want, as we don't want our audience having to think too much about the visual that were placing in front of them. These work product graphics, these visuals that were using to just discover the the patterns in our data and really find stories, they don't have to have refined execution. Look, these things are for our our eyes only. At this point we just want to create them as quickly and efficiently as we can so that we can get to that story. After that will bring effort and often two or more applications to transform those visuals into something that is presentation worthy. What Baron Otto again would call everyday dataviz, or we would call client ready dataviz. Something that we could trot out to an audience and used to confidently indicate some kind of insight. Most importantly of the guidelines that Dona Wong has given us is this idea just removing clutter from a chart, it's the most impactful way that we can improve readability. Removing that clutter keeps elements that will distract our audience from our main point away. It helps to really center us on the few important elements of our chart that we want to communicate. And in that way clearly communicate the insights to our audience.
