Hi, great to see you again. When I first learned R, it was the visuals that really got me hooked. I still think it's so cool that you can write a little bit of code, press a button and, presto, out pops an awesome data visualization. Before we get into all the details, I thought it would be fun to give you a quick sneak peek and show you what R can do. What follows will be a preview of what you're going to learn. By the end of this course, you'll not only understand all this code, you'll be able to write and execute it as well. For now, just sit back, relax and enjoy the show. Let's start by loading a library and getting a dataset to work with. We can use the palmer penguins dataset, which contains size measurements for three penguin species that live on the Palmer Archipelago in Antarctica. This includes data on stuff like body mass, flipper length and bill length. The dataset has 344 rows of information sorted into eight columns. The palmer penguins data is popular with analysts and is great for fun exploration, visualization and teaching concepts. We'll see more of this data set later on in the course. Let's say we want to visualize the relationship between body mass and flipper length. You may guess the larger the penguin, the longer the flipper. We can find out for sure by creating a plot. Let's make a scatter plot. A scatter plot uses points to display the relationship between two variables. So the two variables were going to compare our body mass and flipper length. No need to memorize all these details right now. You'll have time to learn more about them later on. Let's check out the parts of this code and how they fit together. The first function starts the plot. If we run the code at this point, all we get is a blank plot. If we add some more code, R will put labels on each access of our plot and add lines for data. Body mass is on the y-axis and flipper length is on the x-axis, but the data points are not yet visible. To get the complete plot, we can add some more code that tells R how to represent our data. For example, we could use points, bars or lines. We'll use points to create a scatter plot. We can go further. For example, we can change how the plot looks. Let's change the color of all of the points to purple. You can hit the Up arrow to pull up the last piece of code you ran, so we'll do that now. And then we'll add in color equals purple inside geom point. Now we can hit Enter to run this. We can also add new information to the plot and use color to highlight it. Let's tell R to assign a different color to each species of penguin. This way we can link data points to each group of penguins. Gentoos are the largest. The legend just to the right of the plot shows us that the blue points refer to the Gentoos. R automatically creates a legend for the plot to help us understand the color-coding. R does everything you tell it to do and even does stuff you don't ask for. It's just that helpful. We can also use shape to highlight the different penguin species. Or we can use both color and shape. In addition to highlighting our data, we can also reorganize it. We can break our data down into smaller groups or subsets and create a plot for each subset. Let's say we want to focus on the data for each species. Facet functions let us create a separate plot for each species. Check this out. Facets are so great. We can even put text on our plot to point to specific data or communicate a message. Let's give our plot a title to clearly indicate its purpose. Finally, we can save our plot, so we can access or share it later on. Now, if we click on the Files tab, we'll find our file in the list. Let's open it up. Well, that's the end of the show. I hope you enjoyed it as much as I did. We were able to take a big dataset and quickly visualize some significant patterns. These are some of the basic functions in R. In other words, this is just the beginning. It's exciting to think of all the ways R can help you realize the full power of data analysis. As you move forward, you learn more about each of the functions we use to create our plots. By the end of this course, you'll be the one writing and executing all of this code. Coming up, we'll learn more about computer programming and how it can help you analyze your data. See you soon.