Learn how to get around in R.
- [Instructor] You have your software installed now, so we're going to take a quick tour of each platform. Let's start with R, the first step is to open up RStudio, we have it here in our dock, so I'm just going to click on it from there, it might be somewhere different on your computer, but let's open up the application. I'm just going to click not now to this message, you may or may not be receiving that message on your end as well, so. You'll see that this is divided into four panes, let's start at the top right. This is our environment, and any data sources we have, loaded into R, will show up in this section here.
Directly below that environment pane, is a section predominantly for our data visualizations and for our graphs. You have a few other tabs here as well, and we'll explain those just in brief, you can access your help information, you can see which packages you have loaded in and you can navigate your files from this section as well. Moving over to the left, is where our console is. In this area you can write our programs and commands and see the output from other R programs that you're running while within the environment.
And finally, in our top left quadrant, once we've loaded a R program in, will be, both our date output, but also our R code that is outside of the terminal screen, so let me provide you with an example of this real quickly. I'm going to go into our exercise files, and I'm going to navigate into 02 02, and I'm simply going to select the exploratory R file, and then drop that right on top of the R icon.
So you can see, in the top left window, now open up another file, and this is our exercise file that will house some of our code, and this area too, we'll be able to see and review some of our data. So that's the R environment in a snapshot. What I like about RStudio, is everything is encapsulated within this view, there are tabs nested within panes, but out of the gate you can see the entire lay of the land. Python and Tableau both work a little bit differently. With that said, I'm going to go ahead and close out of RStudio, and I'm going to open up Python.
If you recall, we're going to bring up our terminal, and from our terminal, we're going to type Jupyter notebook. Now what you're seeing is your Jupyter dashboard, and all of the files that exist from within that directory. So from here, there's one primary view that you need to know about, and that's why I say it works a little bit differently from RStudio, because there we saw everything on one screen, but in Jupyter we have to navigate into what are called our notebooks, and it's in our notebooks where we can edit our code, we can review our data, and we can do all of the things that we have to do with Python.
Let me give you an example here. So I'm going to go into our desktop, going to navigate into exercise files, I'm going to click on 02 03, and like we looked at previously, I'm simply going to click on that exploratory notebook. This brings us into, the view that allows for us to edit our code. So again, that's really the idea of Python in a snapshot, what we do is we click on the notebook file itself and that opens up our edit view. Now I'm going to select the running notebook, which is denoted by the green icon here, and I'm going to shut it down.
I'm also going to close out of this browser window, and then I'm going to select control c, on my keyboard, and I'm going to select yes. And now we've officially closed out of the Python environment. Now, let's have a look at Tableau. Here I have my application icon in the dock, so let me click on that and open it up. Now the first thing we see is the start page, and there are three sections, there's the connect section, on the far left, this is where we will actually connect to our data.
There's the open section here in the center, and that allows us to review open workbooks in the Tableau community and then on the far right side there is the discover pane, with access to other data visualizations and resources that we can take a look at. Now, I'm going to go ahead and click on text file and connect to some data, let's navigate to our desktop and to our exercise files, and choose 02 04, and select exploratory Tableau. So again, this will help us to see our data.
And then if I click on sheet one, now we're in a Tableau workspace. On the far left side, you can see that this is our data, this is the data we were just looking at in the data source window, actually let me click back on that tab real quickly, so we can see our data. So here we've got key words, campaigns, clicks, and again if I come into my sheet, you see that same data organized here, so we've got data listed out as measures, and measures our numeric data, and then we've got our dimensions.
And the dimensions are what our measures are categorized by. Moving over to our right, we have our marks card, and our marks card allows for us to really take control of the different aspects of our visualizations. So we can change color, we can change the font face, we change the different sizes of our visualization. Moving over to the right even more, we have our two shelves. We have our shelves for our column data, we have our shelves for our row data, so these shelves house the data that we're looking to visualize.
So let me give you a quick example, I can simply drag clicks into columns shelf, and I can grab our Ctr and drop that real quickly into our row shelf. And so this helps us to begin to visualize that data. The last thing I'd like to point out, in terms of the Tableau interface, is the show me palette. So, this can be found on the right side of your screen, so I'm going to move my mouse over here, and I'm going to click on show me, now what this reveals is a number of different data visualization types that I can apply to my data.
And we'll see that come to life more as we go through the course. So there's your overview for Tableau. Now let's go ahead and shut down out of this. We don't want to save this. The good news, is that in just a little bit of time, you now have a high level understanding on how to get around these applications. If you can open, navigate and close these different applications, then you're well on your way to becoming a citizen data scientist and for tackling the rest of this course. Congratulations, let's move on.
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment