 Now we're going to get into some of the data types that are recognized by R starting with the most common. The most common data type that's used in R is called a vector, which is just a collection of values. You might think of this when you're doing collecting data in a spreadsheet. The rows of that spreadsheet, the collection of values in each of those cells in a single row would be considered or stored as a vector. So let's start out by creating a new section here called vectors. So here we're going to create a new variable called HHMembers, which just stands for household members. And this is an attribute, one of the field names from the SAFI dataset. And we're going to use a function, the C function, which just stands for combine. And we're going to throw a list of values in here. Okay, I'm just going to use these four, two, three, four, one, and run that. Now you can see here that in the global environment, there's more information provided about this new vector than our previous variables that we've created. So not only we told the values that we stored in that vector, but we're also told that it's the numeric data type. In other words, that all the values are of the same data type, and that's numeric. And also how many elements are stored in the vector. Okay, and this is one difference between R and Python. R counts from one to four. So the first index of those values is one, and four is last. Whereas in Python, for instance, this would start at zero, and so it would go from zero to three elements. Okay, the next variable that we're going to create here is called a respondent wall type. There's another field name from our dataset. We use the same combined function, but this time instead of entering numeric data, we're going to use what's called strings or text data. So any string data is going to be stored using these quotation marks. And R is indifferent if you use the double quotations or the single quotation marks. But following the exercise, we're going to go ahead and use the double quotation marks just for readability. So we're going to insert three elements here, mud dom, bricks, and sun bricks. And we're going to run this. When we do respondent wall type appears there. And just in the previous, as in the previous case, we have the elements of that vector stored here, but instead of numeric data type, we now have the character data type. And instead of four elements, we have three elements. So as I mentioned, this data type, the vector data type is often used to store the contents of entire data sets, entire rows of data with lots and lots of different values. And sometimes it can be helpful to get kind of a really quick view of the contents of that data. So maybe you want to know, for instance, the length of the vector, how many elements it contains. So for that purpose, you can use the link function. And within the parentheses, you just type in the name of the variable that you want to return the length of. When I do this in a console with Prince three telling me that there are three elements in that vector. Likewise, you might want to know something about the data type, the type of data that's contained in that variable. For that purpose, you use the class function, again with parentheses, the parentheses just into the name of that variable. And now you can see that it prints out these are the character data type. You can see it's different if I was to use HH members. Now it's going to tell me this is of the numeric data type. And one last function will introduce you to is the str function, which just stands for structure. The type that responded wall type. And run that. And what you see down here in the output is basically all the same information that's stored up here in our global environment. This just gives us an overview of basically all the characteristics of that vector, both the data type and the number of elements and the actual contents of that vector. And finally, you can also use the combined function to add new values to an existing vector. So I'm going to show you how that works using respondent wall type. So I'm changing the contents of the variable. I'm going to want to use the assignment operator, the C function. And then I'm just going to call up respondent wall type, which is going to bring in all the first three values that are already stored in that variable. Then I'm going to add one more entry of mud dog. And the fourth household that I survey in this study is also mud dog. So I want to add that value to this, this data set. I run that and then that variable, the contents of that variable again. Now you can see that the fourth value has been added. Okay, and you can also imagine that maybe these households have an order and instead of visiting the fourth household, maybe I'm actually going back and filling in an earlier household that I missed the first time. You can do the same thing. And I'm just going to borrow this line of code that I've already started up here. I'm going to use the same function, but now I'm going to call, let's go ahead, but mud dog at the beginning, followed by respondent wall type. Now you should all expect that when I print this, now I'm going to have five values with a new mud dog at both the beginning and the end. And sure enough, our new mud dog here for household number one is here, followed by our original three values, and the one I just added at the end. Okay, great that wraps up our second segment. If you want additional practice again there are exercises at the end of this section, just refer to pages 18 and 19 in the PDF.