Once you've loaded your data into R, then you might want to do is manipulate that data, and so you can set it up to be a tidy data set. Variables in the columns and observations in the rows, and only the observations that you want to be able to analyze. So this, first lec, part of this lecture is going to recover some of the information that you've already seen about subsetting, that you've probably seen in your R programming class, in case you don't remember it off the top of your head. So what I'm going to do, is I'm going to create a data frame here, and I'm going to create that data frame with three variables that they're labeled var one, var two, and var three. And then what want to do is I'm actually going to scramble up those variables so that they're not in a specific order. And I'm going to make some of the values be missing. So I'm going to make some of the values NA. Once I've done all that, I see that this is the data set that I've created. So, you can see in any of the columns that the va, values aren't necessarily in order, and you can see that for variable two, there's a couple of missing values with the NAs. So, the first thing that I can do is I can subset a specific column by doing x comma one, where I have x open bracket comma one. And what that'll do is actually open up just the first column of that data frame. The other thing I can do is if I want to subset by a column, I can again do x open bracket comma. And then I can actually use the variable name before the close bracket to subset just that column. I can subset by both rows and columns at the same time. So for example, this command here, x, open bracket, one colon two, comma var two, will actually output the first two rows of x, and the first and the second column of x. So you could subset both on rows and columns at the same time. So, the other thing is that you can do is you can subset using logical statements. So, for example, suppose I want to find out all the rows of x where variable one is less than or equal to three. And variable three is greater than 11, so to define those rows I can pass it this logical argument like that, to subsetting the rows and I end up with just the rows where both of those conditions are met. I can also use an or, so I can try to find the places the rows where, variable one is less than or equal to three or,using type command here variable three is greater than 15 ,and the result is this data frame here where one or the other of those two conditions is met. Finally, if I'm dealing with NA values subsetting on NAs will not produce the actual rows, and so what you want to do, is you want to use the which command, so if you use which, it will return the indices where variable two is greater than eight in this case. And so when it returns the indices where variable two is greater than eight, it doesn't return the NAs, and so you can subset the data set. Even dealing with the NAs that you might have. You can also sort variables. So using the sort command. So if you type sort and then pass it x of variable one, it'll sort the values in increasing increasing order. You can tell it to sort them in decreasing order with the parameter decreasing set equal to TRUE. And then the other thing you can do is, when you're dealing with a variable that has missing values, you can tell it to put all the NA values at the end of the sort. So you can say, na.last equals TRUE. The other thing that you can do is, you can actually order a data frame by a particular variable. So suppose that I want to order the data frame, in other word I want to sort all of these rows. By the values of variable one, so that the varia, values of variable one are in increasing order as I go down. So what I can do is take this function order and apply it to that variable one, and then pass the results of that ordering as the selection to the rows of the matrix x, and what that'll do is reorder the rows so that variable one is in increasing order. You can also order by multiple variables, so for example, you can pass it variable one and variable three and so what it'll do, is it will first sort so variable one is in increasing order. And then if there are multiple values of variable one that are the same, it will sort the values of variable three in increasing order within those values. So it sorts the first variable first, and the second variable in the first variable. You can actually do that the same thing with the plyr package, which is a package written by Hadley Wickham. And what you can do, is once you've loaded that library, you can use the arrange command. So the arrange command takes a data frame and then you can tell it a variable. And that variable will the variable that the data frame is sorted on. So now you can see we've sorted on variable one, and so variable one is in an increasing order here. You can also tell it to do decreasing order by just passing the, to the arrange command, the data frame, and variable one but wrapped with the desc function which will tell it to put it in ascending order. You can also add rows and columns to data frames, quite straight forward process so you just basically say x of variable four, so this is a variable that wasn't in the data frame before, we are going to assign to that variable. A new vector that that's random normal vector of length the same dimension as the number of rows of the data set. And then you can see that, that variable has just been added to the data set like that. Another way that you can do this is by using the cbind command, so what that will do is it will take the data set, the data frame x, and it will column-bind the new vector to [UNKNOWN] basically add a column to basically add a column on the right hand side of x. If I put these in any other order, if I put rnorm five here, and then put x second, what it would do is it would bind that column to the left side of x. Which is sort of intuitive. If you want to bind the rows, you can use the rbind command. So it's the same command, only with an r at the beginning. And so if you put the rnorm at the end what it'll do is it'll bind it to the bottom of the data frame, and if you put it before, it'll bind it to the top of the data frame. There are a lot more notes that are to be had on subsetting and reordering and sorting in the R programming course in the data science track. And I've taken some of these information from these nice set of lecture notes by Andrew Jaffe, who teaches the Windsor Institute here at the John Hopkins School of Public Health. And you can check out his lecture notes for more information as well. [SOUND]
