[SOUND] Welcome back to this second of three videos. Where we'll be developing a project together. We're going to build a map that visualizes life expectancy around the world and we've broken up that task into three pieces. In the first video we talked about setting up the map. And I hoped that you've opened up the clips and tried out that beginning setup procedure. And that now you're ready to work with me to read in the data file from the world bank with the life expectancy data. And then we'll display the map in that third video. So, this is our end goal what we'd like to see when we finish this project is the range of colors from bright red to indicates countries with a very low average life expectancy at birth to a bright blue color for countries with a very relatively high life expectancy, average life expectancy at birth. And all of that data is encapsulated in this data file that we can read in from the World Bank, and this file is formatted as a whole bunch of rows, and each row contains data about a single country. So there's some header information in the file and then we see that each of the fields is going to be separated by commas. And so understanding how to read files and manipulate files where we have formated data. Rows each with a similar format and each row containing fields separated by commas will be very powerful because lots of data is passed around in this way. So what we'd like to do is to store data's associated, associating countries with average live expectancy at birth. And a really powerful data structure for doing so is the Map data structure. Now take a minute, we're gonna use Map in two different ways in this video. And so you want to make sure to keep straight which Map we're talking about. And so at this point, when we say Map, we mean an association or a function that relates something that you wanna think of input values to output values. So a map is going to associate keys like countries with values, their average life expectancy at birth. And so, we're going to take the data from that file, that CSV file, and store it in a data structure in our class and that data structure is going to keep track of what life expectancy belongs to each country. So this is an example of a general data type, an abstract data type called Map. And you can keep in mind this image of keys being associated with values. And what's important here is that every key is associated with exactly one value. You're not going to have one country. With two different values associated with it. And so in our particular example, the keys are the country identifiers. And the values that we want to store for each country is that average life expectancy. And both the keys and the values have types, and those types are going to be different because they're different kinds of objects. But there is some relationship between them, which is why we're storing them as Ordered pairs. So there is that word abstract. We're talking about abstract data types and we haven't talked about those in this lesson yet. Or nor in this course. We will talk about those. Stay tuned in an upcoming module. In our application, we'd like to create a map with country IDs as the keys. And the life expectancy as the values. Now, it's a little bit complicated to read in the data from a file and create this data structure and then put the information from the data file into the data structure. And we don't want to clutter up our setup method that talks about setting up the applet that we're displaying to the user. We don't want to clutter up that method with all of this code that's very detailed and maybe isn't so hard to follow, it is pretty hard to follow. And so what we're going to do is carve it out of the setup method and create a new helper method that we can follow through separately. And so in the setup all we say is that the map, lifeExpByCountry is going to be created by invoking this helper method. Now in terms of class design, a good rule of thumb, to extend what Christine earlier said is that when we have helper methods that are really for this organization purpose, we want to keep them private. We don't need to send them out into the world. Okay so, we want to have a private helper method and let's think about how we're specifying what it takes in is the filename. That's going to lead us to the data and then what we want to return after executing this method is the Map that associates countries with expected. With life expectancy. Okay, so we can go ahead and think about what we to have in there, and the first thing we need to do is to construct the map. Just construct that object that's gonna hold the counties and the life expectancy. Now there's something a little funky here, and you'll notice that the data type of the map that we're declaring here is just map. But when we invoke a constructor, we're saying we want a new HashMap. And that's different from other times that we've used constructors. Other times that we've used constructors when we've created a new object, the type of the object that we're creating matches the constructor that we're using. Now this is a subtle point and it comes. You'll see a lot more details about that one when we talk about abstract classes tying into that abstract data type. But for now the way that you can think about this is that we want a map and the kind of map, the way it's being implemented, the details of it is going to be a HashMap, but the functionality that we care about is that it's a map that it has keys and values. Okay, so that's the first thing we do. We construct it. Once we have the map declared, what we'd like to do is populate it. And we're populating it from fields in the file. And so we'd like to read in all the files and we're going to do that taking it one row at a time and sticking the contents of a single row into an array of strings. Now we iterate through all of the strings that we've just read in. So each row one at a time. And we can use the for each row in the array of strings rows construct. So it's a special for loop that lets us iterate over an entire array. And what we're going to do is for each row, we'd like to parse away just the relevant information. If you remember in the data file each row had some beginning junk columns that told us the title of the data series and then it had that relevant piece that we cared about. The country and then also the average life expectancy at birth. And so what we'd like to do is parse the string that comes to us from the file, the entire row, and pick off just the important parts. And so what we can do is take a single String, which is that entire row, and split it based on a separating character. And so we know that in a comma separated values file, a CSV file, every time there's a comma we have a new field. And so we're going to split the rows String into an array of strings, each of the elements in the array of strings is another string that is a field. Okay, but now what we'd like to do is just read off the fields in particular values and so we have some logic to pick out exactly the columns that we need okay? So looking in a little bit deeper into what we're doing, what we'd like is the country ID and the life expectancy. Now the country ID is a string, will be a string forever and after. We don't need to worry about that. But we are doing something in that the first line that is highlighted in that box. And that is we want to read off a float, a number type for the life expectancy, but in the data file we just read it in as a string, and so we're casting this string. We're converting the string to a float, and so we have a method that lets us do that. So now that we've read all of the rows from the data file into our map, we have access to all of the information that pairs the country ID with its average life expectancy at birth, and we are ready to move to the next part of the big picture of our project, which is to display the data that we've just read in and convert it into some visualization on the map.
