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    Load and Inspect Data with D3 v4


    You probably use a framework or standalone library to load data into your apps, but what if that’s overkill for your needs? What if you’re just putting together a quick demo? This lesson demonstrates D3’s APIs for loading data on its own, as well as some helpful methods for inspecting your data and preparing it for use with D3.



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    D3 has a lot of useful methods for dealing with data, but first of all you need to get the data. Now, in a real application you're probably going to be loading data as part of your application framework. D3 does have methods to load data itself in the case that you do need it. I've got some sample data here in three different formats.

    We've got data.json, which is just an array of five simple objects that each have a name and an age property. We also have the same data in CSV format, and the same data again in TSV format, or tab-separated values. What we're going to look at is just the methods for loading that data in D3.

    D3.json is a method that you can call, passing in the URL to the data file that you want to load and then providing a callback which will then receive the load and then parse the data. If we now call this and load our json file you can see we get an array with five items in it, which are just the objects that we looked at before.

    If we comment this out, we can do the same basic thing but use the d3.csv method and load in the CSV file. In this case we get back an array-like object. We've got this columns property here, which is just the different column names that we have, so age and name. Then besides that object, we've got all of our actual data objects.

    If we change the CSVs to TSV, we get the same exact subject here. But obviously, most of the time you're going to be working with json data, so we'll just move right back to that call.

    I mentioned some of the methods that D3 makes available for working with your data. The first one we'll look at is just D3.min. In this case, we're going to pass it our data array as well as a callback function that will return the age property off of each object.

    If our data was just a plain array of numbers, we wouldn't need to pass in this access or function at all. We could simply pass the data and be done. If we run this and then log out the value, we'll see that it spits out 13, which is in fact the minimum age that was found in our data.

    Not unexpectedly, there's a D3.max corollary, which will do the same thing but return the maximum value. Then finally, if you want to find both the minimum and the maximum, you can use D3.extent and that will return a two-element array where you've got the minimum element first and then the maximum element.

    If this sort of array looks familiar it's because we used it in our scales. What we can actually do now is we can create a linear scale and pass that extent that we've calculated from our data as the domain. Then we'll use a range of 0to 600 again, just like before, so maybe our chart is going to be 600 pixels wide.

    Now if we log out, passing some random value, say 24, to our scale, we get 264. We know that if the value of 24 is in our data, that's going to map to a pixel value of 264. We can change that to 37, which we know is 1 shy of our maximum of 38 and we get 576. You can see it's mapping everything properly based on the actual data we're working with.

    Speaking of that 37, you can see here that we have two of those, and sometimes that's fine and other times you need to deal with that fact. In the case that you want to get a unique set of values from your data, you can use the D3.set method. This again, uses the same function signature, where we pass it the data itself and then we access their function where we'll return the age.

    What is then returned is this set object. You can see here we've got some sort of strange property names here, and what we actually really want is just the values, so we're going to call .values on that. Now you can see we have an array that holds the unique values from the data that was loaded. You could end up using this as part of a quantized scale, an ordinal scale, or however you need to.