Skip to main content

Datum-Data difference in map behavior in d3

I'm pretty new to d3js and trying to understand the difference between using data and datum to attach data to elements. I've done a fair bit of reading the material online and I think I theoretically understand what's going on but I still lack an intuitive understanding. Specifically, I have a case where I'm creating a map using topojson. I'm using d3js v7.

In the first instance, I have the following code to create the map within a div (assume height, width, projection etc. setup correctly):

var svg = d3.select("div#map").append("svg")
    .attr("width", width)
    .attr("height", height)
    .attr("transform", "translate(" + 15 + "," + 0 + ")"); 

var path = d3.geoPath()
          .projection(projection);

var mapGroup = svg.append("g");

d3.json("json/world-110m.json").then(function(world){
  console.log(topojson.feature(world, world.objects.land))

  mapGroup.append("path") 
     .datum(topojson.feature(world, world.objects.land))
     .attr("class", "land") 
     .attr("d", path); 

});

The console log for the topojson feature looks like this: enter image description here

And the map comes out fine (with styling specified in a css file):enter image description here

But if I change datum to data, the map disappears. I'm trying to improve my understanding of how this is working and I'm struggling a little bit after having read what I can find online. Can someone explain the difference between data and datum as used in this case and why one works and the other doesn't?

Thanks for your help!

Via Active questions tagged javascript - Stack Overflow https://ift.tt/2FdjaAW

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...