Recently I visited Austin and many of my friends had mentioned about the variety in food options here. So my wife and I decided to search for places to eat on the foursquare app. As a standard search filter with high rating we ended up at pretty good places and foursquare did alert us to checkins whenever we reached a place. Post the trip I wanted to see how many people do checkins using this app and how the checkins are correlated with the ratings.
The first step here is to get the data . So I started to play around with the foursquare API and started working around the URL on what category(food,places to see, etc) to get the data . The authentication process for the foursquare API was a bit tricky but with my google-fu (( and special mention to the GIS tribe ) I was able to get going. Below is how you would get the client id and client secret when you create a new app.
The idea was how to do it for many places across the country. So I decided to use R to scrap and clean the data. You can find the code here.
Once this was done the next part was to how do I visualize this data . Since I have been trying my hands on d3js I used the cleaned output from R in CSV format to display how checkins and ratings vary for these places using bubble chart.
<script> src = "https://d3js.org/d3.v4.min.js" ></script>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/d3/4.4.4/d3.min.js" type="text/JavaScript"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/d3-queue/3.0.3/d3-queue.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/2.2.0/topojson.min.js"></script>
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<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
<script>
var svg = d3.select("svg"),
width = +svg.attr("width"),
height = +svg.attr("height");
var format = d3.format(",d");
var color = d3.scaleOrdinal(d3.schemeCategory10);
var pack = d3.pack()
.size([width, width])
.padding(1.5);
var inputs = {};
d3.csv("austin_fsq.csv", function(d) {
d.sno = +d.sno;
return d;
}, function(error, data) {
if (error) throw error;
d3.selectAll("input").on("change", function(){
inputs[this.id] = +this.value;
console.log(inputs.myValue + "-" + inputs.myRating)
if(inputs.myValue && inputs.myRating){
var classes = data.filter(d => d.value < inputs.myValue && d.rating >= inputs.myRating);
draw(classes);
}
})
function draw(classes) {
d3.selectAll("svg > *").remove();
console.log(classes.length);
var root = d3.hierarchy({
children: classes
})
.sum(function(d) {
return d.value;
})
.each(function(d) {
if (id = d.data.id) {
var id, i = id.lastIndexOf(".");
d.id = id;
d.package = id.slice(0, i);
d.class = id.slice(i + 1);
}
});
var node = svg.selectAll(".node")
.data(pack(root).leaves())
.enter().append("g")
.attr("class", "node")
.attr("transform", function(d) {
return "translate(" + d.x + "," + d.y + ")";
});
node.append("circle")
.attr("id", function(d) {
return d.id;
})
.attr("r", function(d) {
return d.r;
})
.style("fill", function(d) {
return color(d.package);
});
node.append("clipPath")
.attr("id", function(d) {
return "clip-" + d.id;
})
.append("use")
.attr("xlink:href", function(d) {
return "#" + d.id;
});
node.append("text")
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return "url(#clip-" + d.id + ")";
})
.selectAll("tspan")
.data(function(d) {
return d.class.split(/(?=[A-Z][^A-Z])/g);
})
.enter().append("tspan")
.attr("x", 0)
.attr("y", function(d, i, nodes) {
return 13 + (i - nodes.length / 2 - 0.5) * 10;
})
.text(function(d) {
return d;
});
node.append("title")
.text(function(d) {
return d.data.id + "\n" + format(d.value);
});
}
});
</script>