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Costs of task switching analysis
Raw
README.md

Costs of task switching

An exploratory data analysis using d3

Raw
index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>
body {
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.box {
font: 10px sans-serif;
}
.box line,
.box rect,
.box circle {
fill: #fff;
stroke: #000;
stroke-width: 1.5px;
}
.box .center {
stroke-dasharray: 3,3;
}
.box .outlier {
fill: none;
stroke: #ccc;
}
</style>
<body>
<script src="//d3js.org/d3.v3.min.js"></script>
<script src="whiskerbox.js"></script>
<h1>Q1: Is there a correlation between tasks done concurrently and hours spent on task</h1>
<h2>Overall box plot</h2>
<h2>Box plots by task type</h2>
<h2>Identifying outliers</h2>
<h2>Linear regression plot excluding outliers</h2>
<h1>Q2: Is there a correlation between tasks done concurrently and number of errors</h1>
</body>
</html>
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regression.js
(function (){
// adapted from http://stackoverflow.com/questions/20507536/d3-js-linear-regression?lq=1
function linear(y,x){
var lr = {};
var n = y.length;
var sum_x = 0;
var sum_y = 0;
var sum_xy = 0;
var sum_xx = 0;
var sum_yy = 0;
for (var i = 0; i < y.length; i++) {
sum_x += x[i];
sum_y += y[i];
sum_xy += (x[i]*y[i]);
sum_xx += (x[i]*x[i]);
sum_yy += (y[i]*y[i]);
}
lr['slope'] = (n * sum_xy - sum_x * sum_y) / (n*sum_xx - sum_x * sum_x);
lr['intercept'] = (sum_y - lr.slope * sum_x)/n;
lr['r2'] = Math.pow((n*sum_xy - sum_x*sum_y)/Math.sqrt((n*sum_xx-sum_x*sum_x)*(n*sum_yy-sum_y*sum_y)),2);
return lr;
};
d3.regression = {}
d3.regression.linear = linear;
})();
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tabulate.js
(function (){
// adapted from http://jsfiddle.net/7WQjr/
function tabulate(columns, data, selection) {
var table = selection.append("table"),
thead = table.append("thead"),
tbody = table.append("tbody");
// append the header row
thead.append("tr")
.selectAll("th")
.data(columns)
.enter()
.append("th")
.text(function(column) { return column; });
// create a row for each object in the data
var rows = tbody.selectAll("tr")
.data(data)
.enter()
.append("tr");
// create a cell in each row for each column
var cells = rows.selectAll("td")
.data(function(row) {
return columns.map(function(column) {
return {column: column, value: row[column]};
});
})
.enter()
.append("td")
.text(function(d) { return d.value; });
return table;
}
d3.tabulate = curryN(3,tabulate);
})();
// curry from Ramda
// https://github.com/ramda/ramda/blob/master/src/internal/_arity.js
function _arity(n, fn) {
switch (n) {
case 0: return function() { return fn.apply(this, arguments); };
case 1: return function(a0) { return fn.apply(this, arguments); };
case 2: return function(a0, a1) { return fn.apply(this, arguments); };
case 3: return function(a0, a1, a2) { return fn.apply(this, arguments); };
case 4: return function(a0, a1, a2, a3) { return fn.apply(this, arguments); };
case 5: return function(a0, a1, a2, a3, a4) { return fn.apply(this, arguments); };
case 6: return function(a0, a1, a2, a3, a4, a5) { return fn.apply(this, arguments); };
case 7: return function(a0, a1, a2, a3, a4, a5, a6) { return fn.apply(this, arguments); };
case 8: return function(a0, a1, a2, a3, a4, a5, a6, a7) { return fn.apply(this, arguments); };
case 9: return function(a0, a1, a2, a3, a4, a5, a6, a7, a8) { return fn.apply(this, arguments); };
case 10: return function(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9) { return fn.apply(this, arguments); };
default: throw new Error('First argument to _arity must be a non-negative integer no greater than ten');
}
};
// https://github.com/ramda/ramda/blob/master/src/internal/_curryN.js
function _curryN(length, received, fn){
return function() {
var combined = [];
var argsIdx = 0;
var left = length;
var combinedIdx = 0;
while (combinedIdx < received.length || argsIdx < arguments.length) {
var result;
if (combinedIdx < received.length &&
argsIdx >= arguments.length) {
result = received[combinedIdx];
} else {
result = arguments[argsIdx];
argsIdx += 1;
}
combined[combinedIdx] = result;
left -= 1;
combinedIdx += 1;
}
return left <= 0 ? fn.apply(this, combined)
: _arity(left, _curryN(length, combined, fn));
};
}
// https://github.com/ramda/ramda/blob/master/src/curryN.js
function curryN(length, fn){
return _arity(length, _curryN(length, [], fn));
}
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whiskerbox.js
(function() {
// Copied from http://bl.ocks.org/mbostock/4061502
// Inspired by http://informationandvisualization.de/blog/box-plot
d3.box = function() {
var width = 1,
height = 1,
duration = 0,
domain = null,
value = Number,
whiskers = boxWhiskers,
quartiles = boxQuartiles,
tickFormat = null;
// For each small multiple…
function box(g) {
g.each(function(d, i) {
d = d.map(value).sort(d3.ascending);
var g = d3.select(this),
n = d.length,
min = d[0],
max = d[n - 1];
// Compute quartiles. Must return exactly 3 elements.
var quartileData = d.quartiles = quartiles(d);
// Compute whiskers. Must return exactly 2 elements, or null.
var whiskerIndices = whiskers && whiskers.call(this, d, i),
whiskerData = whiskerIndices && whiskerIndices.map(function(i) { return d[i]; });
// Compute outliers. If no whiskers are specified, all data are "outliers".
// We compute the outliers as indices, so that we can join across transitions!
var outlierIndices = whiskerIndices
? d3.range(0, whiskerIndices[0]).concat(d3.range(whiskerIndices[1] + 1, n))
: d3.range(n);
// Compute the new x-scale.
var x1 = d3.scale.linear()
.domain(domain && domain.call(this, d, i) || [min, max])
.range([height, 0]);
// Retrieve the old x-scale, if this is an update.
var x0 = this.__chart__ || d3.scale.linear()
.domain([0, Infinity])
.range(x1.range());
// Stash the new scale.
this.__chart__ = x1;
// Note: the box, median, and box tick elements are fixed in number,
// so we only have to handle enter and update. In contrast, the outliers
// and other elements are variable, so we need to exit them! Variable
// elements also fade in and out.
// Update center line: the vertical line spanning the whiskers.
var center = g.selectAll("line.center")
.data(whiskerData ? [whiskerData] : []);
center.enter().insert("line", "rect")
.attr("class", "center")
.attr("x1", width / 2)
.attr("y1", function(d) { return x0(d[0]); })
.attr("x2", width / 2)
.attr("y2", function(d) { return x0(d[1]); })
.style("opacity", 1e-6)
.transition()
.duration(duration)
.style("opacity", 1)
.attr("y1", function(d) { return x1(d[0]); })
.attr("y2", function(d) { return x1(d[1]); });
center.transition()
.duration(duration)
.style("opacity", 1)
.attr("y1", function(d) { return x1(d[0]); })
.attr("y2", function(d) { return x1(d[1]); });
center.exit().transition()
.duration(duration)
.style("opacity", 1e-6)
.attr("y1", function(d) { return x1(d[0]); })
.attr("y2", function(d) { return x1(d[1]); })
.remove();
// Update innerquartile box.
var box = g.selectAll("rect.box")
.data([quartileData]);
box.enter().append("rect")
.attr("class", "box")
.attr("x", 0)
.attr("y", function(d) { return x0(d[2]); })
.attr("width", width)
.attr("height", function(d) { return x0(d[0]) - x0(d[2]); })
.transition()
.duration(duration)
.attr("y", function(d) { return x1(d[2]); })
.attr("height", function(d) { return x1(d[0]) - x1(d[2]); });
box.transition()
.duration(duration)
.attr("y", function(d) { return x1(d[2]); })
.attr("height", function(d) { return x1(d[0]) - x1(d[2]); });
// Update median line.
var medianLine = g.selectAll("line.median")
.data([quartileData[1]]);
medianLine.enter().append("line")
.attr("class", "median")
.attr("x1", 0)
.attr("y1", x0)
.attr("x2", width)
.attr("y2", x0)
.transition()
.duration(duration)
.attr("y1", x1)
.attr("y2", x1);
medianLine.transition()
.duration(duration)
.attr("y1", x1)
.attr("y2", x1);
// Update whiskers.
var whisker = g.selectAll("line.whisker")
.data(whiskerData || []);
whisker.enter().insert("line", "circle, text")
.attr("class", "whisker")
.attr("x1", 0)
.attr("y1", x0)
.attr("x2", width)
.attr("y2", x0)
.style("opacity", 1e-6)
.transition()
.duration(duration)
.attr("y1", x1)
.attr("y2", x1)
.style("opacity", 1);
whisker.transition()
.duration(duration)
.attr("y1", x1)
.attr("y2", x1)
.style("opacity", 1);
whisker.exit().transition()
.duration(duration)
.attr("y1", x1)
.attr("y2", x1)
.style("opacity", 1e-6)
.remove();
// Update outliers.
var outlier = g.selectAll("circle.outlier")
.data(outlierIndices, Number);
outlier.enter().insert("circle", "text")
.attr("class", "outlier")
.attr("r", 5)
.attr("cx", width / 2)
.attr("cy", function(i) { return x0(d[i]); })
.style("opacity", 1e-6)
.transition()
.duration(duration)
.attr("cy", function(i) { return x1(d[i]); })
.style("opacity", 1);
outlier.transition()
.duration(duration)
.attr("cy", function(i) { return x1(d[i]); })
.style("opacity", 1);
outlier.exit().transition()
.duration(duration)
.attr("cy", function(i) { return x1(d[i]); })
.style("opacity", 1e-6)
.remove();
// Compute the tick format.
var format = tickFormat || x1.tickFormat(8);
// Update box ticks.
var boxTick = g.selectAll("text.box")
.data(quartileData);
boxTick.enter().append("text")
.attr("class", "box")
.attr("dy", ".3em")
.attr("dx", function(d, i) { return i & 1 ? 6 : -6 })
.attr("x", function(d, i) { return i & 1 ? width : 0 })
.attr("y", x0)
.attr("text-anchor", function(d, i) { return i & 1 ? "start" : "end"; })
.text(format)
.transition()
.duration(duration)
.attr("y", x1);
boxTick.transition()
.duration(duration)
.text(format)
.attr("y", x1);
// Update whisker ticks. These are handled separately from the box
// ticks because they may or may not exist, and we want don't want
// to join box ticks pre-transition with whisker ticks post-.
var whiskerTick = g.selectAll("text.whisker")
.data(whiskerData || []);
whiskerTick.enter().append("text")
.attr("class", "whisker")
.attr("dy", ".3em")
.attr("dx", 6)
.attr("x", width)
.attr("y", x0)
.text(format)
.style("opacity", 1e-6)
.transition()
.duration(duration)
.attr("y", x1)
.style("opacity", 1);
whiskerTick.transition()
.duration(duration)
.text(format)
.attr("y", x1)
.style("opacity", 1);
whiskerTick.exit().transition()
.duration(duration)
.attr("y", x1)
.style("opacity", 1e-6)
.remove();
});
d3.timer.flush();
}
box.width = function(x) {
if (!arguments.length) return width;
width = x;
return box;
};
box.height = function(x) {
if (!arguments.length) return height;
height = x;
return box;
};
box.tickFormat = function(x) {
if (!arguments.length) return tickFormat;
tickFormat = x;
return box;
};
box.duration = function(x) {
if (!arguments.length) return duration;
duration = x;
return box;
};
box.domain = function(x) {
if (!arguments.length) return domain;
domain = x == null ? x : d3.functor(x);
return box;
};
box.value = function(x) {
if (!arguments.length) return value;
value = x;
return box;
};
box.whiskers = function(x) {
if (!arguments.length) return whiskers;
whiskers = x;
return box;
};
box.quartiles = function(x) {
if (!arguments.length) return quartiles;
quartiles = x;
return box;
};
return box;
};
function boxWhiskers(d) {
return [0, d.length - 1];
}
function boxQuartiles(d) {
return [
d3.quantile(d, .25),
d3.quantile(d, .5),
d3.quantile(d, .75)
];
}
})();
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z.errors.csv
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
errors data
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z.worktime.csv
We can make this file beautiful and searchable if this error is corrected: No commas found in this CSV file in line 0.
worktime data
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