Reading in external data in d3 JavaScript - an R r2d3 Use Case












0















EDIT: link for all data/code used in example: https://drive.google.com/open?id=16MpDptwV7m4nOkoT3ImlKffl4rYqc5ms



Hello friends and roasters alike,



I'm about as novice as can be with D3 visualization. My background is all in Plotly and integrated R platform plots. I have written very very light js/css for Shiny apps, but I'm trying to branch out into more custom and free visual methods.



So I've been diving through the r2d3 package for d3 integration in R. I've searched through all of the examples and pored through whatever documentation I could find in the master repo and overview pages here: https://rstudio.github.io/r2d3/articles/gallery/calendar/



But, for the life of me I simply can't wrap my head around how the js is actually pulling in the data



An example here: the visual, following by the script that produces it, and finally the csv provided as the data source to visualize



Visual:
Visual Constructed



calendar.js script:



// !preview r2d3 data = read.csv("dji-latest.csv"), d3_version = 4, 

container = "div", options = list(start = 2006, end = 2011)

// Based on https://bl.ocks.org/mbostock/4063318

var height = height / (options.end - options.start),
cellSize = height / 8;

var formatPercent = d3.format(".1%");

var color = d3.scaleQuantize()
.domain([-0.05, 0.05])
.range(["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"]);

var svg = div
.style("line-height", "0")
.selectAll("svg")
.data(d3.range(options.start, options.end))
.enter().append("svg")
.attr("width", width)
.attr("height", height)
.append("g")
.attr("transform", "translate(" + cellSize * 3.5 + "," + (height - cellSize * 7 - 1) + ")");

svg.append("text")
.attr("transform", "translate(-" + (6 * height / 60) + "," + cellSize * 3.5 + ")rotate(-90)")
.attr("font-family", "sans-serif")
.attr("font-size", 2 + 8 * height / 60)
.attr("text-anchor", "middle")
.text(function(d) { return d; });

var rect = svg.append("g")
.attr("fill", "none")
.attr("stroke", "#ccc")
.attr("stroke-width", "0.25")
.selectAll("rect")
.data(function(d) { return d3.timeDays(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("rect")
.attr("width", cellSize)
.attr("height", cellSize)
.attr("x", function(d) { return d3.timeWeek.count(d3.timeYear(d), d) * cellSize; })
.attr("y", function(d) { return d.getDay() * cellSize; })
.datum(d3.timeFormat("%Y-%m-%d"));

svg.append("g")
.attr("fill", "none")
.attr("stroke", "#000")
.attr("stroke-width", "0.25")
.selectAll("path")
.data(function(d) { return d3.timeMonths(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("path")
.attr("d", pathMonth);

r2d3.onRender(function(csv, div, width, height, options) {
var data = d3.nest()
.key(function(d) { return d.Date; })
.rollup(function(d) { return (d[0].Close - d[0].Open) / d[0].Open; })
.object(csv);

rect.filter(function(d) { return d in data; })
.attr("fill", function(d) { return color(data[d]); })
.append("title")
.text(function(d) { return d + ": " + formatPercent(data[d]); });
});

function pathMonth(t0) {
var t1 = new Date(t0.getFullYear(), t0.getMonth() + 1, 0),
d0 = t0.getDay(), w0 = d3.timeWeek.count(d3.timeYear(t0), t0),
d1 = t1.getDay(), w1 = d3.timeWeek.count(d3.timeYear(t1), t1);
return "M" + (w0 + 1) * cellSize + "," + d0 * cellSize
+ "H" + w0 * cellSize + "V" + 7 * cellSize
+ "H" + w1 * cellSize + "V" + (d1 + 1) * cellSize
+ "H" + (w1 + 1) * cellSize + "V" + 0
+ "H" + (w0 + 1) * cellSize + "Z";
}


And this is the .csv fed in



screenshot of .csv pulled from github



And I know this is completely a source of my own understanding of js function call and data handling, but this is simply stumping me to no end. I can see some .data inits and function calls within, but no where do I find any indication of what this visualization is supposed to catch. How does it know which of the columns denotes the dates? Where is the variable specified to actually visualize?



Any inkling of help here would be immensely appreciated. I've gotten some d3 tutorials on my horizon, but any pointers can at least get me playing with the sandboxes those smarter than I have built :)



Thank you!










share|improve this question

























  • Pls provide data, no pictures. Check out ?dput.

    – vaettchen
    Nov 15 '18 at 4:17











  • you construct an SVG on the fly, that is the visualization

    – rioV8
    Nov 15 '18 at 11:54











  • @vaettchen I've posted a Google drive link that has all necessary code/data downloadable. Thank you for not yelling at me about missing that formality. I don't think I follow on the dput lead though

    – fattmagan
    Nov 15 '18 at 18:26













  • @rioV8 I guess this may be above my knowledge for some time, but thank you for your response. What I can't grasp is how the SVG understands what data to pull in and how to organize it. E.g. the heatmap included - what are the values here? How is this js script organizing those values? These are the pieces I cannot understand or grasp Again, if this is simply to neophytic of a question, just let me know and I'll continue researching/learning elsewhere. Thank you for your time

    – fattmagan
    Nov 15 '18 at 18:29











  • this is not the place to copy the whole of d3 documentation, you can read it at the d3 site, learn what each and every line of code is doing by reading the docs.

    – rioV8
    Nov 15 '18 at 18:44
















0















EDIT: link for all data/code used in example: https://drive.google.com/open?id=16MpDptwV7m4nOkoT3ImlKffl4rYqc5ms



Hello friends and roasters alike,



I'm about as novice as can be with D3 visualization. My background is all in Plotly and integrated R platform plots. I have written very very light js/css for Shiny apps, but I'm trying to branch out into more custom and free visual methods.



So I've been diving through the r2d3 package for d3 integration in R. I've searched through all of the examples and pored through whatever documentation I could find in the master repo and overview pages here: https://rstudio.github.io/r2d3/articles/gallery/calendar/



But, for the life of me I simply can't wrap my head around how the js is actually pulling in the data



An example here: the visual, following by the script that produces it, and finally the csv provided as the data source to visualize



Visual:
Visual Constructed



calendar.js script:



// !preview r2d3 data = read.csv("dji-latest.csv"), d3_version = 4, 

container = "div", options = list(start = 2006, end = 2011)

// Based on https://bl.ocks.org/mbostock/4063318

var height = height / (options.end - options.start),
cellSize = height / 8;

var formatPercent = d3.format(".1%");

var color = d3.scaleQuantize()
.domain([-0.05, 0.05])
.range(["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"]);

var svg = div
.style("line-height", "0")
.selectAll("svg")
.data(d3.range(options.start, options.end))
.enter().append("svg")
.attr("width", width)
.attr("height", height)
.append("g")
.attr("transform", "translate(" + cellSize * 3.5 + "," + (height - cellSize * 7 - 1) + ")");

svg.append("text")
.attr("transform", "translate(-" + (6 * height / 60) + "," + cellSize * 3.5 + ")rotate(-90)")
.attr("font-family", "sans-serif")
.attr("font-size", 2 + 8 * height / 60)
.attr("text-anchor", "middle")
.text(function(d) { return d; });

var rect = svg.append("g")
.attr("fill", "none")
.attr("stroke", "#ccc")
.attr("stroke-width", "0.25")
.selectAll("rect")
.data(function(d) { return d3.timeDays(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("rect")
.attr("width", cellSize)
.attr("height", cellSize)
.attr("x", function(d) { return d3.timeWeek.count(d3.timeYear(d), d) * cellSize; })
.attr("y", function(d) { return d.getDay() * cellSize; })
.datum(d3.timeFormat("%Y-%m-%d"));

svg.append("g")
.attr("fill", "none")
.attr("stroke", "#000")
.attr("stroke-width", "0.25")
.selectAll("path")
.data(function(d) { return d3.timeMonths(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("path")
.attr("d", pathMonth);

r2d3.onRender(function(csv, div, width, height, options) {
var data = d3.nest()
.key(function(d) { return d.Date; })
.rollup(function(d) { return (d[0].Close - d[0].Open) / d[0].Open; })
.object(csv);

rect.filter(function(d) { return d in data; })
.attr("fill", function(d) { return color(data[d]); })
.append("title")
.text(function(d) { return d + ": " + formatPercent(data[d]); });
});

function pathMonth(t0) {
var t1 = new Date(t0.getFullYear(), t0.getMonth() + 1, 0),
d0 = t0.getDay(), w0 = d3.timeWeek.count(d3.timeYear(t0), t0),
d1 = t1.getDay(), w1 = d3.timeWeek.count(d3.timeYear(t1), t1);
return "M" + (w0 + 1) * cellSize + "," + d0 * cellSize
+ "H" + w0 * cellSize + "V" + 7 * cellSize
+ "H" + w1 * cellSize + "V" + (d1 + 1) * cellSize
+ "H" + (w1 + 1) * cellSize + "V" + 0
+ "H" + (w0 + 1) * cellSize + "Z";
}


And this is the .csv fed in



screenshot of .csv pulled from github



And I know this is completely a source of my own understanding of js function call and data handling, but this is simply stumping me to no end. I can see some .data inits and function calls within, but no where do I find any indication of what this visualization is supposed to catch. How does it know which of the columns denotes the dates? Where is the variable specified to actually visualize?



Any inkling of help here would be immensely appreciated. I've gotten some d3 tutorials on my horizon, but any pointers can at least get me playing with the sandboxes those smarter than I have built :)



Thank you!










share|improve this question

























  • Pls provide data, no pictures. Check out ?dput.

    – vaettchen
    Nov 15 '18 at 4:17











  • you construct an SVG on the fly, that is the visualization

    – rioV8
    Nov 15 '18 at 11:54











  • @vaettchen I've posted a Google drive link that has all necessary code/data downloadable. Thank you for not yelling at me about missing that formality. I don't think I follow on the dput lead though

    – fattmagan
    Nov 15 '18 at 18:26













  • @rioV8 I guess this may be above my knowledge for some time, but thank you for your response. What I can't grasp is how the SVG understands what data to pull in and how to organize it. E.g. the heatmap included - what are the values here? How is this js script organizing those values? These are the pieces I cannot understand or grasp Again, if this is simply to neophytic of a question, just let me know and I'll continue researching/learning elsewhere. Thank you for your time

    – fattmagan
    Nov 15 '18 at 18:29











  • this is not the place to copy the whole of d3 documentation, you can read it at the d3 site, learn what each and every line of code is doing by reading the docs.

    – rioV8
    Nov 15 '18 at 18:44














0












0








0








EDIT: link for all data/code used in example: https://drive.google.com/open?id=16MpDptwV7m4nOkoT3ImlKffl4rYqc5ms



Hello friends and roasters alike,



I'm about as novice as can be with D3 visualization. My background is all in Plotly and integrated R platform plots. I have written very very light js/css for Shiny apps, but I'm trying to branch out into more custom and free visual methods.



So I've been diving through the r2d3 package for d3 integration in R. I've searched through all of the examples and pored through whatever documentation I could find in the master repo and overview pages here: https://rstudio.github.io/r2d3/articles/gallery/calendar/



But, for the life of me I simply can't wrap my head around how the js is actually pulling in the data



An example here: the visual, following by the script that produces it, and finally the csv provided as the data source to visualize



Visual:
Visual Constructed



calendar.js script:



// !preview r2d3 data = read.csv("dji-latest.csv"), d3_version = 4, 

container = "div", options = list(start = 2006, end = 2011)

// Based on https://bl.ocks.org/mbostock/4063318

var height = height / (options.end - options.start),
cellSize = height / 8;

var formatPercent = d3.format(".1%");

var color = d3.scaleQuantize()
.domain([-0.05, 0.05])
.range(["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"]);

var svg = div
.style("line-height", "0")
.selectAll("svg")
.data(d3.range(options.start, options.end))
.enter().append("svg")
.attr("width", width)
.attr("height", height)
.append("g")
.attr("transform", "translate(" + cellSize * 3.5 + "," + (height - cellSize * 7 - 1) + ")");

svg.append("text")
.attr("transform", "translate(-" + (6 * height / 60) + "," + cellSize * 3.5 + ")rotate(-90)")
.attr("font-family", "sans-serif")
.attr("font-size", 2 + 8 * height / 60)
.attr("text-anchor", "middle")
.text(function(d) { return d; });

var rect = svg.append("g")
.attr("fill", "none")
.attr("stroke", "#ccc")
.attr("stroke-width", "0.25")
.selectAll("rect")
.data(function(d) { return d3.timeDays(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("rect")
.attr("width", cellSize)
.attr("height", cellSize)
.attr("x", function(d) { return d3.timeWeek.count(d3.timeYear(d), d) * cellSize; })
.attr("y", function(d) { return d.getDay() * cellSize; })
.datum(d3.timeFormat("%Y-%m-%d"));

svg.append("g")
.attr("fill", "none")
.attr("stroke", "#000")
.attr("stroke-width", "0.25")
.selectAll("path")
.data(function(d) { return d3.timeMonths(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("path")
.attr("d", pathMonth);

r2d3.onRender(function(csv, div, width, height, options) {
var data = d3.nest()
.key(function(d) { return d.Date; })
.rollup(function(d) { return (d[0].Close - d[0].Open) / d[0].Open; })
.object(csv);

rect.filter(function(d) { return d in data; })
.attr("fill", function(d) { return color(data[d]); })
.append("title")
.text(function(d) { return d + ": " + formatPercent(data[d]); });
});

function pathMonth(t0) {
var t1 = new Date(t0.getFullYear(), t0.getMonth() + 1, 0),
d0 = t0.getDay(), w0 = d3.timeWeek.count(d3.timeYear(t0), t0),
d1 = t1.getDay(), w1 = d3.timeWeek.count(d3.timeYear(t1), t1);
return "M" + (w0 + 1) * cellSize + "," + d0 * cellSize
+ "H" + w0 * cellSize + "V" + 7 * cellSize
+ "H" + w1 * cellSize + "V" + (d1 + 1) * cellSize
+ "H" + (w1 + 1) * cellSize + "V" + 0
+ "H" + (w0 + 1) * cellSize + "Z";
}


And this is the .csv fed in



screenshot of .csv pulled from github



And I know this is completely a source of my own understanding of js function call and data handling, but this is simply stumping me to no end. I can see some .data inits and function calls within, but no where do I find any indication of what this visualization is supposed to catch. How does it know which of the columns denotes the dates? Where is the variable specified to actually visualize?



Any inkling of help here would be immensely appreciated. I've gotten some d3 tutorials on my horizon, but any pointers can at least get me playing with the sandboxes those smarter than I have built :)



Thank you!










share|improve this question
















EDIT: link for all data/code used in example: https://drive.google.com/open?id=16MpDptwV7m4nOkoT3ImlKffl4rYqc5ms



Hello friends and roasters alike,



I'm about as novice as can be with D3 visualization. My background is all in Plotly and integrated R platform plots. I have written very very light js/css for Shiny apps, but I'm trying to branch out into more custom and free visual methods.



So I've been diving through the r2d3 package for d3 integration in R. I've searched through all of the examples and pored through whatever documentation I could find in the master repo and overview pages here: https://rstudio.github.io/r2d3/articles/gallery/calendar/



But, for the life of me I simply can't wrap my head around how the js is actually pulling in the data



An example here: the visual, following by the script that produces it, and finally the csv provided as the data source to visualize



Visual:
Visual Constructed



calendar.js script:



// !preview r2d3 data = read.csv("dji-latest.csv"), d3_version = 4, 

container = "div", options = list(start = 2006, end = 2011)

// Based on https://bl.ocks.org/mbostock/4063318

var height = height / (options.end - options.start),
cellSize = height / 8;

var formatPercent = d3.format(".1%");

var color = d3.scaleQuantize()
.domain([-0.05, 0.05])
.range(["#a50026", "#d73027", "#f46d43", "#fdae61", "#fee08b", "#ffffbf", "#d9ef8b", "#a6d96a", "#66bd63", "#1a9850", "#006837"]);

var svg = div
.style("line-height", "0")
.selectAll("svg")
.data(d3.range(options.start, options.end))
.enter().append("svg")
.attr("width", width)
.attr("height", height)
.append("g")
.attr("transform", "translate(" + cellSize * 3.5 + "," + (height - cellSize * 7 - 1) + ")");

svg.append("text")
.attr("transform", "translate(-" + (6 * height / 60) + "," + cellSize * 3.5 + ")rotate(-90)")
.attr("font-family", "sans-serif")
.attr("font-size", 2 + 8 * height / 60)
.attr("text-anchor", "middle")
.text(function(d) { return d; });

var rect = svg.append("g")
.attr("fill", "none")
.attr("stroke", "#ccc")
.attr("stroke-width", "0.25")
.selectAll("rect")
.data(function(d) { return d3.timeDays(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("rect")
.attr("width", cellSize)
.attr("height", cellSize)
.attr("x", function(d) { return d3.timeWeek.count(d3.timeYear(d), d) * cellSize; })
.attr("y", function(d) { return d.getDay() * cellSize; })
.datum(d3.timeFormat("%Y-%m-%d"));

svg.append("g")
.attr("fill", "none")
.attr("stroke", "#000")
.attr("stroke-width", "0.25")
.selectAll("path")
.data(function(d) { return d3.timeMonths(new Date(d, 0, 1), new Date(d + 1, 0, 1)); })
.enter().append("path")
.attr("d", pathMonth);

r2d3.onRender(function(csv, div, width, height, options) {
var data = d3.nest()
.key(function(d) { return d.Date; })
.rollup(function(d) { return (d[0].Close - d[0].Open) / d[0].Open; })
.object(csv);

rect.filter(function(d) { return d in data; })
.attr("fill", function(d) { return color(data[d]); })
.append("title")
.text(function(d) { return d + ": " + formatPercent(data[d]); });
});

function pathMonth(t0) {
var t1 = new Date(t0.getFullYear(), t0.getMonth() + 1, 0),
d0 = t0.getDay(), w0 = d3.timeWeek.count(d3.timeYear(t0), t0),
d1 = t1.getDay(), w1 = d3.timeWeek.count(d3.timeYear(t1), t1);
return "M" + (w0 + 1) * cellSize + "," + d0 * cellSize
+ "H" + w0 * cellSize + "V" + 7 * cellSize
+ "H" + w1 * cellSize + "V" + (d1 + 1) * cellSize
+ "H" + (w1 + 1) * cellSize + "V" + 0
+ "H" + (w0 + 1) * cellSize + "Z";
}


And this is the .csv fed in



screenshot of .csv pulled from github



And I know this is completely a source of my own understanding of js function call and data handling, but this is simply stumping me to no end. I can see some .data inits and function calls within, but no where do I find any indication of what this visualization is supposed to catch. How does it know which of the columns denotes the dates? Where is the variable specified to actually visualize?



Any inkling of help here would be immensely appreciated. I've gotten some d3 tutorials on my horizon, but any pointers can at least get me playing with the sandboxes those smarter than I have built :)



Thank you!







javascript r d3.js r2d3






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Feb 14 at 12:28









ismirsehregal

1,7601212




1,7601212










asked Nov 15 '18 at 3:59









fattmaganfattmagan

276




276













  • Pls provide data, no pictures. Check out ?dput.

    – vaettchen
    Nov 15 '18 at 4:17











  • you construct an SVG on the fly, that is the visualization

    – rioV8
    Nov 15 '18 at 11:54











  • @vaettchen I've posted a Google drive link that has all necessary code/data downloadable. Thank you for not yelling at me about missing that formality. I don't think I follow on the dput lead though

    – fattmagan
    Nov 15 '18 at 18:26













  • @rioV8 I guess this may be above my knowledge for some time, but thank you for your response. What I can't grasp is how the SVG understands what data to pull in and how to organize it. E.g. the heatmap included - what are the values here? How is this js script organizing those values? These are the pieces I cannot understand or grasp Again, if this is simply to neophytic of a question, just let me know and I'll continue researching/learning elsewhere. Thank you for your time

    – fattmagan
    Nov 15 '18 at 18:29











  • this is not the place to copy the whole of d3 documentation, you can read it at the d3 site, learn what each and every line of code is doing by reading the docs.

    – rioV8
    Nov 15 '18 at 18:44



















  • Pls provide data, no pictures. Check out ?dput.

    – vaettchen
    Nov 15 '18 at 4:17











  • you construct an SVG on the fly, that is the visualization

    – rioV8
    Nov 15 '18 at 11:54











  • @vaettchen I've posted a Google drive link that has all necessary code/data downloadable. Thank you for not yelling at me about missing that formality. I don't think I follow on the dput lead though

    – fattmagan
    Nov 15 '18 at 18:26













  • @rioV8 I guess this may be above my knowledge for some time, but thank you for your response. What I can't grasp is how the SVG understands what data to pull in and how to organize it. E.g. the heatmap included - what are the values here? How is this js script organizing those values? These are the pieces I cannot understand or grasp Again, if this is simply to neophytic of a question, just let me know and I'll continue researching/learning elsewhere. Thank you for your time

    – fattmagan
    Nov 15 '18 at 18:29











  • this is not the place to copy the whole of d3 documentation, you can read it at the d3 site, learn what each and every line of code is doing by reading the docs.

    – rioV8
    Nov 15 '18 at 18:44

















Pls provide data, no pictures. Check out ?dput.

– vaettchen
Nov 15 '18 at 4:17





Pls provide data, no pictures. Check out ?dput.

– vaettchen
Nov 15 '18 at 4:17













you construct an SVG on the fly, that is the visualization

– rioV8
Nov 15 '18 at 11:54





you construct an SVG on the fly, that is the visualization

– rioV8
Nov 15 '18 at 11:54













@vaettchen I've posted a Google drive link that has all necessary code/data downloadable. Thank you for not yelling at me about missing that formality. I don't think I follow on the dput lead though

– fattmagan
Nov 15 '18 at 18:26







@vaettchen I've posted a Google drive link that has all necessary code/data downloadable. Thank you for not yelling at me about missing that formality. I don't think I follow on the dput lead though

– fattmagan
Nov 15 '18 at 18:26















@rioV8 I guess this may be above my knowledge for some time, but thank you for your response. What I can't grasp is how the SVG understands what data to pull in and how to organize it. E.g. the heatmap included - what are the values here? How is this js script organizing those values? These are the pieces I cannot understand or grasp Again, if this is simply to neophytic of a question, just let me know and I'll continue researching/learning elsewhere. Thank you for your time

– fattmagan
Nov 15 '18 at 18:29





@rioV8 I guess this may be above my knowledge for some time, but thank you for your response. What I can't grasp is how the SVG understands what data to pull in and how to organize it. E.g. the heatmap included - what are the values here? How is this js script organizing those values? These are the pieces I cannot understand or grasp Again, if this is simply to neophytic of a question, just let me know and I'll continue researching/learning elsewhere. Thank you for your time

– fattmagan
Nov 15 '18 at 18:29













this is not the place to copy the whole of d3 documentation, you can read it at the d3 site, learn what each and every line of code is doing by reading the docs.

– rioV8
Nov 15 '18 at 18:44





this is not the place to copy the whole of d3 documentation, you can read it at the d3 site, learn what each and every line of code is doing by reading the docs.

– rioV8
Nov 15 '18 at 18:44












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