There are nice introductions to animations in plot.ly out there. However, no examples of this case: flipping through (bar) charts plotting different years of similar data (e.g., different columns of some 100 rows). Isn't it what grids and columns are supposed to do? How does one define such a structure?
Related
Though I can write dc.js applications, I still don't understand how dc uses crossfilter objects, ie the dimensions and groups in various charts. When we click on an graph element, for instance, a pie chart slice, I believe dc is applying filters on the dimension, but does it manipulate the crossfilter object as well? Anyone knows of any document/article explaining how dc interacts with crossfilter objects? I know of http://www.codeproject.com/Articles/693841/Making-Dashboards-with-Dc-js-Part-Using-Crossfil
which is really good for beginners, but it does not go deep dive on this specific subject.
For instance, I have this dc chart: http://bit.ly/1nStSh3
Basically the dataset has object names (4 of them, P, Q, S, T) and its size for various dates. The two piecharts show the size for dates and objects respectively. There is a line chart which shows the data growth over a period of time. Now, when I click on the second graph, ie object names, both line chart and the first pie chart auto adjusts, but when I click on the first pie chart, the line chart does not change.
Your particular question is covered by the crossfilter documentation and the dc.js FAQ: a dimension does not observe its own filters, but only the filters on other dimensions.
To get the charts to respond to each other, create a duplicate of the dimension (construct another one with the same arguments) and put the charts on separate dimensions. (There is also work underway to reflect the brushing/filter state between charts that share the same dimension.)
As to your larger question, no, there is no documentation on the interaction between dc.js and crossfilter that I know of. As the principle maintainer (but not the original author) of dc.js, I hope to write such documentation in the next year.
There actually isn't much magic to it: charts just update the dimension filters and then trigger redraws on the charts in their group. The d3 transitions within each chart are what make it look fancier than that.
I am using NVD3.js and want to create following chart:
As you can see - bars are stacked, two axis and grouped by x-axis
Using multiChart I got :
It is stacked, two axis, but not grouped by x-axis.
Maybe I need to use different chart type - not multiChart, but I didn't find bar charts where are two y-axis.
1) How can I achieve this using NVD3.js?
2) If it can not be done in NVD3.js, then which solution I can properly integrate?
Thanks!
The NVD3 Javascript library is, to quote their website, "an attempt to build re-usable charts and chart components". It's creators have made a couple key decisions in order to emphasize the reusability of the charts:
They have focused on implementing standard chart designs (line graphs, bar graphs, scatterplots), but implemented in flexible, interactive ways.
They have used the same data structure requirement for all the graphs:
The main data array contains multiple data series, each of which represents a logical grouping of the data;
Each series is an array of individual data objects containing two or more variables.
All the graphs have a similar style and reuse important pieces of code.
The NVD3 library allows you to create a grouped bar chart or a stacked bar chart, and even a chart that interactively animates between the two.
Adapting that chart to create a stacked and grouped bar chart is not a simple task, in part because the data structure would be different. You would need a three-level data structure (series > sub-series > datapoints, representing groups > stacks > bars) instead of the two-level (series > datapoints) structure used by NVD3.
All is not lost, however. NVD3 is built on the d3 Javascript library. D3 is much more flexible and open-ended; it doesn't define specific chart types, it defines a way of manipulating a webpage to make it match your data. You can use it to create any type of chart that can be drawn with HTML or SVG. But of course, that means that it is much more work, since you have to explicitly create all the parts of the graph, and make all the design decisions yourself!
I strongly recommend, if you want to use d3, start with the basics in the tutorials list or one of the introductory books. However, you'll also want to check out the gallery of examples, and from there you'll find the following charts that will be of particular interest:
Mike Bostock's Stacked Bar Chart
Bostock's Grouped Bar Chart of the same data
Ali Gencay's adaptation of those examples to create a stacked, grouped bar chart
Once you have become familiar with building charts in d3, you may want to open up the NVD3 source code to see if you can borrow some of their reusable code components (being sure to respect their licence terms, of course). However, I would not recommend doing so as a beginner -- it is a lot of code, and uses a lot of complex techniques to put all the pieces together.
I'm quite confused and might need help just formulating the question, so please give good comments...
I'm trying to crossfilter some data where each data point has its own sub-dataset that I want to chart and filter on as well. Each point represents a geographic region, and associated with each point is a time series which measures a certain metric over time.
Here's what I've got so far: http://michaeldougherty.info/dcjs/
The top bar chart shows a particular value for 10 regions, and the choropleth is linked with the same data. Now, below that are two composite line charts. Each line corresponds to a region -- there are 10 lines in each graph, and each graph is measuring a different metric over time. I would like the lines to be filtered as well, so if one bar is selected, only one line will show on the line chart.
Moreover, I want to be able to filter by time on the line charts (through brushing) in addition to some other filter, so I can make queries like "filter out all regions whose line value between 9 AM and 5 PM is less than 20,000", which would also update the bar and choropleth charts.
This is where I'm lost. I'm considering scrapping DC.js for this and using crossfilter and d3.js directly because it seems so complicated, but I would love it if I'm missing something and DC.js can actually handle this. I'd also love some ideas on where to start implementing this in straight crossfilter, because I haven't fully wrapped my head around that yet either.
How does one deal with datasets within datasets?
Screenshot of the link above included for convenience:
I am new to d3.js but have managed to make two individual charts as in introduction.
I have a map chart, which has dots representing monitoring stations.
I also have a line chart which has multiple timeseries (data from json) from one monitoring station.
What I would like to do. Have the two charts on one page. When you mouseover or click on a station on the map the data is loaded and displayed on the line chart. When a new station is selected on the map, the data transitions on the line chart
The question I have is one of style. With the two separate charts what is the best way to combine them?
With the transition, I have searched but have not found any simple examples that has two charting elements where interacting with one effects the other. Should I combine all the timeseries data into one json file (say 4 timeseries times 50 stations) or have 50 json files?
Thanks
Unless your timeseries data is very large, I would just put everything in one JSON file to make things simpler and so that changing stations can take place entirely client side.
I need some advice about how to improve the visualization of cartographic information.
User can select different species and the webmapping app shows its geographical distribution (polygonal degree cells), each specie with a range of color (e.g darker orange color where we find more info, lighter orange where less info).
The problem is when more than one specie overlaps. What I am currently doing is just to calculate the additive color mix of two colors using http://www.xarg.org/project/jquery-color-plugin-xcolor/
As you can see in the image below, the resulting color where two species overlap (mixed blue and yellow) is not intuitive at all.
Someone has any idea or knows similar tools where to get inspiration? for creating the polygons I use d3.js, so if more complex SVG features have to be created I can give a try.
Some ideas I had are...
1) The more data on a polygon, the thicker the border (or each part of the border with its corresponding color)
2) add a label at the center of polygon saying how many species overlap.
3) Divide polygon in different parts, each one with corresponding species color.
thanks in advance,
Pere
My suggestion is something along the lines of option #3 that you listed, with a twist. Rather painting the entire cell with species colors, place a dot in each cell, one for each species. You can vary the color of each dot in the same way that you currently are: darker for more, ligher for less. This doesn't require you to blend colors, and it will expose more of your map to provide more context to the data. I'd try this approach with the border of the cell and without, and see which one works best.
Your visualization might also benefit from some interactivity. A tooltip providing more detailed information and perhaps a further breakdown of information could be displayed when the user hovers his mouse over each cell.
All of this is very subjective. However one thing's for sure: when you're dealing with multi-dimensional data as you are, the less you project dimensions down onto the same visual/perceptual axis, the better. I've seen some examples of "4-dimensional heatmaps" succeed in doing this (here's an example of visualizing latency on a heatmap, identifying different sources with different colors), but I don't think any attempt's made to combine colors.
My initial thoughts about what you are trying to create (a customized variant of a heat map for a slightly crowded data set, I believe:
One strategy is to employ a formula suggested for
n + 1
with regards to breaks in bin spacing. This causes me some concern regarding how many outliers your set has.
Equally-spaced breaks are ideal for compact data sets without
outliers. In many real data sets, especially proteomics data sets,
outliers can make this representation less effective.
One suggestion I have would be to consider the idea of adding some filters to your categories if you have not yet. This would allow slimming down the rendered data for faster reading by the user.
another solution would be to use something like (Comprehensive) R
or maybe even DanteR
Tutorial in displaying mass spectrometry-based proteomic data using heat maps
(Particularly worth noting I felt, was 'Color mapping'.)