crossfilter | Fast n-dimensional filtering and grouping of records | Data Visualization library

Thanks to Gordon in the comments pointing me into the right direction I was able to solve my problem using the valueAccesor.

Thanks for including a reproducible example. It's really hard to debug D3 and dc.js code by just staring at it without running it.

The problem here is that the scatter plot expects the group keys to be a two-element array of numbers, not just a single number. You can see that the key_function(), in the regression example which you started from, returns a function returning such keys.

Changing your dimension accordingly:

Thanks for including a complete working example with your question.

That looks like a plot of counts by time. If you want to sum the bananas, you could use

I think you're on the right track but

1. You may have your ?:s reversed
2. You may need to take the total for each category, and
3. Not use the overall total

Let's take this line as an example:

I agree that Equation B is easier to define using crossfilter, so I figured out one way to do it.

Equation A could probably work but it's unclear which day's close should be used under filtering - the last day which is not in the current bin? The day before the first day in the current bin?

Equation B needs the earliest row for the current bin, and that requires maintaining the array of all rows for each bin. This is not built into crossfilter but it's a feature which we have talked about adding.

The complex reduce example does this, and we can reuse some of its code. It calculates the median/mode/min/max value from the arrays of rows which fall in each bin, using these functions to generate those arrays:

Managed to figure to out thanks to Gordon!

I did capacityChart.xAxis().ticks(maxWeek) to fix the issue

As we discussed in the comments, the important problem is that dc.js will only draw the data it receives. It doesn't know if data is missing, so we will need to fill in the nulls in order to draw gaps in the line.

I linked to a previous question, where the data is timestamps. The answer there uses a d3 time interval to generate the missing timestamps.

However, your data uses integers for keys (even though it represents weeks), so we will need to change the function a little bit:

The answer is no. Your stated assumptions are correct. That's not efficient, and there is no more efficient way of using this library for temporary groups.

Crossfilter is designed for quick interaction between a fixed set of dimensions and groups.

It's stateful by design, adding and removing just the specific rows from each group that have changed based on the changes to filters.

This matters especially for range-based filters, since if you drag a brush interactively a small segment of domain is added and a small segment is removed at each mousemove.

There are also array indices created to track the mapping from data -> key -> bin. One array of keys and one integer array of indices for the dimension, and one integer array of bin indices for the group. This makes updates fast, but it may be inefficient for temporary groups.

If you don't have a consistent set of charts, it would be more efficient in principle to do the calculation yourself, using typed arrays and e.g. d3-array.

On the other hand, if you are doing this to comply with Vue's data model, you might see if Vue has a concept similar to React's "context", where shared state is associated with a parent component. This is how e.g. react-dc-js holds onto crossfilter objects.

Ah, I see this is because every field needs to have a value. It's actually listed in the crossfilter gotchas wiki: https://github.com/crossfilter/crossfilter/wiki/Crossfilter-Gotchas

If the key function returns undefined for a single row, say due to a missing field, this can corrupt the entire index.