frivol | Simple Redis backed temporary storage | Data Processing library

I use DataGrip as an IDE for Google BigQuery. DataGrip has a warning pane that warns about items that are frivolous. An example below, "Unable to resolve column 'DAYS_DIFF_CURRENT_ETA', this is occurring because I capitalized the word when I was selecting it.

The query runs fine, however I would prefer to only see warnings where my query may not execute due to an error in how I wrote it.

See the screenshot:

I'm trying to add a polynomial trend line to a plotly go chart with 2 y axes, the trend line should follow the trace charting the weekly column against time.

The purpose of the chart is to look at an outcome over time and identify the inflection in the dataset when a new variable is introduced.

Dataset:

I have to parse HTML and "HTML" from emails. I've already managed to create a function that cleans most of the errors such as improper nesting of elements.

I'm trying to determine how best to tackle the issue of HTML attributes that are missing values. We must parse everything ultimately as XML so well-formed HTML is a must as well.

The cleaning function starts off simple enough:

So I have a large data set of webinar conference attendees - about 30k+ rows. I need to write a function (either in Excel or Google Sheets) to determine if each participant is a first time or returning attendee. The data set is sorted chronologically by event from oldest to most recent. Let's use the simplified version below to better illustrate the business case here.

Check out the simple sample set here.

I considered using named ranges for each of the webinar sets and searching to see if the user exists in that range but given the scale and various webinar attendee sizes that would be tough. I also thought about index-matching the various attendees but because the number of attendees per event is so variable (anywhere from 5 to 500) that too would be frivolous. I feel like I might be missing a relatively simple solution here - please assist! Thanks!

My apologies if this is difficult to follow.

I am challenging myself to use only CSS and HTML to build this site in order to improve my skills with CSS. I have hit a road block with two rows that contain three "card" elements each. The cards are all over the place in height and I've not been able to find a solution to fix it. I have added line-height defined in px, em, and % values. I can't define the height of the card in px and still keep it responsive, so I defined it in %. Height: 100% causes the cards to overflow and stagger even when there is not enough content to justify the behavior. The bottom of the middle cards drops down and the two on the right side overlap. I have tried max-height: 100% to keep them from overflowing but that did nothing because the content doesn't require any of them to overflow on its own. I tried min-height: 100% but that gives me the same problems of height: 100%. I know I must be missing something here (and it's probably something obvious and painfully simple) but I'm at a loss as to what it is.

I have used time series in R for data analysis occasionally, but I am not familiar with plotting with functions like ARIMA.

The following question stems from a comment on the number of daily cases of COVID in the US following a cubic. Indeed it looks like that, and I wanted to simply run a cubic regression with the humble (and frivolous) intent of plotting a polynomial curve on the scatterplot. Being that it is a time series I don't think using the lm() function would work.

Here is the code:

I have two machines and both of them are set to UTC by the same NTP server. I have them set up so that Machine A sends messages to Machine B at time T and Machine B receives these messages at T + N. The messages seem to be receivable at Machine B for any positive value of N and Im wondering if anyone can tell me of a way to get Machine B to receive these messages at values of N which are less than zero.

The application of this is for frivolous message passing around magnetised water coolers where varying flux densities are related through Brown Capacitors on the fly.

I dont think this problem is one which can be solved in an instant but might merit some responses from those who know what Im talking about.