Was this dataset analysed correctly?
TL;DR
While the data comes from the NYTimes and seems legit, the presentation is intentionally misleading and the subsequent assertions are baseless. I say &quot;intentionally&quot; because an unbiased and reputable analysis would not propagate such major allegations from the data they have presented. The data does not prove nor disprove voter fraud, so the unfounded assertions are simply disinformation.

Before diving into the data
We are going to see many more such threads/posts in the coming months and years, so I need to address this bit before delving into the question.
Consider the following whenever you view anything online:

Fake news is much more viral than true news! Always air on the side of caution.
Is the source credible? In this case, you have no reason to believe the random twitter user &quot;CulturalHusbandry @Aphilosophae&quot;.
Do other credible sources corroborate the findings? Your confidence in the findings should increase with every independent credible source that come to the same conclusions. In this case, we do not have a credible source to begin with.
Is the language charged and the rhetoric conspiratorial? This is unfortunately becoming a prominent red flag for disinformation campaigns that are developed to sow doubt and distrust in democratic institutions.


How this Twitter thread is disinformation (step-by-step)
1. False premise
He/she first establishes the fact that, in total, batches of votes do not conspicuously favor one candidate or another (they do so via the tweets below). The distribution of the D/R ratio in vote batches over time is not reflected in this aggregate plot. However, this could mislead viewers to believe that they should expect 50/50 Democrat/Republican consistency throughout the vote counting process, which cannot be assumed from this information. The &quot;anonymous data scientist&quot; seems to encourage this logical fallacy by using this as his/her premise and posting his/her follow-up tweet to the plot (image below).

2. Misleading visualizations
He/she then establishes that the initial counts are &quot;random&quot; and noisy as one might expect from a 50/50 split. However, these batches do not have associated sizes with them! A &quot;batch&quot; may be 100K votes or it could fewer than 10. An actually informative portrayal of the data would have made the size of the circle/point reflect the size of the batch (bubble chart) rather than a scatter plot. Although this plot may technically be accurate, this can still be quite misleading, particularly because the &quot;anonymous data scientist&quot; made no note of this deficiency (lack of batch size information). But for the sake of argument, let's go with it.

3. Unsubstantiated claims and ignoring legitimate possibilities
Now they start talking about an &quot;anomaly&quot; and introducing conspiratorial allegations. They note the jump toward D, given the D/R ratio per batch in Milwaukee at some point in time. There are many legitimate explanations for this.
For example, a high/dense population such as a city (likely Democrat-leaning) would of course take more time to collect/count ballots, so they may not have started their reporting until after low/sparse populations (likely Republican-leaning). This can cause a sudden jump in favor of Democrats.
Also, the majority of the early ballots are in favor of Republican. This is to be expected; of course the candidate who consistently attempted to delegitimize mail-in ballots would receive fewer mail-in ballots from his followers. By contrast, Democrats were encouraged to vote by mail (and they did), making the later mail-in vote counts lean Democrat. This is another factor that could explain why the initial vote counts are skewed toward Republican in most states when compared to the final counts.