It’s been a while. I hope you are all well. Shall we make some charts?
Update August 7 2020
The gene symbol renaming is now official. Here’s the publication (not open access, should be), coverage at The Verge and more coverage at The Register. The latter with quotes from me.
It’s been 3 years since we last visited that old favourite recurring topic, data corruption by Excel. Specifically, the unwanted auto-conversion of identifiers that look like dates, e.g. SEPT1, to – well, dates.
Here’s a new twist – well, a two year-old twist in fact, as I don’t keep up to date with this field any longer:
Yes, in 2017 the HGNC decided that the solution to this long-standing issue is to rename the offending genes to prevent the auto-conversion. I’m yet to determine whether anything more came of the proposal.
It is I suppose a practical suggestion that will work. The newsletter states that:
Our initial consultation with the research community publishing on these genes had very mixed results
I bet it did. However, given that ongoing consultation with the research community about the inappropriate use of software has had essentially no results in 15+ years, perhaps it is the most effective solution to the problem.
When Marlion Pickett runs onto the M.C.G for Richmond in the AFL Grand Final this Saturday, he’ll be only the sixth player in 124 finals to debut on the big day.
library(dplyr) library(fitzRoy) afldata <- get_afltables_stats() afldata %>% select(Season, Round, Date, ID, First.name, Surname, Playing.for, Home.team, Home.score, Away.team, Away.score) %>% group_by(ID) %>% arrange(Date) %>% # a player's first game slice(1) %>% ungroup() %>% # grand finals only filter(Round == "GF") %>% # get the winning/losing margin mutate(Margin = case_when(Playing.for == Home.team ~ Home.score - Away.score, TRUE ~ Away.score - Home.score)) %>% select(-Home.team, -Away.team, -Home.score, -Away.score)
Each tweet takes one of two forms and is consistently formatted, making it easy to parse and extract information. Here are a couple of examples with the interesting parts highlighted in bold:
Between 16:00 and 18:30 today, 26% of trips experienced delays. #sydneytrains
The worst delay was 16 minutes, on the 18:16 City to Berowra via Gordon service. #sydneytrains
The take-home message: expect delays somewhere most days but in particular on Monday mornings, when students return to school after the holidays, and if you’re travelling in the far south-west or north-west of the network.
I’m not saying this is a good idea, but bear with me.
This week we return to Australian Rules Football, the R package fitzRoy and some statistics to ask – why can’t Geelong win after a bye?
(with apologies to long-time readers who used to come for the science)
Why would you even ask that? Well, because this.
I sense problems immediately. First, the story is tagged “evolution”. The horns are not arising through inheritance of advantageous mutations, so that isn’t evolution.
Yes last time I checked, horns were external and pointed upwards. The X-ray seems to show an internal, downward-pointing bone growth.
But wait, there’s more.
Melvyn and I hail from the same part of the world, and I learned as a child that many of the local place names there were derived from Old Norse or Danish. Notably: places ending in -by denote a farmstead, settlement or village; those ending in -thwaite mean a clearing or meadow.
So how local are those names? Time for some quick and dirty maps using R.
When this blog moved from bioinformatics to data science I ran a Twitter poll to ask whether I should start afresh at a new site or continue here. “Continue here”, you said.