My old job at the Health & Safety Laboratory is being advertised, and at a higher pay grade to boot. (Though it is still civil service pay, and thus not going to make you rich.)
You’ll need to have solid mathematical modelling skills, particularly solving systems of ODEs, and be proficient at writing scientific code, preferably R or MATLAB or acslX. From chats with a few people at the lab, management are especially keen to get someone who can bring in money so grant writing and blagging skills are important too.
It’s a smashing place to work and the people are lovely. Also, you get flexitime and loads of holiday. If you are looking for a maths job in North West* England then I can heartily recommend applying.
*Buxton is sometimes North West England (when we get BBC local news) and sometimes in the East Midlands (like when we vote in European elections).
This is a silly problem that bit me again recently. It’s an elementary mistake that I’ve somehow repeatedly failed to learn to avoid in eight years of R coding. Here’s an example to demonstrate.
Suppose we create a data frame with a categorical column, in this case the heights of ten adults along with their gender.
(heights <- data.frame( height_cm = c(153, 181, 150, 172, 165, 149, 174, 169, 198, 163), gender = c("female", "male", "female", "male", "male", "female", "female", "male", "male", "female") ))
Using a factory fresh copy of R, the gender column will be assigned a factor with two levels: “female” and then “male”. This is all well and good, though the column can be kept as characters by setting
stringsAsFactors = FALSE.
Now suppose that we want to assign a body weight to these people, based upon a gender average.
avg_body_weight_kg <- c(male = 78, female = 63)
Pop quiz: what does this next line of code give us?
Well, the first value of
heights$gender is “female”, so the first value should be 63, and the second value of
heights$gender is “male”, so the second value should be 78, and so on. Let’s try it.
avg_body_weight_kg[heights$gender] # male female male female female male male female female male # 78 63 78 63 63 78 78 63 63 78
Uh-oh, the values are reversed. So what really happened? When you use a factor as an index, R silently converts it to an integer vector. That means that the first index of “female” is converted to 1, giving a value of 78, and so on.
The fundamental problem is that there are two natural interpretations of a factor index – character indexing or integer indexing. Since these can give conflicting results, ideally R would provide a warning when you use a factor index. Until such a change gets implemented, I suggest that best practice is to always explicitly convert factors to integer or to character before you use them in an index.