Feed: R-bloggers.
Author: Tony Hirst.

A bit more tinkering with F1 data from the ergast db, this time trying to generating trivia / facts around races.

The facts are identified using SQL queries:

#starts for a team
q=paste0('SELECT d.code, COUNT(code) AS startsforteam, c.name AS name FROM drivers d JOIN races r JOIN results rs JOIN constructors c WHERE c.constructorId=rs.constructorId AND d.driverId=rs.driverId AND r.raceId=rs.raceId AND d.code IN (',driversThisYear_str,') ',upto,' GROUP BY d.code, c.name HAVING (startsforteam+1) % 50 = 0')
startsTeammod50=dbGetQuery(ergastdb, q)

#looking for poles to date modulo 5 
q=paste0('SELECT d.code, COUNT(code) AS poles FROM drivers d JOIN qualifying q JOIN races r WHERE r.raceId=q.raceId AND d.code IN (',driversThisYear_str,') AND d.driverId=q.driverId AND q.position=1',upto,' GROUP BY code HAVING poles>1 AND (poles+1) % 5 = 0')
lookingpolesmod5=dbGetQuery(ergastdb, q)

Some of the queries also embed query fragments, which I intend to develop further…

upto=paste0(' AND (year
I'm using knitr to generate Github flavoured markdown (gfm) from my Rmd docs – here’s part of the header:

---
output:
  md_document:
    variant: gfm
---

The following recipe then takes results from the trivia queries and spiels the output:

if (nrow(startsTeammod50)>0) {
  for (row in 1:nrow(startsTeammod50)) {
    text = '- `r startsTeammod50[row, "code"]` is looking for their `r toOrdinal(startsTeammod50[row, "startsforteam"]+1)` start for `r startsTeammod50[row, "name"]`'
    cat(paste0(knit_child(text=text,quiet=TRUE),'n'))
  }
}

if (nrow(lookingpolesmod5)>0) {
  for (row in 1:nrow(lookingpolesmod5)) {
    text = '- `r lookingpolesmod5[row, "code"]` is looking for their `r toOrdinal(lookingpolesmod5[row, "poles"]+1)` ever pole position'
    cat(paste0(knit_child(text=text,quiet=TRUE),'n'))
  }
}

We then get outputs of the form:

• BOT is looking for their 100th race start
• HAM is looking for their 100th start for Mercedes

See more example outputs here: Bahrain F1 2018 – Race Trivia.

This is another recipe I need to work up a little further and add to Wrangling F1 Data With R.