Founder of No More Marking Ltd, psychometrician.
Published
Edited
Jun 15, 2021
1 fork
md`# Appendix`
runs = FileAttachment("running.csv").csv({ typed: true })
runs.map(r => moment(r.Date, 'DD-MM-YYYY'))
runDates = runs.map(row => ({ ...row, dateM: moment(row.Date, 'DD-MM-YYYY') }))
date1m = nextRun ? moment(nextRunDate, 'YYYY-MM-DD') : moment()
date2m = nextRun
? moment(nextRunDate, 'YYYY-MM-DD').subtract(33, 'days')
: moment().subtract(33, 'days')
date2w = nextRun
? moment(nextRunDate, 'YYYY-MM-DD').subtract(6, 'days')
: moment().subtract(6, 'days')
moment
monthlyRuns = runDates.filter(
run => (run.dateM < date2w) & (run.dateM >= date2m)
)
initialValue = 0
monthlySum = monthlyRuns.reduce(
(accumulator, currentValue) => accumulator + currentValue.Distance,
initialValue
)
monthyAverage = Math.round((monthlySum / 4) * 10) / 10
weeklySum = weeklyRuns.reduce(
(accumulator, currentValue) => accumulator + currentValue.Distance,
initialValue
) + gain
weeklyRuns = runDates.filter(
run => (run.dateM <= date1m) & (run.dateM >= date2w)
)
acuteChronic = Math.round((weeklySum / monthyAverage) * 100) / 100
date1m.format("DD-MM-YYYY")
date2m.format("DD-MM-YYYY")
date2w.format("DD-MM-YYYY")
nextRun = gain > 0
moment = require('moment')
d3 = require("d3@5")
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.