Pontifications

(Part 2)

# This time, you can run this file and generate the chart with only files in this
# directory. 1_gen_data.R generates the activity.tsv file that is used in here, if you're
# looking to do similar analyses on other activtites.


library(tidyverse)
source('henrik.r') # for the horribly named theme_henrik

df <- read_tsv('activity.tsv')
  • I still haven't figured out the tidyverse malarkey :-) ! But hopefully not critical to do so for now. I do want to learn this!
  • henrik.r I guess is a theme that I can also ignore for now.
  • here is the first few lines of [activity.tsv](https://github.com/halhen/viz-pub/blob/master/sports-time-of-day/activity.tsv)
  • I guess p is the "number of participants throughout the day compared to peak popularity```
  • i.e. p = number_of_participants for the that time measured in hours divided by the number of participants at the peak time?
activity    time     p
Running 0.0 6.45223543281662e-5
Running 5.0 6.45223543281662e-5
Running 10.0    6.45223543281662e-5
Running 15.0    6.45223543281662e-5
Running 20.0    6.45223543281662e-5
Running 25.0    6.45223543281662e-5
Running 30.0    6.45223543281662e-5
Running 35.0    6.45223543281662e-5
Running 40.0    6.45223543281662e-5
Running 45.0    6.45223543281662e-5
Running 50.0    6.45223543281662e-5
Running 55.0    6.45223543281662e-5
Running 60.0    6.45223543281662e-5
Running 65.0    6.45223543281662e-5
Running 70.0    7.549121524369304e-5
Running 75.0    7.549121524369304e-5
Running 80.0    7.549121524369304e-5
Running 85.0    7.549121524369304e-5
Running 90.0    7.549121524369304e-5
Running 95.0    0.0