Computing your Eddington number using the eddington package.

Introduction

One statistic that cyclists might be interested to track is their Eddington number. The Eddington number for cycling, E, is the maximum number where a cyclist has ridden E miles on E distinct days. So to get a number of 30, you need to have ridden 30 miles or more on 30 separate days.

This package allows the user to compute Eddington numbers and more. For example, users can determine if a specific Eddington number is satisfied or how many rides of the appropriate distance are needed to increment their Eddington number. It also contains simulated data to demonstrate the package use.

The Data

Loading the simulated data is simple. Let’s take a quick look at the first few lines.

library(eddington)
head(rides)
#>    ride_date ride_length
#> 1 2009-01-04        5.97
#> 2 2009-01-06       11.59
#> 3 2009-01-10       17.10
#> 4 2009-01-11       12.51
#> 5 2009-01-18       10.70
#> 6 2009-01-18        7.73

First, we need to establish the granularity of the data. As you can see above, there are at least two entries for 2009-01-18. Since this data simulates a rider who tracked each individual ride, there could be more than one ride per day in this dataset. Therefore, we need to transform the data to aggregate on day.

library(dplyr)

days <- rides %>%
  group_by(ride_date) %>%
  summarize(n = n(), total = sum(ride_length))

head(days)
#> # A tibble: 6 × 3
#>   ride_date      n total
#>   <date>     <int> <dbl>
#> 1 2009-01-04     1  5.97
#> 2 2009-01-06     1 11.6 
#> 3 2009-01-10     1 17.1 
#> 4 2009-01-11     1 12.5 
#> 5 2009-01-18     2 18.4 
#> 6 2009-01-19     1 13.4

Let’s just take a quick peek at the summary stats:

summary(days)
#>    ride_date                n             total      
#>  Min.   :2009-01-04   Min.   :1.000   Min.   : 2.17  
#>  1st Qu.:2009-04-07   1st Qu.:1.000   1st Qu.:11.37  
#>  Median :2009-07-15   Median :1.000   Median :15.02  
#>  Mean   :2009-07-05   Mean   :1.404   Mean   :19.21  
#>  3rd Qu.:2009-09-28   3rd Qu.:2.000   3rd Qu.:25.04  
#>  Max.   :2009-12-31   Max.   :5.000   Max.   :77.71

Computing Eddington Numbers

To compute the Eddington number, we use the E_num() function like so:

E_num(days$total)
#> [1] 29

days$E <- E_cum(days$total)

head(days)
#> # A tibble: 6 × 4
#>   ride_date      n total     E
#>   <date>     <int> <dbl> <int>
#> 1 2009-01-04     1  5.97     1
#> 2 2009-01-06     1 11.6      2
#> 3 2009-01-10     1 17.1      3
#> 4 2009-01-11     1 12.5      4
#> 5 2009-01-18     2 18.4      5
#> 6 2009-01-19     1 13.4      5

Addtional Functionality

E_next(days$total)
#> Your current Eddington Number is 29. You need 3 rides of 30 or greater
#> to get to an Eddington number of 30.

E_req(days$total, 50)
#> [1] 46

E_sat(days$total, 30)
#> [1] FALSE

Conclusion

The text above should give you a good start in using the eddington package. Although this package was developed with bicycling in mind, it has applications for other users as well. The Eddington number is a specific application of computing the side length of a Durfee square. Another application is the Hirsch index, or h-index, which a popular number in bibliometrics.