Eddington {eddington}

An R6 Class for Tracking Eddington Numbers for Cycling

Description

An R6 Class for Tracking Eddington Numbers for Cycling

An R6 Class for Tracking Eddington Numbers for Cycling

Details

The class will maintain the state of the algorithm, allowing for efficient updates as new rides come in.

Warnings

The implementation uses an experimental base R feature utils::hashtab.

Cloning of Eddington objects is disabled. Additionally, Eddington objects cannot be serialized; they cannot be carried between sessions using base::saveRDS or base::save and then loaded later using base::readRDS or base::load.

Active bindings

current

The current Eddington number.

cumulative

A vector of cumulative Eddington numbers.

number_to_next

The number of rides needed to get to the next Eddington number.

n

The number of rides in the data.

hashmap

The hash map of rides above the current Eddington number.

Methods

Public methods

• Eddington$new()

• Eddington$print()

• Eddington$update()

• Eddington$getNumberToTarget()

• Eddington$isSatisfied()

Method new()

Create a new Eddington object.

Usage

Eddington$new(rides, store.cumulative = FALSE)

Arguments

rides

A vector of rides

store.cumulative

logical, indicating whether to keep a vector of cumulative Eddington numbers

Returns

A new Eddington object

Method print()

Print the current Eddington number.

Usage

Eddington$print()

Method update()

Add new rides to the existing Eddington object.

Usage

Eddington$update(rides)

Arguments

rides

A vector of rides

Method getNumberToTarget()

Get the number of rides of a specified length to get to a target Eddington number.

Usage

Eddington$getNumberToTarget(target)

Arguments

target

Target Eddington number

Returns

An integer representing the number of rides of target length needed to achieve the target number.

Method isSatisfied()

Test if an Eddington number is satisfied.

Usage

Eddington$isSatisfied(target)

Arguments

target

Target Eddington number

Returns

Logical

Examples

# Randomly generate a set of 15 rides
rides <- rgamma(15, shape = 2, scale = 10)

# View the rides sorted in decreasing order
setNames(sort(rides, decreasing = TRUE), seq_along(rides))

# Create the Eddington object
e <- Eddington$new(rides, store.cumulative = TRUE)

# Get the Eddington number
e$current

# Update with new data
e$update(rep(25, 10))

# See the new data
e$cumulative