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Type: Package
Title: Analysis and Prediction of Bicycle Rental Amount
Version: 0.1.1
Maintainer: Jiwon Min <miw5281@gmail.com>
Description: Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions. Functions on this package interacts with data on 'tashudata' package, a 'drat' repository. 'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. To install this data package, see the instructions at https://github.com/zeee1/Tashu_Rpackage. top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths. daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental. create_train_dataset(), create_test_dataset() is data processing function for prediction. Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset. Users can make random-forest prediction model by using create_train_model() and predict amount of bicycle rental in 2015 by using predict_bike_rental().
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding: UTF-8
LazyData: true
Imports: ggplot2, lubridate, dplyr, randomForest, plyr, reshape2, RColorBrewer, drat
Suggests: knitr, rmarkdown, tashudata
Additional_repositories: https://zeee1.github.io/drat
VignetteBuilder: knitr
RoxygenNote: 7.1.1
Depends: R (≥ 3.5.0)
NeedsCompilation: no
Packaged: 2021-01-13 09:03:15 UTC; miw52
Author: Jiwon Min [aut, cre]
Repository: CRAN
Date/Publication: 2021-01-13 09:50:02 UTC

Create training dataset on specific station for prediction

Description

A function to create training dataset on 'station_number' bicycle station by preprocessing bicycle rental history and weather data from 2013 to 2014.

Usage

create_test_dataset(station_number)

Arguments

station_number

number that means the number of each station.(1 ~ 144)

Value

a dataset containing feature and rental count data on 'station_number' station, 2013 ~ 2014

Examples

## Not run: test_dataset <- create_test_dataset(1)


Create training dataset on specific station for prediction

Description

A function to create training dataset on 'station_number' bicycle station by preprocessing bicycle rental history and weather data from 2013 to 2014.

Usage

create_train_dataset(station_number)

Arguments

station_number

number that means the number of each station.(1 ~ 144)

Value

a dataset containing feature and rental count data on 'station_number' station, 2013 ~ 2014

Examples

## Not run: train_dataset <- create_train_dataset(1)


Create random-forest training model for bicycle rental prediction.

Description

Create random-forest training model for bicycle rental prediction.

Usage

create_train_model(train_dataset)

Arguments

train_dataset

Training dataset created by create_train_dataset()

Value

random forest training model

Examples

## Not run: train_dataset <- create_train_dataset(3)
rf_model <- create_train_model(train_dataset)
## End(Not run)

Visualize amount of bicycle rental at each day of week.

Description

A function analyzing bicycle rental pattern on each day of week and visualizing analyzed result.

Usage

daily_bicycle_rental()

Examples

## Not run: daily_bicycle_rental()

Extract feature columns from train/test dataset

Description

Extract feature columns from train/test dataset

Usage

extract_features(data)

Arguments

data

data with feature columns and others

Value

data containing only feature columns


Visualize the change of bicycle rental amount by temperature and each month.

Description

A function drawing a plot that shows change of temperature and bicycle rental ratio in each month.

Usage

monthly_bicycle_rental()

Examples

## Not run: monthly_bicycle_rental()

Predict hourly Demand of bicycle in 2015.

Description

predict hourly amount of bicycle rental in 2015 using random forest algorithm. Create prediction model using 'train_dataset' and forecast demand of bicycle rental according to the condition of 'test_dataset'

Usage

predict_bicycle_rental(rf_model, test_dataset)

Arguments

rf_model

random forest prediction model create by create_train_model()

test_dataset

testing dataset

Value

test_dataset with predictive result.

Examples

## Not run: train_dataset <- create_train_dataset(3)
test_dataset <- create_test_dataset(3)
rf_model <- create_train_model(train_dataset)
test_dataset <- predict_bicycle_rental(rf_model, test_dataset)
## End(Not run)


Visualize Top 10 Pathes that were most used from 2013 to 2015.

Description

Visualize Top 10 Pathes that were most used from 2013 to 2015.

Usage

top10_paths()

Examples

## Not run: top10_paths()


Visualize top 10 stations that were most used from 2013 to 2015.

Description

Draw a plot that visualized most used top 10 stations on barchart.

Usage

top10_stations()

Value

Data frame that contains top 10 most used stations from 2013 to 2015

Examples

## Not run: top10_stations()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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