Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return back has become automatic.
Through these systems, user is able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousands bicycles.
Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues.
Opposed to other transport services such as bus or subway, the duration of travel, departure and arrival position is explicitly recorded in these systems. This feature turns bike sharing system into a virtual sensor network that can be used for sensing mobility in the city. It is expected that most of important events in the city could be detected via monitoring these data.
In the finaly project, you will be analyzing the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA. The data set contains recourds of 17379 hourly counts of rentals. It was originally compiled by Fanaee and Gama in "Event labeling combining ensemble detectors and background knowledge" (2013).
You will download the data set bikeshares.csv. The data set contains
- instant: record index
- dteday : date
- season : season (1:springer, 2:summer, 3:fall, 4:winter)
- yr : year (0: 2011, 1:2012)
- mnth : month ( 1 to 12)
- hr : hour (0 to 23)
- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday- schedule)
- weekday : day of the week
- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
- weathersit :
- 1: Clear, Few clouds, Partly cloudy, Partly cloudy
- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
- temp : Normalized temperature in Celsius. The values are divided to 41 (max)
- atemp: Normalized feeling temperature in Celsius. The values are divided to 50 (max)
- hum: Normalized humidity. The values are divided to 100 (max)
- windspeed: Normalized wind speed. The values are divided to 67 (max)
- casual: count of casual users
- registered: count of registered users
- cnt: count of total rental bikes including both casual and registered
This analysis is intentionally open ended. While you explore the data, recall the tools you have learned in class.
The maximum length of your report should be fifteen pages, including plots and tables.
The general format should be an introduction outlining the aspects you plan to explore and describing the data, the analysis itself, including plots, diagrams, and tables, and a discussion/conclusion section summarizing your findings. use R code in the analysis.
However, keep it on a separate document.
I need help with a assignment and want it to be done impeccably.
I do not have time to do it so want someone very well qualified in R studio and statistics to do this for me.
Attachment:- bikesharedata.rar