Description

This dataset contains the daily count of rental bike transactions between years 2011 and 2012 in Capital bikeshare system with the corresponding weather and seasonal information.

Usage

Format

A tibble: 731 x 16

• instant: record index

• dteday : date

• season : season (1:winter, 2:spring, 3:summer, 4:fall)

• yr : year (0: 2011, 1:2012)

• mnth : month ( 1 to 12)

• hr : hour (0 to 23)

• holiday : weather day is holiday or not

• 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 derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)

• atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)

• 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

References

Fanaee-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.

Examples