A dataset containing hourly bike rental demand in Seoul, South Korea, together with weather conditions, seasonal information, holiday status, and whether the bike sharing system was operating on that day.
data(bike_demand)A data frame with 8760 observations and 14 variables:
Date of observation.
Hour of the day, ranging from 0 to 23.
Temperature in degrees Celsius.
Humidity percentage.
Wind speed in meters per second.
Visibility in units recorded by the source dataset.
Dew point temperature in degrees Celsius.
Solar radiation in megajoules per square meter.
Rainfall in millimeters.
Snowfall in centimeters.
Season of the year: "spring", "summer", "autumn", or "winter".
Holiday status: "holiday" or "no holiday".
Whether the bike rental system was operating: "yes" or "no".
Number of rented bikes (target variable).
This dataset was obtained from the UCI Machine Learning Repository and renamed
bike_demand for inclusion in the liver package. It can be used to
illustrate methods for regression, exploratory data analysis, and predictive
modeling in R.
Reza Mohammadi (2025). Data Science Foundations and Machine Learning with R: From Data to Decisions. https://book-data-science-r.netlify.app.
mortgage,
bank,
churn_mlc,
churn,
churn_tel,
adult,
cereal,
advertising,
marketing,
drug,
house,
house_price,
red_wines,
white_wines,
insurance,
caravan,
loan
data(bike_demand)
str(bike_demand)
summary(bike_demand)
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