Bike sharing systems are a new generation of traditional bike rentals where the process of signing up, renting and returning is automated. Through these systems, users are able to easily rent a bike from one location and return them to another. We will be analyzing bike sharing data from Washington D. C. In this assignment, you will perform tasks to clean, visualize, and explore the bike sharing data. Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D. C., USA which is publicly available at http://capitalbikeshare. com/system-data. Data description (column name definitions):
Date: Date.
Season: Season (1 to 4).
Year: Year (2011 to 2012).
Month: Month (1 to 12).
Holiday: Weather a particular day is a holiday or not. (1: Holiday, 0: Nonholiday)
Weekday: Day of the week (1: Monday, 7: Sunday).
WorkingDay: If day is neither weekend nor holiday is 1, otherwise is 0.
Weather:
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: Temperature in Celsius.
ATemp: Feeling temperature in Celsius.
Humidity: Humidity.
WindSpeed: Wind speed.
Casual: Count of casual users.
Registered: Count of registered users.
Count: Count of total bike riders including both casual and registered.
Descriptive Data Analysis:
Given the above described data we would like to have a good understanding of the variables that are being used. Use R to analyze the data and answer the following question:
Find the average of bike riders (Count) for each weekday and calculate the percentages.
Day Weekday Average of bike riders Percentage
1 Mon
2 Tue
3 Wed
4 Thu
5 Fri
6 Sat
7 Sun
Plot a line plot in R to show the results from (a). (x axis represents "Weekday", and y axis represents the average of bike riders for each weekday).
What is the total number of casual bike riders in 2012?
What is the average temperatures for each season (1 to 4)?
What is the average daily bike riders for each month in 2011? Plot a line plot in R to show your results. (x axis represents "Month", and y axis represents the average daily bike riders for each month).
Month (in 2011) Average daily bike riders for each month
1
2
…
12
Find the correlation coefficient of "Temp" and "Count".