#Reading in NYC 2016 Bike Share Analysis Dataset
nyc <- read.csv("NYC-2016-Summary.csv")
head(nyc)
## duration month hour day_of_week user_type
## 1 13.98333 1 0 Friday Customer
## 2 11.43333 1 0 Friday Subscriber
## 3 5.25000 1 0 Friday Subscriber
## 4 12.31667 1 0 Friday Subscriber
## 5 20.88333 1 0 Friday Customer
## 6 8.75000 1 0 Friday Subscriber
#Creating lists of valid month levels and replacing integers with month abreviations.
month_levels<- c("Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec")
nyc$month <- factor(nyc$month, levels = 1:12, labels = month_levels)
head(nyc)
## duration month hour day_of_week user_type
## 1 13.98333 Jan 0 Friday Customer
## 2 11.43333 Jan 0 Friday Subscriber
## 3 5.25000 Jan 0 Friday Subscriber
## 4 12.31667 Jan 0 Friday Subscriber
## 5 20.88333 Jan 0 Friday Customer
## 6 8.75000 Jan 0 Friday Subscriber
#Creating lists of valid time levels and replacing integers with time of day.
time_levels <- sprintf("%02d:00", 0:23)
nyc$hour <- factor(nyc$hour, levels = 0:23, labels = time_levels)
head(nyc)
## duration month hour day_of_week user_type
## 1 13.98333 Jan 00:00 Friday Customer
## 2 11.43333 Jan 00:00 Friday Subscriber
## 3 5.25000 Jan 00:00 Friday Subscriber
## 4 12.31667 Jan 00:00 Friday Subscriber
## 5 20.88333 Jan 00:00 Friday Customer
## 6 8.75000 Jan 00:00 Friday Subscriber
#Converting day of week from character to factor.
nyc$day_of_week <- factor(nyc$day_of_week, levels = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
#Converting user_type from characetr to factor
nyc$user_type <- factor(nyc$user_type, levels = c("Customer", "Subscriber"))
head(nyc)
## duration month hour day_of_week user_type
## 1 13.98333 Jan 00:00 Friday Customer
## 2 11.43333 Jan 00:00 Friday Subscriber
## 3 5.25000 Jan 00:00 Friday Subscriber
## 4 12.31667 Jan 00:00 Friday Subscriber
## 5 20.88333 Jan 00:00 Friday Customer
## 6 8.75000 Jan 00:00 Friday Subscriber