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bike <- read.csv('D:/dataset/bike.csv')
summary(bike)
##      Date           Rented.Bike.Count      Hour        Temperature    
##  Length:8760        Min.   :   0.0    Min.   : 0.00   Min.   :-17.80  
##  Class :character   1st Qu.: 191.0    1st Qu.: 5.75   1st Qu.:  3.50  
##  Mode  :character   Median : 504.5    Median :11.50   Median : 13.70  
##                     Mean   : 704.6    Mean   :11.50   Mean   : 12.88  
##                     3rd Qu.:1065.2    3rd Qu.:17.25   3rd Qu.: 22.50  
##                     Max.   :3556.0    Max.   :23.00   Max.   : 39.40  
##     Humidity       Wind.speed      Visibility   Dew.point.temperature
##  Min.   : 0.00   Min.   :0.000   Min.   :  27   Min.   :-30.600      
##  1st Qu.:42.00   1st Qu.:0.900   1st Qu.: 940   1st Qu.: -4.700      
##  Median :57.00   Median :1.500   Median :1698   Median :  5.100      
##  Mean   :58.23   Mean   :1.725   Mean   :1437   Mean   :  4.074      
##  3rd Qu.:74.00   3rd Qu.:2.300   3rd Qu.:2000   3rd Qu.: 14.800      
##  Max.   :98.00   Max.   :7.400   Max.   :2000   Max.   : 27.200      
##  Solar.Radiation     Rainfall          Snowfall         Seasons         
##  Min.   :0.0000   Min.   : 0.0000   Min.   :0.00000   Length:8760       
##  1st Qu.:0.0000   1st Qu.: 0.0000   1st Qu.:0.00000   Class :character  
##  Median :0.0100   Median : 0.0000   Median :0.00000   Mode  :character  
##  Mean   :0.5691   Mean   : 0.1487   Mean   :0.07507                     
##  3rd Qu.:0.9300   3rd Qu.: 0.0000   3rd Qu.:0.00000                     
##  Max.   :3.5200   Max.   :35.0000   Max.   :8.80000                     
##    Holiday          Functioning.Day   
##  Length:8760        Length:8760       
##  Class :character   Class :character  
##  Mode  :character   Mode  :character  
##                                       
##                                       
##

#data documentation

Data includes information like ,the number of bikes rented per hour , date, weather like (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall) Information of the attributes Date - year-month-day Rented Bike count - Count of bikes rented per hour Hour - Hour of he day Temperature -Temperature in Celsius Humidity - humidity measure in % Windspeed - speed of wind in m/s Visibility - 10m Dew point temperature- measure of dew point temperature in Celsius Solar radiation -measure of solar radiation in MJ/m2 Rainfall - amount of rainfall noted in mm Snowfall - amount of snowfall noted in cm Seasons - Winter, Spring, Summer, Autumn Holiday - Holiday/No Holiday Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)

#about the data set/ purpose

Renting bikes has become a popular mode of transportation in many large areas right now. The public should get access to the rental bikes at the proper time because it cuts down on waiting. The city’s ability to rent bikes continues to be a primary goal throughout time. It is critical to forecast the number of bikes required to keep a continuous supply of rental bikes available every hour.

standard_deviation <- sd(bike$Windspeed, na.rm= TRUE)
variation <- var(bike$Humidity, na.rm= TRUE)
summ <- sum(bike$Visibility)

print(standard_deviation)
## [1] NA
print(variation)
## [1] 414.6279
print(summ)
## [1] 12586594
plot(bike$Hour, bike$Windspeed, main="Scatter Plot of X vs Y", xlab="Hour", ylab="Windspeed")

hist(bike$Hour, main="Histogram of Values", xlab="Hour")

boxplot(bike$Visibility, main="Box Plot of Values")

pie(table(bike$Seasons), main="Pie Chart of Category")