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bike <- read.csv('C:/Users/rosha/Downloads/datasets/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 include weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information. Attribute Information Date : year-month-day Rented Bike count - Count of bikes rented at each hour Hour - Hour of he day Temperature-Temperature in Celsius Humidity - % Windspeed - m/s Visibility - 10m Dew point temperature - Celsius Solar radiation - MJ/m2 Rainfall - mm Snowfall - cm Seasons - Winter, Spring, Summer, Autumn Holiday - Holiday/No holiday Functional Day - NoFunc(Non Functional Hours), Fun(Functional hours)

#goals/purpose

Currently Many big communities have adopted the use of rental bikes to improve transportation comfort. It is crucial to make the rental bikes accessible and available to the general public at the appropriate time since it reduces waiting. Eventually, maintaining a steady supply of rental bikes for the city emerges as a top priority. Predicting the number of bikes needed to maintain a steady supply of rental bikes at each hour’s interval is essential.

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

print(standard_deviation)
## [1] 20.36241
print(variation)
## [1] 370027.3
print(summ)
## [1] 112854.4
plot(bike$Hour, bike$Humidity, main="Scatter Plot of X vs Y", xlab="Hour", ylab="Humidity")

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")

result <- aggregate(bike$Humidity,by=list(bike$Seasons), mean)
result
##   Group.1        x
## 1  Autumn 59.22848
## 2  Spring 58.77672
## 3  Summer 64.98143
## 4  Winter 49.74491
barplot(result$x, names.arg=result$Group.1, xlab="Season", ylab="Average Humidity", col=rainbow(6),
        main="Season vs Humidity",border="black")