bike_sharing_data<- read.csv("bike_sharing_data.csv")

4) Codes to extract bike sharing datasets.

bike3 <- read.csv("bike_sharing_data.csv")
bike1 <- read.table("bike_sharing_data.csv", sep=",", header=TRUE)

5) Number of Rows and Columns (Total Number of Observations and Variables)

nrow(bike_sharing_data)
## [1] 17379
ncol(bike_sharing_data)
## [1] 16

6) The data type of humidity perceived by R

str(bike_sharing_data)
## 'data.frame':    17379 obs. of  16 variables:
##  $ datetime  : chr  "1/1/2011 0:00" "1/1/2011 1:00" "1/1/2011 2:00" "1/1/2011 3:00" ...
##  $ season    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ holiday   : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ workingday: int  0 0 0 0 0 0 0 0 0 0 ...
##  $ weather   : int  1 1 1 1 1 2 1 1 1 1 ...
##  $ temp      : num  9.84 9.02 9.02 9.84 9.84 ...
##  $ atemp     : num  14.4 13.6 13.6 14.4 14.4 ...
##  $ humidity  : chr  "81" "80" "80" "75" ...
##  $ windspeed : num  0 0 0 0 0 ...
##  $ casual    : int  3 8 5 3 0 0 2 1 1 8 ...
##  $ registered: int  13 32 27 10 1 1 0 2 7 6 ...
##  $ count     : int  16 40 32 13 1 1 2 3 8 14 ...
##  $ sources   : chr  "ad campaign" "www.yahoo.com" "www.google.fi" "AD campaign" ...
##  $ X         : logi  NA NA NA NA NA NA ...
##  $ X.1       : logi  NA NA NA NA NA NA ...
##  $ X.2       : chr  "for peak hours" "filter data: numerical approach" "show data through a plot" "" ...
class(bike_sharing_data$humidity)
## [1] "character"

7) Value of season in row 6251

bike3[6251, "season"]
## [1] 4

8) How many observations have the season winter?

season_table <- table(bike3$season)
season_table["4"]
##    4 
## 4232

10) High wind threshold

high_wind_threshold <- 36
high_wind_obs <- subset(bike_sharing_data, 
                        windspeed >= high_wind_threshold & 
                        (season == "4" | season == "2"))
num_high_wind_obs <- nrow(high_wind_obs)