Q4: Import data

bike3 <- read.csv("bike_sharing_data.csv")
bike1 <- read.table("bike_sharing_data.csv", sep = ",", header = TRUE)
bike2 <- read.table("bike_sharing_data.txt", sep="\t", header=TRUE)
bike4 <- read.delim("bike_sharing_data.txt")

Q6:

str(bike1)

## 'data.frame':    17379 obs. of  13 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" ...

Q7

# Get the value of season in row 6251
bike1[6251, "season"]

## [1] 4

Q8

# Create a frequency table of seasons
table(bike1$season)

## 
##    1    2    3    4 
## 4242 4409 4496 4232

# If you want only winter (season = 4)
table(bike1$season)[4]

##    4 
## 4232

Q10

# Count rows with windspeed > 40 and season is winter (4) or spring (1)
sum(bike1$windspeed >= 40 & bike1$season %in% c(1, 4))

## [1] 46