> # Directories
> setwd("directory")
> mydata<- read.csv("mly532.csv")
> attach(mydata)
The following objects are masked from mydata (pos = 4):

    date, maxtp

The following objects are masked from mydata (pos = 8):

    date, maxtp

The following objects are masked from mydata (pos = 9):

    date, maxtp

The following objects are masked from mydata (pos = 14):

    date, maxtp

>
> # Load libraries
> library(MASS)
> library(tseries)
> library(forecast)
>
> # Plot and convert to ln format
> lnweather=log(mydata$maxtp[1:741])
> lnweather
  [1] 2.639057 2.541602 2.476538 2.451005 2.785011 2.785011
  [7] 3.039749 3.182212 3.100092 3.104587 3.049273 2.944439
........
[733] 2.766319 2.595255 2.541602 2.509599 2.681022 2.912351
[739] 3.095578 3.034953 3.173878
>
> # ACF, PACF and Dickey-Fuller Test
> acf(lnweather, lag.max=20)
> pacf(lnweather, lag.max=20)
> adf.test(lnweather)

	Augmented Dickey-Fuller Test

data:  lnweather
Dickey-Fuller = -9.2629, Lag order = 9, p-value = 0.01
alternative hypothesis: stationary

Warning message:
In adf.test(lnweather) : p-value smaller than printed p-value

> # Time series and seasonality
> weatherarima <- ts(lnweather, start = c(1941,11), frequency = 12)
> plot(weatherarima,type="l")
> title("Maximum Air Temperature - Dublin")