# Below are a few R command lines to get you started.
# Note: The # symbol means "comment".

# First download the file temp.txt from the following address:
# http://www.met.rdg.ac.uk/cag/courses/Stats/Data/temp.txt
#
# This file contains daily temperatures measured at the University 
# of Reading during 2003. The file has three columns, each containing 
# 365 daily temperature measurements recorded in degrees Celsius at 
# the University of Reading meteorology station between 1 January and 
# 31 December 2003.  The three columns contain daily mean, minimum and
# maximum temperatures, respectively. Missing values are given the value `NA'.

# Read file temp.txt and stores the content in variable name temp
temp <- read.table("temp.txt",header=T) 

# Look at the first 5 lines of the file
temp[1:5,]

# Plot mean temperature time series
plot(temp[,1],type='l')

# Produce boxplot and histogram of daily mean temp (firt column of temp)
boxplot(temp[,1])
hist(temp[,1])


# Plot histogram with normal density superinposed
x<-seq(-5,30,0.1)
y<-dnorm(x,mean(temp[,1], na.rm = TRUE),sd(temp[,1], na.rm = TRUE))
hist(temp[,1], freq=FALSE, ylim=c(0,0.08)) # freq=FALSE plots density instead of frequency
lines(x,y)


# Compute mean, standard deviation, median, IQR, quartiles and range
# of daily mean temperature

mean(temp[,1], na.rm = TRUE) # na.rm = TRUE is used to remove NA from computation of the mean
sd(temp[,1], na.rm = TRUE)
median(temp[,1], na.rm= TRUE)
IQR(temp[,1], na.rm= TRUE)
quantile(temp[,1], na.rm= TRUE)
range(temp[,1], na.rm= TRUE) # returns min and max of temp[,1]

# help about quantile() function
help(quantile)			

# For more help either go to www.r-project.org
# or type help.start() when in R

# To exit do this and then say yes to save workspace
quit()