###############################

 ##  R Tutorial - First Part  ##

 ###############################



# Please read the tutorial's general instructions first, and prepare 

# for loading an external datafile.

# The content of this file can be pasted directly into the R console.

# It should blurp lost of errors only at the point where you 

# are supposed to have an external dataset available.

# However, it is much handier to use the "display file" command 

# within the R menu to look at this file, and paste command by command 

# to the console, using control-V.  Take your time to experiment a bit 

# with the listed commands.



#---------------------------------------------------------------------





# .Rfirst: A Very Short Introduction to R including Data Manipulation





#---------------------------------------------------------------------





# -----------------

# | Introduction  |

# -----------------



# Advantages and disadvantages of R:

#

# + Freely available 

# + Contains basic and advanced statistical analysis routines

# + The code is easy to modify

#

# - In comparison to S-Plus, the menu system is less developed

# - Not as widely used yet as SAS or SPSS

#

# Important websites, books, information:

#

# Look at: www.r-project.org ;

# from there, follow the links under "Documentation"

# 

# One thing is important for using any package, and 

# that is knowing how to reach the help files.

# There are two easy ways to do this in R. That is 

# either by typing, for example, 



help.search("sort")



# which will list all functions that contain the word "sort" 

# in their description, or via typing



help(sort)



# when you already know the name of a function, 

# the one named "sort" in this case, and you want to know 

# what it can do and how you have to supply the arguments.

# Try to search for some statistical terms you know, and take a 

# look at the functions where they appear.



help.search("Shapiro")

help.search("mixed")



# ---------------------

# | Data Manipulation |

# ---------------------



# Content:

# --------

#

# I.	Making data within R

# II.	Reading data in from elsewhere

# III.	Manipulating/modifying existing data objects

# IV.	Summary statistics, calculations

# V.	Writing a function

# V.	Tables







# I. Making data within R

#----------------------------



# It is quite easy to type in data directly into R, 

# or tho construct lists of observations on a variable.

#! In general, missing values in R are coded "NA" 

# For instance, let's type in data on numbers of beetles 

# caught in pitfall traps:



trapped<-c(0,1,2,1,0,3,3)

trapped # will list the observations.



# We type c() around the observations because this data becomes 

# stored as a column vector(hence the "c").

# Another way of doing the same, uses the scan() function.

# In that case, you have to type in observations yourself.

# Try using it.



testtrapped<-scan() 



# Supply observations separated by spaces, or returns.

# You only need to hit return twice to end adding observations.



# Different variables can have similar names. You can

# for example use a dot to structure a bit more.

# Assume that you have a second set of trappings 

# c(4,1,2,1,0,3,3) that differs ony in a single 

# observation from the first one.

# An economic way to make the second column vector is:



trapped.day1<-trapped #make a new variable from old

trapped.day2<-trapped.day1 # make a second from old

trapped.day2[1]<-4 # modify trapped.day2

trapped.day2 # take a look at it



# Typing ls() returns all the objects stored in memory:



ls()



# By writing trapped.day2[1] we accessed the fourth #element of the column vector trapped.day2.



trapped.day2[2] # prints element two.

trapped.day2[c(1,2,3)] # prints elements 1,2,3



# Beware, most datasets in R become matrices, and then an element like trapped.day2[1] is not well defined anymore. We see below how we can access the variables within such a data matrix.



# R also has a list of datasets that come with the program.

# You can view the list by typing



data() 

 

# To read a dataset in from that list, say dataset "trees", type 



data(trees)

ls() # now contains trees

summary(trees)



# Dataframe trees contains three variables.

# We can access the first variable "Girth" by typing



trees$Girth



# or



trees[,1] # note the comma



# a data matrix has rows and columns, therefore two indices 

# (separated by a comma) are necessary to code each element. 

# Each variable has a separate column usually, 

# and each observation is on a row.



trees[1,] # gives observation one

trees[1,1] # returns the first variable of observation one.





# II. Reading data in from elsewhere

#--------------------------------------



#!!Please stop here for a moment and read following the instructions.



#! We now give an easy way to read data into R, and at the same time

#! saving them in a data-object



# Prepare the datafile in, for example, the excell software program.

# In that spreadsheet, the first line of the data matrix 

# will contain the names of the variables (single words).

# This line is called the header line.

# Every observation is written on a separate line below the header line.

# Save your spreadsheet as a text file (e.g. "namefile.txt").

# In R: Change the working directory to the directory where the data are. 

# Use the R menu for that.



# Now type the following command at the command line:



dataset<-read.table("namefile.txt",header=TRUE)



# The data will be read in as data-object with name "dataset". 

# You can check that this object exists by typing



ls()



# ls prints all objects in your workspace. Dataset should be be among 

# the objects listed.

# Or get a summary of what dataset contains as follows:



summary(dataset)



dim(dataset) 



# gives the numbers of lines (observations) and columns (variables) in it.

ls("dataset") 



#or



objects("dataset") # lists the variables in the objects with name dataset.



# If you type the following command, the variables in the object dataset 

# can be accessed directly by giving their names:



attach(dataset)



# Try this for a variable within dataset you have read in.

# For instance type loc at the prompt, if "dataset" contains a variable 

# with that name.

# You can also type



Girth # not found ....

attach(trees)

Girth # aha!



# The function detach() is used to make a dataset less accessible as soon 

# as you don't really need the variables anymore.

# This avoids messing up variables from different data-objects.



detach(trees)





# III. Manipulating/modifying existing data objects

#----------------------------------------------------



#  It is relatively easy to manipulate datasets within R.

# Also concatenating different datasets, extracting pasrts of them

# and so on is possible.



# For instance, consider the object



trapped.day2



# We can access element number eight of it by typing



trapped.day2[8]



# ....replace it by typing 



trapped.day2[8]<-5



#such that trapped.day2 becomes



trapped.day2



# Replacing several elements at a time can be done as well:



trapped.day2[9:11]<-rep(1,3) #replaces elements 9 to 11 by ones.



#See



help(rep) #for explanation on the rep() function used here.



# Try to figure out what the ":" does for you.



trapped.day2[c(1,8)]<-c(1,2) #replaces elements 1, 8 by 1 and 2 resp.





# We can also add observations, even ones with missing values



trapped.day2[12]<-NA

trapped.day2[13]<-12



# spreadsheet facilities are available by using



data.entry(trapped.day2)



# or we can use another way of editing still,



edit(trapped.day2)



# The function edit() can use different text editors such as 

# vi, pico, emacs etc... For details, take a look at



help(edit)



# Using data.entry() to type in a new dataset is a little bit tricky.

# Suppose you want to make trapped.day3.

# This does not work:



data.entry(trapped.day3)



# But the following does work, since we initialize trapped.day3 immediately



data.entry(trapped.day3=c(NA))



# Concatenating column vectors goes as follows:



trapped.all<-c(trapped.day1,trapped.day2)



# For combining data matrices, you use rbind() or cbind().



# Some functions return logical TRUE or FALSE, for instance 



trapped.day2==1



# returns TRUE for all elements of trapped.day2 that equal 4, 

# FALSE otherwise.

# Sometimes you just want the positions where that occurs:



which(trapped.day2==3)



# The functions is.na() is also extremely handy.

# It tests for occurrence of missing values "NA"



is.na(trapped.all)

help(is.na)



# You can for instance replace missing values by zeroes as follows:



trapped.all[is.na(trapped.all)]<-0

trapped.all



# If trapped.all contained any missing values, 

# they would become replaced by zeroes.





# IV. Summary statistics, calculations

#----------------------------------------



# Obviously, you can use R as a pocket calculator, 

# but there's much more.



# We can do calculations on entire vectors,



trapped.day2-trapped.day1 # calculates the elementwise differences between them



# The following functions do what you would expect already

# from reading their names:



length(trapped.all) #length of the vector

diff(trapped.all) # differences between successive observations

diff(c(1:10)) # Predict what this will do...



# Several sample statistics are available as well:



median(trapped.all)

mean(trapped.all)

var(trapped.all)

max(trapped.all)

sum(trapped.all)



# If your data vector contains NA's, missing values, 

# you can add an option to remove missing values, see help(mean) for this.



# Cumsum returns a vector whose elements are

# the cumulative sums of the argument



cumsum(trapped.all) 



cummax(trapped.all) # Similarly, cumulative maxima.



# It is also possible to calculate a statistic on a subset of a 

# datavector, 



mean(subset(trapped.day2,trapped.day2>2))



# beware, the following command calcuates the proportion of

# observations with values larger than 2:



mean(trapped.day2>2)





# V.	Writing a function



# Suppose you want the standard deviation of a variable

# and not just the variance, for which we used 

# var() in the last section.



std(trapped.all) # This does not work.



# Lets' make a function that calculates a standard deviation ourselves.

# We will call it std. 

# For a vector x, it has to calculate the square root of the variance.

# It must become a function with one argument x, 

# that uses other functions sqrt(var(x)) on x.

# Voila:



std<-function(x)sqrt(var(x))



std(trapped.all) # This works!



# We could have avoided writing this function by looking a bit harder for



help.search("standard deviation")



# Then we would have seen that this is available:



sd(trapped.all)



# Which produces the same result.



# V.	Tables

#--------------



# The function table()  builds a contingency

# table of the counts.

# For instance, when we do that for trapped.all,

# we get a table listing the frequencies of counts.



table(trapped.all)

help(table)



# -------------------------------------------------------

# When finished running this file, please continue with 

# the ".Rgraphics" or ".Rclassics" tutorial files.

# -------------------------------------------------------





# Much of the material in this short tutorial comes from



# Using R for data analysis and graphics. An Introduction

# by J. H. Maindonald (2001)



# Using R for introductory Statistics

# by John Verzani (2002)



# both are acessible via links on the documentation section of

# the R-project website. 



# Tom Van Dooren, version 30/01/2003