# wordLengths.R
#
# (c) 2007 by Mary E. Beckman for Linguistics H286
#
# Script for making sample histograms of word lengths for data analysis 
# report on "Counting words of different lengths." 

# Any line that has a hashmark (#) at the beginning is a comment. 
# Most of these are explanations of the R code in the neighboring 
# lines.  A good strategy is to read the comments around the R 
# code and then experiment with running different parts of the 
# code, to see what each part does.  For example ....

# Start by setting the working directory to the folder where you have 
# stored the file wordsInClassAgroup.txt (if you are in the A group) 
# or wordsInClassStupendous.txt (if you are in group Stupendous) and 
# so on.  These files are on our class web page in the directory 
# http://ling.osu.edu/~mbeckman/Lx286/scripts 
# On Mary's laptop, this folder is called Lx286 and it is a subdirectory 
# of her C: drive, so she would use the following command:  
setwd('C:/Lx286')

# The syntax of this line is the simplest possible.  It uses the 
# command setwd() using the argument 'c:/Lx286' to name the "path"  
# to the folder.

###
# Next read the file for your group and assign the contents to a 
# variable called "words" using this next command.  
words=read.table("wordsInClassStupendous.txt", header=T, sep="\t")

# This command has two parts.  The part to the right of the 
# assignment operator (=) uses the command read.table() with the 
# argument "wordsInClassAgroup.txt", which is the name of the file 
# to be read in as a table.  (The other two arguments tell R that 
# there is a "header" row and that the columns of data are separated 
# by tabs rather than by spaces.)  The part before the assignment 
# operator specifies the name of the variable which then stands in 
# for the table that you read in.  Try running just this part:
read.table("wordsInClassStupendous.txt", header=T, sep="\t")
# to compare the effects of running the command without assigning 
# the results to anything.  Also, no try typing just:
words
# to see what you did in the first command.  

###
# Use the following command to see how big the table is.
dim(words)

# You should get something that looks like this on your R console:
# [1] 29  2
# This tells you that the table words has 29 rows and 2 columns. 

# Use the following command to see what the names of the columns are:
names(words)
# You should get something that looks like this on your R console:
# [1] "word"   "noSyls"
# This tells you that the 2 columns are called "word" (for the word's 
# spelled form) and "noSyls" (for the number of syllables that group 
# Stupendous assigned to each word). 

# You can now refer to the column either by its number, like this:
words[,1]

# or by its name, like this:
words[,"word"]

# And you can refer to rows similarly, using the number(s) of the 
# row(s), like this (for the first 3 rows):
words[1:3,]

# You can use these commands to see how many syllables there are in 
# the shortest and longest words:
min(words[,"noSyls"])
max(words[,"noSyls"])
# You should see that the shortest are 1 and the longest are 4.  

# You can now count the number of words that have each number of 
# syllables, like this:
summary(as.factor(words[,"noSyls"]))
# You should see something like this, which says that there are 
# 6 words with length 1 syllable, 8 with length 2 syllables, and 
# so on:
#  1  2  3  4 
#  6  8 11  4

# You can assign this summary to a variable called sylCount, like 
# this:
sylCount=summary(as.factor(words[,"noSyls"]))

# And then you can now make your histogram, like this:
barplot(sylCount)

# This draws a pretty box around the graph:
box()