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Exploring Data Structures: Arrays, String Manipulation, and Scaling Techniques

This document recaps essential concepts in data manipulation using arrays, focusing on splitting names and numbers from a file and searching through them. The practical assignment involves reading a line-oriented file, extracting information into arrays, and implementing searching functions. It further explains scaling data to a specified range by calculating scaled values based on minimum and maximum inputs. The structured approach enables students to learn about string handling, data organization, and visualizing outputs through histograms.

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Exploring Data Structures: Arrays, String Manipulation, and Scaling Techniques

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  1. CISC 130 - Today’s Class • Recap • Problem 11 • Problem 12 R. Smith - University of St Thomas - Minnesota

  2. Recap • Splitting with ‘Extract’ • Searching for strings in a string array • Pulling the Pieces Together R. Smith - University of St Thomas - Minnesota

  3. A practical look at #11 • We read data from a file containing 2 columns • We split data from the 2 columns • Each goes into a separate array: numbers and names • Index values ‘match’ for the column • If names[5] = “Joe” then numbers[5] = his phone number • We search for names in ‘names’ array • then we print out the corresponding name and number R. Smith - University of St Thomas - Minnesota

  4. Let’s create a sample file • Go to the assignment, copy/paste the data R. Smith - University of St Thomas - Minnesota

  5. How the assignment works • Call a function to read the number/name strings into 2 separate arrays • One keeps the name strings • One keeps the number strings • Name[i] is the person whose number is in number[i] • Do a loop till a blank line is entered • Read a line from input • Look it up in the ‘names’ array; retrieve the index • If it’s a valid index, print out the name and number • Create a file of numbers/names for next step R. Smith - University of St Thomas - Minnesota

  6. Writing the extract() function • Local variables • We need 2 array indices • We need a variable for the ‘split’ index • First, find the split point • The end of the number • Next, copy out the name and number • Option: copy the number • Option: copy the name R. Smith - University of St Thomas - Minnesota

  7. Searching • We write a loop to search the array • Compare each line with the line typed in • We can use a ‘full match’ from the library • We must write a ‘partial match’ R. Smith - University of St Thomas - Minnesota

  8. Assignment 12: Scaling • Date Due: Next Monday • The problem: • Taking a set of data and scaling it to fit a particular range • A common lab data problem • What we’ll do • Read in a file of data to be scaled • Calculate scaled values • Print out the original data w/scaled horizontal histogram R. Smith - University of St Thomas - Minnesota

  9. Assignment 12: The File • Line-oriented file again, like in A11 • Name/Number instead of Number/Name • Separated by tab character “\t” • Read “names” into one array • Read “numbers” into a separate array • Use a third integer array for scaled values R. Smith - University of St Thomas - Minnesota

  10. How Scaling Works • Scan the raw data in the array • Find the minimum and maximum values • Minimum maps to 0 in scaled values • Maximum maps to 31 in scaled values • Calculate the scaling ratio • Range of desired values / range of actual values • scale = 31.0 / (max – min) • A real number, not an int • Apply the ratio and round the result • Subtract ‘min’ before scaling • Use “rint()” function from math.h • Save scaled values in the scaled array R. Smith - University of St Thomas - Minnesota

  11. Unscaled Values 200 1000 253 600 999 823 410 Scaled Values 0 31 2 16 31 24 8 Example Scaling R. Smith - University of St Thomas - Minnesota

  12. The Histogram • Horizontal Format • Left side of display: name and unscaled value • Right side: histogram of scaled value R. Smith - University of St Thomas - Minnesota

  13. What to Start On • Create a sample file • Read in names and numbers • Looking for the Tab • Can use a ‘string.h’ function • See pp. 249-50 in the book • strspn() – search against a ‘set’ of chars • strcspn() – opposite of strspn() • strchr() – points to first occurrence of a char • Converting Text to Integer • Use the function in the library R. Smith - University of St Thomas - Minnesota

  14. Sample Input • Moose Mountain 986 • Mystery Mountain 650 • Eagle Mountain 650 • Ullr Mountain 350 • Welch Village 360 • Spirit Mountain 700 • Keystone 3128 • Steamboat Springs 3668 • Sunday River 2340 R. Smith - University of St Thomas - Minnesota

  15. Creative Commons License This work is licensed under the Creative Commons Attribution-Share Alike 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. R. Smith - University of St Thomas - Minnesota

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