1. Using the Power of Excel… To help with cutting a budget

2. About Karen Harker MLS, MPH Collection Assessment University of North Texas

3. About you

4. Where you are

5. Objectives Poll on your Excel skillsTake advantage of Excel’s features & functionsKey features & functions to cover: VLookup() – for organizing data PercentRank.inc() – for ranking items Conditional formatting – for visualizing data Pareto distributions (80/20) – for evaluating Big Deals

6. About the Webinar Intermediate to advancedFunction WizardPauses built-in

7. Criteria Used Purpose Select resources to cut from budget based on evidence of value. Objective Rank resources from least to most value based on criteria Price Use Cost per use Pareto number (80/?) Inflation factor Subject librarians’ ratings

8. About the Data Usage 3 year average annual usage Highest & best measure of usage for type of resource Pareto Distribution Distribution of usage across titles in a package Benchmark: 80% of usage from 20% of titles Comparisons of the second number (80/??)

9. Highest & Best Uses Full-text downloads Individual journals Full-text downloads Distribution of usage across titles Ejournal packages Items streamed/full-text downloads Audiovisual Abstracts/record views Literature databases Abstract/record views Full-text downloads Full-text databases Abstract/record views Online reference (miscellaneous)

10. Data Sources Integrated Library System (ILS) Sierra Bibliographic information Order record number (“o999999”) Key Identifier COUNTER Reports of Usage JR1: Full-Text DB1: Abstracts Export Excel CSV

11. Functions Making Excel do the Work

12. Functions What are functions? Mini programs that return a value What are they made of? Equal sign (=) Tag or label Inputs or parameters, in parentheses and separated by commas Example =Sum($E$2:E10) adds the numbers or the values of cell references and returns the total. Order matters The order of the inputs or parameters matters.

13. Two Key Functions VLOOKUP() Look something up PERCENTRANK.INC() Distribution

14. What does VLOOKUP() do? Master List ID Title Price Usage Inflation Ratings Resource Type ID Title Price Usage Inflation Ratings  V is for Vertical  Looks down the first column of a list for a specific value, then…  …returns the value of a specific column in that row.  Allows you to link lists by an ID number

15. VLOOKUP() Parameters Decoded =VLOOKUP(A2, 'Master List'!$A$1:$D$305, 2, FALSE) Lookup_value Table_array Col_index_ num Range_lookup What are you looking up? Number or cell reference Where are you looking it up? The range that has the data you are needing. File or Worksheet Cell Range

16. VLOOKUP() Parameters Column index # Lookup range Range lookup Lookup value What are you looking up? Number or cell reference Where are you looking it up? The range that has the data you are needing. What do you want to return? Column Number These are numbers, NOT letters. How precise do you want to be? True - Approximate match is OK False - Only exact match.

17. Simple Example  You want to look up an ID (#39) and return the name:  Lookup value - 39  Lookup range - A1:C10  Column index number - 3 (column C or Full Name)  Range lookup - False (exact matches only) =VLOOKUP(39,A1:C10,3, False) returns "Suroor Fatima" Column C is the 3 rd Column

18. Challenges  Challenges questions: what do these return?  =VLOOKUP(42,A1:C10,2,False)  Operations  =vlookup(35,A1:C10,3,False)  Yossi Banai  =vlookup(38,A1:C10,2,False)&", "&vlookup(38,A1:C10,3,False)  Operations, Axel Delgado  =vlookup(54,A1:C10,3,False)  #N/A

19. IFERROR()  =IFERROR(some function, what to return if there is an error)  Embedded functions  Excel processes innermost functions and works outward  =IFERROR(VLOOKUP(52,A1:C10,3, False),”N/A”)  If “52” can’t be found, returns “N/A”

20. Applying VLOOKUP()

21. Setting up the files

22. Multiple Files or Worksheets Master List Columns Resource Type Worksheets A. Order # (ID) B. Title C. Renewal Price D. Type 1. Filter Master List on Type 2. Copy & Paste Order #  From Master List  To Worksheet 3. Use VLOOKUP()  Title  Renewal Price 4. Add Other Data  Usage  Ratings

23. Master List Worksheet ID in first column Resource Type Individual titles Ejournal Literature A&I or Full-text Database Big Deals Package Online reference source Reference

24. Resource Type Worksheets 1. Filter Master List on Type 2. Copy Order # 3. Paste in resource type worksheet (E-journal)

25. Use VLOOKUP() in Resource Type sheet to get Title from Master List Title = B = Col. 2

26. Filling in the cells  Copy & paste the formula is quick & easy - BUT…  Use Relative cell references for the Lookup_value – A2  Use Absolute cell references for the Table_array - $A$1:$D$305

27. Use VLOOKUP() to Get Price Price in 4 th Column

28. Use VLOOKUP() to Get Price

29. Add Usage Data to Resource Type Worksheet VLOOKUP() from Master List VLOOKUP() to Master List

30. Titles & Price from Master List to Resource Type Worksheets Master List Ejournals Database Package

31. Usage Data & Rankings to Master List from Resource Type Worksheets Master List EjournalDatabasePackage

32. It’s all relative Using PercentRank.inc() to Compare Resources

33. Comparing Resources Against Each Other Relativity How a resource "stacks up" against others of its kind. Sort by some value CPU Usage Cost Distributions vary Wide Inconsistent Use percentiles Understand the distribution

34. Check it out Title A has 45 uses Title B has 155 uses Where does Title A fall relative to all the other titles? Title B?

35. PERCENTRANK.INC() Returns The rank of a value as a percentage 0 to 1.00 inclusive Parameters Array: Column of interest X: The value of interest Significance: # of significant digits Example =PercentRank.inc(E:E,E2,2)

36. PercentRank() of CPU 2 digits past decimal

37. Compare the Ranks of Different Measures 50 th percentile for usage 80 th percentile for CPU CPU: Lower is better. Usage: Higher is better.

38. Directions of Comparisons Comparisons should be in the same direction High = good Low = bad Decide… Low = good High = bad …OrReverse directions, when needed

39. Original Ranks Low is good Cost High is good Use Low is good CPU Low is good Inflation High is good Ratings

40. Transformed Ranks High is good Transformed Cost High is good Use High is good Transformed CPU High is good Transformed Inflation High is good Ratings

41. Compare the (Transformed) Ranks 1 minus % Rank for CPU 50 th percentile for usage 20 th percentile for CPU Higher is Better

42. Efficiency of ‘big deals’ Distribution of Usage Across Titles Within a Package

43. Power Law Distribution  In statistics, a power law is a functional relationship between two quantities, where one quantity varies as a power of another.  Wikipedia Wikipedia

44. Pareto Distribution in Libraries AKA The 80/20 Rule 80% of the usage is from 20% of the collection. 80% of the uses are from 20% of the users. Efficiency of an Ejournal Package 80% of usage is from ??% of the titles. 20% is a benchmark. Higher is better.

45. 1.List titles in package. 2.Gather usage data. 3.Sort by usage Z-A. Title Name2011201220133yr. Avg. Package 12, Title 453783409445624146.33 Package 12, Title 571722122611621370.00 Package 12, Title 291313135112521305.33 Package 12, Title 531263124213351280.00 Package 12, Title 431081125512501195.33 Package 12, Title 50107698613641142.00 Package 12, Title 3215729187651085.00 Package 12, Title 13949115610101038.33 Package 12, Title 207409211018893.00 Package 12, Title 581002805789865.33 Package 12, Title 31970902680850.67 Package 12, Title 95686751148797.00 Package 12, Title 40703731870768.00 Package 12, Title 46599846838761.00 Package 12, Title 24583709844712.00 Package 12, Title 21639590568599.00 Package 12, Title 42585592459545.33 Package 12, Title 36517459491489.00 Package 12, Title 1466469450461.67

46. Calculations for Pareto Distribution % of Uses% of Titles  Cumulative sum ∕ total uses  =SUM($E$2:E2)/ SUM(E:E)  Locate the value closest to your benchmark (e.g. 80%)  Cumulative count ∕ total # titles  =COUNT($E$2:E2)/ COUNT(E:E)  Read the value next to the benchmark % uses

47. Pareto Distribution Title Name3yr. Avg.% Uses% Titles Package 12, Title 454146.3317.59%1.75% Package 12, Title 571370.0023.40%3.51% Package 12, Title 291305.3328.94%5.26% Package 12, Title 531280.0034.37%7.02% Package 12, Title 431195.3339.44%8.77% Package 12, Title 501142.0044.29%10.53% Package 12, Title 321085.0048.89%12.28% Package 12, Title 131038.3353.29%14.04% Package 12, Title 20893.0057.08%15.79% Package 12, Title 58865.3360.75%17.54% Package 12, Title 31850.6764.36%19.30% Package 12, Title 9797.0067.74%21.05% Package 12, Title 40768.0071.00%22.81% Package 12, Title 46761.0074.23%24.56% Package 12, Title 24712.0077.25%26.32% Package 12, Title 21599.0079.79%28.07% Package 12, Title 42545.3382.11%29.82% Package 12, Title 36489.0084.18%31.58% Package 12, Title 1461.6786.14%33.33% Title 45 has over 17% of uses. In this package, 20% of titles account for 2/3 of total uses. About 80% of uses are used by nearly 30% of titles.

48. Compare Distributions of All Packages ORDER #Title Renewal Price# TitlesCost/ Title3 yr Avg UsesCPUPareto % o1044667Package 13 $ 1,974.976 $ 329.1669 $ 28.6250% o4518731Package 26 $ 3,919.838 $ 489.981305 $ 3.0050% o3099891Package 268 $ 7,214.2614 $ 515.3089 $ 81.0650% o3679408Package 87 $ 4,168.5141 $ 101.671482 $ 2.8147% o3462341Package 17 $ 12,305.6139 $ 315.534817 $ 2.5545% o3874291Package 89 $ 2,383.447 $ 340.491577 $ 1.5143% o1638543Package 240 $ 22,557.40355 $ 63.5413756 $ 1.6435% o3906115Package 25 $ 15,400.5339 $ 394.89509 $ 30.2634% o4616935Package 262 $ 217,544.85599 $ 363.1863401 $ 3.4333% o4203276Package 28 $ 3,794.6522 $ 172.48685 $ 5.5430% o2978969Package 12 $ 64,795.2159 $ 1,098.2223585 $ 2.7528% o4081791Package 227 $ 55,241.67315 $ 175.3726803 $ 2.0627% o3014782Package 126 $ 137,240.355766 $ 23.8028400 $ 4.8324% o2741003Package 280 $ 288,666.481718 $ 168.0225830 $ 11.1824% o1653441Package 9 $ 38,135.8312 $ 3,177.996870 $ 5.5523% o380186xPackage 260 $ 12,332.9837 $ 333.326032 $ 2.0423% o3768284Package 239 $ 3,661.6642 $ 87.1847 $ 77.9121% o4096083Package 295 $ 485,336.561571 $ 308.9375883 $ 6.4021% o3798161Package 43 $ 55,446.98437 $ 126.889035 $ 6.1419% o2612380Package 177 $ 39,781.002062 $ 19.29230620 $ 0.1719% o3933416Package 20 $ 2,189.8852 $ 42.112171 $ 1.0117% o3006785Package 143 $ 116,987.74110 $ 1,063.524789 $ 24.4317% o3244064Package 301 $ 22,390.0037 $ 605.14292 $ 76.6814% o1745232Package 5 $ 5,529.101249 $ 4.432463 $ 2.242%

49. Conditional formatting Quick way to highlight outliers or visually represent distributions

50. Ways to Use Conditional Formatting  Highlight based on a specific value  Usage Measure (e.g. Abstracts, FTD’s, etc.)  Greater than.7,.3-.7, and lower than.3  Visually represent distributions  A visualization of PercentileRank()  CPU  Pareto

51. Conditional Formatting CPU Set the Conditional Formatting Highlight the CPU column Select Conditional Formatting->3 color scale Red – Yellow – Green (High – Medium – Low) Highest is red; Lowest is green Highest & Lowest 10 th Percentile Conditional Formatting->Manage Rules->Edit Rule Change “Highest” and “Lowest” to “Percentile”.

52. Conditional Formatting CPU

53. Conditional Formatting CPU Changing Rule to Percentile Change “Lowest” to “Percentile” Change “Highest” to “Percentile”

54. Conditional Formatting CPU

55. Altogether, Now Master List - Summary columns Use VLOOKUP to "grab" the summary data from your Resource Type worksheets 3-yr avg uses CPU Pareto Distribution (Packages only) Use Conditional Formatting Highlight important text Visualize distributions

56. Compiled Master List Imported from ILS VLookUp() from Resource Type Worksheets

57. Master List Visualizing Use Rank by 3 categories. CPU by Percentile Rank

58. Caveats Use Table Formatting Can name your tables Automatically copies & pastes formulas Easier to add columns Adjusts formulas for absolute & relative cell ranges Don't rename your files References will not change Save all of your files in one folder Preserves relationships Use the same structure in all of the worksheets Easier to set up

59. What (I hope) you’ve learned for organizing data VLookup() for ranking items PercentRank.inc() for visualizing data Conditional formatting for evaluating the efficiency of Big Deals Pareto distributions (80/20)

60. Questions and Comments  Karen.harker@unt.edu Karen.harker@unt.edu  Libraries are for Use  Librariesareforuse.wordpre ss.com Librariesareforuse.wordpre ss.com  UNT Faculty Profile UNT Faculty Profile  Karen Harker in UNT Scholarly Works Karen Harker in UNT Scholarly Works  Charleston Pre- Conference Workshop:  Keeping it Real: A Comprehensive and Transparent Evaluation of Electronic Resources Keeping it Real: A Comprehensive and Transparent Evaluation of Electronic Resources  Cost: $150  Presenters:  Karen R. Harker  Laurel Crawford  Todd Enoch