Data-informed collection decisions using R or, learning R using collection data

Transcription

1 Data-informed collection decisions using R or, learning R using collection data Heidi Tebbe Collections & Research Librarian for Engineering and Data Science NCSU Libraries

2 Collections & Research Librarian for Engineering and Data Science Image courtesy Hilary Davis

3 Today s talk 1. R Overview 2. Examples

4 R Overview

5 What is R? The Friendly Beginners' R Course, written by Dr. Toby Marthew

6 Brought to you by the letter R Started by Robert Gentleman and Ross Ihaka from the Statistics Department in the University of Auckland in the mid 90s Ernie and a Letter R : Muppet Wiki

7 Why R? Free! Runs on Mac, Windows, Unix Open source Thousands of available packages Robust graphical capabilities Vast user community

8 Open source software Software with source code that anyone can inspect, modify, and enhance In general, open source licenses grant computer users permission to use open source software for any purpose they wish

9 R Packages A package is the fundamental unit of shareable code Over 10,000 packages on the Comprehensive R Archive Network (CRAN) Can also find packages on GitHub CatterPlots package

10 RStudio Cross-platform integrated development environment (IDE)

11 Why R instead of Excel? R for librarians: data analysis and processing, Lisa Federer, 1. R is free and open source 2. R enhances research reproducibility 3. R is a workhorse

12 Why R instead of Excel? Part 2 Data Manipulation Easier Automation Faster Computation It Reads Any Type of Data Easier Project Organization It Supports Larger Data Sets Reproducibility Accuracy Easier to Find and Fix Errors State-of-the-Art Graphics

13 Why Excel instead of R? Data Entry Basic Calculations Viewing/Scrolling Data at a Glance Data Presentation and Summarization

14 Why R instead of Python?

15 Examples Safari Books Online

16 Safari Books Online

17 Merging usage statistics with the full collection title list Usage report: 59 titles Book list: 191 titles

18 Merging usage statistics with the full collection title list

19 Merging usage statistics with the full collection title list

20 Added titles

21 Added titles

22 Added titles

23 Added titles

24 Dropped titles

25 Dropped titles

26 Examples Fund codes

27 GOBI data Library Activity search full record headers

28 GOBI data Fund Group Purchasing Account Invoice Number Invoice Date Fund code on the invoice Blanket P.O. ISBN Title Author Edition Statement Imprint ID Imprint Name Binding Adult Code Level Pagination Unit list price on the invoice Discount % on invoice Net Unit price on invoice Series ID Series Title Subject Heading Approval Notes Full LC Call Number LC Class Item LC Class Sort ebook purchase option Publication Year retro search headers

29 WorkFlows data library ordertype fiscalcycle orderid OrderDateCreate OrderDateMod orderline# vendorid amt_encumb totalamtpaid #copies_received #copies_invoiced #copies_paid amt_invoiced #ofcopies Acq_Mat_Type xinfo_location E_Access VEND_TITLE RELATION Title Pub{260} {050} {090} {022} {001} Percent_funded Funding_Amt FundID #cancelled custom WorkFlows report

30 List_orderlines.R

31 List_orderlines.R

32 fund_code_check.r

33 fund_code_check.r regular expression

34 fund_code_check.r

35 fund_code_check.r

36 What s next?

37 Analysis! print(summary(myfulldata)) print(summary(myfulldata$subject.heading))

38 Visualization!

39 Reports!

40 Combine GOBI and WorkFlows data!

41 Share my code! [Coming soon] me!

42 Packages used readr plyr dplyr tidyr ggplot

43 Lessons learned Save as csv, not csv as UTF RStudio s Import Dataset functionality # ARGH RStudio View doesn't show all columns - need to figure out how to add Library Notes columns Know when to quit Data cleaning takes up so much time

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