Data-informed collection decisions using R or, learning R using collection data
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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
44
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