An R primer for SQL folks
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This document summarizes a presentation given by Thomas Hütter on using R for data analysis and visualization. The presentation provided an overview of R's history and ecosystem, introduced basic data types and functions, and demonstrated connecting to a SQL Server database to extract and analyze sales data from a Dynamics Nav system. It showed visualizing the results with ggplot2 and creating interactive apps with the Shiny framework. The presentation emphasized that proper data understanding is important for reliable analysis and highlighted resources for learning more about R.
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An R primer for SQL folks
- 1. SQL Bits - the great data heist Manchester 2019 An R primer for SQL folks Thomas Hütter
- 2. Thomas Hütter, Diplom-Betriebswirt • Application developer, consultant, accidental DBA, author • Worked at consultancies, ISVs, end user companies • Speaker at SQL events around Europe • SQL Server > 6.5, Dynamics Nav > 3.01, R > 3.1.2 @DerFredo https://twitter.com/DerFredo de.linkedin.com/in/derfredo www.xing.com/profile/Thomas_Huetter An R primer for SQL folks
- 3. Agenda • History: what is R, how did R come to be, what does the R ecosystem look like today • Introduction: R IDE, RStudio, basic data types / objects, packages, in-/output, data analysis, visualization • Business case demo: • Extracting ‘sales’ data from a Nav DB on SQL Server • Basic analysis and visualization • Advanced visualization using the Shiny framework • Example: data science going wrong, round-up, resources • This is an introductory walk-through, no deep dive - so no fancy predictions, regression, big data science :-(
- 4. History: R - then and now • Programming language for statistical computing, analysis and visualization, widely used by statisticians, data miners, analysts, data scientists • Created by Ross Ihaka and Robert Gentleman, Uni Auckland, in 1993 as an open source implementation of the (1970s) S language • GNU project, maintained by the R Foundation for Statistical Computing, compiled builds for Mac OS, Linux, Windows, supported by R Consortium • Extensible through user-created packages, > 13700 available on CRAN • Commercial support, e.g. since 2007 by Revolution Analytics, acquired by Microsoft in 2015, now provide Microsoft R Open, R Server • IDEs: R.App, RStudio, R Tools for Visual Studio (deprecated from VS 2019) • Support for R now in SQL Server, Power BI, Azure ML, Data science VM
- 5. Introduction: data objects • Data types - numeric, integer, complex - character - logical - factor - Posix types for date/time - NA = Not available • Data structures - vector: 1 dim, 1 data type - matrix: 2 dim rect, 1 data type - list: collection of other objects - table: > 2 dimensions - data frame 2 dim rect, cols = vectors DemoBasics1
- 6. Introduction: packages •Extensions to the R base system, containing code, data, documentation. Key factor to the success of R; flexible, user contributable. -> CRAN •installed.packages() lists all installed packages incl. versions, dependencies, license and other info •search() lists currently attached packages •install.packages() downloads and installs packages •library() loads/attaches packages, also require() •Hadley Wickham, chief scientist at RStudio, professor of statistics „Tidyverse“: dplyr, tidyr, lubridate, readr, httr, ggplot2 + many more: hadley.nz DemoBasics2
- 7. Introduction: basic data in-/output • Generic functions read.table and write.table - read.csv / read.csv2 comma/semicolon delimited - read.delim / read.delim2 Tab delimited, decimal point/comma - read.fwf fixed width format • Some additional I/O packages - reader functions flexibly load multiple formats fast - foreign reads data from Minitab, S, SAS, SPSS, Stata, dBase… - DBI/ODBC database access via ODBC - xlsx and readxl read and write Excel 97/XP/200X files - XML reads XML and tables from http web sites
- 8. Introduction: basic data analysis + visualization • Analyzing (numeric) data: str() structure = data types and ranges summary() Min, max, mean, median, quartiles; for factors: count of levels head()/tail() shows top/bottom n rows (default = 6) • Distribution of values: hist() shows frequency distribution, boxplot() for min, max, quartiles, outliers, mosaicplot() contingency mosaic DemoBasics3
- 9. Continued… data analysis + visualization • Libraries: tidy for data tidying/reshaping, ggplot2 implements grammar of graphics, raster for geo data • apply() family of functions applies functions to the margins of an array or a matrix • gather()/spread() convert between wide/long format • ggplot() very powerful plot function, plots point, line or bar geometrics etc with versatile parameters DemoBasics4
- 10. Business case demo • We are the distributor for all German petrol stations, with two subsidiaries: NorthTank and SouthFuel • Business calls „We need some analysis of our 2015 Diesel sales“, preferably some visualizations, and „maybe something is wrong…“ • Of interest: distribution by post code zones • Source: Dynamics Nav ERP database, on the customer card (table „Customer“) there’s a field called „Sales (LCY)“ (= Local currency) • Publicly available shape- and data files for post code zones
- 11. Extracting data & first analysis • Using ODBC and the DBI package (also available: JDBC, RODBC and others) • dbConnect() to establish a connection, then dbGetQuery() to query the database • Calculate aggregates (sums) using ddply() • Bar plot: ggplot() + geom_bar() • Line diagram: ggplot() + geom_line()
- 12. Analysis & visualization • Calculate intervals for sales sums: cut() • libraries raster, rgeos for visualizing geospatial data • shapefiles: open vector data format for GIS software, describes points, lines or polygons in these files: .shp shapes, .shx shape index, .dbf attributes, .prj projection • merge shape and sales data: merge() • plot maps, colouring post code zones according to sales DemoTankData
- 13. Use of Shiny framework • Framework for interactive web applications in R apps consist of server.R and ui.R or just app.R • ui defines screen appearance & controls • server handles any data processing, plotting etc. • apps can be run in web browser DemoShiny/app
- 14. Example: data science going wrong? • Anscombe’s quartet: • 4 data sets, each with 11 completely different x-y pairs • yet nearly identical statistical properties - Mean of x = 9 - Mean of y = 7.5 - Correlation between x and y = 0.816 - Linear regression y = 3 + 0.5 x Anscombe
- 15. Round-up / conclusions • With R, a lot is possible in terms of analysis and visualization • There’s probably always a package for that But please: • Know your data • Look at your data ▪ Think - does it make sense? • Consider the influence of outliers • Don’t blindly rely on R ‘doing the trick’
- 16. Resources online • https://en.wikipedia.org/wiki/R_(programming_language) • https://www.r-project.org/ -> Mirrors of CRAN = Comprehensive R Archive Network • https://www.r-consortium.org/ • http://www.r-bloggers.com/ • www.kdnuggets.com • www.rseek.org Pimped Google search for R-related subjects • Twitter hashtag #rstats • LinkedIn groups R Developers und Users Group, R Programming, The R Project for… • www.swirlstats.com „Learn R, in R“ • www.coursera.org Data Science specialization (10 courses) MOOC • www.edx.org
- 17. Resources offline • Beginning R, The statistical programming language Dr. Mark Gardener, Wrox/Wiley, ISBN 978-1118164303 • R Cookbook, Paul Teetor, O’Reilly, ISBN 978-0596809157 • R Graphics Cookbook, Winston Chang, O’Reilly, ISBN 978-1449316952 • R in a Nutshell, Joseph Adler, O’Reilly, ISBN 978-1449312084 • Practical Data Science with R, Nina Zumel + John Mount, Manning publications, ISBN 978-1617291562
- 18. Credits • Titanic data set: www.kaggle.com/c/titanic/data • SQL Database structure: mbs.microsoft.com Dynamics Nav 2016 demo database • Customer and „sales“ data: www.tankerkoenig.de (license CC BY 4.0) • Shape files: - www.suche-postleitzahl.org (Open database license, © OpenStreetMap contributors) - Bundesamt für Kartographie und Geodäsie, Frankfurt am Main, 2011 • Some icons made by: http://www.flaticon.com/authors/hanan (license CC BY 3.0) • Anscombe’s quartet: Francis J. Anscombe 1973
- 19. An R primer for SQL folks Time for some Q & A: That is: questions that might be of common interest, and their answers might fit into the remaining time For slides and scripts: follow link on final slide or check the SQLBits homepage ;-)
- 20. An R primer for SQL folks Thank you for your interest & keep in touch: @DerFredo https://twitter.com/DerFredo de.linkedin.com/in/derfredo www.xing.com/profile/Thomas_Huetter Slides and scripts to this presentation will be at https://github.com/SQLThomas/Conferences/tree/master/Bits2019