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- 1. Introduction Enter base base: statistical functions for large datasets Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.com BNOSAC - Belgium Network of Open Source Analytical Consultants: www.bnosac.be Centraal Bureau voor Statistiek: www.cbs.nl July 10 2013, useR! 2013 Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 2. Introduction Enter base Overview Introduction Who are we Large data Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 3. Introduction Enter base Who are we Large data About Jan) Founder of www.bnosac.be and very recent father of Midas (2013-06-19) and therefore not present at useR! 2013. Providing consultancy services in open source analytical engineering Poor man's BI: Python/PostgreSQL/Pentaho/R/Hadoop/Sencha/ExtJS... ... Expertise in predictive data mining, biostatistics, geostats, python programming, GUI building, articial intelligence, process automation, analytical web development R implementations R application maintenance Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 4. Introduction Enter base Who are we Large data About Edwin Working at Statistics Netherlands, that produces all dutch ocial statistics. Co-author of several R packages: editrules tabplot whisker ffbase Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 5. Introduction Enter base Who are we Large data Large data Statistical data is becoming larger. If nrow(data) 106 everything works ne in R For larger data.frame's you run quickly into memory problems # create an integer of length = 1 billion integer(1e+09) ## Error: cannot allocate vector of size 3.7 Gb Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 6. Introduction Enter base Who are we Large data If it ts in memory plain R is just ne! If it doesn't t on your hard disk, you'll need some big data stu (Hadoop, rmr, RHadoop) For data sizes range of 106 - 109 several options provided by external packages. Popular one is ff1 1Daniel Adler, Christian Gläser, Oleg Nenadic, Jens Oehlschlägel, Walter Zucchini Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 7. Introduction Enter base Who are we Large data Popularity based on RStudio download log les 0 100 200 300 jan feb mrt apr mei jun jul #uniqueip ff bigmemory filehash ffbase colbycol mmap pbdBASE Rstudio logs 2013, downloads/week Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 8. Introduction Enter base Who are we Large data ff package provides: numerical, integer, boolean and factor variables of type ff stored on disk (via memory mapping) a sort of data.frame of type ffdf ecient indexing, retrieval and sorting of ff vectors and ffdf. ecient chunk-wise retrieval of data. ff-based matrix storage. vectors up-to length of 2 · 109. Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 9. Introduction Enter base Who are we Large data What's the catch? ff is nice, but: Handling ff vectors often results in non-standard R code It oers no statistical functions on ff and ffdf objects No mean, max, min, sd, etc. on ff vectors Requires that you process the data chunkwise. Has no support for character vectors. Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 10. Introduction Enter base Who are we Large data Typical chunkwise code for ff library(ff) x - ff(0, length = 1e+08) # calculating the max value of x m - -Inf for (i in chunk(x)) { m - max(x[i], m, na.rm = T) } Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 11. Introduction Enter base Who are we Large data Value vs reference Note that out-of-memory objects have issues with value vs reference semantics. x - ff(0, length = 1e+07) y - x y[1] - 100 print(x[1]) ## [1] 100 This is not what a normal R vector would do! Trade-of between copying large object and side-eects. Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 12. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets ffbase2 attempts to add basic statistical functions to make code as standard R as possible make working with ff more pleasant. connect ff with big* methods. 2de Jonge/Wijels/van der Laan Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 13. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Basic operations Most methods works via S3 dispatch (but not all...) mean,min,max,range, sum, all, cumsum, cumprod, quantile, tabulate.ff, table.ff, cut, c, unique, duplicated, Math.Ops x - 1:10 x_ff - ff(x) mean(x) ## [1] 5.5 mean(x_ff) ## [1] 5.5 Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 14. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Idiosyncratic R Use S3 dispatch (works only for generic methods...) ffbase adds subset3, with, within and transform to ff Makes code interchangeable with normal R code: iris_ff - as.ffdf(iris) iris_ff - transform( iris_ff , Sepal.Ratio = Sepal.Width/Sepal.Length ) str(iris_ff[,]) ## 'data.frame': 150 obs. of 6 variables: ## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3. ## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 ## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 ## $ Species : Factor w/ 3 levels setosa,versicolor, ## $ Sepal.Ratio : num 0.686 0.612 0.681 0.674 0.72 ... 3This creates a copy of all selected data Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 15. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Under the hood ffbase rewrites the expression into chunked expression transform( iris_ff , Sepal.Ratio = Sepal.Width/Sepal.Length ) into4 iris_ff2 - iris_ff for (.i in chunk(iris_ff2)){ iris_ff2[.i,] - transform( iris_ff2[.i,] , Sepal.Ratio=Sepal.Width/Sepal.Length ) } return(iris_ff2) 4greatly simplied Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 16. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets ltering: ffwhich Often we need an index for a subselection, but even this may be to big for memory. ffwhich(dat, expression) returns a ff index vector result can be used to index ffdf data.frame. idx - ffwhich(iris_ff, Sepal.Width 2) iris_ff[idx, ] Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 17. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Importing data Package ff: read.table.ffdf, read.csv.ffdf etcetera Package ETLutils: read.dbi.ffdf, read.odbc.ffdf, read.jdbc.ffdf SQL Databases (SQLite / PostgreSQL / Oracle / MySQL / SQL Server (read.odbc.df) / Hive (read.jdbc.df) / ...) Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 18. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets ffbase ffbase adds laf_to_ffdf using LaF for importing large csv and fwf les ffappend for appending vectors to an existing ff object x - ffappend(x, 1:10) ffdfappend for appending data.frame's to an existing ffdf dat - ffdfappend(dat, iris) # Note the pattern of assigning the result of the function ap # ff object to itself Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 19. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets storage When data is in ff format processing is fast! Time bottle neck can be loading the data into ff Keeping data in ff format can save time Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 20. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets ff stores all ff vectors on disk, however lenames are not user-friendly. basename(filename(iris_ff$Sepal.Length)) ## [1] ffdf1ad05b442183.ff Furthermore each ff vector stores the absolute path. Makes moving data around more dicult ff provides: ffsave and ffload, which archives and unarchives vectors and df data.frames into a zip le. Note that this still can be time-consuming. Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 21. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets ffbase has: save.ffdf and load.ffdf that store and load ffdf data.frames into a directory with sensible R names. save.ffdf(iris_ff) basename(filename(iris_ff$Sepal.Length)) ## [1] iris_ff$Sepal.Length.ff pack.ffdf and unpack.ffdf that do the same but zip/unzip the result. Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 22. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Several methods for ff ffbase allows statistical models directly on data.5 Classication + Regression with bigglm.ffdf (biglm + base packages) Least angle regression with biglars.fit (biglars + base packages) Randomforest classication with bigrfc (bigrf + base packages) Clustering with clustering features of package stream 5These work on large data but you can also sample from your df to build your stat model and predict chunkwise. Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 23. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Example of bigglm Using biglm (with bigglm.df from base) class(iris_ff) ## [1] ffdf nrow(iris_ff) # that is 1e7! ## [1] 1000000 mymodel - bigglm(Sepal.Length ~ Petal.Length, data = iris_ff) coef(mymodel) ## (Intercept) Petal.Length ## 4.3051 0.4093 Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets
- 24. Introduction Enter base Goal Basic statistical functions Normal R code Getting data into storage Using big statistical methods on datasets Thank you! Questions? Jan Wijels Edwin de Jonge: jwijels@bnosac.be edwindjonge@gmail.combase: statistical functions for large datasets

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