R Lightning Talks @ BURN (2014-01-15)

7/22/2019 R Lightning Talks @ BURN (2014-01-15)

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2014. janur 15.


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01 Arat Bence: Ki szereti az R-t?02 Bod Lszl: R s !""0= Bur#er !sa$a: % nz'i d(nt se) *izs#lata l+e4-#'el04 ,arczi er#el': 9z(*e#es jelent se) ) sz>t se05 /or*t er#el': R a i*atalos statiszti)$an0 Kocsis +re: R/adoo . a Reduce R-$en0@ K(les ri Lszl: 3, atc ,as $oard0 dud*ari '(r#': R 6 %'t on colla$oration

0 7ttucs) '(r#': 7nline 8orecastin# A lication10 9aln)i #nes: Ano+liadete)tls R-rel11 ;t er#el': R +int 9 esz)(z12


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Arat Bence:Ki szereti az R-t?

BI Consulting


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Arat Bence:Ki szereti az R-t?

Bod Lszl:

R s !""


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Bod Lszl:R s !""

Burg Analytics

Bur#er !sa$a:

% nz'i d(nt se)*izs#lata l+e4-#'el


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Pnzgyi dntsek vizsglata az lme4 csom

Burger Csaba, PhD

Budapest Users of R Network Meetup2014. Janur 15.


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Vrhat llami nyugdj az utols vi fizets

szzalkbantlagbrt keres frfi alkalmazott, megszakts nlkli karrier

Germany (reform el tt)

E iSlovak RepublicDenmark

IrelandSweden

United KingdomJapan

SwitzerlandCanadaUnited States

Germany (reform utn)Belgium

KoreaNorway

Czech RepublicPortugalFinland

Italy

Austria


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Mennyit teszel flre a nyugdjra?

Attl fgg, hogy Hny ves vagy N vagy frfi vagy Meg akarsz takartani (nem a munkltat finans ...

ezeket az sszefggseket befolysolja, hogy hol lsz


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tlagos megtakartsi sszegek

p.a.


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Megtakartsi sszeg magyarzata egy sima

regresszival

++= k

k k X Savings )ln(

Hny ves N vagy frfi

Finanszrozsi forma


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Megtakartsi sszeg magyarzata egy sima

regresszival

++= k

k k X Savings )ln(

Hny ves N vagy frfi

Finanszrozsi forma

Nem standardizlt rezi


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A megtakartsi sszeg az letkorral s a lakhellyel

ltszik egyttmozogni

600

800

1 000

1 200

1 400

400

200

050 5445 4940 4435 3930 3425 2920 2420 l tt

p.a.


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Gender gap : frfiak s n k megtakartsa kz

klnbsg lakhely- s letkor-fgg

12

76

96

155

107

64

41

-42

126129134

113

4036

-18

frfi - n (kelet)

frfi - n (nyugat)

p.a.


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Lme4-csomag: mixed effects regresszi, aho

fggvny crossed effect-et is lehet v tesz

library(lme4)

fm


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Nem s Bundesland interakciBundesland s nem fixed effects rtkek

-0,3

-0,2

-0,1

0,0

0,1

0,2

0,3

0,4

RP MSLBWNIHBNWHH HE BE SH BY BB


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38

43

47

52

55

55

59

61

62

68

72

74

77

Nordrhein-Westfalen

Niedersachsen

Sachsen-Anhalt

Hessen

Schleswig-HolsteinHamburg

Thringen

Bremen

Sachsen

BayernBaden-Wrttemberg

Rheinland-Pfalz

Saarland

N

K

Becslt megtakartsi gender gapEgy 40 ves biztostott esetben


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Random-effect a nemre s klnbsgk: sz

megosztottsg


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Mik a terveid a nyugdjas korodra?


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Bur#er !sa$a:% nz'i d(nt se) *izs#lata l+e4-#'el

rapporter net

,arczi er#el':.ander


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,arczi -er#el':.ander

KSH

/or*t0 -er#el':

R a 0i*atalosstatiszti)$an

R a hivatalos statisztikban


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BURN lightning talk1

R a hivatalos statisztikban

Kitekints s

tapasztalatok

Mit csinl a statisztika?


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Mit csinl a statisztika?


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De mit adhat a hivatalos

statisztiknak az

R kzssg?

CRAN official statistics task vie


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CRAN official statistics task vie

Komple! mintk Mintavtel

"#l$ozs

kalib%ls &ecslsek 'hibaszm(ts)

Adatfeldolgozs


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Adatfeldolgozs

*ditls 'edit%+les) ,ibake%ess s sszef-ggsek ellen.%zse

,in$z/ adatok *lemzse '01M) s 2/tlsa


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Mindenkinek van eg$ t%tnete

M i R t ?


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M i R t ?

*!cel "A"3 "2"" Adatbziskezel.

'Mi%t ne?)

Mi%t lenne 4/?


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Mi%t lenne 4/?

Adat5viz+alizci/


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Adat5viz+alizci/

6 lib%a%$'R7D&C)


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6 lib%a%$ R7D&C)

Adatel%s eg$sze%8en Adatok Metaadatok

"9: megszelid(tse ";l p%og%am felhasznlsa R5ben

6 lib%a%$'tables)


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6 lib%a%$ tables)

/l alak(that/

# 9. minta, kor, nemetab_9


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p

>/l hasznlhat/ *setenknt 4obban3 mint a nag$ok@

&%mil$en feladat%a

+1 R"t+dio

K+tat/szoba


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K+tat/ k+tasson Kevss vdett adat *g$%e tbb el%het. llomn$

R el%het. lesz ,og$an?

K+tat/szoba B


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Adatvdelmi ellen.%zs Nem m csak #g$ k+tatgat+nk o+tp+t checking

Rep%od+klhat/sg

%ts-k az R5t


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BURN lightning talk 15

g

s mg eg$ kicsit tbb%e isE


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/or*t0 er#el':R a 0i*atalos statiszti)$an

BME MIT

Kocsis 1+re:

R/adoo .2a Reduce R-$en


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Budapesti Mszaki s Gazdasgtudomnyi EgyetemMrstechnika s Informcis Rendszerek Tanszk

RHadoop: MapReduce R-ben

Kocsis [email protected]

BURN Meetup, 2014.01.15.

Egy/A Big Data problma
mailto:[email protected]:[email protected]


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Elosztott trols

Computation to data

At rest Big Datao

Nincs updateo Mindent elemznk

Not true, but a very, very good lie! (T. Pratchett, Nightwatch)

MapReduce


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DistributedFile System

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SHUFFLE

Map

Reduce

[ , ] [ , ] [ , ] [ , ] [ , ]

Szszmlls


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MapReduce stlusban szervezhet


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Ami zavarbaejten prhuzamos o embarrassingly parallel

Statistical Query Model o Locally Weighted Linear Regression, Naive Bayes, Gaussian

Discriminative Analysis, k-means, Logistic Regression,Neural Network, PCA, ICA, EM, SVM,

Generalized Iterative Matrix-Vector mult. o PageRank, grftmr, sszefgg komponensek,

RHadoop = Hadoop + R


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Hadoop

HDFS

[ , ] [ , ] [ , ]

SHUFFLE

M a p R e d u c e map(k,v)

reduce(k,vv)mapreduce(...)

RHadoop


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github.com/RevolutionAnalytics/RHadoop/

The most mature [] project for R and Hadoop isRHadoop. (OReilly , R In a Nutshell, 2012)

rmr : mapreduce rhdfs : HDFS llomnykezels

rhbase, plyrmr

rmr: mapreduce


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Local backend


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rmr.options(backend="local")

Helyi llomnyrendszerSzekvencilis vgrehajts

Debug!

Input/output itt isllomnyrendszer

Input/output format


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text json csv

native (R sorosts) sequence.typedbytes (Hadoop) pig.hive

hbase

ElnykM


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Map s Reduce: R-beno

Csomagok!o MR algoritmus- prototipizls

+ a vezrls is: knyelem

Hadoop Job: egy fggvnyhvs!o Pl. iteratv MapReduce teljesen R-ben

o Map s Reduce : ~a hv krnyezetben

Hogyan lehet ilyenem?L l b k d db VM k


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Local backend, sandbox VM-eko

Cloudera, Hortonworks

Sajt Hadoop klaszter

Amazon Elastic MapReduce (EMR)o Brelhet Hadoop klaszter

Sajt felh megolds

Rhadoop az Apache Virtual Computing Lab-ban

El k ht k


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Elnyk s htrnyok

Htrnyok?N h k d b


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Nehzkes debug

+1 hangolsi rteg MAHOUT-kln Sok Hadoop funkc.

Kevs plda

Ritka esemnyek kategorizlsa RHadooppal


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Infrastr.-adatokSalnki gnes

Mkdik.Jpr gotcha De inkbb, mint

Java-ban


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Kocsis 1+re:R/adoo . 2a Reduce R-$en

Planimter

K(les ri Lszl:

23, 4atc0,as0$oard


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2014.01.15 Budapest Users of R Network 2

Data Source ( FDA )


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( )

2012 Q4 Safety Alerts for Human MedicalProducts (Drugs, Biologics, Medical Devices,Special Nutritionals, and Cosmetics)

The alerts contain actionable information thatmay impact both treatment and diagnosticchoices for healthcare professional andpatient .

MED Watch Dashboard Viewershttp://medwatch.co.nf


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pDecember of 2013


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Data Clean - 1


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The raw reported data have been cleanedaccording to the International Conference onHarmonisation (ICH) of TechnicalRequirements for Registration ofPharmaceuticals for Human Use .

The verbatim reactions/indications have beencoded into the system organ class (SOC)

using the Medical Dictionary for Regulatory Activities (MedDRA version 13.1) for coding of diseases/medical conditions.

Data Clean - 2


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! The raw reported datasets have beentransferred into CDISC SDTM datasets, andalso into CDISC ADaM datasets which are thebasis for production of statistical graphs in Rstatistical package including: Shiny ,

vcd (The conditional density plot - Hofmann and Theus2005) ,

Basic Hexagon Binning Functions ( hexbin )

rworldmap (joinCountryData2Map, mapCountryData) ,

ggplot2 .


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LIE Factor - Edward Tufte


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Define the maximum ideas to the audience: In the shortest time ,

Minimize the number of "ink " , with the smallest optimal representation.

Tell the truth about the data.


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K(les ri Lszl:23, 4atc0 ,as0$oard

Qanopt

5dud*ari -'(r#':R 6 %'t0on

colla$oration


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Budapesti Mszaki s Gazdasgtudomnyi Egyetem Mrstechnika s Informcis Rendszerek Tanszk

TekR edik a kgy avagy az R s Python sszekapcsolsnak lehetsgei

Ndudvari Gyrgy [email protected]

2014. j anur 15

Kapcsolatom az R- rel s a Pythonnal


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Hello World!2002.12.26.- n Szmomra A nyelv

o Webo Rendszeradminisztrci o Knyelmi funkcik

Elszr 2012-bentallkoztunk Kevs tapasztalat

o Logelemzs o Vizulis adatelemzs

Erssgek s gyengesgek


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ltalnos hasznlatra Egyszer

Statisztikai nyelvSzmomra nehzkes

Plottok forrsa: http://ghalib.me/blog/a-superficial-comparison-of

Erssgek s erssgek

+
http://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-ofhttp://ghalib.me/blog/a-superficial-comparison-of


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Hatkony, gyorsfejleszts Kzssg

Plottols Statisztikai csomagok

Kzssg

+

=Profit

Lehetsgek


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R Python rPythonRSPython


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R Python system()


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Python R rpy2


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Forrs: rpy2 dokumentci

Python R rpy2 szintek, csomagok


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Python R rpy2 R session- k


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Python R rpy2Egy plda:


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Alkalmazs egy sajt projektnl


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IndokokHatrid Tbb t t l t P th l


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Tbb tapasztalat PythonnalMr meglv R-es kdbzis

Elnyk Gyorsabb fejleszts E bb i t i


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Egyszerbb integrci Ksz R-es fggvnyek minimlis mdostsa Ignyes grafikonok

sszefoglals Merjnk kilpni az R-es vilgbl! A k l gh tk bb k t h lj k!


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A szmunkra leghatkonyabb eszkzt hasznljuk! A lehetsg megvan

Ksznm a figyelmet!

@reedcourty


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5dud*ari -'(r#':R 6 %'t0on colla$oration

7ttucs) -'(r#':7nline 8orecastin#

A..lication


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Using R in Production

Case Study:Online Forecasting

Gyuri [email protected]

1

Background


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US startup

My first big R project

Early 2010

Start as research pilot (plan: proto in R, afterJava:)

2

Sequential Sales Forecasting


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US Grocery stores

Input Data previous days and earlier days sales data Historical & future Price (Promotion Calendars) Other inputs

Forecast horizon: next 7-days

3

Sequential Sales Forecasting


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US Grocery stores

Input Data previous days and earlier days sales data Historical & future Price (Promotion Calendars) Other inputs

Forecast horizon: next 7-days

4

Some numbers


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2010 ~200 fcsts/day, 1 developer, laptop

2013 ~100,000 fcsts/day, 3 developers+1 data ops,

server (8 cores)

~2MB R code

5

Challenges


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Speed couple hours time window to process data &

generate the forecast (should submit before 6amEastern time)

More CPU cores

Training/Back Testing File mutex

6

Challenges


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Speed couple hours time window to process data &

generate the forecast (should submit before 6amEastern time)

More CPU cores

Training/Back Testing File mutex

7

Challenges II


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Maintenance

Requirements High Availability Fault tolerance (e.g. Tornado)

8

Maintenance


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Early months manual operation Manually move the data/start the process Check the data quality

Put together a doc and hand it over to ops

9


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10

Maintenance 2.0 First idea: crontab, etc..


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Second: Hudson-CI, http://hudson-ci.org/ Overkill?

11

Hudson
http://hudson-ci.org/http://hudson-ci.org/http://hudson-ci.org/http://hudson-ci.org/


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Monitoring executions of externally-run jobs

Nice web UI

Cron + procmail and lot more

Tons of plugins

12


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13

Conclusion: R in production


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Pros: Quick prototyping No issue with speed

Cons: Code maintenance is hard after several thousand

lines

14


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Thank you

15


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7ttucs) '(r#':7nline 8orecastin# A..lication

BME MIT

9aln)i #nes:Ano+liadete)tlsR-rel


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Budapesti Mszaki s Gazdasgtudomnyi EgyetemMrstechnika s Informcis Rendszerek Tanszk

Anomliadetektls R-rel

Salnki gnes

2014.01.15.

Egy motivci (1949)

Hadlum vs. Hadlum


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Forrs: http://www.siam.org/meetings/sdm10/tutorial3.pdf

Egy motivci(1949)

tlag:280 nap
http://www.siam.org/meetings/sdm10/tutorial3.pdfhttp://www.siam.org/meetings/sdm10/tutorial3.pdf


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Forrs: http://www.siam.org/meetings/sdm10/tutorial3.pdf

(40 ht)

Mrs. Hadlum:

349

Anomlia definci?ms a generl folyamat
http://www.siam.org/meetings/sdm10/tutorial3.pdfhttp://www.siam.org/meetings/sdm10/tutorial3.pdf


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Anomlia definci?

exception


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anomaly

surprise

rare eventnoveltyoutlier

aberration

peculiarity

discordant observations

CsoportostsTvolsg alap

o Befoglal burok:depth


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o MVE, MCD: MASSo BACON: robustXo DB: fields

Srsg alapo LOF:DMwR

o NNDB

Tvolsg?

Befoglal burok1D: min, max


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(bels: medin)

2D: bef. poligon

3 D:

Befoglal burok: depth::depth

MVEMinimum Volume

Ellipsoid


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Kimert keresssel

MVE: MASS::cov.rob

BACON


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Ha sszefgg,sszefgg

BACON: robustX::mvBacon

DBHiba vagyunk a

kzppontban, hanincsenek szomszdaink

Distance-basedapproach


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DB: fields::fields.rdist.near

LOF motivci2 sem,

vagy 1 is?


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LOFLocal outlier factor


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Ha aszomszdaim

is

magnyosak,nincs nagygond

LOF: DMwR::lofactor

NNDB

Hol vannak a


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nagy vltsok?

Amire mi hasznljuk: teljestmnymen.


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Tvolsg vagy srsg alap?BACON: elg messze

vanNNDB: de homogn

srsg


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9 l )i #


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9aln)i #nes:Ano+liadete)tls R-rel

rapporter.net

;t0 er#el':

R +int 19 esz)(z


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Tth Gergely, Rapporter -Easystats/PERIPATO

library (sp ) # Alap: a terleti adatok kezelse (classes )


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library (maptools ) # ESRI standardok kezelse

library (rgeos ) # Tri objektumok manipullsa (GEOS )

library (raster ) # grid, raster

library (rasterVis ) # raster megjelents

# sok- sok fggsg

library (dismo ) # google maps hvsok (eredetileg: gmap +szksges rgdal )


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+szksges rgdal )

mymap


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library (RgoogleMaps )


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PlotOnStaticMap (lat = c(36.3 , 35.8 , 36.4 ),lon = c(-5.5 , -5.6 , -5.8), zoom = 10,cex = 4, pch = 19, col = "red" ,

FUN = points, add = F)# Ments fjlba: ujterkep


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# Mintaadatok: Nemes babr

library (dismo )


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library (dismo ) laurus


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Goog e trkpen:locs.sp.coords


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# rworldmaplibrary (rworldmap ) data (coastsCoarse )

plot (locs.sp, pch = 20, cex = 2,col = "steelblue" )title (Nemes babr


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( )data (countriesLow ) plot (coastsCoarse, add = T)

(Spanyolorszgban ") plot (countriesLow, add = T)

library (googleVis )data (Exports ) # minta adatbzis # 'data.frame': 10 obs. of 3 variables:# $ C F / 10 l l

# Andrew hurrikn: data (Andrew )M1


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# $ Country: Factor w/ 10 levels"Brazil","France",..: 3 1 10 2 4 6 5 7 8 9

# $ Profit : num 3 4 5 4 3 2 1 4 5 1# $ Online : logi TRUE FALSE TRUE TRUE

FALSE TRUE ..Geo


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library (rworldmap )

data(" countryExData ",


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data( cou t y Data ,envir=environment(),package=" rworldmap ")

sPDF


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Kontrtrkpek Trbeli autokorrelci

Francisco Rodriguez-Sanchez: Spatial data inR U i R GIS
http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/


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Francisco Rodriguez Sanchez: Spatial data inR: Using R as a GIS

CRAN Task View: Analysis of Spatial Data Making Maps with R

Csomag dokumentcik

;t0 -er#el':
http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://cran.r-project.org/web/views/Spatial.htmlhttp://www.molecularecologist.com/2012/09/making-maps-with-r/http://www.molecularecologist.com/2012/09/making-maps-with-r/http://www.molecularecologist.com/2012/09/making-maps-with-r/http://www.molecularecologist.com/2012/09/making-maps-with-r/http://www.molecularecologist.com/2012/09/making-maps-with-r/http://www.molecularecologist.com/2012/09/making-maps-with-r/http://www.molecularecologist.com/2012/09/making-maps-with-r/http://cran.r-project.org/web/views/Spatial.htmlhttp://cran.r-project.org/web/views/Spatial.htmlhttp://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/http://pakillo.github.io/R-GIS-tutorial/


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;t0 er#el :R +int -19 esz)(z

Qanopt


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1. Quanopt Ltd.

MonetDB.Rcsomagbl

Urbanics Gbor < [email protected]>

Villmelads R meetup Budapest

Problmafelvets

Az R j, de a memriban kell lenniemindennek hamar elfogyhat


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2. Quanopt Ltd.

j ,mindennek hamar elfogyhatErre a legegyszerbb megoldsok

o File-backed csomagok hasznlata, pl.

bigmemory csomagcsald, ff csomag o Amennyire lehet, hasznljunk adatbzist

Trols + Feldolgozs

MonetDB

Relcis

ID Day Discount10 4/4/98 0.195

11 9/4/98 0.06512 1/2/98 0.175


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3. Quanopt Ltd.

Oszlopszervezso V. soronknti

13 7/2/98 0

OID ID

100 10101 11

102 12

103 13

104 14

OID Day

100 4/4/98101 9/4/98

102 1/2/98

103 7/2/98

104 1/2/99

OID Discount

100 0.195101 0.065

102 0.175

103 0

104 0.065

3 db kln fjl a diszken

MonetDB elnyk

A teljes oszlopo(ka )t rint lekrdezseknllesz hatkony (IO hozzfrs jobb)


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4. Quanopt Ltd.

lesz hatkony (IO hozzfrs jobb)

Egy oszlopot egyszer cache-elnio

Memory mapped fileOszloponknt jobban tmrthetek az adatok

o kisebb trhely

o CPU- val fizetnk, a kevesebb IO- rt

Frtbe is szervezhet

MonetDB htrnyok

Nem silver-bullet : mindenre nem lesz j


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5. Quanopt Ltd.

Read-mostly hozzfrsre javasolt

Itt is a kd a legbiztosabb dokumentci

MonetDB s R integrci

RODBC-n keresztl elrhet


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6. Quanopt Ltd.

o Ilyet lttunk mr ez nem segtene sokat

MonetDB.R csomag

o MonetDB specifikus funkcionalits R - bl

o Alapvet DB management (start/stop)

o Adatelrs (termszetesen)

MonetDB.R csomag DBI elrs

DBI drivero dbConnect


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7. Quanopt Ltd.

o dbSendUpdate ,

# using DBI with MonetDB

> conn result print(result)

L11 29722533> str(result)'data.frame': 1 obs. of 1 variable: $ L1: num 29722533

MonetDB.R csomag monet.frame

Egy data.frame szer osztly


155/163

8. Quanopt Ltd.

o Proxy objektum egy adatbzis tblhoz

Egyszer mveletek adatbzis oldalon

o Az R hvsokat SQL lekrdezsekk rja t

MonetDB.R csomag monet.frame> mframe


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9. Quanopt Ltd.

> str(mframe)

MonetDB-backed data.frame surrogate 3 columns, 12500 rows Query: SELECT * FROM demotable Columns: col1 (numeric), col2 (numeric), col3 (numeric)

lekrdezseket!

MonetDB.R csomag monet.frame> res


157/163

10. Quanopt Ltd.

QQ: 'SELECT col3 FROM demotable QQ: 'SELECT 10*(col3) FROM demotable QQ: 'SELECT COS(10*(col3)) FROM demotable QQ: 'SELECT SQRT(COS(10*(col3))) FROM demotable

QQ: 'SELECT ((col1)+(col2))/(SQRT(COS(10*(col3)))) FROMdemotable'

a lekrdezseket,de nem hajtja vgre

MonetDB.R csomag monet.frame> head(res)

QQ: 'SELECT ((col1)+(col2))/(SQRT(COS(10*(col3)))) FROM

demotable LIMIT 6 OFFSET 0 II: 'Re- Initializing column info. EX 'SELECT (( l1) ( l2))/(SQRT(COS(10*( l3)))) FROM


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11. Quanopt Ltd.

EX: 'SELECT ((col1)+(col2))/(SQRT(COS(10*(col3)))) FROMdemotable LIMIT 6 OFFSET 0

sql_div_sql_add_col11 4.6613272 NA3 NA4 6.2608295 6.6401086 -21.836528

Szmos R fggvny s opertor SQL oldaliimplementcija szerepel a csomagban

b [

MonetDB.R csomag monet.frame


159/163

12. Quanopt Ltd.

o Subset , [ opertor o Aritmetikai mveletek: +, -, *, /, ^, %%, %/%o Logikai opertorok: &, |, !o min, max, mean, sd, var, median, quantile,

tabulateo abs, sign, sqrt, floor, ceiling, trunc, round, signifo exp, log, expm1, log1p, cos, sin, tan, acos, asin,

atan, cosh, sinh, tanh, acosh,asinh, atanh

MonetDB.R csomag htrnyok

Az tlet (R SQL trs) j , deo Az implementciban vannak azrt hibk( l li i l h lj k )


160/163

13. Quanopt Ltd.

(pl. limit nev vltozt ne hasznljunk )o Az mveletek elemkszlete korltozott

Teljesen transzparens nem lesz

MonetDB.R csomag htrnyok

Tetszleges meglv fggvnyt nemfogunk tudni hasznlni


161/163

14. Quanopt Ltd.

> ggplot(data=mframe, aes(x=col1)) + geom_histogram()Error: ggplot2 doesn't know how to deal with data of classmonet.frame

Termszetesen mi rhatunk olyan sajt fggvnyt, amitmogatja

MonetDB.R hasonl megoldsok

Oracle R Enterpriseo R fggvnyek vgrehajtsa Oracled tb i k


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15. Quanopt Ltd.

adatbzisokon IBM Netezza R csomagok

o

nzR, nzA, nzMatrixTeradata

o teradataR csomag

01 Arat Bence: Ki szereti az R-t?

02 Bod Lszl: R s !""0= Bur#er !sa$a: % nz'i d(nt se) *izs#lata l+e4-#'el04 i # l' 9 (* # j l ) ) >


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04 ,arczi er#el': 9z(*e#es jelent se) ) sz>t se05 /or*t er#el': R a i*atalos statiszti)$an0 Kocsis +re: R/adoo . a Reduce R-$en0@ K(les ri Lszl: 3, atc ,as $oard0 dud*ari '(r#': R 6 %'t on colla$oration0 7ttucs) '(r#': 7nline 8orecastin# A lication10 9aln)i #nes: Ano+liadete)tls R-rel

11 ;t er#el': R +int 9 esz)(z12

Documents

R Lightning Talks @ BURN (2014-01-15)