DATA VISUALIZATION WITH R PACKAGES

R ile Veri Madenciliği Yaz Okulu, 07 – 13 Eylül 2015, Muğla,
TOVAK ULUSLARARSI MARMARİS AKADEMİSİ
DATA VISUALIZATION
WITH R PACKAGES
FATMA ÇINAR, MBA, CAPITAL MARKETS BOARD OF TURKEY
E-mail: fatma.cinar@spk.gov.tr @fatma_cinar_ftm @TRUserGroup
Kutlu MERİH, PhD, e-mail: kutmerih@gmail.com @cortexien
https://www.riskonomi.com

Visualization of multidimensional multi factorial big data is
not large data, big data is complex data.
What is big data?

 We are trainnig decipher this complexcity
data Visualization.
 Data Visualization packages of R software
lattice and ggplot 2.

 What is data analysis?
 Why use a programming language?
 Why use R ?
 Why lattice packages?
 What is lattice packages grammer of
graphics?
 Why ggplot2 ?
 What is ggplot2 grammer of graphics?
Agenda • Case study: BRSA NUTS and Sectoral
Loans Default Chart of Turkey

Sectoral Loans Dataset Graphics Data-Mining
Analysis
Action
 Real Time Interactive Data Management
for
 Effect and Response Analysis
Technique:
 #Lattice and #ggplot2 Graphical Packages
using #R Software

 #library(lattice)
 #library(ggplot2)
 # This example uses the
ENGTOVAKLOANS dataset, which comes
with ggplot2
 names(dataset)
Wednesday, September 02, 2015

names(dataset)
 names(dataset)
 [1] "NYEAR" "SYEAR" "QUARTERS"
 [4] "CITY" "CITYCODE" "NREGION"
 [7] "REGION" "NUTS3CODE" "NUTS2CODE"
 [10] "NUTS1CODE" "TRNUTS1REGION" "NUTS1REGION"
 [13] "TRGROUP" "SECTORAL" "CASHLOANS"
 [16] "NONCASHLOANS" "TOTALCASHLOANS" "AUTO"
 [19] "MORTGAGE" "OVERDRAFTACCOUNT" "CREDITCARDS"
 [22] "FOOD" "BUILDING" "MINERALS "
 [25] "FINANCIAL" "TEXTILE" "WHOSESALE "
 [28] "TOURISM" "AGRICULTURE" "ENERGY"
 [31] "MARITIME" "OTHERCONSUMER" "DEFRECEIVABLE"
 [34] "DEFCREDITCARDS" "DEFAUTO" "DEFMORTGAGE"
 [37] "DEFOTHERCONSUMER" "DEFFOOD" "DEFBUILDING"
 [40] "DEFMINERALS" "DEFFINANCIAL" "DEFTEXTILE"
 [43] "DEFWHOLESALE " "DEFTOURISM" "DEFAGRICULTURE"
 [46] "DEFENERGY" "DEFMARITIME" "NONCASHFOOD"
 [49] "NONCAHBUILDING" "NONCASHMINERALS" "NONFINANCIAL"
 [52] "NONCASHTEXTILE" "NONCASHWHOLESALE " "NONCASHTOURISM"
 [55] "NONCASHAGRICULTURE" "NONCASHENERGY" "NONCASHMARITIME"

• [1] "NYEAR" "SYEAR" "QUARTERS"
• [4] "CITY" "CITYCODE" "NREGION"
• [7] "REGION" "NUTS3CODE" "NUTS2CODE"
• [10] "NUTS1CODE" "TRNUTS1REGION"
"NUTS1REGION"
• [13] "TRGROUP" "SECTORAL" "CASHLOANS"
• [16] "NONCASHLOANS" "TOTALCASHLOANS" "AUTO"
• [19] "MORTGAGE" "OVERDRAFTACCOUNT"
"CREDITCARDS"
• [22] "FOOD" "BUILDING" "MINERALS "
• [25] "FINANCIAL" "TEXTILE" "WHOSESALE "
• [28] "TOURISM" "AGRICULTURE" "ENERGY"
• [31] "MARITIME" "OTHERCONSUMER" "DEFRECEIVABLE"
• [34] "DEFCREDITCARDS" "DEFAUTO" "DEFMORTGAGE"
• [37] "DEFOTHERCONSUMER" "DEFFOOD" "DEFBUILDING"
• [40] "DEFMINERALS" "DEFFINANCIAL" "DEFTEXTILE"
• [43] "DEFWHOLESALE " "DEFTOURISM" "DEFAGRICULTURE"
• [46] "DEFENERGY" "DEFMARITIME" "NONCASHFOOD"
• [49] "NONCAHBUILDING" "NONCASHMINERALS" "NONFINANCIAL"
• [52] "NONCASHTEXTILE" "NONCASHWHOLESALE "
"NONCASHTOURISM"
• [55] "NONCASHAGRICULTURE" "NONCASHENERGY"
"NONCASHMARITIME"

NUTS-1:12 Region of
Turkey
 MEDITERRANEAN
 SOUTHEAST ANATOLIA
 EAGEAN REGION
 NORTHEAST ANATOLIA
 MIDDLE ANATOLIA
 WEST BLACK SEA
 WEST ANATOLIA
 EAST BLACK SEA
 WEST MARMARA
 MIDDLE EAST ANATOLIA
 ISTANBUL
 EAST MARMARA
•NUTS-1: 12 Regions
•NUTS-2: 26 Subregions
•NUTS-3: 81 Provinces

(Nomenclature of Territorial Units for
Statistics, NUTS)

İstanbul
Region
West
Marmara
Region
Aegean
Region
East
Marmara
West
Anatolia
Region
Mediterranea
n Region
Anatolia
Region
West Black
Sea Region
East Black
Sea Region
Northeast
Anatolia
Region
East
Anatolia
Region
Southea
st
Anatoli
a
İstanbul
(Subregion)
Tekirdağ
(Subregion)
İzmir
(Subregion)
Bursa
(Subregion)
Ankara
(Subregion)
Antalya
(Subregion)
Kırıkkale
(Subregion)
Zonguldak
(Subregion)
Trabzon
(Subregion)
Erzurum
(Subregion)
Malatya
(Subregion)
Gaziant
ep
(Subreg
ion)
Edirne
Aydın
(Subregion)
Eskişehir
Konya
(Subregion)
Isparta Aksaray Karabük Ordu Erzincan Elazığ
Adıyam
an
Kırlareli Denizli Bilecik Karaman Burdur Niğde Bartın Giresun Bayburt Bingöl Kilis
Balıkesir
(Subregion)
Muğla
Kocaeli
(Subregion)
Adana
(Subregion)
Nevşehir
Kastamonu
(Subregion)
Rize
Ağrı
(Subregion)
Dersim
Şanlıurf
a
(Subreg
ion)
Çanakkale
Manisa
(Subregion)
Sakarya Mersin Kırşehir Çankırı Artvin Kars
Van
(Subregion)
Diyarba
kır
A.Karahisar Düzce
Hatay
(Subregion)
Kayseri
(Subregion)
Sinop Gümüşhane Iğdır Muş
Mardin
(Subreg
ion)
Kütahya Bolu Kahramanmaraş Sivas
Samsun
(Subregion)
Ardahan Bitlis Batman
Uşak Yalova Osmaniye Yozgat Tokat Hakkari Şırnak
Çorum Siirt
Amasya
1 Province 5 Province 8 Province 8 Province 3 Province 8 Province 8 Province 10 Province 6 Province 7 Province 8 Province
9
Provinc
e

1. Lattice Graphics Packages
 How to create basic plots (xyplot,
scatterplots, histograms, boxwhisper,
dotplot and bar using qplot()
 Setting vs. mapping
 How to add group and factor=numerical
variable
Çarşamba 7.Ekim.2015

1.1. XYPlot Graphic Module
 library(lattice)
 p<-xyplot(NUMERIC ~ NUMERIC) | FACTOR1,
group= FACTOR2, data=dataset)
 p
 p<-xyplot( NUM ~ NUM ) | FAC1+FAC2, group=
FAC3, data=dataset)
 p
 p<-xyplot(DEFENERGY ~ ENERGY |
CITY+factor(NYEAR), group=SECTORAL,
data=dataset)
 p
Description
of XYPlot
Graphs
1. Lattice Graphics Packages

p<-xyplot(DEFENERGY ~ ENERGY | CITY+factor(NYEAR),
group=SECTORAL, data=dataset)
XYPlot graph of the lattice packakge for 2 numerical 3 factors
values

p<-xyplot(DEFENERGY ~ ENERGY | CITY+factor(NYEAR),
group=SECTORAL, data=dataset)

p<-xyplot(DEFENERGY ~ ENERGY |
SECTORAL+factor(NYEAR), group=NUTS1REGION,
data=dataset)

1.1.1. XYPlot Graphic Module and Legand
 library(lattice)
 p<-xyplot(NUMERIC ~ NUMERIC) | FACTOR1, group= FACTOR2,
data=dataset)
 p<-xyplot(log10(DEFENERGY) ~ log10(ENERGY) |
data=dataset)
 p
 p<-xyplot(log10(DEFENERGY) ~ log10(ENERGY) |
auto.key=list(border=TRUE),data=dataset)
 p<-xyplot(log10(DEFENERGY) ~ log10(ENERGY) | SECTORAL,
group=factor(NYEAR),
auto.key=list(border=TRUE),data=dataset)

p<-xyplot(log10(DEFENERGY) ~ log10(ENERGY) | SECTORAL+factor(NYEAR),
group=NUTS1REGION, data=dataset)

p<-xyplot(log10(DEFENERGY) ~ log10(ENERGY) | SECTORAL+factor(NYEAR),
group=NUTS1REGION, auto.key=list(border=TRUE),data=dataset)

p<-xyplot(log10(DEFENERGY) ~ log10(ENERGY) | SECTORAL,
group=factor(NYEAR), auto.key=list(border=TRUE),data=dataset)

• We do factor-based analysis for the
begining with the simplest graphical form
of the histogram.
• Histograms of a single numeric value by
one factor are the starting point of the
factor based graphical analysis
1.2. Histogram Graphic Module
Description
of
Histogram
Graphs

p<-histogram( ~ log10(DEFENERGY) | SECTORAL, data=dataset)

p<-bwplot(SECTORAL ~ log10(DEFENERGY))

p<-bwplot(SECTORAL ~ log10(DEFENERGY) | NUTS1REGION)

1.3.DotPlot Graphic Module
#p<-dotplot (FACTOR1 ~ NUMERIC | FACTOR2,
group=FACTOR3, data=dataset)
p enter
**********CITY!!!!!!***************
p<-dotplot (CITY ~ DEFENERGY | SECTORAL,
group=factor(NYEAR), data=dataset)
p<-dotplot (CITY ~ log10(DEFENERGY) | SECTORAL,
p<-dotplot (NUTS1REGION ~ DEFENERGY |
SECTORAL, group=factor(NYEAR), data=dataset)
p<-dotplot(SECTORAL ~ log10(DEFENERGY) | CITY,
Description
of DotPlot
Graphs

p<-dotplot(SECTORAL ~ log10(DEFENERGY) |
NUTS1REGION,group=factor(NYEAR),data=dataset)

p<-dotplot(SECTORAL ~ log10(DEFENERGY) |
NUTS1REGION,group=factor(NYEAR),auto.key=list(border=TRUE),data=dataset)

p<-dotplot(SECTORAL ~ DEFENERGY | CITY, group=factor(NYEAR), data=dataset)

p<-dotplot(SECTORAL ~ log10(DEFENERGY) | CITY, group=factor(NYEAR),
data=dataset)

p<-dotplot(CITY ~ DEFENERGY | SECTORAL, group=factor(NYEAR),
data=dataset)

p<-dotplot (CITY ~ log10(DEFENERGY) | SECTORAL, group=factor(NYEAR),
data=dataset)

2. Ggplot2 Graphics
Packages
 How to create basic plots (xyplot, scatterplots,
histograms, baloon, facet, density and violin)
using qplot()
 Setting vs. mapping
 How to add extra variables with aesthetics
(like color, shape, and size) or faceting
 https://plot.ly/ggplot2/geom_bar/

What is ggplot2 ?
 Grammer of graphics represents and abstraction of
graphics ideas/objects
 Think ‘verb’, ‘noun’, ‘adjective’ for graphics
 Allows for a ‘theory’ of graphics on which to build
new graphics and graphics ogjects
 ‘Shorten the distence from mind to page’

Grammer of Graphics ?
‘In brief, the grammer tells us that a statistical graphic
is a mapping from data to aesthetic attributes (color,
shape, size) of geometric object (point, lines, bars).
The plot may also contain stastistical transformations
of data and drawn on a specific coordinate system’
Hadley Wickham

2.1.Logarithm Module
 library(ggplot2)
 ds<-ggplot(dataset)
 #as<-aes(log10(NUMERIC), log10(NUMERIC), color=FACTOR)
 as<-aes(log10(ENERGY), log10(DEFENERGY),
color=SECTORAL)
 lx<-scale_x_log10()
 ly<-scale_y_log10()
 p<-ds+as+gp+lx+ly
 p

How to add extra variables with aesthetics (like
color, shape, and size)
#as<-(NUMERIC, NUMERIC, color=FACTOR, shape=factor(NUMERIC),
size=NUMERIC
as<-aes(ENERGY,DEFENERGY, color=NUTS1REGION, shape=factor(NYEAR),
size=DEFRECEIVABLE
gp<-geom_point()
ds<-ggplot(dataset)
ds<-ggplot(dataset)
p<-ds+as+gp
p enter
2.1.1. Baloon Graphic Module

Description
of Baloon
Graphs
Baloon graphs of ggplot2 package can show us
3-dimensional relations distributed according 1-3
factors in scatterplot form.
With this type 2-dimensional numerical relations
can be represented under effect of 3rd numerical
value.

as<-aes(Log10(ENERGY), (log10(DEFENERGY), color=factor(NYEAR),
shape=SECTORAL), size=DEFRECEIVABLE

ae<-aes(log10(ENERGY), log10(DEFENERGY),
color=SECTORAL)
gp<-geom_point()
ds<-ggplot(dataset)
dataset=subset(dataset, ENERGY!=0)
dataset=subset(dataset, DEFENERGY!=0)
ss<-stat_smooth(method = "lm", formula = y ~ x, size = 2)
p<-ds+ae+gp+ss
p
2.1.2. PowerLaw Graphic Module
Description
of Baloon
Graphs

ae<-aes(log10(ENERGY), log10(DEFENERGY), color=SECTORAL)
p<-ds+ae+gp+ss

ae<-aes(log10(ENERGY), log10(DEFENERGY), color=NUTS1REGION)
p<-ds+ae+gp+ss

3.Density Graphic Module
#ad<-aes(NUMERIC, color=FACTOR)
ad<-aes(ENERGY, color=SECTORAL)
#as<-aes(log10(NUMERIC), fill=FACTOR
ad<-aes(log10(ENERGY), fill=SECTORAL)
gd<-geom_density()
gd<-geom_density(alpha=0.5)
ds<-ggplot(dataset)
p<-ds+ad+gd
p enter
P.S It will be one Numeric Variable
Description
of Density
Graphs

• Density Graphs are the continuous version of
Histograms
• They plot a single numerical variable against their
frequancy.
• We can detect single or multiple peaks of density
graphs and pinpoint the effective factors.
• On the other hand soperposing density graphs
acording the factors with different colors provide us
with information of the effect of the factors
• Logarithmic scale leads a more stable density
formations for financial data.Description of
Density
Graphs

ad<-aes(log10(ENERGY),fill=SECTORAL)
p<-ds+ad+gd

NUTS
Eagean
Regions
Log10
Energy Vs
Log10
Default
Energy,
Baloon
Defreceivable
Explained by
Sectoral and
Year Factors
Density/
Violin
Graphics

3.1.Density Bar Graphic Module
#ad<-aes(NUMERIC, color=FACTOR)
ad<-aes(ENERGY, color=SECTORAL)
#as<-aes(log10(NUMERIC), fill=FACTOR
ab<-aes(log10(ENERGY), fill=SECTORAL)
gbd<-geom_bar(position="dodge")
gbs<-geom_bar(position="stack")
ds<-ggplot(dataset)
p<-ds+ab+gbs
p enter

gbs<-geom_bar(position="stack")
p<-ds+ab+gbs

gbd<-geom_bar(position="dodge")
p<-ds+ab+gbd

4.Facet Graphic Module
 f<-facet_grid(FACTOR ~ NUMERIC)
 f<-facet_grid(NUTS1REGION ~ NYEAR)
 f<-facet_grid(SECTORAL ~ NYEAR)
 f<-facet_grid(NYEAR ~ SECTORAL)***
 gv<-geom_violin(),gp<-geom_point(),gd<-geom_density()
 p<-ds+as+gp+f
 p<-ds+as+gv+f
 p<-ds+as+gd+f
av<-aes(ENERGY,DEFENERGY,fill=SECTORAL,color=NUTS1REGION)
f<-facet_grid(NYEAR ~ SECTORAL)
p<-ds+av+gp+(lx+ly)+f

av<-aes(ENERGY,DEFENERGY,fill=SECTORAL,color=NUTS1REGION)
p<-ds+av+gp+f

 Facet graphs of ggplot2 package can
show us 3-dimensional graphs distributed
according 3 factors in matrix form.
 In which we can see the anomalies occurs
on which year and which region and
which period.
 Here we investigate default energy versus
default loans bloonad by total loans
according to region, year and period
factors.
 Colors period, balloons Total Cash
loans.Description
of Facet
Graphs

4.1.Facet Violin Graphic Module
 f<-facet_grid(FACTOR ~ NUMERIC)
 f<-facet_grid(NUTS1REGION ~ NYEAR)
 f<-facet_grid(SECTORAL ~ NYEAR)
 f<-facet_grid(NYEAR ~ SECTORAL)***
 gv<-geom_violin(),gp<-geom_point(),gd<-geom_density()
 p<-ds+av+gv+lx+ly+f
 p<-ds+as+gv+f
Çarşamba7.Ekim.2015

av<-aes(ENERGY, DEFENERGY, fill=SECTORAL)
p<-ds+av+gv+f

5.Violin Graphic Module
 subset
 dataset=subset(dataset,ENERGRY!=0)
 dataset=subset(dataset,DEFENERGRY!=0)
 Subset Justify
 m<-length(dataset[,1])
 m enter
 [m] 3046 ….
Çarşamba7.Ekim.2015
• ds<-ggplot(dataset)
• av<aes(ENERGY,DEFENERGY,fill=SECTORAL)
• gv<-geom_violin()
• gj<-geom_jitter()
• p<-ds+av+gv+gj+lx+ly
• p enter

Description
of Violin
Graphs
•Violin Graphs can be seen as two-dimensional
density graphs
•Usually Violin Graphs comes with Mushroom, Potter
and Bottle formations
•Violin Graphs are very important for Risk Analysis of
financial Data
•Through the mean of X-axis Y-density graph ocuurs
with nirror copy
•Mushroom formation represents a risk concentration
on hig order values of financial data
•Potter means risk on the medium order and the
bottle menas risk on the lower orders

av<-aes(ENERGY,DEFENERGY, fill=NUTS1REGION)
p<-ds+av+gv+gj+ly

av<-aes(log10(ENERGY),log10(DEFENERGY), fill=NUTS1REGION)
p<-ds+av+gv+lx+ly

I would like to express my deep gratitude to;
Dr. Kutlu MERİH,
Dr. C. Coşkun KÜÇÜKÖZMEN
for their valuable contibutions,
Fatma ÇINAR

Contact
kutmerih@gmail.com
kutlu@merih.net
coskun.kucukozmen@ieu.edu.tr
http://www.ieu.edu.tr/tr
coskunkucukozmen@gmail.com
http://www.coskunkucukozmen.com
fatma.cinar@spk.gov.tr
http://www.spk.gov.tr/
http://www.riskonomi.com
@TRUserGroup
@CORTEXIEN
@Riskonometri
@Riskonomi
@datanalitik
@Riskanalitigi
@RiskLabTurkey
@fatma_cinar_ftm
tr.linkedin.com/in/fatmacinar
tr.linkedin.com/pub/kutlu-merih
tr.linkedin.com/in/coskunkucukozmen

KÜÇÜKÖZMEN, C. C. AND ÇINAR F., (2014). “MODELLİNG OF CORPORATE PERFORMANCE IN MULTİ-
DİMENSİONAL COMPLEX STRUCTURED ORGANİZATİONS “CBBC” MANAGEMENT”, SUBMİTTED TO THE
“2ND INTERNATİONAL SYMPOSİUM ON CHAOS, COMPLEXİTY AND LEADERSHİP (ICCLS), DECEMBER
17-19 AT MİDDLE EAST TECHNİCAL UNİVERSİTY (METU), ANKARA, TURKEY.
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ANALİZİ”, TROUGBI/DW SIG, NİSAN 2014 İSTANBUL, HTTP://WWW.TROUG.ORG/?P=684
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ÇÖZÜM, TEMMUZ 2014, HTTP://EKONOMİK-COZUM.COM.TR/GORSEL-VERİ-ANALİZİNDE-DEVRİM-
Mİ.HTML.
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TEMMUZ 2014, HTTP://EKONOMİK-COZUM.COM.TR/GORSEL-TEKNİKLER-CAGİ.HTML
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GRAPHİCAL DATA MİNİNG APPROACH”, SUBMİTTED TO THE 34TH NATİONAL CONFERENCE FOR
OPERATİONS RESEARCH AND INDUSTRİAL ENGİNEERİNG (YAEM 2014), GÖRÜKLE CAMPUS OF
ULUDAĞ UNİVERSİTY İN BURSA, TURKEY ON 25-27 JUNE 2014.
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COMPLEX STRUCTURED ORGANİZATİONS: “CBBC” APPROACH”, SUBMİTTED TO THE ECONANADOLU
2013: ANADOLU INTERNATİONAL CONFERENCE İN ECONOMİCS III JUNE 19-21, 2013, ESKİŞEHİR.
HTTP://WWW.ECONANADOLU.ORG/EN/İNDEX.PHP/ARTİCLES2013/3683
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DEFAULTS: GRAPHİC-DATA MİNİNG ANALYSİS”, SUBMİTTED TO THE ICEF 2014 CONFERENCE, YILDIZ
TECHNİCAL UNİVERSİTY İN İSTANBUL, TURKEY ON 08-09 SEP. 2014.
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CONCEPTS”, ‘A CASE STUDY İN STRUCTURİNG AN EDUCATİONAL DOMAİN’, CHAİR OF SOFTWARE
ENGİNEERİNG, ETH ZURİCH, SWİTZERLAND. FMİCHELA.PEDRONİ|BERTRAND.MEYERG@İNF.ETHZ.CH
KÜÇÜKÖZMEN, C. C. AND ÇINAR F., (2015). “VİSUAL ANAYSİS OF ELECTRİCİTY DEMAND ENERGY
DASHBOARD GRAPHİCS” SUBMİTTED TO THE 5TH MULTİNATİONAL ENERGY AND VALUE CONFERENCE
MAY 7-9, 2015 KADİR HAS UNİVERSİTY İN İSTANBUL, TURKEY

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