UYGULAMALI EKONOMETİR ÖDEV 2

library(quantmod)
## Zorunlu paket yükleniyor: xts
## Zorunlu paket yükleniyor: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Zorunlu paket yükleniyor: TTR
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo
help("quantmod")
## starting httpd help server ... done

1-Yaho finance SORUSU

Yaho finance den 2 şirket kodu olarak amazon ve tesla şeçtim

1-A-Amazon(AMZN)

getSymbols("AMZN")
## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
## 
## This message is shown once per session and may be disabled by setting 
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.
## [1] "AMZN"
dim(AMZN)
## [1] 3817    6
head(AMZN)
##            AMZN.Open AMZN.High AMZN.Low AMZN.Close AMZN.Volume AMZN.Adjusted
## 2007-01-03     38.68     39.06    38.05      38.70    12405100         38.70
## 2007-01-04     38.59     39.14    38.26      38.90     6318400         38.90
## 2007-01-05     38.72     38.79    37.60      38.37     6619700         38.37
## 2007-01-08     38.22     38.31    37.17      37.50     6783000         37.50
## 2007-01-09     37.60     38.06    37.34      37.78     5703000         37.78
## 2007-01-10     37.49     37.70    37.07      37.15     6527500         37.15
tail(AMZN)
##            AMZN.Open AMZN.High AMZN.Low AMZN.Close AMZN.Volume AMZN.Adjusted
## 2022-02-22   3009.57   3059.65  2969.71    3003.95     3306400       3003.95
## 2022-02-23   3033.01   3035.26  2893.02    2896.54     3212200       2896.54
## 2022-02-24   2796.75   3034.98  2790.00    3027.16     5039300       3027.16
## 2022-02-25   3011.00   3079.80  2984.27    3075.77     3119800       3075.77
## 2022-02-28   3048.50   3089.00  3017.00    3071.26     2878500       3071.26
## 2022-03-01   3054.65   3081.98  2999.54    3022.84     2238900       3022.84
  AMZN.OPEN O GÜNKÜ AÇILIŞ
  AMZN.HİGH O GÜNKÜEN YÜKSEK
  AMZN.LOW O GÜNKÜ EN DÜŞÜK
  AMC.CLOSE O GÜNKÜ KAPANIŞ FİYATINI GÖSTERİRİ
  
chartSeries(AMZN)

1-B-Microsoft

getSymbols("MSFT")
## [1] "MSFT"
dim(MSFT)
## [1] 3817    6
head(MSFT)
##            MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2007-01-03     29.91     30.25    29.40      29.86    76935100      21.78468
## 2007-01-04     29.70     29.97    29.44      29.81    45774500      21.74821
## 2007-01-05     29.63     29.75    29.45      29.64    44607200      21.62417
## 2007-01-08     29.65     30.10    29.53      29.93    50220200      21.83575
## 2007-01-09     30.00     30.18    29.73      29.96    44636600      21.85764
## 2007-01-10     29.80     29.89    29.43      29.66    55017400      21.63877
tail(MSFT)
##            MSFT.Open MSFT.High MSFT.Low MSFT.Close MSFT.Volume MSFT.Adjusted
## 2022-02-22    285.00    291.54   284.50     287.72    41736100        287.72
## 2022-02-23    290.18    291.70   280.10     280.27    37811200        280.27
## 2022-02-24    272.51    295.16   271.52     294.59    56989700        294.59
## 2022-02-25    295.14    297.63   291.65     297.31    32546700        297.31
## 2022-02-28    294.31    299.14   293.00     298.79    34585700        298.79
## 2022-03-01    296.40    299.97   292.15     294.95    31188900        294.95
MSFT.OPEN O GÜNKÜ AÇILIŞ
MSFT.HİGH O GÜNKÜEN YÜKSEK
MSFT.LOW O GÜNKÜ EN DÜŞÜK
MSFT.CLOSE O GÜNKÜ KAPANIŞ FİYATINI GÖSTERİRİ
chartSeries(MSFT, theme="white")

2 FRED VERİ SEÇİMİ SORUSU

2 FRED verisi yada doviz şeçilecek ve grafikleri çizilecek ben 1 veri 1 de doviz seçtim

2-A-İlk veri Türkiye’de Toplam Sanayi Üretimi

getSymbols("TURPROINDMISMEI",src="FRED")
## [1] "TURPROINDMISMEI"
chartSeries(TURPROINDMISMEI)

2-B-Seçtiğim dovizler KORE WON U VE TL

getFX("USD/JPY")
## [1] "USD/JPY"
chartSeries(USDJPY)

3 WDI DATA SORUSU

Diğer sorulardaki gibi 2 veri seçmemiz gerekiyor gene

library(WDI)

3-A-İlk olarak fosil yakıt kullanım oranını aldım(Fossil fuel energy consumption)

seçtiğim data daki sitedeki güncellemeler 2015de kaldığı için bitiş tarihi olarak öyle yapdım

df = WDI(indicator='EG.USE.COMM.FO.ZS', country=c('FR','US','DE'), start=1960, end=2015)
head(df)
##   iso2c country EG.USE.COMM.FO.ZS year
## 1    DE Germany          78.86255 2015
## 2    DE Germany          79.71053 2014
## 3    DE Germany          81.08827 2013
## 4    DE Germany          80.62522 2012
## 5    DE Germany          80.37021 2011
## 6    DE Germany          79.56035 2010
library(reshape2)
data_genis <- dcast(df, year ~ country, value.var="EG.USE.COMM.FO.ZS")
head(data_genis)
##   year   France  Germany United States
## 1 1960 95.52018 99.02016      95.52567
## 2 1961 95.86188 99.04099      95.56680
## 3 1962 96.43237 99.19267      95.60551
## 4 1963 95.89151 99.30221      95.76932
## 5 1964 96.69443 99.39761      95.80162
## 6 1965 95.82039 99.10231      95.86016
tail(data_genis)
##    year   France  Germany United States
## 51 2010 49.83638 79.56035      84.15059
## 52 2011 48.67699 80.37021      83.71405
## 53 2012 49.01203 80.62522      83.43718
## 54 2013 48.54358 81.08827      82.94051
## 55 2014 46.22592 79.71053      83.08904
## 56 2015 46.48797 78.86255      82.42783

2009-2015 arası veriler için View(df2) kodu 3 ülkenin en yakın verilerini gösterir bize

df2 <- WDI(country=c('FR','US','DE'),indicator = "NY.GDP.PCAP.KD", start = 2009,end = 2015,extra = TRUE,cache = NULL,latest = NULL,)

1960-2020 arası bütün ülkeler içinde View(df3) kodu bütün ülkelerin fosil yakıt kullanımını gösterir

df3 <- WDI(country = "all",indicator = "NY.GDP.PCAP.KD", start = 1960,end = 2020,extra = TRUE,cache = NULL,latest = NULL,)
View(df2)
View(df3)

3-B-İkinci olarak bütün nüfustaki 0-14yaş arası oranını geçtim(Population ages 0-14 (% of total population))

pf = WDI(indicator='SP.POP.0014.TO.ZS', country=c('KR','TR','JP'), start=1960, end=2020)
head(pf)
##   iso2c country SP.POP.0014.TO.ZS year
## 1    JP   Japan          12.44856 2020
## 2    JP   Japan          12.57303 2019
## 3    JP   Japan          12.69685 2018
## 4    JP   Japan          12.81439 2017
## 5    JP   Japan          12.91436 2016
## 6    JP   Japan          12.98938 2015
tail(pf)
##     iso2c country SP.POP.0014.TO.ZS year
## 178    TR  Turkey          42.50036 1965
## 179    TR  Turkey          42.58883 1964
## 180    TR  Turkey          42.52218 1963
## 181    TR  Turkey          42.32978 1962
## 182    TR  Turkey          42.10249 1961
## 183    TR  Turkey          41.90168 1960
data_genis2 <- dcast(pf, year ~ country, value.var="SP.POP.0014.TO.ZS")
head(data_genis2)
##   year    Japan Korea, Rep.   Turkey
## 1 1960 30.26390    43.23885 41.90168
## 2 1961 29.40904    43.67100 42.10249
## 3 1962 28.47836    43.78000 42.32978
## 4 1963 27.52080    43.67605 42.52218
## 5 1964 26.63051    43.52409 42.58883
## 6 1965 25.86565    43.39824 42.50036
tail(data_genis2)
##    year    Japan Korea, Rep.   Turkey
## 56 2015 12.98938    13.78172 25.59979
## 57 2016 12.91436    13.49793 25.32427
## 58 2017 12.81439    13.22550 25.00276
## 59 2018 12.69685    12.97332 24.64941
## 60 2019 12.57303    12.74644 24.29082
## 61 2020 12.44856    12.54281 23.94206