odev 2
Beril Yamer
2022-02-24
YAHOO FİNANCE
örnek 1 TESLA
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 zoogetSymbols("TSLA")## '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] "TSLA"dim(TSLA)## [1] 2937 6head(TSLA)## TSLA.Open TSLA.High TSLA.Low TSLA.Close TSLA.Volume TSLA.Adjusted
## 2010-06-29 3.800 5.000 3.508 4.778 93831500 4.778
## 2010-06-30 5.158 6.084 4.660 4.766 85935500 4.766
## 2010-07-01 5.000 5.184 4.054 4.392 41094000 4.392
## 2010-07-02 4.600 4.620 3.742 3.840 25699000 3.840
## 2010-07-06 4.000 4.000 3.166 3.222 34334500 3.222
## 2010-07-07 3.280 3.326 2.996 3.160 34608500 3.160tail(TSLA)## TSLA.Open TSLA.High TSLA.Low TSLA.Close TSLA.Volume TSLA.Adjusted
## 2022-02-17 913.26 918.50 874.10 876.35 18392800 876.35
## 2022-02-18 886.00 886.87 837.61 856.98 22710500 856.98
## 2022-02-22 834.13 856.73 801.10 821.53 27762700 821.53
## 2022-02-23 830.43 835.30 760.56 764.04 31752300 764.04
## 2022-02-24 700.39 802.48 700.00 800.77 45107400 800.77
## 2022-02-25 809.23 819.50 782.40 809.87 25309500 809.87chartSeries(TSLA)
örnek 2 APPLE
library(quantmod)getSymbols("AAPL")## [1] "AAPL"dim(AAPL)## [1] 3815 6head(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2007-01-03 3.081786 3.092143 2.925000 2.992857 1238319600 2.562706
## 2007-01-04 3.001786 3.069643 2.993571 3.059286 847260400 2.619587
## 2007-01-05 3.063214 3.078571 3.014286 3.037500 834741600 2.600933
## 2007-01-08 3.070000 3.090357 3.045714 3.052500 797106800 2.613777
## 2007-01-09 3.087500 3.320714 3.041071 3.306071 3349298400 2.830903
## 2007-01-10 3.383929 3.492857 3.337500 3.464286 2952880000 2.966379tail(AAPL)## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2022-02-17 171.03 171.91 168.47 168.88 69589300 168.88
## 2022-02-18 169.82 170.54 166.19 167.30 82614200 167.30
## 2022-02-22 164.98 166.69 162.15 164.32 91162800 164.32
## 2022-02-23 165.54 166.15 159.75 160.07 90009200 160.07
## 2022-02-24 152.58 162.85 152.00 162.74 141147500 162.74
## 2022-02-25 163.84 165.12 160.87 164.85 91881700 164.85chartSeries(AAPL)
FRED ECONOMİC DATA
örnek 1 TÜRKİYE’DE 15-74 YAŞ KADIN İŞSİZLİK ORANI
getSymbols("LRUN74FETRQ156S",src="FRED")## [1] "LRUN74FETRQ156S"chartSeries(LRUN74FETRQ156S,theme="white") 
örnek 2 TÜRKİYE GENÇ İŞSİZLİK ORANI
getSymbols("SLUEM1524ZSTUR",src="FRED")## [1] "SLUEM1524ZSTUR"chartSeries(SLUEM1524ZSTUR,theme="white")
getFX("USD/TRY",from="2022-01-01")## [1] "USD/TRY"chartSeries(USDTRY,theme="white")
WORLD BANK
ÖRNEK 1 CO2 EMİSYONLARI (KİŞİ BAŞINA METRİK TON)
library(WDI)df = WDI(indicator='EN.ATM.CO2E.PC', country=c('MX','TR','US'), start=1960, end=2018)head(df)## iso2c country EN.ATM.CO2E.PC year
## 1 MX Mexico 3.741478 2018
## 2 MX Mexico 3.781216 2017
## 3 MX Mexico 3.885809 2016
## 4 MX Mexico 3.878195 2015
## 5 MX Mexico 3.808063 2014
## 6 MX Mexico 3.954147 2013library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:xts':
##
## first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniondf <- df %>%
rename(ulkekodu = 1,
ulke = 2,
karbondioksit = 3,
sene = 4)library(reshape2)
data_genis <- dcast(df, sene ~ ulke, value.var="karbondioksit")head(data_genis)## sene Mexico Turkey United States
## 1 1960 1.670990 0.6122715 15.99978
## 2 1961 1.675962 0.6168793 15.68126
## 3 1962 1.587485 0.7502431 16.01394
## 4 1963 1.600528 0.7676379 16.48276
## 5 1964 1.736658 0.8707899 16.96812
## 6 1965 1.705355 0.8842807 17.45173tsveri <- ts(data_genis, start=1960, frequency=1)head(tsveri)## sene Mexico Turkey United States
## [1,] 1960 1.670990 0.6122715 15.99978
## [2,] 1961 1.675962 0.6168793 15.68126
## [3,] 1962 1.587485 0.7502431 16.01394
## [4,] 1963 1.600528 0.7676379 16.48276
## [5,] 1964 1.736658 0.8707899 16.96812
## [6,] 1965 1.705355 0.8842807 17.45173library(ggplot2)
library(ggfortify)
autoplot(tsveri[,"Turkey"]) +
ggtitle("Türkiye'nin kişi başına metrik ton olarak karndioksit emisyonu") +
xlab("Sene") +
ylab("")
autoplot(tsveri[,"Turkey"], ts.colour = 'red', ts.linetype = 'dashed')
ÖRNEK 2 TAHIL VERİMİ (HEKTAR BAŞINA KG)
df = WDI(indicator='AG.YLD.CREL.KG', country=c('MX','TR','US'), start=1960, end=2018)head(df)## iso2c country AG.YLD.CREL.KG year
## 1 MX Mexico 3826.3 2018
## 2 MX Mexico 3799.7 2017
## 3 MX Mexico 3748.7 2016
## 4 MX Mexico 3470.1 2015
## 5 MX Mexico 3581.8 2014
## 6 MX Mexico 3386.6 2013library(dplyr)
df <- df %>%
rename(ulkekodu = 1,
ulke = 2,
tahıl = 3,
sene = 4)library(reshape2)
data_genis <- dcast(df, sene ~ ulke, value.var="tahıl")head(data_genis)## sene Mexico Turkey United States
## 1 1960 NA NA NA
## 2 1961 1104.9 989.4 2522.3
## 3 1962 1124.5 1136.0 2683.1
## 4 1963 1130.1 1343.4 2800.6
## 5 1964 1302.0 1117.9 2639.3
## 6 1965 1335.6 1138.6 3040.8tsveri <- ts(data_genis, start=1960, frequency=1)head(tsveri)## sene Mexico Turkey United States
## [1,] 1960 NA NA NA
## [2,] 1961 1104.9 989.4 2522.3
## [3,] 1962 1124.5 1136.0 2683.1
## [4,] 1963 1130.1 1343.4 2800.6
## [5,] 1964 1302.0 1117.9 2639.3
## [6,] 1965 1335.6 1138.6 3040.8library(ggplot2)
library(ggfortify)
autoplot(tsveri[,"Turkey"]) +
ggtitle("Hektar Başın kg Tahıl Verimi") +
xlab("Sene") +
ylab("")## Warning: Removed 1 row(s) containing missing values (geom_path).
autoplot(tsveri[,"Turkey"], ts.colour = 'red', ts.linetype = 'dashed')## Warning: Removed 1 row(s) containing missing values (geom_path).