library(quantmod)
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
getSymbols("RACE")
## '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] "RACE"
dim(RACE)
## [1] 1601 6
head(RACE)
## RACE.Open RACE.High RACE.Low RACE.Close RACE.Volume RACE.Adjusted
## 2015-10-21 60.00 60.97 55.00 55.00 22498800 52.29956
## 2015-10-22 57.07 58.20 55.70 56.75 4545100 53.96364
## 2015-10-23 57.77 58.00 56.27 56.38 1967600 53.61180
## 2015-10-26 57.00 57.00 54.54 55.02 1466300 52.31858
## 2015-10-27 54.80 54.99 49.36 53.85 5949200 51.20602
## 2015-10-28 54.02 54.16 50.10 51.87 2411300 49.32324
tail(RACE)
## RACE.Open RACE.High RACE.Low RACE.Close RACE.Volume RACE.Adjusted
## 2022-02-22 217.00 219.900 214.04 216.43 344200 216.43
## 2022-02-23 221.03 221.450 215.07 215.30 301200 215.30
## 2022-02-24 204.88 215.875 204.30 215.55 732800 215.55
## 2022-02-25 212.20 215.460 210.30 213.42 460300 213.42
## 2022-02-28 212.04 218.000 211.68 215.31 376300 215.31
## 2022-03-01 214.90 215.370 207.63 208.48 357700 208.48
chartSeries(RACE)
getSymbols("AAPL")
## [1] "AAPL"
dim(AAPL)
## [1] 3817 6
head(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 2.619588
## 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 2.966379
tail(AAPL)
## AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 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 91974200 164.85
## 2022-02-28 163.06 165.42 162.43 165.12 94869100 165.12
## 2022-03-01 164.70 166.60 161.97 163.20 83361500 163.20
chartSeries(AAPL)
getSymbols("INTDSRTRM193N",src="FRED")
## [1] "INTDSRTRM193N"
chartSeries(INTDSRTRM193N)
getSymbols("TURGDPNQDSMEI",src="FRED")
## [1] "TURGDPNQDSMEI"
chartSeries(TURGDPNQDSMEI)
library(WDI)
df = WDI(indicator='NY.GDP.MKTP.CD', country=c('MX','TR','US'), start=1960, end=2018)
head(df)
## iso2c country NY.GDP.MKTP.CD year
## 1 MX Mexico 1.222408e+12 2018
## 2 MX Mexico 1.158913e+12 2017
## 3 MX Mexico 1.078491e+12 2016
## 4 MX Mexico 1.171868e+12 2015
## 5 MX Mexico 1.315351e+12 2014
## 6 MX Mexico 1.274443e+12 2013
library(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, union
df <- df %>%
rename(ulkekodu = 1,
ulke = 2,
cıro = 3,
sene = 4)
library(reshape2)
data_genis <- dcast(df, sene ~ ulke, value.var="cıro")
head(data_genis)
## sene Mexico Turkey United States
## 1 1960 1.304e+10 13995067818 5.433e+11
## 2 1961 1.416e+10 7988888889 5.633e+11
## 3 1962 1.520e+10 8922222222 6.051e+11
## 4 1963 1.696e+10 10355555556 6.386e+11
## 5 1964 2.008e+10 11177777778 6.858e+11
## 6 1965 2.184e+10 11966666667 7.437e+11
tsveri <- ts(data_genis, start=1960, frequency=1)
head(tsveri)
## sene Mexico Turkey United States
## [1,] 1960 1.304e+10 13995067818 5.433e+11
## [2,] 1961 1.416e+10 7988888889 5.633e+11
## [3,] 1962 1.520e+10 8922222222 6.051e+11
## [4,] 1963 1.696e+10 10355555556 6.386e+11
## [5,] 1964 2.008e+10 11177777778 6.858e+11
## [6,] 1965 2.184e+10 11966666667 7.437e+11
library(ggplot2)
library(ggfortify)
autoplot(tsveri[,"Turkey"]) +
ggtitle("GSYIH (carı ABD doları) - Turkıye ") +
xlab("Sene") +
ylab("")
autoplot(tsveri[,"Turkey"], ts.colour = 'red', ts.linetype = 'dashed')
plot(tsveri[,"Turkey"])
plot(tsveri[,2:4])
plot(tsveri[,2:4], plot.type = "single")
data_uzun <- melt(data_genis, id.vars = "sene")
ggplot(data_uzun,
aes(x = sene,
y = value,
col = variable)) +
geom_line()
df = WDI(indicator='SP.DYN.LE00.IN', country=c('MX','TR','US'), start=1960, end=2018)
head(df)
## iso2c country SP.DYN.LE00.IN year
## 1 MX Mexico 74.992 2018
## 2 MX Mexico 74.947 2017
## 3 MX Mexico 74.917 2016
## 4 MX Mexico 74.904 2015
## 5 MX Mexico 74.908 2014
## 6 MX Mexico 74.930 2013
library(dplyr)
df <- df %>%
rename(ulkekodu = 1,
ulke = 2,
yasamsuresı = 3,
sene = 4)
data_genis <- dcast(df, sene ~ ulke, value.var="yasamsuresı")
head(data_genis)
## sene Mexico Turkey United States
## 1 1960 57.077 45.369 69.77073
## 2 1961 57.668 46.093 70.27073
## 3 1962 58.193 46.830 70.11951
## 4 1963 58.656 47.573 69.91707
## 5 1964 59.069 48.312 70.16585
## 6 1965 59.447 49.035 70.21463
tsveri <- ts(data_genis, start=1960, frequency=1)
head(tsveri)
## sene Mexico Turkey United States
## [1,] 1960 57.077 45.369 69.77073
## [2,] 1961 57.668 46.093 70.27073
## [3,] 1962 58.193 46.830 70.11951
## [4,] 1963 58.656 47.573 69.91707
## [5,] 1964 59.069 48.312 70.16585
## [6,] 1965 59.447 49.035 70.21463
autoplot(tsveri[,"Turkey"]) +
ggtitle("Doğumda beklenen yaşam süresi, toplam (yıl) - Türkiye") +
xlab("Sene") +
ylab("")
