R Markdown
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("COIN")
## '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] "COIN"
dim(COIN)
## [1] 221 6
head(COIN)
## COIN.Open COIN.High COIN.Low COIN.Close COIN.Volume COIN.Adjusted
## 2021-04-14 381.00 429.54 310.00 328.28 81065700 328.28
## 2021-04-15 348.90 349.20 317.27 322.75 39777900 322.75
## 2021-04-16 327.50 345.99 321.03 342.00 22654500 342.00
## 2021-04-19 337.26 341.01 326.79 333.00 11405600 333.00
## 2021-04-20 333.43 334.83 312.02 320.82 18082300 320.82
## 2021-04-21 312.52 327.47 302.10 311.92 10103900 311.92
tail(COIN)
## COIN.Open COIN.High COIN.Low COIN.Close COIN.Volume COIN.Adjusted
## 2022-02-17 202.10 206.31 188.93 191.92 4740600 191.92
## 2022-02-18 191.08 192.72 180.54 189.16 4494100 189.16
## 2022-02-22 180.87 185.75 173.16 176.76 4472800 176.76
## 2022-02-23 180.69 183.34 171.77 172.74 3892500 172.74
## 2022-02-24 159.08 183.00 155.92 179.56 8134000 179.56
## 2022-02-25 180.24 182.09 170.40 176.83 6822500 176.83
chartSeries(COIN)
chartSeries(COIN, theme="white")
library(quantmod)
loadSymbols("XTEXVA01CNM659S",src="FRED")
## [1] "XTEXVA01CNM659S"
chartSeries(XTEXVA01CNM659S,theme="white")
chartSeries(COIN,theme="white")
library(WDI)
df = WDI(indicator='FP.CPI.TOTL.ZG', country=c('TR','CN','CL'), start=1960, end=2018)
head(df)
## iso2c country FP.CPI.TOTL.ZG year
## 1 CL Chile 2.434890 2018
## 2 CL Chile 2.182718 2017
## 3 CL Chile 3.786194 2016
## 4 CL Chile 4.348774 2015
## 5 CL Chile 4.718675 2014
## 6 CL Chile 1.789556 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,
enflasyon = 3,
sene = 4)
library(reshape2)
data_genis <- dcast(df, sene ~ ulke, value.var="enflasyon")
head(data_genis)
## sene Chile China Turkey
## 1 1960 NA NA 5.664740
## 2 1961 NA NA 3.172857
## 3 1962 NA NA 3.888320
## 4 1963 NA NA 6.362707
## 5 1964 NA NA 1.119638
## 6 1965 NA NA 4.555534
df2 <- WDI(
country = "all",
indicator = "FP.CPI.TOTL.ZG",
start = 2010,
end = 2020,
extra = TRUE,
cache = NULL,
latest = NULL,
language = "tr"
)
## Warning in WDI(country = "all", indicator = "FP.CPI.TOTL.ZG", start = 2010, :
## This language is only supported partially.
tsveri <- ts(data_genis, start=1960, frequency=1)
head(tsveri)
## sene Chile China Turkey
## [1,] 1960 NA NA 5.664740
## [2,] 1961 NA NA 3.172857
## [3,] 1962 NA NA 3.888320
## [4,] 1963 NA NA 6.362707
## [5,] 1964 NA NA 1.119638
## [6,] 1965 NA NA 4.555534
library(ggplot2)
library(ggfortify)
autoplot(tsveri[,"Turkey"]) +
ggtitle("Türkiye'nin kisi basina düsen milli geliri") +
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")
plot(tsveri[,2],
type = "l",
col = 2,
ylim = c(0, 25),
xlab = "Sene",
ylab = "Enflasyon")
lines(tsveri[,3],
type = "l",
col = 3)
lines(tsveri[,4],
type = "l",
col = 4)
legend("topright",
c("Sili", "Türkiye", "Çin"),
lty = 1,
col = 2:4)
data_uzun <- melt(data_genis, id.vars = "sene")
ggplot(data_uzun,
aes(x = sene,
y = value,
col = variable)) +
geom_line()
## Warning: Removed 38 row(s) containing missing values (geom_path).
library(quantmod)
getSymbols("TSLA")
## [1] "TSLA"
dim(TSLA)
## [1] 2937 6
head(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 3.160
tail(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 809.87
chartSeries(TSLA)
chartSeries(TSLA, theme="white")
getSymbols("LRHU24TTTRM156S",src="FRED")
## [1] "LRHU24TTTRM156S"
chartSeries(LRHU24TTTRM156S,theme="white")
getFX("USD/TRY",from="2022-01-01")
## [1] "USD/TRY"
chartSeries(USDTRY,theme="white")
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 2013
library(dplyr)
df <- 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 17.45173
df2 <- WDI(
country = "all",
indicator = "NY.GDP.PCAP.KD",
start = 2010,
end = 2020,
extra = TRUE,
cache = NULL,
latest = NULL,
language = "tr"
)
## Warning in WDI(country = "all", indicator = "NY.GDP.PCAP.KD", start = 2010, :
## This language is only supported partially.
WDIsearch("electricity")
## indicator
## [1,] "1.1_ACCESS.ELECTRICITY.TOT"
## [2,] "1.2_ACCESS.ELECTRICITY.RURAL"
## [3,] "1.3_ACCESS.ELECTRICITY.URBAN"
## [4,] "110400"
## [5,] "2.0.cov.Ele"
## [6,] "2.0.hoi.Ele"
## [7,] "4.1.1_TOTAL.ELECTRICITY.OUTPUT"
## [8,] "4.1.2_REN.ELECTRICITY.OUTPUT"
## [9,] "4.1_SHARE.RE.IN.ELECTRICITY"
## [10,] "9060000"
## [11,] "CC.ESG.EUFE"
## [12,] "CC.ESG.EUMA"
## [13,] "CC.GHG.EMSE.EH"
## [14,] "CC.GHG.SDEG.EH"
## [15,] "EG.ELC.ACCS.RU.ZS"
## [16,] "EG.ELC.ACCS.UR.ZS"
## [17,] "EG.ELC.ACCS.ZS"
## [18,] "EG.ELC.COAL.KH"
## [19,] "EG.ELC.COAL.ZS"
## [20,] "EG.ELC.FOSL.ZS"
## [21,] "EG.ELC.HYRO.KH"
## [22,] "EG.ELC.HYRO.ZS"
## [23,] "EG.ELC.NGAS.KH"
## [24,] "EG.ELC.NGAS.ZS"
## [25,] "EG.ELC.NUCL.KH"
## [26,] "EG.ELC.NUCL.ZS"
## [27,] "EG.ELC.PETR.KH"
## [28,] "EG.ELC.PETR.ZS"
## [29,] "EG.ELC.PROD.KH"
## [30,] "EG.ELC.RNEW.KH"
## [31,] "EG.ELC.RNEW.ZS"
## [32,] "EG.ELC.RNWX.KH"
## [33,] "EG.ELC.RNWX.ZS"
## [34,] "EG.TEG.RNEW.ZS"
## [35,] "EG.TEG.VAR.ZS"
## [36,] "EN.CO2.ETOT.MT"
## [37,] "EN.CO2.ETOT.ZS"
## [38,] "EN.PRD.ELEC"
## [39,] "EN.PRD.ELEC.POP.ZS"
## [40,] "HOU.ELC.ACSN.ZS"
## [41,] "IC.CNS.ELEC.ZS"
## [42,] "IC.ELC.ACES.DFRN.DB1015"
## [43,] "IC.ELC.ACES.DFRN.DB1619"
## [44,] "IC.ELC.ACES.RK.DB19"
## [45,] "IC.ELC.ACS.COST"
## [46,] "IC.ELC.ACS.COST.DFRN"
## [47,] "IC.ELC.COMM.TRFF.CG.01.DB1619"
## [48,] "IC.ELC.GEN.ZS"
## [49,] "IC.ELC.LMTG.OUTG.01.DB1619"
## [50,] "IC.ELC.MONT.OUTG.01.DB1619"
## [51,] "IC.ELC.OUTG.FREQ.DURS.03.DB1619"
## [52,] "IC.ELC.OUTG.MN.DB1619"
## [53,] "IC.ELC.PRI.KH.DB1619"
## [54,] "IC.ELC.PROC.NO"
## [55,] "IC.ELC.PROC.NO.DFRN"
## [56,] "IC.ELC.REGU.MONT.01.DB1619"
## [57,] "IC.ELC.RSTOR.01.DB1619"
## [58,] "IC.ELC.RSTT.XD.08.DB1619"
## [59,] "IC.ELC.RSTT.XD.08.DFRN.DB1619"
## [60,] "IC.ELC.SAID.XD.DB1619"
## [61,] "IC.ELC.SAIF.XD.DB1619"
## [62,] "IC.ELC.TIME"
## [63,] "IC.ELC.TIME.DFRN"
## [64,] "IC.ELEC.COST.PC.ZS"
## [65,] "IC.ELEC.PROC"
## [66,] "IC.ELEC.TIME"
## [67,] "IC.ELEC.XQ"
## [68,] "IC.FRM.ELEC.ZS"
## [69,] "IC.FRM.INFRA.IN10_C"
## [70,] "IC.FRM.INFRA.IN12"
## [71,] "IC.FRM.OBS.OBST8"
## [72,] "IN.ENRGY.ELEC.GEN"
## [73,] "NA.GDP.ELEC.GAS.SNA08.CR"
## [74,] "NA.GDP.ELEC.GAS.SNA08.KR"
## [75,] "NV.IND.GELW.CD"
## [76,] "NV.IND.GELW.CN"
## [77,] "NV.IND.GELW.KN"
## [78,] "SE.PRM.INFR.4"
## [79,] "SE.PRM.INFR.4.R"
## [80,] "SE.PRM.INFR.4.U"
## [81,] "SE.PRM.ISTD.7"
## [82,] "SE.PRM.ISTD.8"
## [83,] "SG.COK.ELEC.ZS"
## [84,] "SI.POV.ELEC.MI"
## [85,] "SL.EMP.ELC"
## [86,] "UIS.SCHBSP.1.WELEC"
## [87,] "UIS.SCHBSP.2.WELEC"
## [88,] "UIS.SCHBSP.3.WELEC"
## name
## [1,] "Access to electricity (% of total population)"
## [2,] "Access to electricity (% of rural population)"
## [3,] "Access to electricity (% of urban population)"
## [4,] "110400:HOUSING, WATER, ELECTRICITY, GAS, AND OTHER FUELS"
## [5,] "Coverage: Electricity"
## [6,] "HOI: Electricity"
## [7,] "Total electricity output (GWh)"
## [8,] "Renewable energy electricity output (GWh)"
## [9,] "Renewable electricity (% in total electricity output)"
## [10,] "9060000:ACTUAL HOUSING, WATER, ELECTRICITY, GAS AND OTHER FUELS"
## [11,] "Employment by sector and gender (% of total) - Electricity and utilities - Female"
## [12,] "Employment by sector and gender (% of total) - Electricity and utilities - Male"
## [13,] "Total GHG emissions by sector (Mt CO2 eq) - Electricity/Heat"
## [14,] "Sectoral drivers of GHG emissions growth in the period 2012-2018 - Electricity/Heat (contribution to total growth, %)"
## [15,] "Access to electricity, rural (% of rural population)"
## [16,] "Access to electricity, urban (% of urban population)"
## [17,] "Access to electricity (% of population)"
## [18,] "Electricity production from coal sources (kWh)"
## [19,] "Electricity production from coal sources (% of total)"
## [20,] "Electricity production from oil, gas and coal sources (% of total)"
## [21,] "Electricity production from hydroelectric sources (kWh)"
## [22,] "Electricity production from hydroelectric sources (% of total)"
## [23,] "Electricity production from natural gas sources (kWh)"
## [24,] "Electricity production from natural gas sources (% of total)"
## [25,] "Electricity production from nuclear sources (kWh)"
## [26,] "Electricity production from nuclear sources (% of total)"
## [27,] "Electricity production from oil sources (kWh)"
## [28,] "Electricity production from oil sources (% of total)"
## [29,] "Electricity production (kWh)"
## [30,] "Electricity production from renewable sources (kWh)"
## [31,] "Renewable electricity output (% of total electricity output)"
## [32,] "Electricity production from renewable sources, excluding hydroelectric (kWh)"
## [33,] "Electricity production from renewable sources, excluding hydroelectric (% of total)"
## [34,] "Share of renewables in total electricity generation (%)"
## [35,] "Share of variable renewables in total electricity generation (%)"
## [36,] "CO2 emissions from electricity and heat production, total (million metric tons)"
## [37,] "CO2 emissions from electricity and heat production, total (% of total fuel combustion)"
## [38,] "Electricity production (million kwh)"
## [39,] "Electricity production (kwh per capita)"
## [40,] "Household Access to Electricity: Total (in % of total household)"
## [41,] "Electricity (% of managers surveyed ranking this as a major constraint)"
## [42,] "Getting electricity (DB10-15 methodology) - Score"
## [43,] "Getting electricity (DB16-20 methodology) - Score"
## [44,] "Rank: Getting electricity (1=most business-friendly regulations)"
## [45,] "Getting electricity: Cost to get electricity (% of income per capita)"
## [46,] "Getting electricity: Cost to get electricity (% of income per capita) - Score"
## [47,] "Getting electricity: Communication of tariffs and tariff changes (0-1) (DB16-20 methodology)"
## [48,] "Electricity from Generator (%)"
## [49,] "Getting electricity: Financial deterrents aimed at limiting outages (0-1) (DB16-20 methodology)"
## [50,] "Getting electricity: Mechanisms for monitoring outages (0-1) (DB16-20 methodology)"
## [51,] "Getting electricity: Total duration and frequency of outages per customer a year (0-3) (DB16-20 methodology)"
## [52,] "Getting electricity: Minimum outage time (in minutes) (DB16-20 methodology)"
## [53,] "Getting electricity: Price of electricity (US cents per kWh) (DB16-20 methodology)"
## [54,] "Getting electricity: Procedures (number)"
## [55,] "Getting electricity: Procedures (number) - Score"
## [56,] "Getting electricity: Regulatory monitoring (0-1) (DB16-20 methodology)"
## [57,] "Getting electricity: Mechanisms for restoring service (0-1) (DB16-20 methodology)"
## [58,] "Getting electricity: Reliability of supply and transparency of tariff index (0-8) (DB16-20 methodology)"
## [59,] "Getting electricity: Reliability of supply and transparency of tariff index (0-8) (DB16-20 methodology) - Score"
## [60,] "Getting electricity: System average interruption duration index (SAIDI) (DB16-20 methodology)"
## [61,] "Getting electricity: System average interruption frequency index (SAIFI) (DB16-20 methodology)"
## [62,] "Time required to get electricity (days)"
## [63,] "Getting electricity: Time (days) - Score"
## [64,] "Cost to get electricity connection (% of income per capita)"
## [65,] "Procedures required to get electricity (number)"
## [66,] "Time required to get electricity (days)"
## [67,] "Getting electricity (rank)"
## [68,] "Electricity (% of firms identifying this as a major constraint)"
## [69,] "If a generator is used, average proportion of electricity from a generator (%)"
## [70,] "Percent of firms identifying electricity as a major constraint"
## [71,] "Percent of firms choosing electricity as their biggest obstacle"
## [72,] "Total-Electricity Generated Gross (GWh)"
## [73,] "GDP on Electricity & Gas Supply Sector (in IDR Million), SNA 2008, Current Price"
## [74,] "GDP on Electricity & Gas Supply Sector (in IDR Million), SNA 2008, Constant Price"
## [75,] "Electricity, gas and water supply, value added (current US$)"
## [76,] "Electricity, gas and water supply, value added (current LCU)"
## [77,] "Electricity, gas and water supply, value added (constant LCU)"
## [78,] "(De Facto) Percent of schools with access to electricity"
## [79,] "(De Facto) Percent of schools with access to electricity - Rural"
## [80,] "(De Facto) Percent of schools with access to electricity - Urban"
## [81,] "(De Jure) Is there a policy in place to require that schools have access to electricity?"
## [82,] "(De Facto) Do you know if there is a policy in place to require that schools have access to electricity?"
## [83,] "Main cooking fuel: electricity (% of households)"
## [84,] "Multidimensional poverty, Electricity (% of population deprived)"
## [85,] "Number of people employed in electricity and utilities sector"
## [86,] "Proportion of primary schools with access to electricity (%)"
## [87,] "Proportion of lower secondary schools with access to electricity (%)"
## [88,] "Proportion of upper secondary schools with access to electricity (%)"
tsveri <- 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 17.45173
library(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')
plot(tsveri[,"Turkey"])
plot(tsveri[,2:4])
plot(tsveri[,2:4], plot.type = "single")
plot(tsveri[,2],
type = "l",
col = 2,
ylim = c(0, 25),
xlab = "Sene",
ylab = "Karbondioksit")
lines(tsveri[,3],
type = "l",
col = 3)
lines(tsveri[,4],
type = "l",
col = 4)
legend("topright",
c("Meksika", "Türkiye", "US"),
lty = 1,
col = 2:4)
data_uzun <- melt(data_genis, id.vars = "sene")
ggplot(data_uzun,
aes(x = sene,
y = value,
col = variable)) +
geom_line()
