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Data.R
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Data.R
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library(tidyverse)
library(tsbox)
library(imputeTS)
library(Hmisc)
# Temperature data
GISS <- read_table("https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.txt",
skip = 7) %>%
slice(1:(n() - 5)) %>%
filter(!row_number() %in% c(22, 43, 64, 85, 106, 127, 148))
GISS[GISS == "****"] <- NA
GISS <- GISS %>% select("Year",
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec")
GISS$Year <- as.numeric(GISS$Year)
GISS$Jan <- as.numeric(GISS$Jan)
GISS$Feb <- as.numeric(GISS$Feb)
GISS$Mar <- as.numeric(GISS$Mar)
GISS$Apr <- as.numeric(GISS$Apr)
GISS$May <- as.numeric(GISS$May)
GISS$Jun <- as.numeric(GISS$Jun)
GISS$Jul <- as.numeric(GISS$Jul)
GISS$Aug <- as.numeric(GISS$Aug)
GISS$Sep <- as.numeric(GISS$Sep)
GISS$Oct <- as.numeric(GISS$Oct)
GISS$Nov <- as.numeric(GISS$Nov)
GISS$Dec <- as.numeric(GISS$Dec)
GISS <- GISS %>%
pivot_longer(c(Jan,
Feb,
Mar,
Apr,
May,
Jun,
Jul,
Aug,
Sep,
Oct,
Nov,
Dec), names_to = "Month", values_to = "anomaly") %>%
mutate(time = dmy(paste(1, Month, Year))) %>%
select(time, anomaly)
GISS$anomaly <- GISS$anomaly/100
GISS <- subset(GISS, time >= "1958-03-01") %>%
rename(Temperature = anomaly)
# CO2 data
co2 <- read_table("https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_mm_mlo.txt",
col_names = FALSE, skip = 58)
co2 <- co2 %>%
select(X1, X2, X4) %>%
rename(Year = X1, Month = X2, CO2 = X4) %>%
mutate(time = make_date(year = Year, month = Month, day = 1 )) %>%
mutate(CO2_rf = 5.35*log(CO2/280)) %>%
select(time, CO2_rf) %>%
rename(CO2 = CO2_rf)
# Sunspot data
sunspots <- read_table("https://www.sidc.be/silso/DATA/SN_m_tot_V2.0.txt",
col_names = FALSE)
sunspots <- sunspots %>%
rename(Year = X1, Month = X2, sunspots = X4) %>%
mutate(time = make_date(year = Year, month = Month, day = 1)) %>%
select(time, sunspots) %>%
subset(time >= "1958-03-01") %>%
rename("Solar" = sunspots)
# ENSO data
ENSO <- read_table("https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt")
ENSO$time <- seq.Date(from = as.Date("1950-01-01"), by = "month", length.out = nrow(ENSO))
ENSO <- subset(ENSO, time >= "1957-09-01") %>%
rename(ENSO = ANOM) %>%
select(time, ENSO)
ENSO$ENSO_lagged <- Lag(ENSO$ENSO, 3)
ENSO <- ENSO %>%
select(time, ENSO_lagged) %>%
rename(ENSO = ENSO_lagged)
# Aerosols data
aerosols <- read_table("https://data.giss.nasa.gov/modelforce/strataer/tau.line_2012.12.txt",
skip = 3) %>%
rename(time = "year/mon", Aerosols = global) %>%
mutate_at(c("time", "Aerosols"), as.numeric) %>%
select(time, Aerosols)
aerosols$time <- format(date_decimal(aerosols$time), "%Y-%m-01") %>%
as.Date(aerosols$time, format = "%Y-%m-%d")
aerosols <- subset(aerosols, time >= "1955-10-01")
## Extrapolate out from 2012-09-01
length_out <- subset(ENSO, time >= "2012-10-01")
length_out <- length(length_out$time)
extrapolation <- data.frame(
time = seq(from = as.Date("2012-10-01"), by = "1 month", length = length_out),
Aerosols = "NaN"
)
aerosols <- rbind(aerosols, extrapolation)
aerosols$Aerosols <- as.numeric(aerosols$Aerosols)
aerosols <- na_interpolation(aerosols, option = "linear")
aerosols$Aerosols_lagged <- Lag(aerosols$Aerosols, 18)
aerosols <- aerosols %>%
select(time, Aerosols_lagged) %>%
rename(Aerosols = Aerosols_lagged)
aerosols <- subset(aerosols, time >= "1958-03-01")
# Make data frame
df_list <- list(GISS, co2, sunspots, ENSO, aerosols)
global_temp <- df_list %>%
reduce(inner_join, by = "time") %>%
mutate(date = decimal_date(time)) %>%
select(date, Temperature, CO2, Solar, ENSO, Aerosols) %>%
rename(time = date)
# Lag function
calculate_lag <- function(timeseries1, timeseries2) {
cross_corr <- stats::ccf(timeseries1, timeseries2)
lag <- which.max(cross_corr$acf)
return(lag)
}