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Data_LoadRawData.R
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Data_LoadRawData.R
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# loading data
#**********************************************************************
# Notes ####
#**********************************************************************
# Goal:
# Load and organize data from each source into the same format that is easy for merge operations.
# The format:
# - data frame
# - Three date variables: year, month, date(in Date format)
# - All variables, within and across data sources, have unique names.
#**********************************************************************
# Packages ####
#**********************************************************************
library(quantmod)
library(Quandl)
library(tidyverse)
library(readxl)
library(lubridate)
library(xts)
library(zoo)
library(magrittr)
library(stringr)
# Intro to quantmod
# 1. quantmod http://statmath.wu.ac.at/~hornik/QFS1/quantmod-vignette.pdf
# 2. https://www.quantinsti.com/blog/a-guide-on-r-quantmod-package-how-to-get-started/
# Quandl for R
# https://www.quandl.com/tools/r
# Intro to zoo cran.r-project.org/web/packages/zoo/vignettes/zoo-quickref.pdf
#**********************************************************************
# Global settings ####
#**********************************************************************
dir_data_raw <- "data_raw/"
dir_data_out <- "data_out/"
Quandl.api_key("rsakY2-RD8pa1JNBk9sd")
# Note for Don: The api key is associated with my Quandl account.
# You may want to register to obtain you own api key (Account settings -> API Key)
#**********************************************************************
# Loading data from FRED ####
#**********************************************************************
# http://rstudio-pubs-static.s3.amazonaws.com/24858_1f006c3965614b0099c963913100e9f0.html
# Major economic variables
FRED_vars <- c(
"GDPC1", # Quarterly, Seasonally adjusted GDP level, billion
"A191RL1Q225SBEA", # Quarterly, seasonally adjusted GDP growth, annual rate
"TB3MS", # 3-Month Treasury-bill: secondary market rate, monthly
"GS2", # 2-Year Treasury constant maturity rate
"GS10", # 10-Year Treasury constant maturity rate
"GS20", # 10-Year Treasury constant maturity rate
"GS30", # 30-Year Treasury constant maturity rate
"CPIAUCSL", # CPI-U, seasonally adjusted
"CPIAUCNS", # CPI_U, not seasonally adjusted
"CPILFESL", # core CPI: CPI-U less food and energy, seasonally adjusted
"GDPDEF", # GDP implicit price deflator,Quarterly, seasonally adjusted, 2009 = 100
"GDPCTPI", # GDP Chain-type price index, Quarterly, seasonally adjusted, 2009 = 100
#"B191RA3Q086SBEA", # GDP chain-type quantity index (discontinuted), quarterly, seasonally adjusted, 2009 = 100
#"AAA", # Moody's Seasoned Aaa Corporate Bond Yield, monthly not seasonally adjusted
"UNRATE" # civilian unemployent rate, seasonally adjusted
)
# loading data through Quandl (2000 calls per 10 mins).
df_FRED <- Quandl(paste0("FRED/", FRED_vars), order = "asc")
names(df_FRED) <- str_replace_all(names(df_FRED), c("FRED." = "", " - Value" = ""))
df_FRED %<>%
mutate(year = year(Date),
month = month(Date),
yearMon = as.yearmon(Date)) %>%
select(year, month, yearMon, everything(), -Date) %>%
rename(GDP_FRED = GDPC1,
GDP_growth_FRED = A191RL1Q225SBEA,
TBill3m_FRED = TB3MS,
TBond2y_FRED = GS2,
Tbond10y_FRED = GS10,
Tbond30y_FRED = GS30,
CPIU_SA_FRED = CPIAUCSL,
CPIU_NA_FRED = CPIAUCNS,
CPIc_SA_FRED = CPILFESL,
GDPdeflator_FRED = GDPDEF,
GDPCTPI_FRED = GDPCTPI,
#GDPCTQI_FRED = B191RA3Q086SBEA,
unrate_SA_FRED= UNRATE
)
# %>% mutate(GDPdeflator_FRED = na.locf(GDPdeflator_FRED, na.rm = FALSE), .
# GDPCTPI_FRED = na.locf(GDPCTPI_FRED, na.rm = FALSE),
names(df_FRED)
df_FRED
# data through quantmod
macroData <- new.env()
getSymbols("AAA", src = "FRED", env = macroData )
df_AAA <- macroData$AAA %>% as.data.frame()
df_AAA %<>%
mutate(Date = row.names(df_AAA),
year = year(Date),
month = month(Date),
yearMon = as.yearmon(Date)) %>%
rename(CBond_Yield_AAA = AAA) %>%
select(year, month, yearMon, everything(), -Date)
# Merging data
df_FRED <- full_join(df_FRED, df_AAA)
head(df_FRED)
tail(df_FRED)
# as.yearmon()
# x <- as.yearmon(date("2001-01-01"))
# x %>% month
# as.Date(x)
# as.yearmon("2010-1")
# Quarterly GDP and price indices are given in month 1, 4, 7, 10.
# Fill other month with value in the first month of the quarter
df_FRED %<>% group_by(year) %>%
mutate(
GDP_FRED = case_when(
month %in% 1:3 ~ as.numeric(GDP_FRED[ 1]),
month %in% 4:6 ~ as.numeric(GDP_FRED[ 4]),
month %in% 7:9 ~ as.numeric(GDP_FRED[ 7]),
month %in% 10:12 ~ as.numeric(GDP_FRED[ 10])
),
GDP_growth_FRED = case_when(
month %in% 1:3 ~ GDP_growth_FRED[1],
month %in% 4:6 ~ GDP_growth_FRED[4],
month %in% 7:9 ~ GDP_growth_FRED[7],
month %in% 10:12 ~ GDP_growth_FRED[10]
),
GDPdeflator_FRED = case_when(
month %in% 1:3 ~ GDPdeflator_FRED[1],
month %in% 4:6 ~ GDPdeflator_FRED[4],
month %in% 7:9 ~ GDPdeflator_FRED[7],
month %in% 10:12 ~ GDPdeflator_FRED[10]
),
GDPCTPI_FRED = case_when(
month %in% 1:3 ~ GDPCTPI_FRED[1],
month %in% 4:6 ~ GDPCTPI_FRED[4],
month %in% 7:9 ~ GDPCTPI_FRED[7],
month %in% 10:12 ~ GDPCTPI_FRED[10]
)
)
#**********************************************************************
# Loading data from SBBI Yearbook ####
#**********************************************************************
# Data from SBBI2016 yearbook appendix B: 1/1926~12/2015
SBBI_AppendB_vars <- c("LCapStock_TRI",
"LCapStock_CAI",
"SCapStock_TRI",
"CBond_TRI",
"LTGBond_TRI",
"MTGBond_TRI",
"TBills_TRI",
"Inflation_Index")
SBBI_AppendA_vars <- c("LTGBond_TR",
"MTGBond_TR",
"LTGBond_Yield",
"MTGBond_Yield",
"CBond_TR")
SBBI_AppendB_file <- paste0(dir_data_raw, "SBBI2016_AppendixB.xlsx")
SBBI_AppendB_cell <- "A3:M94"
SBBI_AppendA_file <- paste0(dir_data_raw, "SBBI2016_AppendixA.xlsx")
SBBI_AppendA_cell <- "A3:M93"
fn <- function(fileName, varName, cells){
# a function to read a single sheet and convert it into long format
read_excel(fileName, varName, cells) %>%
gather(month, var, -Year) %>%
mutate(month = match(month, substr(month.name, 1, 3)),
varName = varName) %>%
arrange(Year, month, var) %>%
rename(year = Year)
}
df_SBBI_AppendB <-
sapply(SBBI_AppendB_vars, fn, fileName = SBBI_AppendB_file, cells = SBBI_AppendB_cell, simplify = FALSE) %>%
bind_rows() %>%
mutate(varName = factor(varName, levels = SBBI_AppendB_vars)) %>%
spread(varName, var) %>%
mutate(yearMon = as.yearmon(paste(year, month, sep = "-"))) %>%
select(year, month, yearMon, everything())
df_SBBI_AppendA <-
sapply(SBBI_AppendA_vars, fn, fileName = SBBI_AppendA_file, cells = SBBI_AppendA_cell, simplify = FALSE) %>%
bind_rows() %>%
mutate(varName = factor(varName, levels = SBBI_AppendA_vars)) %>%
spread(varName, var) %>%
mutate(yearMon = as.yearmon(paste(year, month, sep = "-"))) %>%
select(year, month, yearMon, everything())
head(df_SBBI_AppendB)
tail(df_SBBI_AppendB)
head(df_SBBI_AppendA)
tail(df_SBBI_AppendA)
#**********************************************************************
# Loading R. Shiller data ####
#**********************************************************************
df_Shiller <- read_xls(paste0(dir_data_raw,"RShiller_data.xls"), sheet = "Data", skip = 7) %>%
mutate(yearMon = str_replace(as.character(Date), "\\.", "-") %>% as.yearmon(),
year = year(yearMon),
month = month(yearMon)) %>%
filter(!is.na(year)) %>%
select(year, month, yearMon, everything(), -Date)
head(df_Shiller)
tail(df_Shiller)
#**********************************************************************
# Loading yahoo finance data ####
#**********************************************************************
yahoo_vars <- c("^GSPC", #SP500 index (price only)
"^SP500TR" #SP500 total return
#"^W5000",
#"^RUA" # Russell 3000 Index
)
# use quantmod package
env_yahoo <- new.env()
getSymbols(yahoo_vars, src = "yahoo", env = env_yahoo,
from = "1900-01-01", periodicity = "monthly")
SP500 <- env_yahoo$GSPC %>% Cl # get closing prices
SP500TR <- env_yahoo$SP500TR %>% Cl # get closing prices
df_yahoo <- cbind(SP500, SP500TR) %>% as.data.frame
df_yahoo %<>%
mutate(Date = row.names(df_yahoo),
year = year(Date),
month = month(Date),
yearMon = as.yearmon(Date)) %>%
rename(SP500 = GSPC.Close,
SP500TR = SP500TR.Close) %>%
select(year, month, yearMon, everything(), -Date)
head(df_yahoo)
tail(df_yahoo)
# # get monthly returns
# GSPC.pctchange <- ClCl(GSPC)
# GSPC.pctchange %>% head()
#**********************************************************************
# Loading estimated monthly GDP data ####
#**********************************************************************
# Stock and Watson estimated monthly real GDP: 1/1959 - 6/2010
df_GDPmonthly_StockWatson <- read_excel(paste0(dir_data_raw, "MonthlyGDP_StockWatson.xlsx"), "InterpolatedGDP_GDI_rename")
df_GDPmonthly_StockWatson %<>%
mutate(yearMon = paste(year, month, sep = "-") %>% as.yearmon()) %>%
rename(GDP_qtr_SW = RealGDP_Q,
GDP_mon_SW = RealGDP_M) %>%
mutate(GDP_qtr_SW = as.numeric(GDP_qtr_SW),
GDP_mon_SW = as.numeric(GDP_mon_SW)) %>%
select(year, month, yearMon, GDP_mon_SW, GDP_qtr_SW)
# Macroeconomic Advisors estimated monthly real GDP 1/1992 - 12/2017
df_GDPmonthly_MA <- read_excel(paste0(dir_data_raw, "MonthlyGDP_MA.xlsx"), "Data")
df_GDPmonthly_MA %<>%
separate(Date, c("year", "month"), " - " , convert = TRUE) %>%
mutate(yearMon = paste(month, year) %>% as.yearmon(),
month = month(yearMon)) %>%
rename(GDP_mon_MA = `Monthly Real GDP Index`,
GDP_nominal_mon_MA = `Monthly Nominal GDP Index`) %>%
select(year, month, yearMon, everything())
head(df_GDPmonthly_MA)
save(df_FRED,
df_SBBI_AppendA,
df_SBBI_AppendB,
df_Shiller, df_yahoo,
df_GDPmonthly_StockWatson, df_GDPmonthly_MA,
file = paste0(dir_data_raw, "dataRaw.RData"))