#' @name macro_loader
#' @param Blank will bring in csv's from
#' @return A data frame
#' @export
macro_loader<- function(){
library(lubridate)
library(timemerge)
df_list<-csv_prep()
test_fun <- function(df) {
df %>%
mutate(Date = parse_date_time2(as.character(Date), orders = "%Y/%m/%d")) %>%
group_by(Date) %>%
gather(Cats, Vals, 2:length(.)) %>%
binner(Date, Cats, Vals, "quarter", method = "avg") %>%
select(-Count)
}
out_df_list <- list()
j <- 1
for (n in df_list) {
out_df_list[[j]] <- test_fun(n)
j <- j + 1
}
out1 <- out_df_list[[1]]
longform <<- out_df_list %>%
bind_rows()
spreadform<<-spread(longform, Cats, Total)
colnames(spreadform)[colnames(spreadform)=="bin_id"] <- "Date"
spreadform$Date <- as.Date(spreadform$Date)
}
# quarterly_macro <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/quarterly macro data.csv", header = TRUE, sep =",")
# Employment_ceasing <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/Reasons Ceasing Employment.csv", header = TRUE, sep =",")
# Employment_Industy <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/EmploymentByIndustry.csv", header = TRUE, sep =",")
# LabourForce <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/LabourForceAggregates.csv", header = TRUE, sep =",")
# population1 <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/PopulationStats.csv", header = TRUE, sep =",")
# weekly_earnings <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/Average Weekly Earnings.csv", header = TRUE, sep =",")
# RandD <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/Research and Development.csv", header = TRUE, sep =",")
# Underemployment <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/UnderemploymentByIndustry.csv", header = TRUE, sep =",")
# WPI_Industry <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/WPI by industry.csv", header = TRUE, sep =",")
# Foreign_Investment <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/ForeignInvestmentData.csv", header = TRUE, sep =",")
# Bonds <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/Bonds.csv", header = TRUE, sep =",")
# House_Index <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/House Index.csv", header = TRUE, sep =",")
# LabourMob_Ind <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/Labour Mobility By Industry.csv", header = TRUE, sep =",")
# LabourMob <- read.csv("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Time series csv's/LabourMobility.csv", header = TRUE, sep =",")
#
# quarterly_macro$Date <- as.Date(quarterly_macro$Date, format = "%d/%m/%Y" )
# Employment_ceasing$Date <- as.Date(Employment_ceasing$Date, format = "%d/%m/%Y" )
# Employment_Industy$Date <- as.Date(Employment_Industy$Date, format = "%d/%m/%Y" )
# LabourForce$Date <- as.Date(LabourForce$Date, format = "%d/%m/%Y" )
# population1$Date <- as.Date(population1$Date, format = "%d/%m/%Y" )
# weekly_earnings$Date <- as.Date(weekly_earnings$Date, format = "%d/%m/%Y" )
# RandD$Date <- as.Date(RandD$Date, format = "%d/%m/%Y" )
# Underemployment$Date <- as.Date(Underemployment$Date, format = "%d/%m/%Y" )
# WPI_Industry$Date <- as.Date(WPI_Industry$Date, format = "%d/%m/%Y" )
# Foreign_Investment$Date <- as.Date(Foreign_Investment$Date, format = "%d/%m/%Y")
# Bonds$Date <- as.Date(Bonds$Date, format = "%d/%m/%Y")
# House_Index$Date <- as.Date(House_Index$Date, format = "%d/%m/%Y")
# LabourMob_Ind$Date<- as.Date(LabourMob_Ind$Date, format = "%d/%m/%Y")
# LabourMob$Date <- as.Date(LabourMob$Date, format = "%d/%m/%Y")
#
# quarterly_macro <<- quarterly_macro
# Employment_ceasing <<- Employment_ceasing
# Employment_Industy <<- Employment_Industy
# LabourForce <<- LabourForce
# population1 <<- population1
# weekly_earnings <<- weekly_earnings
# RandD <<- RandD
# Underemployment <<- Underemployment
# WPI_Industry <<- WPI_Industry
# Foreign_Investment <<- Foreign_Investment
# Bonds <<- Bonds
# House_Index <<- House_Index
# LabourMob_Ind <<- LabourMob_Ind
# LabourMob <<- LabourMob
#
# df_list <- list(quarterly_macro,Employment_ceasing, Employment_Industy,LabourForce,population1,weekly_earnings,RandD,Underemployment,WPI_Industry,Foreign_Investment,Bonds,House_Index,LabourMob_Ind,LabourMob)
#
# final<-mutate( final,riskfree = Aus10yr-inflation.rate, na.rm= F)
#
# final$riskfree
#
# class(final$inflation.rate)
# class(final$Aus10yr)
#
#
#
# plot(final$inflation.rate,final$Aus10yr)
#
# ggplot(final, aes(x = inflation.rate&Aus10yr, y = Total, colour = inflation.rate,Aus10yr)) + geom_line()
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