R/Macro_loader.R

In youngstreetinitiative/AusMacroTimeSeries: Analyses Australia

#' #' @name macro_loader
#' #' @param  Blank will bring in csv's from
#' #' @return A data frame
#' #' @export
#'
#'
#' macro_loader<- function(){
#'
#' 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 =",")
#'
#'
#' 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")
#'
#' 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
#'
#' df_list <- list(quarterly_macro,Employment_ceasing, Employment_Industy,LabourForce,population1,weekly_earnings,RandD,Underemployment,WPI_Industry,Foreign_Investment,Bonds,House_Index,LabourMob_Ind)
#'
#'
#'
#' 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]]
#' all_done <<- out_df_list %>%
#'   bind_rows()
#' final<<-spread(all_done, Cats, Total)
#' }
#'
#' macro_loader()
#'
#'
#'
#'
#'
#'
#'
#' 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()