scripts/labour graphs.R
In youngstreetinitiative/AusMacroTimeSeries: Analyses Australia
macro_loader()
library(scales)
library(tidyverse)
library(stringr)
library(lubridate)
library(zoo)
# spreadform <- spreadform %>%
# mutate(WPI_growth = (Productivity_hourly - lag(Productivity_hourly, 4))/lag(Productivity_hourly, 4))
# spreadform$Productivity_hourly
# labour force simple geom lines
RPIbase <- spreadform$WPI.real[spreadform$Date=="2000-01-01"]
Prodbase <- spreadform$Productivity_hourly[spreadform$Date=="2000-01-01"]
spreadform <- spreadform %>%
mutate(WPI.real2=(WPI.real/RPIbase)*100)
Productivity <-Productivity %>%
mutate(Productivity_hourly2=(Productivity_hourly/Prodbase)*100)
WPIandProd_real<- ggplot(data = spreadform ) +
ylab('Index') +
geom_line(aes(x=Date, y=WPI.real2 , col='WPIreal'), size=1, alpha=.5) +
geom_line(data = Productivity, aes(x=Date, y=Productivity_hourly2 , col='Productivity_hourly'), size=1, alpha=.5) +
theme(legend.position=c(.15,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(15),limits = as.Date(c('1/1/1997', '1/6/2017'),format="%d/%m/%Y"))
WPIandProd_real
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/WPIandProd_real.png", WPIandProd_real)
WPI_growth<- ggplot(data = spreadform ) +
ylab('WPI_growth') +
geom_line(aes(x=Date, y=wage.growth ), size=1, alpha=.5, col="darkblue") +
theme(legend.position=c(.15,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_y_continuous(labels=scales::percent,limits=c(0,0.045))+
scale_x_date(breaks = pretty_breaks(15),limits = as.Date(c('1/1/1997', '1/6/2017'),format="%d/%m/%Y"))
WPI_growth
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/WPI_growth.png", WPI_growth)
AverageWeekly<- ggplot(data = `Average Weekly Earnings` ) +
ylab('average weekly earnings (AUD)') +
geom_line(aes(x=Date, y=Total), size=1, alpha=.5, col ="orange") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1994', '1/6/2017'),format="%d/%m/%Y"))
AverageWeekly
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/AverageWeekly.png", AverageWeekly)
underemp<- ggplot(data = LabourForceAggregates ) +
ylab('Underemployed (%)') +
geom_line(aes(x=Date, y=UnderEmpRate_Trend ), size=1, alpha=.5,col= "purple") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
underemp
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/underemp.png", underemp)
prod<- ggplot(data = Productivity ) +
ylab('Productivity (Index)') +
geom_line(aes(x=Date, y=Productivity_hourly ), size=1, alpha=.5, col='darkgreen') +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
prod
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/prod.png", prod)
underutil<- ggplot(data = LabourForceAggregates ) +
ylab('Underutilisation (Percent)') +
geom_line(aes(x=Date, y=UnderutilisationRate_Trend ), size=1, alpha=.5, col="red") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
underutil
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/underutil.png", underutil)
EmploymentByIndustry$PT_ratio<-EmploymentByIndustry$Total_EmpPT/EmploymentByIndustry$Total_EmpTotal
part_time<- ggplot(data = EmploymentByIndustry ) +
ylab('ratio employed part time') +
geom_line(aes(x=Date, y=rollmean(PT_ratio,4,na.pad = T)), size=1, alpha=.5,col ="darkred") +
#geom_line(aes(x=Date, y=Total_EmpFT , col='Total_EmpFT'), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))+
scale_y_continuous(labels=scales::percent,limits=c(0.1,0.4))
part_time
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/part_time.png", part_time)
################################################### more complex plots
industry <- filter(longform, Cats %in% c("Agriculture_EmpTotal","Mining_EmpTotal","Manufacturing_EmpTotal","Utilities_EmpTotal",
"Construction_EmpTotal","Wholesale_EmpTotal","Retail_EmpTotal","Hospitality_EmpTotal",
"Transport_EmpTotal","Media_EmpTotal","Financial_EmpTotal","RentalRealEstate_EmpTotal",
"ProfScientificTech_EmpTotal","Administration_EmpTotal","PublicAdmin_EmpTotal","Education_EmpTotal","HealthSocial_EmpTotal",
"ArtsRec_EmpTotal","OtherServices_EmpTotal"))
industry2 <- filter(longform, Cats %in% c("Agriculture_EmpTotal","Mining_EmpTotal","Manufacturing_EmpTotal","Utilities_EmpTotal",
"Construction_EmpTotal","Wholesale_EmpTotal","Retail_EmpTotal","Hospitality_EmpTotal",
"Transport_EmpTotal","Media_EmpTotal","Financial_EmpTotal","RentalRealEstate_EmpTotal",
"ProfScientificTech_EmpTotal","Administration_EmpTotal","PublicAdmin_EmpTotal","Education_EmpTotal","HealthSocial_EmpTotal",
"ArtsRec_EmpTotal","OtherServices_EmpTotal",
"Agriculture_EmpPT","Mining_EmpPT","Manufacturing_EmpPT","Utilities_EmpPT",
"Construction_EmpPT","Wholesale_EmpPT","Retail_EmpPT","Hospitality_EmpPT",
"Transport_EmpPT","Media_EmpPT","Financial_EmpPT","RentalRealEstate_EmpPT",
"ProfScientificTech_EmpPT","Administration_EmpPT","PublicAdmin_EmpPT","Education_EmpPT","HealthSocial_EmpPT",
"ArtsRec_EmpPT","OtherServices_EmpPT"))
industry_S <- spread(industry2,Cats,Total)
industry_S_C <- spread(industry2,Cats,Total)
industry_S_C$PrimaryIndusty_PT <- industry_S_C$Agriculture_EmpPT + industry_S_C$Mining_EmpPT + industry_S_C$Utilities_EmpPT
industry_S_C$SecondaryIndusty_PT <- industry_S_C$Manufacturing_EmpPT + industry_S_C$Construction_EmpTotal + industry_S_C$Wholesale_EmpPT + industry_S_C$Transport_EmpPT
industry_S_C$TertiaryIndusty_PT <- industry_S_C$Retail_EmpPT + industry_S_C$Hospitality_EmpPT + industry_S_C$Media_EmpPT + industry_S_C$ArtsRec_EmpPT
industry_S_C$BusnessServices_PT <- industry_S_C$Financial_EmpPT + industry_S_C$RentalRealEstate_EmpPT + industry_S_C$ProfScientificTech_EmpPT +
industry_S_C$Administration_EmpPT + industry_S_C$OtherServices_EmpPT
industry_S_C$Govdominated_PT <- industry_S_C$PublicAdmin_EmpPT + industry_S_C$Education_EmpPT + industry_S_C$HealthSocial_EmpPT
industry_S_C$PrimaryIndusty_Total <- industry_S_C$Agriculture_EmpTotal + industry_S_C$Mining_EmpTotal + industry_S_C$Utilities_EmpTotal
industry_S_C$SecondaryIndusty_Total <- industry_S_C$Manufacturing_EmpTotal + industry_S_C$Construction_EmpTotal + industry_S_C$Wholesale_EmpTotal + industry_S_C$Transport_EmpTotal
industry_S_C$TertiaryIndusty_Total <- industry_S_C$Retail_EmpTotal + industry_S_C$Hospitality_EmpTotal + industry_S_C$Media_EmpTotal + industry_S_C$ArtsRec_EmpTotal
industry_S_C$BusnessServices_Total <- industry_S_C$Financial_EmpTotal + industry_S_C$RentalRealEstate_EmpTotal + industry_S_C$ProfScientificTech_EmpTotal +
industry_S_C$Administration_EmpTotal + industry_S_C$OtherServices_EmpTotal
industry_S_C$Govdominated_Total <- industry_S_C$PublicAdmin_EmpTotal + industry_S_C$Education_EmpTotal + industry_S_C$HealthSocial_EmpTotal
industry_S_C[2:39] <- NULL
industry_S_C$PrimaryIndusty_part_time <- industry_S_C$PrimaryIndusty_PT/industry_S_C$PrimaryIndusty_Total
industry_S_C$SecondaryIndusty_part_time <- industry_S_C$SecondaryIndusty_PT/industry_S_C$SecondaryIndusty_Total
industry_S_C$TertiaryIndustry_part_time <- industry_S_C$TertiaryIndusty_PT/industry_S_C$TertiaryIndusty_Total
industry_S_C$BusnessServices_part_time <- industry_S_C$BusnessServices_PT/industry_S_C$BusnessServices_Total
industry_S_C$GovDominated_part_time <- industry_S_C$Govdominated_PT/industry_S_C$Govdominated_Total
industry_S_C[2:11] <- NULL
industry_S_C <- gather(industry_S_C,Industry, Proportion, -bin_id )
industry_S_C$bin_id <- as.Date(industry_S_C$bin_id)
colnames(industry_S_C)[1] <- "Date"
part_time_ratio<-ggplot(industry_S_C, aes(x = Date, y = Proportion, group = Industry)) +
geom_line(colour='blue')+
facet_wrap(~Industry)+
scale_y_continuous(labels=scales::percent,limits=c(0,1))+
scale_x_date(breaks = pretty_breaks(10),limits = as.Date(c('1/1/1990', '1/1/2017'),format="%d/%m/%Y"))
part_time_ratio
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/part_time_ratio.png", part_time_ratio)
ggplotly(part_time_ratio)
x_S <- spread(x,Category,Value)
x2 <- x %>%
group_by(Category) %>%
mutate(Roll_mean = rollmean(Value,4,fill=NA))
c<- ggplot(data = x2 ) +
ylab('PT_ratio') +
geom_line(aes(x=bin_id, y=Roll_mean, col=Category, alpha=.5, linetype = Category))
c
colnames(industry) <- c("Date","sector","Employed")
jobs<-ggplot(industry, aes(x = Date, y = Employed, fill = sector)) +
geom_area(col="black", size=0.2, alpha=.4)+
ylab("number employed (000's)")
jobs
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/jobs.png", jobs)
test2 <- Consumption %>%
gather(Consumption_Catergory,ConsumptionDollars,-Date) %>%
filter(Consumption_Catergory != "consumtpion.TOTAL") %>%
group_by(ConsumptionDollars) %>%
mutate(Total = sum(ConsumptionDollars),
ConsumptionPerc = ConsumptionDollars/Total)
consumption<-ggplot(test2, aes(x = Date, y = ConsumptionDollars, fill = Consumption_Catergory)) + geom_area(position = "fill",colour="black", size=0.2, alpha=.4)
consumption
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/consumption.png", consumption)
######################################################
#ARRRR and DEEEEE
######################################################
randD<- ggplot(data = Research and Development ) +
ylab('Underemployed (%)') +
geom_line(aes(x=Date, y=UnderEmpRate_Trend ), size=1, alpha=.5,col= "purple") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
underemp
youngstreetinitiative/AusMacroTimeSeries documentation built on May 3, 2019, 5:21 p.m.
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macro_loader()
library(scales)
library(tidyverse)
library(stringr)
library(lubridate)
library(zoo)
# spreadform <- spreadform %>%
# mutate(WPI_growth = (Productivity_hourly - lag(Productivity_hourly, 4))/lag(Productivity_hourly, 4))
# spreadform$Productivity_hourly
# labour force simple geom lines
RPIbase <- spreadform$WPI.real[spreadform$Date=="2000-01-01"]
Prodbase <- spreadform$Productivity_hourly[spreadform$Date=="2000-01-01"]
spreadform <- spreadform %>%
mutate(WPI.real2=(WPI.real/RPIbase)*100)
Productivity <-Productivity %>%
mutate(Productivity_hourly2=(Productivity_hourly/Prodbase)*100)
WPIandProd_real<- ggplot(data = spreadform ) +
ylab('Index') +
geom_line(aes(x=Date, y=WPI.real2 , col='WPIreal'), size=1, alpha=.5) +
geom_line(data = Productivity, aes(x=Date, y=Productivity_hourly2 , col='Productivity_hourly'), size=1, alpha=.5) +
theme(legend.position=c(.15,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(15),limits = as.Date(c('1/1/1997', '1/6/2017'),format="%d/%m/%Y"))
WPIandProd_real
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/WPIandProd_real.png", WPIandProd_real)
WPI_growth<- ggplot(data = spreadform ) +
ylab('WPI_growth') +
geom_line(aes(x=Date, y=wage.growth ), size=1, alpha=.5, col="darkblue") +
theme(legend.position=c(.15,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_y_continuous(labels=scales::percent,limits=c(0,0.045))+
scale_x_date(breaks = pretty_breaks(15),limits = as.Date(c('1/1/1997', '1/6/2017'),format="%d/%m/%Y"))
WPI_growth
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/WPI_growth.png", WPI_growth)
AverageWeekly<- ggplot(data = `Average Weekly Earnings` ) +
ylab('average weekly earnings (AUD)') +
geom_line(aes(x=Date, y=Total), size=1, alpha=.5, col ="orange") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1994', '1/6/2017'),format="%d/%m/%Y"))
AverageWeekly
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/AverageWeekly.png", AverageWeekly)
underemp<- ggplot(data = LabourForceAggregates ) +
ylab('Underemployed (%)') +
geom_line(aes(x=Date, y=UnderEmpRate_Trend ), size=1, alpha=.5,col= "purple") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
underemp
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/underemp.png", underemp)
prod<- ggplot(data = Productivity ) +
ylab('Productivity (Index)') +
geom_line(aes(x=Date, y=Productivity_hourly ), size=1, alpha=.5, col='darkgreen') +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
prod
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/prod.png", prod)
underutil<- ggplot(data = LabourForceAggregates ) +
ylab('Underutilisation (Percent)') +
geom_line(aes(x=Date, y=UnderutilisationRate_Trend ), size=1, alpha=.5, col="red") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
underutil
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/underutil.png", underutil)
EmploymentByIndustry$PT_ratio<-EmploymentByIndustry$Total_EmpPT/EmploymentByIndustry$Total_EmpTotal
part_time<- ggplot(data = EmploymentByIndustry ) +
ylab('ratio employed part time') +
geom_line(aes(x=Date, y=rollmean(PT_ratio,4,na.pad = T)), size=1, alpha=.5,col ="darkred") +
#geom_line(aes(x=Date, y=Total_EmpFT , col='Total_EmpFT'), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))+
scale_y_continuous(labels=scales::percent,limits=c(0.1,0.4))
part_time
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/part_time.png", part_time)
################################################### more complex plots
industry <- filter(longform, Cats %in% c("Agriculture_EmpTotal","Mining_EmpTotal","Manufacturing_EmpTotal","Utilities_EmpTotal",
"Construction_EmpTotal","Wholesale_EmpTotal","Retail_EmpTotal","Hospitality_EmpTotal",
"Transport_EmpTotal","Media_EmpTotal","Financial_EmpTotal","RentalRealEstate_EmpTotal",
"ProfScientificTech_EmpTotal","Administration_EmpTotal","PublicAdmin_EmpTotal","Education_EmpTotal","HealthSocial_EmpTotal",
"ArtsRec_EmpTotal","OtherServices_EmpTotal"))
industry2 <- filter(longform, Cats %in% c("Agriculture_EmpTotal","Mining_EmpTotal","Manufacturing_EmpTotal","Utilities_EmpTotal",
"Construction_EmpTotal","Wholesale_EmpTotal","Retail_EmpTotal","Hospitality_EmpTotal",
"Transport_EmpTotal","Media_EmpTotal","Financial_EmpTotal","RentalRealEstate_EmpTotal",
"ProfScientificTech_EmpTotal","Administration_EmpTotal","PublicAdmin_EmpTotal","Education_EmpTotal","HealthSocial_EmpTotal",
"ArtsRec_EmpTotal","OtherServices_EmpTotal",
"Agriculture_EmpPT","Mining_EmpPT","Manufacturing_EmpPT","Utilities_EmpPT",
"Construction_EmpPT","Wholesale_EmpPT","Retail_EmpPT","Hospitality_EmpPT",
"Transport_EmpPT","Media_EmpPT","Financial_EmpPT","RentalRealEstate_EmpPT",
"ProfScientificTech_EmpPT","Administration_EmpPT","PublicAdmin_EmpPT","Education_EmpPT","HealthSocial_EmpPT",
"ArtsRec_EmpPT","OtherServices_EmpPT"))
industry_S <- spread(industry2,Cats,Total)
industry_S_C <- spread(industry2,Cats,Total)
industry_S_C$PrimaryIndusty_PT <- industry_S_C$Agriculture_EmpPT + industry_S_C$Mining_EmpPT + industry_S_C$Utilities_EmpPT
industry_S_C$SecondaryIndusty_PT <- industry_S_C$Manufacturing_EmpPT + industry_S_C$Construction_EmpTotal + industry_S_C$Wholesale_EmpPT + industry_S_C$Transport_EmpPT
industry_S_C$TertiaryIndusty_PT <- industry_S_C$Retail_EmpPT + industry_S_C$Hospitality_EmpPT + industry_S_C$Media_EmpPT + industry_S_C$ArtsRec_EmpPT
industry_S_C$BusnessServices_PT <- industry_S_C$Financial_EmpPT + industry_S_C$RentalRealEstate_EmpPT + industry_S_C$ProfScientificTech_EmpPT +
industry_S_C$Administration_EmpPT + industry_S_C$OtherServices_EmpPT
industry_S_C$Govdominated_PT <- industry_S_C$PublicAdmin_EmpPT + industry_S_C$Education_EmpPT + industry_S_C$HealthSocial_EmpPT
industry_S_C$PrimaryIndusty_Total <- industry_S_C$Agriculture_EmpTotal + industry_S_C$Mining_EmpTotal + industry_S_C$Utilities_EmpTotal
industry_S_C$SecondaryIndusty_Total <- industry_S_C$Manufacturing_EmpTotal + industry_S_C$Construction_EmpTotal + industry_S_C$Wholesale_EmpTotal + industry_S_C$Transport_EmpTotal
industry_S_C$TertiaryIndusty_Total <- industry_S_C$Retail_EmpTotal + industry_S_C$Hospitality_EmpTotal + industry_S_C$Media_EmpTotal + industry_S_C$ArtsRec_EmpTotal
industry_S_C$BusnessServices_Total <- industry_S_C$Financial_EmpTotal + industry_S_C$RentalRealEstate_EmpTotal + industry_S_C$ProfScientificTech_EmpTotal +
industry_S_C$Administration_EmpTotal + industry_S_C$OtherServices_EmpTotal
industry_S_C$Govdominated_Total <- industry_S_C$PublicAdmin_EmpTotal + industry_S_C$Education_EmpTotal + industry_S_C$HealthSocial_EmpTotal
industry_S_C[2:39] <- NULL
industry_S_C$PrimaryIndusty_part_time <- industry_S_C$PrimaryIndusty_PT/industry_S_C$PrimaryIndusty_Total
industry_S_C$SecondaryIndusty_part_time <- industry_S_C$SecondaryIndusty_PT/industry_S_C$SecondaryIndusty_Total
industry_S_C$TertiaryIndustry_part_time <- industry_S_C$TertiaryIndusty_PT/industry_S_C$TertiaryIndusty_Total
industry_S_C$BusnessServices_part_time <- industry_S_C$BusnessServices_PT/industry_S_C$BusnessServices_Total
industry_S_C$GovDominated_part_time <- industry_S_C$Govdominated_PT/industry_S_C$Govdominated_Total
industry_S_C[2:11] <- NULL
industry_S_C <- gather(industry_S_C,Industry, Proportion, -bin_id )
industry_S_C$bin_id <- as.Date(industry_S_C$bin_id)
colnames(industry_S_C)[1] <- "Date"
part_time_ratio<-ggplot(industry_S_C, aes(x = Date, y = Proportion, group = Industry)) +
geom_line(colour='blue')+
facet_wrap(~Industry)+
scale_y_continuous(labels=scales::percent,limits=c(0,1))+
scale_x_date(breaks = pretty_breaks(10),limits = as.Date(c('1/1/1990', '1/1/2017'),format="%d/%m/%Y"))
part_time_ratio
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/part_time_ratio.png", part_time_ratio)
ggplotly(part_time_ratio)
x_S <- spread(x,Category,Value)
x2 <- x %>%
group_by(Category) %>%
mutate(Roll_mean = rollmean(Value,4,fill=NA))
c<- ggplot(data = x2 ) +
ylab('PT_ratio') +
geom_line(aes(x=bin_id, y=Roll_mean, col=Category, alpha=.5, linetype = Category))
c
colnames(industry) <- c("Date","sector","Employed")
jobs<-ggplot(industry, aes(x = Date, y = Employed, fill = sector)) +
geom_area(col="black", size=0.2, alpha=.4)+
ylab("number employed (000's)")
jobs
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/jobs.png", jobs)
test2 <- Consumption %>%
gather(Consumption_Catergory,ConsumptionDollars,-Date) %>%
filter(Consumption_Catergory != "consumtpion.TOTAL") %>%
group_by(ConsumptionDollars) %>%
mutate(Total = sum(ConsumptionDollars),
ConsumptionPerc = ConsumptionDollars/Total)
consumption<-ggplot(test2, aes(x = Date, y = ConsumptionDollars, fill = Consumption_Catergory)) + geom_area(position = "fill",colour="black", size=0.2, alpha=.4)
consumption
ggsave("C:/Users/User/Dropbox (YSI)/YSI Team Folder/Content/Economy/Houses and wages/Jackson graphs/consumption.png", consumption)
######################################################
#ARRRR and DEEEEE
######################################################
randD<- ggplot(data = Research and Development ) +
ylab('Underemployed (%)') +
geom_line(aes(x=Date, y=UnderEmpRate_Trend ), size=1, alpha=.5,col= "purple") +
#geom_line(aes(x=bin_id, y=(wage.Index-CPI), col = "Real Wage"), size=1, alpha=.5) +
theme(legend.position=c(.1,.85),panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "grey"),
panel.border = element_rect(colour = "black", fill=NA, size=1)) +
scale_x_date(breaks = pretty_breaks(20),limits = as.Date(c('1/1/1990', '1/6/2017'),format="%d/%m/%Y"))
underemp
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