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AllData <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/AllData.xlsx")
OutsideData <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/OutsideData.xlsx")
LumberData <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/LumberData.xlsx")
CESData <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/CESData.xlsx")
Permit_HIRL_Data <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/Permit_HIRL_Data.xlsx")
EastPrecipDiff <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/EastPrecipDiff.xlsx")
BurnsData <- read.xlsx("C:/Users/ikennedy/JBREC/BP research public use microdata coding and data - General/AHSProject/OutsideDataSources_SingleYearApprox/OutsideData_fredr/BurnsData.xlsx")1 Plots for Potential Predictors (1995-2009)
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ggplot() +
geom_line(data = AllData, aes(Year, DisRepairDiff, color = "Disaster Repair Growth"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, GasolineStations, color = "Gas Station Growth"), size = 1.5) +
geom_line(data = EastPrecipDiff, aes(Year, May, color = "Eastern US, May Precipitation Growth"), size = 1.5) +
scale_x_continuous(breaks = c(seq(1999, 2009, 2)), limits = c(1999, 2009)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Disaster Repair R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Disaster Repair Growth" = "black", "Gas Station Growth" = "red", "Eastern US, May Precipitation Growth" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, MiniDiff, color = "Mini Project Growth"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, TotalPrivateConstructionSpendingManufacturingintheUnitedStates, color = "Electronics/Appliance Store Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_FloorCoveringsbyIncomeBeforeTaxes_$40_000to$49_999`, color = "Spending on Floor Coverings, Incomes between 40-50K, Growth"), size = 1.5) +
scale_x_continuous(breaks = c(seq(1999, 2009, 2)), limits = c(1999, 2009)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Mini R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Mini Project Growth" = "black", "Electronics/Appliance Store Growth" = "red", "Spending on Floor Coverings, Incomes between 40-50K, Growth" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, SmallDiff, color = "Small Project Growth"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, ElectronicsandApplianceStores, color = "Electronics/Appliance Store Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_HouseholdFurnishingsandEquipmentbyQuintilesofIncomeBeforeTaxes_Fourth20Percent(61stto80thPercentile)`, color = "4th Quintile Spending on Household Furninshings and Equipment Growth"), size = 1.5) +
scale_x_continuous(breaks = c(seq(1999, 2009, 2)), limits = c(1999, 2009)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Small R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Small Project Growth" = "black", "Electronics/Appliance Store Growth" = "red", "4th Quintile Spending on Household Furninshings and Equipment Growth" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36 , linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, MediumDiff, color = "Medium Project Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, Expenditures_MajorAppliances_AllConsumerUnits, color = "Major Appliance Spending Growth, All Consumers"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, RetailTrade_ExcludingMotorVehicleandPartsDealers, color = "Retail Trade Growth, No Car/Parts Dealers"), size = 1.5) +
scale_x_continuous(breaks = c(seq(1999, 2009, 2)), limits = c(1999, 2009)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Medium R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Medium Project Growth" = "black", "Major Appliance Spending Growth, All Consumers" = "red", "Retail Trade Growth, No Car/Parts Dealers" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36 , linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, LargeDiff, color = "Large Project Growth"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, NewPrivately_OwnedHousingUnitsUnderConstructionUnitsinBuildingswith2_4Units, color = "Growth in New Housing UNits Under Construction, 2-4 Unit Buildings"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, RetailTrade_ExcludingMotorVehicleandPartsDealers, color = "Retail Trade Growth, No Car/Parts Dealers"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Highest20Percent(81stto100thPercentile)`, color = "Major Appliance Spending Growth, Top Quintile of Earners"), size = 1.5) +
scale_x_continuous(breaks = c(seq(1999, 2009, 2)), limits = c(1999, 2009)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Large R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Large Project Growth" = "black", "Growth in New Housing UNits Under Construction, 2-4 Unit Buildings" = "red", "Retail Trade Growth, No Car/Parts Dealers" = "blue", "Major Appliance Spending Growth, Top Quintile of Earners" = 'green')) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36 , linewidth = 2)
2 Plots for Potential Predictors (2010-Current)
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ggplot() +
geom_line(data = AllData, aes(Year, DisRepairDiff, color = "Disaster Repair Growth"), size = 1.5) +
geom_line(data = EastPrecipDiff, aes(Year, AMJ, color = "Eastern US, April-June Precipitation Growth"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, GasolineStations, color = "Gas Station Growth"), size = 1.5) +
scale_x_continuous(breaks = c(seq(2011, 2021, 2)), limits = c(2011, 2021)) +
scale_y_continuous(breaks = c(seq(-0.4, .9, 0.1))) +
labs(title = 'Disaster Repair R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Disaster Repair Growth" = "black", "Eastern US, April-June Precipitation Growth" = "red", "Gas Station Growth" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, MiniDiff, color = "Mini Project Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, Expenditures_MajorAppliancesbySizeofConsumerUnit_TwoPeopleinConsumerUnit, color = "Major Appliance Spending Growth, 2-Person HH"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Second20Percent(21stto40thPercentile)`, color = "Major Appliance Spending Growth, 2nd Lowest Income Quintile"), size = 1.5) +
scale_x_continuous(breaks = c(seq(2011, 2021, 2)), limits = c(2011, 2021)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Mini R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Mini Project Growth" = "black", "Major Appliance Spending Growth, 2-Person HH" = "red", "Major Appliance Spending Growth, 2nd Lowest Income Quintile" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, SmallDiff, color = "Small Project Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Fourth20Percent(61stto80thPercentile)`, color = "Major Appliance Spending Growth, 2nd Highest Income Quintile"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_FurniturebyQuintilesofIncomeBeforeTaxes_Third20Percent(41stto60thPercentile)`, color = "Furniture Spending Growth, Middle Income Quintile"), size = 1.5) +
scale_x_continuous(breaks = c(seq(2011, 2021, 2)), limits = c(2011, 2021)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Small R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Small Project Growth" = "black", "Major Appliance Spending Growth, 2nd Highest Income Quintile" = "red", "Furniture Spending Growth, Middle Income Quintile" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36 , linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, MediumDiff, color = "Medium Project Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, Expenditures_MajorAppliancesbySizeofConsumerUnit_FiveorMorePeopleinConsumerUnit, color = "Major Appliance Spending Growth, 5+ Person HHs"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_FurniturebyQuintilesofIncomeBeforeTaxes_Third20Percent(41stto60thPercentile)`, color = "Furniture Spending Growth, Middle Income Quintile"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_Maintenance_Repairs_Insurance_OtherExpensesforOwnedDwellingbyQuintilesofIncomeBeforeTaxes_Highest20Percent(81stto100thPercentile)`, color = "Maint/Repairs Spending Growth, Highest Income Quintile"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Highest20Percent(81stto100thPercentile)`, color = "Major Appliance Spending Growth, Highest Income Quintile"), size = 1.5) +
scale_x_continuous(breaks = c(seq(2011, 2021, 2)), limits = c(2011, 2021)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Medium R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Medium Project Growth" = "black", "Major Appliance Spending Growth, 5+ Person HHs" = "red", "Furniture Spending Growth, Middle Income Quintile" = "blue", "Maint/Repairs Spending Growth, Highest Income Quintile" = 'green', "Major Appliance Spending Growth, Highest Income Quintile" = 'purple')) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36 , linewidth = 2)
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ggplot() +
geom_line(data = AllData, aes(Year, LargeDiff, color = "Large Project Growth"), size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_FurniturebyQuintilesofIncomeBeforeTaxes_Fourth20Percent(61stto80thPercentile)`, color = "Furniture Spending Growth, 2nd Highest Income Quintile"), size = 1.5) +
geom_line(data = OutsideData, aes(Date, BuildingMaterialsandSuppliesDealers, color = "Building Mat./Supplies Dealers Spending Growth"), size = 1.5) +
scale_x_continuous(breaks = c(seq(2011, 2021, 2)), limits = c(2011, 2021)) +
scale_y_continuous(breaks = c(seq(-0.4, 1.1, 0.1))) +
labs(title = 'Large R/R Project Growth Rate vs Possible Predictors') +
ylab('Growth Rate') +
scale_color_manual(values = c("Large Project Growth" = "black", "Furniture Spending Growth, 2nd Highest Income Quintile" = "red", "Building Mat./Supplies Dealers Spending Growth" = "blue")) +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36 , linewidth = 2)