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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
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ggplot(AllData, aes(Year, DisRepairDiff)) +
geom_line(size = 1.5) +
geom_line(data = OutsideData, aes(Date, GasolineStations), color = 'red', size = 1.5) +
geom_line(data = OutsideData, aes(Date, FuelDealers), color = 'green', size = 1.5) +
geom_line(data = OutsideData, aes(Date, ManufacturersNewOrdersConsumerGoods), color = 'orange', size = 1.5) +
geom_line(data = EastPrecipDiff, aes(Year, Apr), color = 'blue', size = 1.5) +
scale_x_continuous(breaks = c(seq(1999,2021,2))) +
scale_y_continuous(breaks = c(seq(-.4,1.1,.1))) +
labs(title = 'Disaster Repair Project Growth Rate vs Possible Predictors',
subtitle = 'Black = Disaster Repair Project Spend \nRed = Gasoline Station Spending \nBlue = East US April Precipitation \nGreen = Fuel Dealer Spending \nOrange = Manufacturers New Orders: Consumer Goods') +
ylab('Growth Rate') +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot(AllData, aes(Year, MiniDiff)) +
geom_line(size = 2) +
geom_point() +
geom_line(data = LumberData, aes(Date, ProducerPriceIndexbyIndustryCutStock_ResawingLumber_andPlaningHardwoodCutStockandDimension), color = 'green', size = 1.5) +
geom_line(data = CESData, aes(Date, Expenditures_MajorAppliancesbySizeofConsumerUnit_TwoPeopleinConsumerUnit), color = 'red', size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyIncomeBeforeTaxes_$30_000to$39_999`), color = 'blue', size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Second20Percent(21stto40thPercentile)`), color = 'orange', size = 1.5) +
scale_x_continuous(breaks = c(seq(1999,2021,2))) +
scale_y_continuous(breaks = c(seq(-.25,.45,.1))) +
labs(title = 'Mini Project Growth Rate vs Possible Predictors', subtitle = 'Black = Mini Project Spend \nRed = Major Appliances: 2 Person Households \nGreen = PI for Cut Stock, Resawing Lumber, & Planing Hardwood Cut Stock/Dimension \nOrange = Major Appliances: Second Lowest Income Quintile') +
ylab('Growth Rate') +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot(AllData, aes(Year, SmallDiff)) +
geom_line(size = 2) +
geom_point() +
geom_line(data = OutsideData, aes(Date, RetailTrade_ExcludingMotorVehicleandPartsDealers), color = 'green', size = 1.5) +
geom_line(data = OutsideData, aes(Date, ElectronicShoppingandMail_OrderHouses), color = 'red', size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Fourth20Percent(61stto80thPercentile)`), color = 'orange', size = 1.5) +
scale_x_continuous(breaks = c(seq(1999,2021,2))) +
scale_y_continuous(breaks = c(seq(-.2,.5,.1))) +
labs(title = 'Small Project Growth Rate vs Possible Predictors', subtitle = 'Black = Small Project Spend \nRed = Electronic Shopping & Mail Order Houses \nGreen = Retail Trade Excluding Car/Parts Dealers \nOrange = Major Appliances: Second Highest Income Quintile') +
ylab('Growth Rate') +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot(AllData, aes(Year, MediumDiff)) +
geom_line(size = 2) +
geom_point() +
geom_line(data = OutsideData, aes(Date, ManufacturersNewOrdersConstructionMaterialsandSupplies), color = 'green', size = 1.5) +
geom_line(data = LumberData, aes(Date, `IndustrialProductionManufacturingDurableGoodsMillwork(NAICS=32191)`), color = 'red', size = 1.5) +
geom_line(data = OutsideData, aes(Date, RetailTrade_ExcludingMotorVehicleandPartsDealers), color = 'orange', size = 1.5) +
geom_line(data = CESData, aes(Date, `Expenditures_MajorAppliancesbyQuintilesofIncomeBeforeTaxes_Highest20Percent(81stto100thPercentile)`), color = 'blue', size = 1.5) +
scale_x_continuous(breaks = c(seq(1999,2021,2))) +
scale_y_continuous(breaks = c(seq(-.4,.3,.1)), limits= c(-.4, .3)) +
labs(title = 'Medium Project Growth Rate vs Possible Predictors', subtitle = 'Black = Medium Project Spend\nRed = Industrial Production: Manufacturing Durable Goods (Millwork)\nGreen = Manufacturers New Orders: Construction Materials and Supplies\nOrange = Retail Trade Excluding Car/Parts Dealers\nBlue = Major Appliances: Highest Income Quintile') +
ylab('Growth Rate') +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)
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ggplot(AllData, aes(Year, LargeDiff)) +
geom_line(size = 2) +
geom_point() +
geom_line(data = OutsideData, aes(Date, NewPrivately_OwnedHousingUnitsUnderConstructionUnitsinBuildingswith2_4Units), color = 'green', size = 1.5) +
geom_line(data = LumberData, aes(Date, `ProducerPriceIndexbyIndustryPrefabricatedWoodBuildingManufacturingPrefabricatedStationaryWoodBuildings_Components(NotSoldasCompleteUnits)`), color = 'red', size = 1.5) +
geom_line(data = OutsideData, aes(Date, BuildingMaterialsandSuppliesDealers), color = 'orange', size = 1.5) +
geom_line(data = OutsideData, aes(Date, Furniture_HomeFurnishings_Electronics_andApplianceStores), color = 'blue', size = 1.5) +
scale_x_continuous(breaks = c(seq(1999,2021,2))) +
scale_y_continuous(breaks = c(seq(-.5,.6,.1)), limits = c(-.5, .6)) +
labs(title = 'Large Project Growth Rate vs Possible Predictors', subtitle = 'Black = Large Project Spend\nRed = PI for Prefab Wood-Building-Manufacturing/Stationary-Wood-Building Components (Not Sold as Complete Units) \nGreen = New Privately Owned Housing Units Under Construction: Units in Buildings with 2-4 Units \nOrange = Building Materials and Supplies Dealers \nBlue = Furniture, Home Furnishings, Electronics, & Appliance Stores') +
ylab('Growth Rate') +
theme(axis.text.x = element_text(angle = 90)) +
theme_jbrec(basesize = 36, linewidth = 2)