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Code containing functions that cleanup CHAS data and reports summary characteristics for Table 1 (income by race) and Table 11 (detailed income distribution) and for Tables 14 and 15 (vacant/available units and units being rented by income range). Demonstrates how to use code with examples for Portland MSA and sub-geographies. CHAS (Comprehensiv…
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| ## Table of contents for CHAS data tables | |
| # T1: Income x race (x housing problems x tenure) | |
| # T2: Income x race (x severe housing problems x tenure) | |
| # T3: Income x housing problem severity, ordered (x tenure) | |
| # T4: Household type x household size x housing problems (x tenure) | |
| # T5: Income x elderly occupant x housing problems (x tenure) | |
| # T6: DISCONTINUED (Income x disability x housing problems) | |
| # T7: Income x cost burden x household type (x tenure) | |
| # T8: Income x cost burden x facilities (x tenure) | |
| # T9: Cost burden x race (x tenure) | |
| # T10: Income x persons per room x family type (x tenure) | |
| # T11: Detailed income x housing problems (x tenure) | |
| # T12: Income x year built x cost burden (x tenure) | |
| # T13: Income x year built x children (x tenure) | |
| # T14A: Home price x bedrooms x facilities (VACANT FOR SALE) | |
| # T14B: Home price x bedrooms x facilities (VACANT FOR RENT) | |
| # T15A: Income x home value x bedrooms x facilities (owner with mortgage) | |
| # T15B: Income x home value x bedrooms x facilities (owner without mortgage) | |
| # T15C: Income x home value x bedrooms x facilities (renter) | |
| # T16: Income x household type x housing problems (x tenure) | |
| # T17A: Asking price x units in structure (FOR SALE) | |
| # T17B: Rent x units in structure (RENTALS) | |
| # T18A: Income x home value x units in structure (owner with a mortgage) | |
| # T18B: Income x home value x units in structure (owner without a mortage) | |
| # T18C: Income x rent x units in structure (renter) | |
| library(tidyverse) | |
| # Function to cast your CHAS data from wide to long format | |
| cast_chas_long <- function(chas_table, header_vars = 7) { | |
| # header_vars is the number of variables that serve as headers before the actual data starts | |
| ## If using the cleanup_chas_*() functions, then the default value is 7 | |
| require(tidyr) | |
| fields_to_melt <- names(chas_table[,as.numeric(header_vars+1):length(names(chas_table))]) | |
| melted <- gather(chas_table, fields_to_melt, key = 'variable', value = 'values') | |
| melted <- separate(data = melted, col = variable, into = c('table', 'variable')) | |
| melted <- separate(data = melted, col = variable, into = c('var_type', 'var_num'), sep = 3) | |
| melted$var_num <- paste0('v',melted$var_num) | |
| melted <- spread(melted, var_type, values) | |
| melted$moesq <- melted$moe * melted$moe | |
| return(melted) | |
| } | |
| # Function to cleanup tract-level CHAS data | |
| cleanup_chas_tract <- function(chas_tract_data, year = 2015) { | |
| refcols <- c('source', 'sumlevel', 'geoid', 'geoid2', 'name', 'st', 'cnty_plc') | |
| chas_tract_data <- chas_tract_data %>% select(-tract) | |
| names(chas_tract_data)[names(chas_tract_data) == 'cnty'] <- 'cnty_plc' | |
| chas_tract_data$geoid2 <- substr(chas_tract_data$geoid, 8, 18) | |
| chas_tract_data <- chas_tract_data[, c(refcols, setdiff(names(chas_tract_data), refcols))] | |
| return(chas_tract_data) | |
| } | |
| # Function to cleanup state-level CHAS data | |
| cleanup_chas_st <- function(chas_st_data, year = 2015) { | |
| refcols <- c('source', 'sumlevel', 'geoid', 'geoid2', 'name', 'st', 'cnty_plc') | |
| chas_st_data$cnty_plc <- '' | |
| chas_st_data$geoid2 <- substr(chas_st_data$geoid, 8, 10) | |
| chas_st_data <- chas_st_data[, c(refcols, setdiff(names(chas_st_data), refcols))] | |
| return(chas_st_data) | |
| } | |
| # Function to cleanup place-level CHAS data | |
| cleanup_chas_plc <- function(chas_plc_data, year = 2015) { | |
| refcols <- c('source', 'sumlevel', 'geoid', 'geoid2', 'name', 'st', 'cnty_plc') | |
| names(chas_plc_data)[names(chas_plc_data) == 'place'] <- 'cnty_plc' | |
| chas_plc_data$geoid2 <- substr(chas_plc_data$geoid, 8, 14) | |
| chas_plc_data <- chas_plc_data[, c(refcols, setdiff(names(chas_plc_data), refcols))] | |
| return(chas_plc_data) | |
| } | |
| # Function to cleanup county-level CHAS data | |
| cleanup_chas_cnty <- function(chas_cnty_data, year = 2015) { | |
| refcols <- c('source', 'sumlevel', 'geoid', 'geoid2', 'name', 'st', 'cnty_plc') | |
| names(chas_cnty_data)[names(chas_cnty_data) == 'cnty'] <- 'cnty_plc' | |
| chas_cnty_data$geoid2 <- substr(chas_cnty_data$geoid, 8, 13) | |
| chas_cnty_data <- chas_cnty_data[, c(refcols, setdiff(names(chas_cnty_data), refcols))] | |
| return(chas_cnty_data) | |
| } | |
| # Function to cast cleaned up long-format chas data to wide format | |
| cast_chas_wide <- function(long_cleaned_chas_table) { | |
| long_cleaned_chas_table <- long_cleaned_chas_table %>% | |
| gather(variable, value, -(source:var_num)) %>% | |
| unite(temp, var_num, variable) %>% | |
| spread(temp, value) | |
| return(long_cleaned_chas_table) | |
| } | |
| # Establish geoid lists for interesting geographies | |
| msa_geoid2_list <- c('41005', '41009', '41051', '41067', '41071', '53011', '53059') | |
| orwa_place_list <- c('4159000', '4123850', '4164900', '4131250', '4105350', '4134100', '4105800', | |
| '4147000', '4173650', '4101650', '4140550', '4155200', '4145000', '4174950', | |
| '4180150', '4148650', '4182800', '4126200', '4183750', '4105950', '4152100', | |
| '4153900', '4167100', '4141650', '4110750', '4172950', '4112150', '4174850', | |
| '4132050', '4129000', '4164600', '4153988', '4165250', '4124250', '4117800', | |
| '4163010', '4112050', '4115550', '4180900', '4134900', '4160900', '4165500', | |
| '5304475', '5309480', '5336710', '5358410', '5374060', '5376405', '5379625') | |
| peer_city_list <- c('0666000', '0667000', '4805000', '1836003', '0820000', '4752006', '2743000', '2758000', '4967000') | |
| sw_corridor_list <- c('41051005800', '41051005900', '41051006001', '41051006002', '41051006200', '41051006402', '41051006403', | |
| '41051006404', '41051006501', '41051006502', '41051006602', '41005020302', '41005020304', '41005020401', | |
| '41067030600', '41067030700', '41067030801', '41067030806', '41067030900', '41067031912', '41067032005') | |
| icura_list <- c('41051002401' , '41051002402' , '41051002501' , '41051002502' , '41051003100' , '41051003200' , '41051003301' , | |
| '41051003302' , '41051003401' , '41051003402' , '41051003501' , '41051003502' , '41051003601' , '41051003602' , | |
| '41051003701' , '41051003702' , '41051003801' , '41051003802' , '41051003803' , '41051003901' , '41051003902' , | |
| '41051002303' , '41051002203') | |
| # Load in Table 1 for various geographic entities / summary levels | |
| chas.tct.2015.t1 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/tract/2011thru2015-140-csv/140/Table1.csv') | |
| chas.cnty.2015.t1 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/county/2011thru2015-050-csv/050/Table1.csv') | |
| chas.st.2015.t1 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/state/2011thru2015-040-csv/040/Table1.csv') | |
| chas.plc.2015.t1 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/place/2011thru2015-160-csv/160/Table1.csv') | |
| # Load in Table 11 for various geographic entities / summary levels | |
| chas.tct.2015.t11 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/tract/2011thru2015-140-csv/140/Table11.csv') | |
| chas.cnty.2015.t11 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/county/2011thru2015-050-csv/050/Table11.csv') | |
| chas.st.2015.t11 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/state/2011thru2015-040-csv/040/Table11.csv') | |
| chas.plc.2015.t11 <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/place/2011thru2015-160-csv/160/Table11.csv') | |
| # Load in Table 14B for various geographic entities / summary levels | |
| chas.tct.2015.t14b <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/tract/2011thru2015-140-csv/140/Table14B.csv') | |
| chas.cnty.2015.t14b <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/county/2011thru2015-050-csv/050/Table14B.csv') | |
| chas.st.2015.t14b <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/state/2011thru2015-040-csv/040/Table14B.csv') | |
| chas.plc.2015.t14b <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/place/2011thru2015-160-csv/160/Table14B.csv') | |
| # Load in Table 15C for various geographic entities / summary levels | |
| chas.tct.2015.t15c <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/tract/2011thru2015-140-csv/140/Table15C.csv') | |
| chas.cnty.2015.t15c <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/county/2011thru2015-050-csv/050/Table15C.csv') | |
| chas.st.2015.t15c <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/state/2011thru2015-040-csv/040/Table15C.csv') | |
| chas.plc.2015.t15c <- read_csv('C:/Users/nkobel/Desktop/Large_files/CHAS/data/place/2011thru2015-160-csv/160/Table15C.csv') | |
| # Clean up pre-loaded geographies so that they are in a consistent format | |
| chas.tct.2015.t1 <- cleanup_chas_tract(chas.tct.2015.t1) | |
| chas.plc.2015.t1 <- cleanup_chas_plc(chas.plc.2015.t1) | |
| chas.cnty.2015.t1 <- cleanup_chas_cnty(chas.cnty.2015.t1) | |
| chas.st.2015.t1 <- cleanup_chas_st(chas.st.2015.t1) | |
| chas.tct.2015.t11 <- cleanup_chas_tract(chas.tct.2015.t11) | |
| chas.plc.2015.t11 <- cleanup_chas_plc(chas.plc.2015.t11) | |
| chas.cnty.2015.t11 <- cleanup_chas_cnty(chas.cnty.2015.t11) | |
| chas.st.2015.t11 <- cleanup_chas_st(chas.st.2015.t11) | |
| chas.tct.2015.t14b <- cleanup_chas_tract(chas.tct.2015.t14b) | |
| chas.plc.2015.t14b <- cleanup_chas_plc(chas.plc.2015.t14b) | |
| chas.cnty.2015.t14b <- cleanup_chas_cnty(chas.cnty.2015.t14b) | |
| chas.st.2015.t14b <- cleanup_chas_st(chas.st.2015.t14b) | |
| chas.tct.2015.t15c <- cleanup_chas_tract(chas.tct.2015.t15c) | |
| chas.plc.2015.t15c <- cleanup_chas_plc(chas.plc.2015.t15c) | |
| chas.cnty.2015.t15c <- cleanup_chas_cnty(chas.cnty.2015.t15c) | |
| chas.st.2015.t15c <- cleanup_chas_st(chas.st.2015.t15c) | |
| # Bind all geographies together into a single file (by year by table) | |
| chas.2015.t1 <- rbind(chas.tct.2015.t1, chas.plc.2015.t1, chas.cnty.2015.t1, chas.st.2015.t1) | |
| chas.2015.t11 <- rbind(chas.tct.2015.t11, chas.plc.2015.t11, chas.cnty.2015.t11, chas.st.2015.t11) | |
| chas.2015.t14b <- rbind(chas.tct.2015.t14b, chas.plc.2015.t14b, chas.cnty.2015.t14b, chas.st.2015.t14b) | |
| chas.2015.t15c <- rbind(chas.tct.2015.t15c, chas.plc.2015.t15c, chas.cnty.2015.t15c, chas.st.2015.t15c) | |
| # Example of casting data into long format for MSA geographies and peer cities | |
| chas.msa.15.t1 <- cast_chas_long(chas.2015.t1 %>% filter( (geoid2 %in% msa_geoid2_list) | | |
| (geoid2 %in% orwa_place_list) | | |
| (geoid2 %in% c('41', '53')) | | |
| (geoid2 %in% peer_city_list) ), 7) | |
| chas.msa.15.t11 <- cast_chas_long(chas.2015.t11 %>% filter( (geoid2 %in% msa_geoid2_list) | | |
| (geoid2 %in% orwa_place_list) | | |
| (geoid2 %in% c('41', '53')) | | |
| (geoid2 %in% peer_city_list) ), 7) | |
| # Get detailed income distribution by tenure | |
| get_detailed_income_distribution <- function(geoid2_list, chas_t11) { | |
| # census places must prepend the state fips (2-dig), e.g. '4159000' wrapped in quotes | |
| chas_t11 <- cast_chas_long(chas_t11 %>% filter(geoid2 %in% geoid2_list), 7) | |
| chas_t11 <- cast_chas_wide(chas_t11) | |
| chas_t11 %>% | |
| summarize(total_hh = sum(v1_est), | |
| total_000_to_020_own = sum(v4_est) + sum(v18_est) + sum(v32_est), | |
| total_000_to_020_rent = sum(v47_est) + sum(v61_est) + sum(v75_est), | |
| total_021_to_030_own = sum(v5_est) + sum(v19_est) + sum(v33_est), | |
| total_021_to_030_rent = sum(v48_est) + sum(v62_est) + sum(v76_est), | |
| total_031_to_040_own = sum(v6_est) + sum(v20_est) + sum(v34_est), | |
| total_031_to_040_rent = sum(v49_est) + sum(v63_est) + sum(v77_est), | |
| total_041_to_050_own = sum(v7_est) + sum(v21_est) + sum(v35_est), | |
| total_041_to_050_rent = sum(v50_est) + sum(v64_est) + sum(v78_est), | |
| total_051_to_060_own = sum(v8_est) + sum(v22_est) + sum(v36_est), | |
| total_051_to_060_rent = sum(v51_est) + sum(v65_est) + sum(v79_est), | |
| total_061_to_065_own = sum(v9_est) + sum(v23_est) + sum(v37_est), | |
| total_061_to_065_rent = sum(v52_est) + sum(v66_est) + sum(v80_est), | |
| total_066_to_080_own = sum(v10_est) + sum(v24_est) + sum(v38_est), | |
| total_066_to_080_rent = sum(v53_est) + sum(v67_est) + sum(v81_est), | |
| total_081_to_095_own = sum(v11_est) + sum(v25_est) + sum(v39_est), | |
| total_081_to_095_rent = sum(v54_est) + sum(v68_est) + sum(v82_est), | |
| total_096_to_100_own = sum(v12_est) + sum(v26_est) + sum(v40_est), | |
| total_096_to_100_rent = sum(v55_est) + sum(v69_est) + sum(v83_est), | |
| total_101_to_115_own = sum(v13_est) + sum(v27_est) + sum(v41_est), | |
| total_101_to_115_rent = sum(v56_est) + sum(v70_est) + sum(v84_est), | |
| total_116_to_120_own = sum(v14_est) + sum(v28_est) + sum(v42_est), | |
| total_116_to_120_rent = sum(v57_est) + sum(v71_est) + sum(v85_est), | |
| total_121_to_140_own = sum(v15_est) + sum(v29_est) + sum(v43_est), | |
| total_121_to_140_rent = sum(v58_est) + sum(v72_est) + sum(v86_est), | |
| total_141_to_999_own = sum(v16_est) + sum(v30_est) + sum(v44_est), | |
| total_141_to_999_rent = sum(v59_est) + sum(v73_est) + sum(v87_est), | |
| total_000_to_020 = total_000_to_020_own + total_000_to_020_rent, | |
| total_021_to_030 = total_021_to_030_own + total_021_to_030_rent, | |
| total_031_to_040 = total_031_to_040_own + total_031_to_040_rent, | |
| total_041_to_050 = total_041_to_050_own + total_041_to_050_rent, | |
| total_051_to_060 = total_051_to_060_own + total_051_to_060_rent, | |
| total_061_to_065 = total_061_to_065_own + total_061_to_065_rent, | |
| total_066_to_080 = total_066_to_080_own + total_066_to_080_rent, | |
| total_081_to_095 = total_081_to_095_own + total_081_to_095_rent, | |
| total_096_to_100 = total_096_to_100_own + total_096_to_100_rent, | |
| total_101_to_115 = total_101_to_115_own + total_101_to_115_rent, | |
| total_116_to_120 = total_116_to_120_own + total_116_to_120_rent, | |
| total_121_to_140 = total_121_to_140_own + total_121_to_140_rent, | |
| total_141_to_999 = total_141_to_999_own + total_141_to_999_rent, | |
| share_000_to_020 = total_000_to_020 / total_hh, | |
| share_021_to_030 = total_021_to_030 / total_hh, | |
| share_031_to_040 = total_031_to_040 / total_hh, | |
| share_041_to_050 = total_041_to_050 / total_hh, | |
| share_051_to_060 = total_051_to_060 / total_hh, | |
| share_061_to_065 = total_061_to_065 / total_hh, | |
| share_066_to_080 = total_066_to_080 / total_hh, | |
| share_081_to_095 = total_081_to_095 / total_hh, | |
| share_096_to_100 = total_096_to_100 / total_hh, | |
| share_101_to_115 = total_101_to_115 / total_hh, | |
| share_116_to_120 = total_116_to_120 / total_hh, | |
| share_121_to_140 = total_121_to_140 / total_hh, | |
| share_141_to_999 = total_141_to_999 / total_hh, | |
| share_000_to_030 = share_000_to_020 + share_021_to_030, | |
| share_031_to_060 = share_031_to_040 + share_041_to_050 + share_051_to_060, | |
| share_061_to_080 = share_061_to_065 + share_066_to_080, | |
| share_081_to_100 = share_081_to_095 + share_096_to_100, | |
| share_101_to_120 = share_101_to_115 + share_116_to_120, | |
| share_121_to_999 = share_121_to_140 + share_141_to_999 | |
| ) | |
| } | |
| # Example for detailed income distribution | |
| glimpse(get_detailed_income_distribution(sw_corridor_list, chas.tct.2015.t11)) | |
| # Simple function to get low-income households by tenure | |
| get_low_income_tenure <- function(geoid2_list, chas_t1) { | |
| # census places must prepend the state fips (2-dig), e.g. '4159000' wrapped in quotes | |
| chas_t1 <- cast_chas_long(chas_t1 %>% filter(geoid2 %in% geoid2_list), 7) | |
| chas_t1 <- cast_chas_wide(chas_t1) | |
| chas_t1 %>% | |
| summarize(total_hh = sum(v1_est), | |
| total_hh_own = sum(v2_est), | |
| total_hh_rent = sum(v126_est), | |
| total_li_own = sum(v4_est) + sum(v12_est) + sum(v20_est) + sum(v45_est) + sum(v53_est) + sum(v61_est) + sum(v86_est) + sum(v94_est) + sum(v102_est), | |
| total_li_rent = sum(v128_est) + sum(v136_est) + sum(v144_est) + sum(v169_est) + sum(v177_est) + sum(v185_est) + sum(v210_est) + sum(v218_est) + sum(v226_est), | |
| total_li = total_li_own + total_li_rent, | |
| share_li = total_li / total_hh, | |
| share_own = total_hh_own / total_hh, | |
| share_rent = total_hh_rent / total_hh, | |
| share_li_that_own = total_li_own / total_li, | |
| share_li_that_rent = total_li_rent / total_li, | |
| share_hh_that_li_own = total_li_own / total_hh, | |
| share_hh_that_li_rent = total_li_rent / total_hh | |
| ) | |
| } | |
| # Examples for MSA total, SW Corridor and Portland | |
| glimpse(get_low_income_tenure(msa_geoid2_list, chas.cnty.2015.t1)) | |
| glimpse(get_low_income_tenure('4159000', chas.plc.2015.t1)) | |
| glimpse(get_low_income_tenure(sw_corridor_list, chas.tct.2015.t1)) | |
| # More complex function to get low-income households by race by tenure | |
| get_income_race_tenure <- function(geoid2_list, chas_t1) { | |
| # census places must prepend the state fips (2-dig), e.g. '4159000' wrapped in quotes | |
| chas_t1 <- cast_chas_long(chas_t1 %>% filter(geoid2 %in% geoid2_list), 7) | |
| chas_t1 <- cast_chas_wide(chas_t1) | |
| chas_t1 <- chas_t1 %>% | |
| summarize(total_hh = sum(v1_est), | |
| total_white = sum(v5_est) + sum(v13_est) + sum(v21_est) + sum(v29_est) + sum(v37_est) + | |
| sum(v46_est) + sum(v54_est) + sum(v62_est) + sum(v70_est) + sum(v78_est) + sum(v87_est) + | |
| sum(v95_est) + sum(v103_est) + sum(v111_est) + sum(v119_est) + sum(v129_est) + sum(v137_est) + | |
| sum(v145_est) + sum(v153_est) + sum(v161_est) + sum(v170_est) + sum(v178_est) + sum(v186_est) + | |
| sum(v194_est) + sum(v202_est) + sum(v211_est) + sum(v219_est) + sum(v227_est) + sum(v235_est) + sum(v243_est), | |
| total_aian = sum(v8_est) + sum(v16_est) + sum(v24_est) + sum(v32_est) + sum(v40_est) + sum(v49_est) + | |
| sum(v57_est) + sum(v65_est) + sum(v73_est) + sum(v81_est) + sum(v90_est) + sum(v98_est) + | |
| sum(v106_est) + sum(v114_est) + sum(v122_est) + sum(v132_est) + sum(v140_est) + sum(v148_est) + | |
| sum(v156_est) + sum(v164_est) + sum(v173_est) + sum(v181_est) + sum(v189_est) + sum(v197_est) + | |
| sum(v205_est) + sum(v214_est) + sum(v222_est) + sum(v230_est) + sum(v238_est) + sum(v246_est), | |
| total_asian = sum(v7_est) + sum(v15_est) + sum(v23_est) + sum(v31_est) + sum(v39_est) + sum(v48_est) + | |
| sum(v56_est) + sum(v64_est) + sum(v72_est) + sum(v80_est) + sum(v89_est) + sum(v97_est) + | |
| sum(v105_est) + sum(v113_est) + sum(v121_est) + sum(v131_est) + sum(v139_est) + sum(v147_est) + | |
| sum(v155_est) + sum(v163_est) + sum(v172_est) + sum(v180_est) + sum(v188_est) + sum(v196_est) + | |
| sum(v204_est) + sum(v213_est) + sum(v221_est) + sum(v229_est) + sum(v237_est) + sum(v245_est), | |
| total_black = sum(v6_est) + sum(v14_est) + sum(v22_est) + sum(v30_est) + sum(v38_est) + sum(v47_est) + | |
| sum(v55_est) + sum(v63_est) + sum(v71_est) + sum(v79_est) + sum(v88_est) + sum(v96_est) + | |
| sum(v104_est) + sum(v112_est) + sum(v120_est) + sum(v130_est) + sum(v138_est) + sum(v146_est) + | |
| sum(v154_est) + sum(v162_est) + sum(v171_est) + sum(v179_est) + sum(v187_est) + sum(v195_est) + | |
| sum(v203_est) + sum(v212_est) + sum(v220_est) + sum(v228_est) + sum(v236_est) + sum(v244_est), | |
| total_hispanic = sum(v10_est) + sum(v18_est) + sum(v26_est) + sum(v34_est) + sum(v42_est) + sum(v51_est) + | |
| sum(v59_est) + sum(v67_est) + sum(v75_est) + sum(v83_est) + sum(v92_est) + sum(v100_est) + | |
| sum(v108_est) + sum(v116_est) + sum(v124_est) + sum(v134_est) + sum(v142_est) + sum(v150_est) + | |
| sum(v158_est) + sum(v166_est) + sum(v175_est) + sum(v183_est) + sum(v191_est) + sum(v199_est) + | |
| sum(v207_est) + sum(v216_est) + sum(v224_est) + sum(v232_est) + sum(v240_est) + sum(v248_est), | |
| total_nhpi = sum(v9_est) + sum(v17_est) + sum(v25_est) + sum(v33_est) + sum(v41_est) + sum(v50_est) + | |
| sum(v58_est) + sum(v66_est) + sum(v74_est) + sum(v82_est) + sum(v91_est) + sum(v99_est) + | |
| sum(v107_est) + sum(v115_est) + sum(v123_est) + sum(v133_est) + sum(v141_est) + sum(v149_est) + | |
| sum(v157_est) + sum(v165_est) + sum(v174_est) + sum(v182_est) + sum(v190_est) + sum(v198_est) + | |
| sum(v206_est) + sum(v215_est) + sum(v223_est) + sum(v231_est) + sum(v239_est) + sum(v247_est), | |
| total_other = sum(v11_est) + sum(v19_est) + sum(v27_est) + sum(v35_est) + sum(v43_est) + sum(v52_est) + | |
| sum(v60_est) + sum(v68_est) + sum(v76_est) + sum(v84_est) + sum(v93_est) + sum(v101_est) + | |
| sum(v109_est) + sum(v117_est) + sum(v125_est) + sum(v135_est) + sum(v143_est) + sum(v151_est) + | |
| sum(v159_est) + sum(v167_est) + sum(v176_est) + sum(v184_est) + sum(v192_est) + sum(v200_est) + | |
| sum(v208_est) + sum(v217_est) + sum(v225_est) + sum(v233_est) + sum(v241_est) + sum(v249_est), | |
| total_poc = total_aian + total_asian + total_black + total_nhpi + total_other + total_hispanic, | |
| li_white_owner = sum(v5_est) + sum(v13_est) + sum(v21_est) + sum(v46_est) + sum(v54_est) + | |
| sum(v62_est) + sum(v87_est) + sum(v95_est) + sum(v103_est), | |
| li_white_renter = sum(v129_est) + sum(v137_est) + sum(v145_est) + sum(v170_est) + sum(v178_est) + | |
| sum(v186_est) + sum(v211_est) + sum(v219_est) + sum(v227_est), | |
| li_white = li_white_owner + li_white_renter, | |
| li_aian_owner = sum(v8_est) + sum(v16_est) + sum(v24_est) + sum(v49_est) + sum(v57_est) + | |
| sum(v65_est) + sum(v90_est) + sum(v98_est) + sum(v106_est), | |
| li_aian_renter = sum(v132_est) + sum(v140_est) + sum(v148_est) + sum(v173_est) + sum(v181_est) + | |
| sum(v189_est) + sum(v214_est) + sum(v222_est) + sum(v230_est), | |
| li_aian = li_aian_owner + li_aian_renter, | |
| li_asian_owner = sum(v7_est) + sum(v15_est) + sum(v23_est) + sum(v48_est) + sum(v56_est) + | |
| sum(v64_est) + sum(v89_est) + sum(v97_est) + sum(v105_est), | |
| li_asian_renter = sum(v131_est) + sum(v139_est) + sum(v147_est) + sum(v172_est) + sum(v180_est) + | |
| sum(v188_est) + sum(v213_est) + sum(v221_est) + sum(v229_est), | |
| li_asian = li_asian_owner + li_asian_renter, | |
| li_black_owner = sum(v6_est) + sum(v14_est) + sum(v22_est) + sum(v47_est) + sum(v55_est) + | |
| sum(v63_est) + sum(v88_est) + sum(v96_est) + sum(v104_est), | |
| li_black_renter = sum(v130_est) + sum(v138_est) + sum(v146_est) + sum(v171_est) + sum(v179_est) + | |
| sum(v187_est) + sum(v212_est) + sum(v220_est) + sum(v228_est), | |
| li_black = li_black_owner + li_black_renter, | |
| li_hispanic_owner = sum(v10_est) + sum(v18_est) + sum(v26_est) + sum(v51_est) + sum(v59_est) + | |
| sum(v67_est) + sum(v92_est) + sum(v100_est) + sum(v108_est), | |
| li_hispanic_renter = sum(v134_est) + sum(v142_est) + sum(v150_est) + sum(v175_est) + sum(v183_est) + | |
| sum(v191_est) + sum(v216_est) + sum(v224_est) + sum(v232_est), | |
| li_hispanic = li_hispanic_owner + li_hispanic_renter, | |
| li_nhpi_owner = sum(v9_est) + sum(v17_est) + sum(v25_est) + sum(v50_est) + sum(v58_est) + | |
| sum(v66_est) + sum(v91_est) + sum(v99_est) + sum(v107_est), | |
| li_nhpi_renter = sum(v133_est) + sum(v141_est) + sum(v149_est) + sum(v174_est) + sum(v182_est) + | |
| sum(v190_est) + sum(v215_est) + sum(v223_est) + sum(v231_est), | |
| li_nhpi = li_nhpi_owner + li_nhpi_renter, | |
| li_other_owner = sum(v11_est) + sum(v19_est) + sum(v27_est) + sum(v52_est) + sum(v60_est) + | |
| sum(v68_est) + sum(v93_est) + sum(v101_est) + sum(v109_est), | |
| li_other_renter = sum(v135_est) + sum(v143_est) + sum(v151_est) + sum(v176_est) + sum(v184_est) + | |
| sum(v192_est) + sum(v217_est) + sum(v225_est) + sum(v233_est), | |
| li_other = li_other_owner + li_other_renter, | |
| li_poc_owner = li_aian_owner + li_asian_owner + li_black_owner + li_hispanic_owner + li_nhpi_owner + li_other_owner, | |
| li_poc_renter = li_aian_renter + li_asian_renter + li_black_renter + li_hispanic_renter + li_nhpi_renter + li_other_renter, | |
| li_poc = li_poc_owner + li_poc_renter, | |
| li_owner = li_white_owner + li_poc_owner, | |
| li_renter = li_white_renter + li_poc_renter, | |
| li_total = li_owner + li_renter, | |
| share_hh_that_li = li_total / total_hh, | |
| share_hh_that_li_owner = li_owner / total_hh, | |
| share_hh_that_li_renter = li_renter / total_hh, | |
| share_poc_that_li = li_poc / total_poc, | |
| share_poc_that_li_renter = li_poc_renter / total_poc, | |
| share_white_that_li = li_white / total_white, | |
| share_white_that_li_renter = li_white_renter / total_white, | |
| share_hh_that_poc = total_poc / total_hh, | |
| share_hh_that_li_poc = li_poc / total_hh, | |
| share_hh_that_li_poc_renter = li_poc_renter / total_hh, | |
| ratio_li_poc_vs_white = share_poc_that_li / share_white_that_li, | |
| ratio_li_renter_poc_vs_white = share_poc_that_li_renter / share_white_that_li_renter, | |
| total_hh_moe = sqrt(sum(v1_moesq)), | |
| total_white_moe = sqrt( sum(v5_moesq) + sum(v13_moesq) + sum(v21_moesq) + sum(v29_moesq) + sum(v37_moesq) + | |
| sum(v46_moesq) + sum(v54_moesq) + sum(v62_moesq) + sum(v70_moesq) + sum(v78_moesq) + | |
| sum(v87_moesq) + sum(v95_moesq) + sum(v103_moesq) + sum(v111_moesq) + sum(v119_moesq) + | |
| sum(v129_moesq) + sum(v137_moesq) + sum(v145_moesq) + sum(v153_moesq) + sum(v161_moesq) + | |
| sum(v170_moesq) + sum(v178_moesq) + sum(v186_moesq) + sum(v194_moesq) + sum(v202_moesq) + | |
| sum(v211_moesq) + sum(v219_moesq) + sum(v227_moesq) + sum(v235_moesq) + sum(v243_moesq) ), | |
| total_aian_moe = sqrt( sum(v8_moesq) + sum(v16_moesq) + sum(v24_moesq) + sum(v32_moesq) + | |
| sum(v40_moesq) + sum(v49_moesq) + sum(v57_moesq) + sum(v65_moesq) + | |
| sum(v73_moesq) + sum(v81_moesq) + sum(v90_moesq) + sum(v98_moesq) + | |
| sum(v106_moesq) + sum(v114_moesq) + sum(v122_moesq) + sum(v132_moesq) + | |
| sum(v140_moesq) + sum(v148_moesq) + sum(v156_moesq) + sum(v164_moesq) + | |
| sum(v173_moesq) + sum(v181_moesq) + sum(v189_moesq) + sum(v197_moesq) + | |
| sum(v205_moesq) + sum(v214_moesq) + sum(v222_moesq) + sum(v230_moesq) + | |
| sum(v238_moesq) + sum(v246_moesq) ), | |
| total_asian_moe = sqrt( sum(v7_moesq) + sum(v15_moesq) + sum(v23_moesq) + sum(v31_moesq) + | |
| sum(v39_moesq) + sum(v48_moesq) + sum(v56_moesq) + sum(v64_moesq) + | |
| sum(v72_moesq) + sum(v80_moesq) + sum(v89_moesq) + sum(v97_moesq) + | |
| sum(v105_moesq) + sum(v113_moesq) + sum(v121_moesq) + sum(v131_moesq) + | |
| sum(v139_moesq) + sum(v147_moesq) + sum(v155_moesq) + sum(v163_moesq) + | |
| sum(v172_moesq) + sum(v180_moesq) + sum(v188_moesq) + sum(v196_moesq) + | |
| sum(v204_moesq) + sum(v213_moesq) + sum(v221_moesq) + sum(v229_moesq) + | |
| sum(v237_moesq) + sum(v245_moesq) ), | |
| total_black_moe = sqrt( sum(v6_moesq) + sum(v14_moesq) + sum(v22_moesq) + sum(v30_moesq) + | |
| sum(v38_moesq) + sum(v47_moesq) + sum(v55_moesq) + sum(v63_moesq) + | |
| sum(v71_moesq) + sum(v79_moesq) + sum(v88_moesq) + sum(v96_moesq) + | |
| sum(v104_moesq) + sum(v112_moesq) + sum(v120_moesq) + sum(v130_moesq) + | |
| sum(v138_moesq) + sum(v146_moesq) + sum(v154_moesq) + sum(v162_moesq) + | |
| sum(v171_moesq) + sum(v179_moesq) + sum(v187_moesq) + sum(v195_moesq) + | |
| sum(v203_moesq) + sum(v212_moesq) + sum(v220_moesq) + sum(v228_moesq) + | |
| sum(v236_moesq) + sum(v244_moesq) ), | |
| total_hispanic_moe = sqrt( sum(v10_moesq) + sum(v18_moesq) + sum(v26_moesq) + sum(v34_moesq) + | |
| sum(v42_moesq) + sum(v51_moesq) + sum(v59_moesq) + sum(v67_moesq) + | |
| sum(v75_moesq) + sum(v83_moesq) + sum(v92_moesq) + sum(v100_moesq) + | |
| sum(v108_moesq) + sum(v116_moesq) + sum(v124_moesq) + sum(v134_moesq) + | |
| sum(v142_moesq) + sum(v150_moesq) + sum(v158_moesq) + sum(v166_moesq) + | |
| sum(v175_moesq) + sum(v183_moesq) + sum(v191_moesq) + sum(v199_moesq) + | |
| sum(v207_moesq) + sum(v216_moesq) + sum(v224_moesq) + sum(v232_moesq) + | |
| sum(v240_moesq) + sum(v248_moesq) ), | |
| total_nhpi_moe = sqrt( sum(v9_moesq) + sum(v17_moesq) + sum(v25_moesq) + sum(v33_moesq) + sum(v41_moesq) + | |
| sum(v50_moesq) + sum(v58_moesq) + sum(v66_moesq) + sum(v74_moesq) + sum(v82_moesq) + | |
| sum(v91_moesq) + sum(v99_moesq) + sum(v107_moesq) + sum(v115_moesq) + sum(v123_moesq) + | |
| sum(v133_moesq) + sum(v141_moesq) + sum(v149_moesq) + sum(v157_moesq) + sum(v165_moesq) + | |
| sum(v174_moesq) + sum(v182_moesq) + sum(v190_moesq) + sum(v198_moesq) + sum(v206_moesq) + | |
| sum(v215_moesq) + sum(v223_moesq) + sum(v231_moesq) + sum(v239_moesq) + sum(v247_moesq) ), | |
| total_other_moe = sqrt( sum(v11_moesq) + sum(v19_moesq) + sum(v27_moesq) + sum(v35_moesq) + sum(v43_moesq) + | |
| sum(v52_moesq) + sum(v60_moesq) + sum(v68_moesq) + sum(v76_moesq) + | |
| sum(v84_moesq) + sum(v93_moesq) + sum(v101_moesq) + sum(v109_moesq) + | |
| sum(v117_moesq) + sum(v125_moesq) + sum(v135_moesq) + sum(v143_moesq) + | |
| sum(v151_moesq) + sum(v159_moesq) + sum(v167_moesq) + sum(v176_moesq) + | |
| sum(v184_moesq) + sum(v192_moesq) + sum(v200_moesq) + sum(v208_moesq) + | |
| sum(v217_moesq) + sum(v225_moesq) + sum(v233_moesq) + sum(v241_moesq) + | |
| sum(v249_moesq) ), | |
| total_poc_moe = sqrt(total_aian_moe^2 + total_asian_moe^2 + total_black_moe^2 + total_nhpi_moe^2 + total_other_moe^2 + total_hispanic_moe^2) ) | |
| return(chas_t1) | |
| } | |
| # Examples for MSA total, SW Corridor and Portland | |
| glimpse(get_income_race_tenure(msa_geoid2_list, chas.cnty.2015.t1)) | |
| glimpse(get_income_race_tenure(sw_corridor_list, chas.tct.2015.t1)) | |
| glimpse(get_income_race_tenure('4159000', chas.plc.2015.t1)) | |
| get_rental_housing_gap <- function(geoid2_list, gtitle, chas_t14b, chas_t15c) { | |
| results <- c() | |
| chas_t15c <- cast_chas_long(chas_t15c %>% filter(geoid2 %in% geoid2_list), 7) | |
| chas_t15c <- cast_chas_wide(chas_t15c) | |
| chas_t14b <- cast_chas_long(chas_t14b %>% filter(geoid2 %in% geoid2_list), 7) | |
| chas_t14b <- cast_chas_wide(chas_t14b) | |
| chas_t14b <- chas_t14b %>% | |
| mutate(vacant_total = v1_est, | |
| vacant_complete = v3_est, | |
| vacant_available_000_to_030 = v4_est, | |
| vacant_available_031_to_050 = v8_est, | |
| vacant_available_051_to_080 = v12_est, | |
| vacant_available_081_to_999 = v16_est) | |
| chas_t14b <- chas_t14b %>% | |
| summarize(vacant_total = sum(vacant_total), | |
| vacant_complete = sum(vacant_complete), | |
| vacant_available_000_to_030 = sum(vacant_available_000_to_030), | |
| vacant_available_031_to_050 = sum(vacant_available_031_to_050), | |
| vacant_available_051_to_080 = sum(vacant_available_051_to_080), | |
| vacant_available_081_to_999 = sum(vacant_available_081_to_999)) | |
| chas_t15c <- chas_t15c %>% | |
| mutate(matched_units_000_to_030 = v5_est, | |
| matched_units_031_to_050 = v30_est, | |
| matched_units_051_to_080 = v55_est, | |
| matched_units_081_to_999 = v80_est + v84_est, | |
| hh_000_to_030_renting_up = v26_est + v47_est + v68_est, | |
| hh_031_to_050_renting_up = v51_est + v72_est, | |
| hh_051_to_080_renting_up = v76_est, | |
| hh_081_to_999_renting_up = 0, | |
| hh_000_to_030_renting_down = 0, | |
| hh_031_to_050_renting_down = v9_est, | |
| hh_051_to_080_renting_down = v13_est + v34_est, | |
| hh_081_to_999_renting_down = v17_est + v21_est + v38_est + v42_est + v59_est + v63_est, | |
| units_000_to_030_rented_to_higher_income = v9_est + v13_est + v17_est + v21_est, | |
| units_031_to_050_rented_to_higher_income = v34_est + v38_est + v42_est, | |
| units_051_to_080_rented_to_higher_income = v59_est + v63_est, | |
| units_081_to_999_rented_to_higher_income = 0, | |
| units_000_to_030_rented_to_lower_income = 0, | |
| units_031_to_050_rented_to_lower_income = v26_est, | |
| units_051_to_080_rented_to_lower_income = v47_est + v51_est, | |
| units_081_to_999_rented_to_lower_income = v68_est + v72_est + v76_est) | |
| chas_t15c <- chas_t15c %>% | |
| summarize(matched_units_000_to_030 = sum(matched_units_000_to_030), | |
| matched_units_031_to_050 = sum(matched_units_031_to_050), | |
| matched_units_051_to_080 = sum(matched_units_051_to_080), | |
| matched_units_081_to_999 = sum(matched_units_081_to_999), | |
| hh_000_to_030_renting_up = sum(hh_000_to_030_renting_up), | |
| hh_031_to_050_renting_up = sum(hh_031_to_050_renting_up), | |
| hh_051_to_080_renting_up = sum(hh_051_to_080_renting_up), | |
| hh_081_to_999_renting_up = 0, | |
| hh_000_to_030_renting_down = 0, | |
| hh_031_to_050_renting_down = sum(hh_031_to_050_renting_down), | |
| hh_051_to_080_renting_down = sum(hh_051_to_080_renting_down), | |
| hh_081_to_999_renting_down = sum(hh_081_to_999_renting_down), | |
| units_000_to_030_rented_to_higher_income = sum(units_000_to_030_rented_to_higher_income), | |
| units_031_to_050_rented_to_higher_income = sum(units_031_to_050_rented_to_higher_income), | |
| units_051_to_080_rented_to_higher_income = sum(units_051_to_080_rented_to_higher_income), | |
| units_081_to_999_rented_to_higher_income = 0, | |
| units_000_to_030_rented_to_lower_income = 0, | |
| units_031_to_050_rented_to_lower_income = sum(units_031_to_050_rented_to_lower_income), | |
| units_051_to_080_rented_to_lower_income = sum(units_051_to_080_rented_to_lower_income), | |
| units_081_to_999_rented_to_lower_income = sum(units_081_to_999_rented_to_lower_income)) | |
| units_at_000_to_030 <- chas_t15c$matched_units_000_to_030 + chas_t14b$vacant_available_000_to_030 + | |
| chas_t15c$units_000_to_030_rented_to_higher_income + chas_t15c$units_000_to_030_rented_to_lower_income | |
| hh_at_000_to_030 <- chas_t15c$matched_units_000_to_030 + chas_t15c$hh_000_to_030_renting_up + | |
| chas_t15c$hh_000_to_030_renting_down | |
| gross_gap_or_surplus_in_units_000_to_030 <- units_at_000_to_030 - hh_at_000_to_030 | |
| effective_gap_or_surplus_in_units_000_to_030 <- gross_gap_or_surplus_in_units_000_to_030 - chas_t15c$units_000_to_030_rented_to_higher_income | |
| units_at_031_to_050 <- chas_t15c$matched_units_031_to_050 + chas_t14b$vacant_available_031_to_050 + | |
| chas_t15c$units_031_to_050_rented_to_higher_income + chas_t15c$units_031_to_050_rented_to_lower_income | |
| hh_at_031_to_050 <- chas_t15c$matched_units_031_to_050 + chas_t15c$hh_031_to_050_renting_up + | |
| chas_t15c$hh_031_to_050_renting_down | |
| gross_gap_or_surplus_in_units_031_to_050 <- units_at_031_to_050 - hh_at_031_to_050 | |
| effective_gap_or_surplus_in_units_031_to_050 <- gross_gap_or_surplus_in_units_031_to_050 - chas_t15c$units_031_to_050_rented_to_higher_income | |
| units_at_051_to_080 <- chas_t15c$matched_units_051_to_080 + chas_t14b$vacant_available_051_to_080 + | |
| chas_t15c$units_051_to_080_rented_to_higher_income + chas_t15c$units_051_to_080_rented_to_lower_income | |
| hh_at_051_to_080 <- chas_t15c$matched_units_051_to_080 + chas_t15c$hh_051_to_080_renting_up + | |
| chas_t15c$hh_051_to_080_renting_down | |
| gross_gap_or_surplus_in_units_051_to_080 <- units_at_051_to_080 - hh_at_051_to_080 | |
| effective_gap_or_surplus_in_units_051_to_080 <- gross_gap_or_surplus_in_units_051_to_080 - chas_t15c$units_051_to_080_rented_to_higher_income | |
| units_at_081_to_999 <- chas_t15c$matched_units_081_to_999 + chas_t14b$vacant_available_081_to_999 + | |
| chas_t15c$units_081_to_999_rented_to_higher_income + chas_t15c$units_081_to_999_rented_to_lower_income | |
| hh_at_081_to_999 <- chas_t15c$matched_units_081_to_999 + chas_t15c$hh_081_to_999_renting_up + | |
| chas_t15c$hh_081_to_999_renting_down | |
| gross_gap_or_surplus_in_units_081_to_999 <- units_at_081_to_999 - hh_at_081_to_999 | |
| effective_gap_or_surplus_in_units_081_to_999 <- gross_gap_or_surplus_in_units_081_to_999 - chas_t15c$units_081_to_999_rented_to_higher_income | |
| gap_analysis = data.frame("income_range" = c("0% to 30% MFI", "31% to 50% MFI", "51% to 80% MFI", "81% MFI or higher"), | |
| "total_households" = c(hh_at_000_to_030, hh_at_031_to_050, hh_at_051_to_080, hh_at_081_to_999), | |
| "total_units" = c(units_at_000_to_030, units_at_031_to_050, units_at_051_to_080, units_at_081_to_999), | |
| "matched_units_to_hh" = c(chas_t15c$matched_units_000_to_030, chas_t15c$matched_units_031_to_050, chas_t15c$matched_units_051_to_080, chas_t15c$matched_units_081_to_999), | |
| "paying_too_much" = c(chas_t15c$hh_000_to_030_renting_up, chas_t15c$hh_031_to_050_renting_up, chas_t15c$hh_051_to_080_renting_up, chas_t15c$hh_081_to_999_renting_up), | |
| "getting_a_deal" = c(chas_t15c$units_000_to_030_rented_to_higher_income, chas_t15c$units_031_to_050_rented_to_higher_income, chas_t15c$units_051_to_080_rented_to_higher_income, chas_t15c$units_081_to_999_rented_to_higher_income), | |
| "vacant_units" = c(chas_t14b$vacant_available_000_to_030, chas_t14b$vacant_available_031_to_050, chas_t14b$vacant_available_051_to_080, chas_t14b$vacant_available_081_to_999), | |
| "gross_existing_gap" = c(gross_gap_or_surplus_in_units_000_to_030, gross_gap_or_surplus_in_units_031_to_050, gross_gap_or_surplus_in_units_051_to_080, gross_gap_or_surplus_in_units_081_to_999), | |
| "gap_in_available_units" = c(effective_gap_or_surplus_in_units_000_to_030, effective_gap_or_surplus_in_units_031_to_050, effective_gap_or_surplus_in_units_051_to_080, effective_gap_or_surplus_in_units_081_to_999)) | |
| gap_analysis_plot_data <- data.frame("income_range" = c("0% to 30% MFI", "0% to 30% MFI", "31% to 50% MFI", "31% to 50% MFI"), | |
| "control" = c("Households", "Units", "Households", "Units"), | |
| "total" = c(hh_at_000_to_030, units_at_000_to_030, hh_at_031_to_050, units_at_031_to_050), | |
| "matched_units_to_hh" = c(chas_t15c$matched_units_000_to_030, chas_t15c$matched_units_000_to_030, chas_t15c$matched_units_031_to_050, chas_t15c$matched_units_031_to_050), | |
| "paying_too_much" = c(chas_t15c$hh_000_to_030_renting_up, chas_t15c$units_000_to_030_rented_to_higher_income, chas_t15c$hh_031_to_050_renting_up, chas_t15c$units_031_to_050_rented_to_higher_income), | |
| "vacant_units" = c(0, chas_t14b$vacant_available_000_to_030, 0, chas_t14b$vacant_available_031_to_050), | |
| "getting_a_deal" = c(chas_t15c$hh_000_to_030_renting_down, chas_t15c$units_000_to_030_rented_to_lower_income, chas_t15c$hh_031_to_050_renting_down, chas_t15c$units_031_to_050_rented_to_lower_income), | |
| "existing_gap" = c(0, as.numeric(gross_gap_or_surplus_in_units_000_to_030*-1), 0, as.numeric(gross_gap_or_surplus_in_units_031_to_050*-1))) | |
| gap_analysis_plot_data <- gather(gap_analysis_plot_data, c(matched_units_to_hh, paying_too_much, vacant_units, getting_a_deal, existing_gap), key = 'variable', value = 'value') | |
| gap_analysis_plot_data$variable <- factor(gap_analysis_plot_data$variable, ordered = T, levels = c('existing_gap', 'getting_a_deal', 'vacant_units', 'paying_too_much', 'matched_units_to_hh')) | |
| gap_analysis_plot_data <- gap_analysis_plot_data %>% | |
| mutate(share = value / total) | |
| group.colors <- c(getting_a_deal = '#D5D1D1', paying_too_much = '#D6B261', matched_units_to_hh = '#726868', vacant_units = '#B88F35', existing_gap = '#FFFFFF') | |
| p <- gap_analysis_plot_data %>% ggplot(aes(x = control, y = value, fill = variable)) + | |
| geom_col() + | |
| facet_wrap(~income_range) + | |
| labs(title = 'RENTAL AFFORDABILITY GAP', subtitle = gtitle, x = "", y = "Number of renter households or units\n") + | |
| scale_y_continuous(label = comma) + | |
| scale_fill_manual(values = group.colors, | |
| name = "", | |
| breaks = c('existing_gap', 'getting_a_deal', 'vacant_units', 'paying_too_much', 'matched_units_to_hh'), | |
| labels = c('Existing gap', 'Renting down\n(getting a deal)', 'Vacant units', 'Renting up\n(paying too much)', 'Matched units')) | |
| print(p) | |
| return(gap_analysis) | |
| } | |
| pdx_affordability_gap <- get_rental_housing_gap('4159000', 'City of Portland, 2015', chas.plc.2014.t14b, chas.plc.2014.t15c) | |
| nopo_affordability_gap <- get_rental_housing_gap(ldist_north_fips_list, 'North Portland Liaison District, 2015', chas.tct.2015.t14b, chas.tct.2015.t15c) |
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