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stellap_4year2023.R
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#stellap
library(dplyr)
library(readxlsb)
library(data.table)
library(ggplot2)
library(readr)
library(lubridate)
library(glue)
setwd("C:/Users/TimBender/North Carolina Coalition to End Homelessness/PM Data Center - Documents/Reporting/Reporting PIT HIC/Statewide Reporting- PIT+HIC/Working Files Mastersheet/Racial Equity Reference data")
# https://ncceh.sharepoint.com/sites/boscoccoordination/Shared%20Documents/Forms/AllItems.aspx?id=%2Fsites%2Fboscoccoordination%2FShared%20Documents%2F4%2D%20BoS%20Developing%20CoC%20System%2FRacial%20Equity%20Subcommittee%2FRacial%20Equity%20Assessment%2F2022%20NOFA%20RE%20Assessment&p=true&ga=1
rm(list=ls());cat('\f')
gc()
# Functions----
sp_man_race22 <- function(x, fieldname = NULL, year = NA){
long_names <- c("All HH",
"American Indian, Alaska Native or Indigenous",
"Asian or Asian American",
"Black or African American or African",
"Multiple Races",
"Native Hawaiian or Pacific Islander",
"White",
"Hispanic / Latino",
"Non-Hispanic / Non-Latino",
"American Indian, Alaska Native or Indigenous; HISP",
"American Indian, Alaska Native or Indigenous; non-HISP",
"Black; HISP",
"Black; non-HISP",
"Multi-Racial or Asian or Native Hawaiian or Pacific Islander - HISP",
"Multi-Racial or Asian or Native Hawaiian or Pacific Islander - non-HISP",
"White; HISP",
"White; non-HISP")
out <- data.frame(names = long_names,
yr = year,
vals = x)
colnames(out)[colnames(out) == "vals"] <- fieldname
return(as_tibble(out))
}
sp_man_race <- function(x, fieldname = NULL, year = NA){
long_names <- c("All HH",
"American Indian, Alaska Native or Indigenous",
"Asian or Asian American",
"Black or African American or African",
"Multiple Races",
"Native Hawaiian or Pacific Islander",
"White",
"Hispanic / Latino",
"Non-Hispanic / Non-Latino",
"American Indian, Alaska Native or Indigenous; HISP",
"American Indian, Alaska Native or Indigenous; non-HISP",
"Black; HISP",
"Black; non-HISP",
"White; HISP",
"White; non-HISP")
out <- data.frame(names = long_names,
yr = year,
vals = x)
colnames(out)[colnames(out) == "vals"] <- fieldname
return(as_tibble(out))
}
# data entry----
# PIT----
# Stella_P----
# 2018----
data.18 <- full_join(sp_man_race(x = c(1414,10,5,626,35,3,735,53,1358,2,8,12,614,32,703),
fieldname = c("perm_exits")),
sp_man_race(x = c(1369,5,5,504,53,1,800,45,1321,0,5,4,500,34,766),
fieldname = c("temp_exits"))) %>%
full_join(., sp_man_race(x = c(1510,16,3,674,33,5,774,43,1460,2,14,13,661,23,751),
fieldname = c("unknown_exits"))) %>%
full_join(., sp_man_race(x = c(76,0,0,33,4,0,39,2,74,0,0,1,32,1,38),
fieldname = c("returns_from_perm_within_6_months"))) %>%
full_join(., sp_man_race(x = c(100,1,0,36,3,0,60,1,99,0,1,0,36,1,59),
fieldname = c("returns_from_perm_within_12_months"))) %>%
full_join(., sp_man_race(x = c(368,1,0,161,12,1,193,11,356,0,1,2,159,7,186),
fieldname = c("returns_from_##_within_6_months"))) %>%
full_join(., sp_man_race(x = c(490,2,0,196,16,1,274,13,476,0,2,2,194,9,265),
fieldname = c("returns_from_##_within_12_months"))) %>%
full_join(., sp_man_race(x = c(73,59,67,77,55,47,71,60,73,124,48,55,77,61,72),
fieldname = c("avg_length_stay_days"))) %>%
full_join(., sp_man_race(x = c(6517,
47,17,2902,171,13,
3367, # white
NA,NA,NA,NA,NA,NA,
128,3222),
fieldname = c("overall_population")))
# data.18 <- rows_append(data.18,
# data.frame(names = "Unknown",
# overall_population = -1))
data.18$yr <- "FY18"
as_tibble(data.18)
# 2019----
data.19 <- full_join(sp_man_race(x = c(1598,13,4,710,36,5,829,62,1535,2,11,9,701,41,788),
fieldname = c("perm_exits")),
sp_man_race(x = c(1581,17,6,682,53,6,815,53,1528,1,16,7,675,36,779),
fieldname = c("temp_exits"))) %>%
full_join(., sp_man_race(x = c(1113,10,4,429,28,5,635,34,1078,1,9,8,421,20,615),
fieldname = c("unknown_exits"))) %>%
full_join(., sp_man_race(x = c(85,0,0,32,3,1,49,3,82,0,0,2,30,1,48),
fieldname = c("returns_from_perm_within_6_months"))) %>%
full_join(., sp_man_race(x = c(156,0,0,64,6,1,85,7,149,0,0,3,61,3,82),
fieldname = c("returns_from_perm_within_12_months"))) %>%
full_join(., sp_man_race(x = c(335,0,0,141,13,2,179,9,326,0,0,2,139,6,173),
fieldname = c("returns_from_##_within_6_months"))) %>%
full_join(., sp_man_race(x = c(616,2,0,268,28,2,316,20,596,0,2,5,263,13,303),
fieldname = c("returns_from_##_within_12_months"))) %>%
full_join(., sp_man_race(x = c(87,52,104,95,62,49,84,59,88,90,49,47,96,68,85),
fieldname = c("avg_length_stay_days"))) %>%
full_join(., sp_man_race(x = c(6572,
54,22,2936,164,21,
3375, # white
NA,NA,NA,NA,NA,NA,
123,3239),
fieldname = c("overall_population")))
# data.19 <- rows_append(data.19,
# data.frame(names = "Unknown",
# overall_population = -1))
data.19$yr <- "FY19"
#2020----
data.20 <- full_join(sp_man_race(x = c(1417,10,8,577,23,4,793,26,1389,1,9,0,577,19,774),
fieldname = c("perm_exits")),
sp_man_race(x = c(1579,18,8,639,30,2,876,47,1527,2,16,8,631,33,843),
fieldname = c("temp_exits"))) %>%
full_join(., sp_man_race(x = c(554,6,3,216,13,0,316,10,543,0,6,1,215,9,307),
fieldname = c("unknown_exits"))) %>%
full_join(., sp_man_race(x = c(57,1,0,16,0,0,40,3,54,1,0,0,16,2,38),
fieldname = c("returns_from_perm_within_6_months"))) %>%
full_join(., sp_man_race(x = c(252,1,0,101,8,1,141,9,243,1,0,1,100,5,136),
fieldname = c("returns_from_perm_within_12_months"))) %>%
full_join(., sp_man_race(x = c(281,4,1,108,7,1,160,9,272,1,3,1,107,6,154),
fieldname = c("returns_from_##_within_6_months"))) %>%
full_join(., sp_man_race(x = c(544,3,1,214,17,2,307,16,528,1,2,4,210,9,298),
fieldname = c("returns_from_##_within_12_months"))) %>%
full_join(., sp_man_race(x = c(101,76,105,108,79,47,97,73,102,59,77,50,109,76,97),
fieldname = c("avg_length_stay_days"))) %>%
full_join(., sp_man_race(x = c(5921,
56,26,2645,117,9,
3068, #white
NA,NA,NA,NA,NA,NA,
95,2961),
fieldname = c("overall_population")))
# data.20 <- rows_append(data.20,
# data.frame(names = "Unknown",
# overall_population = -1))
data.20$yr <- "FY20"
# 2021----
data.21 <- full_join(sp_man_race(x = c(1146,17,6,514,30,3,572,42,1104,1,16,9,505,28,544),
fieldname = c("perm_exits")),
sp_man_race(x = c(1685,20,7,593,35,2,1018,75,1602,2,18,9,584,53,965),
fieldname = c("temp_exits"))) %>%
full_join(., sp_man_race(x = c(599,3,4,274,16,2,299,18,581,2,1,5,269,6,293),
fieldname = c("unknown_exits"))) %>%
full_join(., sp_man_race(x = c(33,0,0,14,0,0,19,1,32,0,0,0,14,1,18),
fieldname = c("returns_from_perm_within_6_months"))) %>%
full_join(., sp_man_race(x = c(92,1,2,29,2,0,58,4,88,1,0,0,29,3,55),
fieldname = c("returns_from_perm_within_12_months"))) %>%
full_join(., sp_man_race(x = c(210,2,1,73,8,0,126,7,203,0,2,1,72,5,121),
fieldname = c("returns_from_##_within_6_months"))) %>%
full_join(., sp_man_race(x = c(387,4,4,144,10,1,224,11,376,1,3,2,142,6,218),
fieldname = c("returns_from_##_within_12_months"))) %>%
full_join(., sp_man_race(x = c(112,100,103,128,94,80,99,81,113,67,104,51,129,91,99),
fieldname = c("avg_length_stay_days"))) %>%
full_join(., sp_man_race(x = c(6715,
83,25,3055,178,15,
3359,# white
NA,NA,NA,NA,NA,NA,
141,3186),
fieldname = c("overall_population")))
# data.21 <- rows_append(data.21,
# data.frame(names = "Unknown",
# overall_population = -1))
data.21$yr <- "FY21"
# 2022----
data.22 <- full_join(sp_man_race22(x = c(1726,35,6,794,54,5,827,79,1644,6,26,15,779,10,60,48,779),
fieldname = c("perm_exits")),
sp_man_race22(x = c(2663,45,17,1112,76,9,1393,105,2548,15,30,17,1095,16,97,57,1336),
fieldname = c("temp_exits"))) %>%
full_join(., sp_man_race22(x = c(515,3,3,227,19,2,261,12,501,0,3,3,224,1,23,8,253),
fieldname = c("unknown_exits"))) %>%
full_join(., sp_man_race22(x = c(54,1,1,21,1,0,30,3,51,0,1,0,21,0,1,2,28),
fieldname = c("returns_from_perm_within_6_months"))) %>%
full_join(., sp_man_race22(x = c(79,1,0,36,2,0,40,5,74,0,1,0,36,0,1,4,36),
fieldname = c("returns_from_perm_within_12_months"))) %>%
full_join(., sp_man_race22(x = c(347,1,1,141,10,0,192,13,332,0,1,2,139,0,10,8,184),#187,0,1,83,5,0,98,5,182,0,0,0,83,4,94),
fieldname = c("returns_from_##_within_6_months"))) %>%
full_join(., sp_man_race22(x = c(442,6,4,167,16,0,248,22,418,1,5,2,165,0,18,16,232),#350,4,1,124,12,0,209,18,331,1,3,2,122,13,196),
fieldname = c("returns_from_##_within_12_months"))) %>%
full_join(., sp_man_race22(x = c(116,105,62,135,100,96,101,99,116,57,120,78,136,93,102,114,100),#132,113,63,155,106,121,113,111,133,65,124,92,156,125,113),
fieldname = c("avg_length_stay_days"))) %>%
full_join(., sp_man_race22(x = c(6251,102,28,2763,183,22,3133,244,5986,24,78,44,2719,34,219,142,2991),
fieldname = c("overall_population")))
# data.22 <- rows_append(data.22,
# data.frame(names = "Unknown",
# overall_population = -1))
data.22$yr <- "FY22"
# 2023----
data.23 <- full_join(sp_man_race(x = c(NA),
fieldname = c("perm_exits")),
sp_man_race(x = c(NA),
fieldname = c("temp_exits"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("unknown_exits"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("returns_from_perm_within_6_months"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("returns_from_perm_within_12_months"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("returns_from_##_within_6_months"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("returns_from_##_within_12_months"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("avg_length_stay_days"))) %>%
full_join(., sp_man_race(x = c(NA),
fieldname = c("overall_population")))
# data.23 <- rows_append(data.23,
# data.frame(names = "Unknown",
# overall_population = -1))
data.23$yr <- "FY23_half"
# consolidated----
all.cons <- rbind(melt(as.data.table(data.20),
id.vars = c("names", "yr")),
melt(as.data.table(data.19),
id.vars = c("names", "yr")),
melt(as.data.table(data.18),
id.vars = c("names", "yr")),
melt(as.data.table(data.21),
id.vars = c("names", "yr")),
melt(as.data.table(data.22),
id.vars = c("names", "yr"))) %>%
dcast(., variable+names~yr,
value.var = "value", fun.aggregate = sum,
fill = 0) %>%
as_tibble()
all.cons
all.cons$sd <- NA
all.cons$avg <- NA
all.cons$var <- NA
all.cons$diff_range <- NA
# all.cons$min <- NA
# all.cons$max <- NA
for(i in 1:nrow(all.cons)){
all.cons$sd[i] <- sd(c(all.cons$FY18[i],
all.cons$FY19[i],
all.cons$FY20[i],
all.cons$FY21[i],
all.cons$FY22half[i]),
na.rm = T)
all.cons$avg[i] <- mean(c(all.cons$FY18[i],
all.cons$FY19[i],
all.cons$FY20[i],
all.cons$FY21[i],
all.cons$FY22half[i]),
na.rm = T)
all.cons$var[i] <- var(c(all.cons$FY18[i],
all.cons$FY19[i],
all.cons$FY20[i],
all.cons$FY21[i],
all.cons$FY22half[i]),
na.rm = T)
# all.cons$min[i] <- min(c(all.cons$FY18[i],
# all.cons$FY19[i],
# all.cons$FY20[i],
# all.cons$FY21[i],
# all.cons$FY22half[i]),
# na.rm = T)
# all.cons$max[i] <- max(c(all.cons$FY18[i],
# all.cons$FY19[i],
# all.cons$FY20[i],
# all.cons$FY21[i],
# all.cons$FY22half[i]),
# na.rm = T)
all.cons$diff_range[i] <- diff(range(c(all.cons$FY18[i],
all.cons$FY19[i],
all.cons$FY20[i],
all.cons$FY21[i],
all.cons$FY22half[i]),
na.rm = T)) /
mean(c(all.cons$FY18[i],
all.cons$FY19[i],
all.cons$FY20[i],
all.cons$FY21[i],
all.cons$FY22half[i])[c(all.cons$FY18[i],
all.cons$FY19[i],
all.cons$FY20[i],
all.cons$FY21[i],
all.cons$FY22half[i])>0],
na.rm = T)
}
# rename variable
all.cons$variable2 <- as.character(all.cons$variable)
unique(all.cons$variable2)
all.cons$variable2[all.cons$variable2 == "perm_exits"] <- "Permanent_Housing_Exits"
all.cons$variable2[all.cons$variable2 == "temp_exits"] <- "Temporary_Housing_Exits"
all.cons$variable2[all.cons$variable2 == "unknown_exits"] <- "Unknown_Housing_Exits"
all.cons$variable2[all.cons$variable2 == "returns_from_perm_within_6_months"] <- "Returns_From_Permanent_Housing_within_6_Months"
all.cons$variable2[all.cons$variable2 == "returns_from_##_within_6_months"] <- "Returns_From_All_Exit_Types_within_6_Months"
all.cons$variable2[all.cons$variable2 == "returns_from_perm_within_12_months"] <- "Returns_From_Permanent_Housing_within_12_Months"
all.cons$variable2[all.cons$variable2 == "returns_from_##_within_12_months"] <- "Returns_From_All_Exit_Types_within_12_Months"
all.cons$variable2[all.cons$variable2 == "avg_length_stay_days"] <- "Average_Length_of_Stay_in_Days"
all.cons$variable2[all.cons$variable2 == "overall_population"] <- "Overall_Population"
# hispanic note
all.cons[order(all.cons$diff_range,decreasing = T),]
all.cons$hisp <- NA
all.cons$hisp[grepl("hisp|hispanic", all.cons$names, ignore.case = T)] <- T
all.cons$hisp[grepl("non-hisp|non-hispanic", all.cons$names, ignore.case = T)] <- F
# ggplot() +
# geom_col(data = all.cons[all.cons$variable=="temp_exits",],
# aes(x = diff_range, y = names)) +
# facet_grid(variable~., scales = "free", space = 'free')+
# theme(strip.text.y = element_text(angle = 0))
all.cons$race <- ifelse(is.na(all.cons$hisp),T,F)
all.cons$ethnicity <- ifelse(is.na(all.cons$hisp) & all.cons$names != "All HH",F,T)
# keep V ----
unique(all.cons$names)
all.cons$intersect <- NA
all.cons$intersect[all.cons$hisp == T &
! is.na(all.cons$hisp)] <- "Hispanic / Latino"
all.cons$intersect[all.cons$hisp == F &
! is.na(all.cons$hisp)] <- "Non-Hispanic / Non-Latino"
all.cons$intersect[is.na(all.cons$hisp)] <- "Unreporting"
all.cons$race_1 <- NA
all.cons$race_1[grep("hawaii|pacific", all.cons$names, ignore.case = T, value = F)] <- "Native Hawaiian or Pacific Islander"
all.cons$race_1[grep("black", all.cons$names, ignore.case = T, value = F)] <- "Black or African American or African"
all.cons$race_1[grep("indian|indigenous", all.cons$names, ignore.case = T, value = F)] <- "American Indian, Alaska Native or Indigenous"
all.cons$race_1[grep("multiple|unknown", all.cons$names, ignore.case = T, value = F)] <- "Multiple Races"
all.cons$race_1[grep("white", all.cons$names, ignore.case = T, value = F)] <- "White"
all.cons$race_1[grep("asian", all.cons$names, ignore.case = T, value = F)] <- "Asian or Asian American"
all.cons$race_1[grep("all hh", all.cons$names, ignore.case = T, value = F)] <- "All Households"
all.cons$race_1[grep("latino|latina", all.cons$names, ignore.case = T, value = F)] <- NA
# keep ^ ----
# make new tables----
all.cons_race <- all.cons %>%
.[.$race == T,] %>%
.[!colnames(.) %in% c("sd", "avg", "var", "diff_range", "variable")]
all.cons_eth <- all.cons %>%
.[.$ethnicity == T,] %>%
.[!colnames(.) %in% c("sd", "avg", "var", "diff_range", "variable")]
all.cons_intersect <- all.cons %>%
.[!is.na(.$intersect),] %>%
.[!colnames(.) %in% c("sd", "avg", "var", "diff_range", "variable")]
# remove NAs from "overall"----
all.cons_race <- all.cons_race[!is.na(all.cons_race$FY18),]
all.cons_eth <- all.cons_eth[!is.na(all.cons_eth$FY18),]
all.cons_intersect <- all.cons_intersect[!is.na(all.cons_intersect$FY18),]
# # older vvv ----
# all.cons_race <- all.cons[all.cons$names %in% c("All HH", "White",
# "Native Hawaiian or Pacific Islander",
# "Multiple Races",
# "Black or African American or African",
# "Asian or Asian American",
# "American Indian, Alaska Native or Indigenous"),] %>%
# .[!colnames(.) %in% c("sd", "avg", "var", "diff_range", "variable")]
#
#
# all.cons_eth <- all.cons[all.cons$names %in%
# c("All HH", unique(grep("Hispanic", all.cons$names, value = T))),] %>%
# .[!colnames(.) %in% c("sd", "avg", "var", "diff_range", "variable")]
#
# all.cons_intersect <- all.cons[all.cons$names %in%
# c("All HH",
# unique(grep("HISP", all.cons$names, value = T)),
# "Native Hawaiian or Pacific Islander",
# "Asian or Asian American",
# "Multiple Races"),] %>%
# .[!colnames(.) %in% c("sd", "avg", "var", "diff_range", "variable")]
all.cons_eth$tbl_type <- "ethnicity"
all.cons_intersect$tbl_type <- "intersectional"
all.cons_race$tbl_type <- "race"
all.cons_eth <- all.cons_eth[,c("tbl_type", "variable2", "names", "hisp", "FY18",
"FY19", "FY20", "FY21", "FY22")] %>%
.[order(.$variable2),]
all.cons_intersect <- all.cons_intersect[,c("tbl_type", "variable2", "names", "hisp", "FY18",
"FY19", "FY20", "FY21", "FY22")] %>%
.[order(.$variable2),]
all.cons_race <- all.cons_race[,c("tbl_type", "variable2", "names", "hisp", "FY18",
"FY19", "FY20", "FY21", "FY22")] %>%
.[order(.$variable2),]
#percent change
all.cons_eth$FY18_to_FY19 <- ifelse(all.cons_eth$FY18 == 0, NA,
(all.cons_eth$FY19 - all.cons_eth$FY18) / all.cons_eth$FY18)
all.cons_eth$FY19_to_FY20 <- ifelse(all.cons_eth$FY19 == 0, NA,
(all.cons_eth$FY20 - all.cons_eth$FY19) / all.cons_eth$FY19)
all.cons_eth$FY20_to_FY21 <- ifelse(all.cons_eth$FY20 == 0, NA,
(all.cons_eth$FY21 - all.cons_eth$FY20) / all.cons_eth$FY20)
all.cons_eth$FY21_to_FY22 <- ifelse(all.cons_eth$FY21 == 0, NA,
(all.cons_eth$FY22 - all.cons_eth$FY21) / all.cons_eth$FY21)
all.cons_race$FY18_to_FY19 <- ifelse(all.cons_race$FY18 == 0, NA,
(all.cons_race$FY19 - all.cons_race$FY18) / all.cons_race$FY18)
all.cons_race$FY19_to_FY20 <- ifelse(all.cons_race$FY19 == 0, NA,
(all.cons_race$FY20 - all.cons_race$FY19) / all.cons_race$FY19)
all.cons_race$FY20_to_FY21 <- ifelse(all.cons_race$FY20 == 0, NA,
(all.cons_race$FY21 - all.cons_race$FY20) / all.cons_race$FY20)
all.cons_race$FY21_to_FY22 <- ifelse(all.cons_race$FY21 == 0, NA,
(all.cons_race$FY22 - all.cons_race$FY21) / all.cons_race$FY21)
all.cons_intersect$FY18_to_FY19 <- ifelse(all.cons_intersect$FY18 == 0, NA,
(all.cons_intersect$FY19 - all.cons_intersect$FY18) / all.cons_intersect$FY18)
all.cons_intersect$FY19_to_FY20 <- ifelse(all.cons_intersect$FY19 == 0, NA,
(all.cons_intersect$FY20 - all.cons_intersect$FY19) / all.cons_intersect$FY19)
all.cons_intersect$FY20_to_FY21 <- ifelse(all.cons_intersect$FY20 == 0, NA,
(all.cons_intersect$FY21 - all.cons_intersect$FY20) / all.cons_intersect$FY20)
all.cons_intersect$FY21_to_FY22 <- ifelse(all.cons_intersect$FY21 == 0, NA,
(all.cons_intersect$FY22 - all.cons_intersect$FY21) / all.cons_intersect$FY21)
#percent share
all.cons_eth$FY18_pct_share <- NA
all.cons_eth$FY19_pct_share <- NA
all.cons_eth$FY20_pct_share <- NA
all.cons_eth$FY21_pct_share <- NA
all.cons_eth$FY22_pct_share <- NA
for(i in 1:nrow(all.cons_eth)){
all.cons_eth$FY18_pct_share[i] <- all.cons_eth$FY18[i] / all.cons_eth$FY18[all.cons_eth$names == "All HH" &
all.cons_eth$variable2 == all.cons_eth$variable2[i]]
all.cons_eth$FY19_pct_share[i] <- all.cons_eth$FY19[i] / all.cons_eth$FY19[all.cons_eth$names == "All HH" &
all.cons_eth$variable2 == all.cons_eth$variable2[i]]
all.cons_eth$FY20_pct_share[i] <- all.cons_eth$FY20[i] / all.cons_eth$FY20[all.cons_eth$names == "All HH" &
all.cons_eth$variable2 == all.cons_eth$variable2[i]]
all.cons_eth$FY21_pct_share[i] <- all.cons_eth$FY21[i] / all.cons_eth$FY21[all.cons_eth$names == "All HH" &
all.cons_eth$variable2 == all.cons_eth$variable2[i]]
all.cons_eth$FY22_pct_share[i] <- all.cons_eth$FY22[i] / all.cons_eth$FY22[all.cons_eth$names == "All HH" &
all.cons_eth$variable2 == all.cons_eth$variable2[i]]
}
all.cons_race$FY18_pct_share <- NA
all.cons_race$FY19_pct_share <- NA
all.cons_race$FY20_pct_share <- NA
all.cons_race$FY21_pct_share <- NA
all.cons_race$FY22_pct_share <- NA
for(i in 1:nrow(all.cons_race)){
all.cons_race$FY18_pct_share[i] <- all.cons_race$FY18[i] / all.cons_race$FY18[all.cons_race$names == "All HH" &
all.cons_race$variable2 == all.cons_race$variable2[i]]
all.cons_race$FY19_pct_share[i] <- all.cons_race$FY19[i] / all.cons_race$FY19[all.cons_race$names == "All HH" &
all.cons_race$variable2 == all.cons_race$variable2[i]]
all.cons_race$FY20_pct_share[i] <- all.cons_race$FY20[i] / all.cons_race$FY20[all.cons_race$names == "All HH" &
all.cons_race$variable2 == all.cons_race$variable2[i]]
all.cons_race$FY21_pct_share[i] <- all.cons_race$FY21[i] / all.cons_race$FY21[all.cons_race$names == "All HH" &
all.cons_race$variable2 == all.cons_race$variable2[i]]
all.cons_race$FY22_pct_share[i] <- all.cons_race$FY22[i] / all.cons_race$FY22[all.cons_race$names == "All HH" &
all.cons_race$variable2 == all.cons_race$variable2[i]]
}
all.cons_intersect$FY18_pct_share <- NA
all.cons_intersect$FY19_pct_share <- NA
all.cons_intersect$FY20_pct_share <- NA
all.cons_intersect$FY21_pct_share <- NA
all.cons_intersect$FY22_pct_share <- NA
for(i in 1:nrow(all.cons_intersect)){
all.cons_intersect$FY18_pct_share[i] <- all.cons_intersect$FY18[i] / all.cons_intersect$FY18[all.cons_intersect$names == "All HH" &
all.cons_intersect$variable2 == all.cons_intersect$variable2[i]]
all.cons_intersect$FY19_pct_share[i] <- all.cons_intersect$FY19[i] / all.cons_intersect$FY19[all.cons_intersect$names == "All HH" &
all.cons_intersect$variable2 == all.cons_intersect$variable2[i]]
all.cons_intersect$FY20_pct_share[i] <- all.cons_intersect$FY20[i] / all.cons_intersect$FY20[all.cons_intersect$names == "All HH" &
all.cons_intersect$variable2 == all.cons_intersect$variable2[i]]
all.cons_intersect$FY21_pct_share[i] <- all.cons_intersect$FY21[i] / all.cons_intersect$FY21[all.cons_intersect$names == "All HH" &
all.cons_intersect$variable2 == all.cons_intersect$variable2[i]]
all.cons_intersect$FY22_pct_share[i] <- all.cons_intersect$FY22[i] / all.cons_intersect$FY22[all.cons_intersect$names == "All HH" &
all.cons_intersect$variable2 == all.cons_intersect$variable2[i]]
}
# order var2
var2order <- c("Average_Length_of_Stay_in_Days",
"Overall_Population",
"Permanent_Housing_Exits",
"Temporary_Housing_Exits",
"Unknown_Housing_Exits",
"Returns_From_Permanent_Housing_within_6_Months",
"Returns_From_All_Exit_Types_within_6_Months",
"Returns_From_Permanent_Housing_within_12_Months",
"Returns_From_All_Exit_Types_within_12_Months")
if(any(!all.cons$variable2 %in% var2order)){
stop("missing variables; see above ^^^")
}else{
print("proceed")
}
all.cons_eth$variable2_f <- factor(all.cons_eth$variable2,
levels = var2order)
all.cons_race$variable2_f <- factor(all.cons_race$variable2,
levels = var2order)
all.cons_intersect$variable2_f <- factor(all.cons_intersect$variable2,
levels = var2order)
all.cons_eth <- all.cons_eth[order(all.cons_eth$variable2_f),]
all.cons_race <- all.cons_race[order(all.cons_race$variable2_f),]
all.cons_intersect <- all.cons_intersect[order(all.cons_intersect$variable2_f),]
# insert blank rows between tables----
new_eth <- NULL
for(i in unique(all.cons_eth$variable2)){
new_eth <- rbind(new_eth,
all.cons_eth[all.cons_eth$variable2 == i,])
new_eth <- rows_append(x = new_eth,
y = data.frame(tbl_type = c("", NA),
variable2 = c("", NA)))
}
new_eth <- rows_append(x = new_eth[is.na(new_eth$tbl_type),][1,],
y = new_eth)
for(i in 1:nrow(new_eth)){
if(is.na(new_eth$names[i]) &
!is.na(new_eth$variable2[i+1])){
new_eth$names[i] <- new_eth$variable2[i+1]
}
}
new_eth
new_race <- NULL
for(i in unique(all.cons_race$variable2)){
new_race <- rbind(new_race,
all.cons_race[all.cons_race$variable2 == i,])
new_race <- rows_append(x = new_race,
y = data.frame(tbl_type = c("", NA),
variable2 = c("", NA)))
}
new_race <- rows_append(x = new_race[is.na(new_race$tbl_type),][1,],
y = new_race)
for(i in 1:nrow(new_race)){
if(is.na(new_race$names[i]) &
!is.na(new_race$variable2[i+1])){
new_race$names[i] <- new_race$variable2[i+1]
}
}
new_race
new_intersect <- NULL
for(i in unique(all.cons_intersect$variable2)){
new_intersect <- rbind(new_intersect,
all.cons_intersect[all.cons_intersect$variable2 == i,])
new_intersect <- rows_append(x = new_intersect,
y = data.frame(tbl_type = c("", NA),
variable2 = c("", NA)))
}
new_intersect <- rows_append(x = new_intersect[is.na(new_intersect$tbl_type),][1,],
y = new_intersect)
for(i in 1:nrow(new_intersect)){
if(is.na(new_intersect$names[i]) &
!is.na(new_intersect$variable2[i+1])){
new_intersect$names[i] <- new_intersect$variable2[i+1]
}
}
new_intersect
new_eth <- new_eth[!colnames(new_eth) %in% c("hisp", "variable2_f")]
new_race <- new_race[!colnames(new_race) %in% c("hisp", "variable2_f")]
new_intersect <- new_intersect[!colnames(new_intersect) %in% c("hisp", "variable2_f")]
# write csv files----
write.csv(x = new_eth,#all.cons_eth,
"all_cons_eth2023.csv")
write.csv(x = new_intersect,#all.cons_intersect,
"all.cons_intersect2023.csv")
write.csv(x = new_race,#all.cons_race,
"all.cons_race2023.csv")
# charts
all.bind <- rbind(all.cons_eth,
all.cons_race,
all.cons_intersect)
all.bind$n_NA <- is.na(all.bind$FY18) +
is.na(all.bind$FY19) +
is.na(all.bind$FY20) +
is.na(all.bind$FY21) +
is.na(all.bind$FY22)
all.bind[all.bind$n_NA > 0,] %>%
group_by(tbl_type, variable2, names) %>%
summarise(n = n())
as.data.table(all.bind[,c("tbl_type", "variable2_f", "names",
"FY18", "FY19", "FY20", "FY21", "FY22")]) %>%
melt(., id.vars = c("tbl_type", "variable2_f", "names")) %>% as_tibble()%>%
.[.$tbl_type == "race",] %>%
ggplot(data = .,
aes(x = as.numeric(variable), y = value, color = names))+
geom_line() +
geom_point()+
facet_wrap(variable2_f~., scales = "free_y")+
theme(legend.position = "bottom", legend.direction = "horizontal")
as.data.table(all.bind[,c("tbl_type", "variable2_f", "names",
"FY18_to_FY19", "FY19_to_FY20", "FY20_to_FY21", "FY21_to_FY22")]) %>%
melt(., id.vars = c("tbl_type", "variable2_f", "names")) %>% as_tibble()%>%
.[.$tbl_type == "race",] %>%
ggplot(data = .,
aes(x = as.numeric(variable), y = value, color = names))+
geom_path() +
geom_point()+
facet_wrap(variable2_f~.,scales = "free_y")+
theme(legend.position = "bottom", legend.direction = "horizontal")
as.data.table(all.bind[,c("tbl_type", "variable2_f", "names",
"FY18_pct_share", "FY19_pct_share", "FY20_pct_share",
"FY21_pct_share", "FY22_pct_share")]) %>%
melt(., id.vars = c("tbl_type", "variable2_f", "names")) %>%
as_tibble() %>%
.[.$tbl_type == "race",] %>%
ggplot(data = .,
aes(x = as.numeric(variable), y = value, color = names))+
geom_path() +
geom_point()+
facet_wrap(variable2_f~.,scales = "free_y")+
theme(legend.position = "bottom", legend.direction = "horizontal")
# charts----
unique(all.cons_intersect$variable2) # returns, exits, length of stay, population
unique(all.cons_intersect$names)
# 6month vs 12month returns for all types----
# white & black vs others
# pct_change
# return types
m6v12_returns <- all.cons_intersect %>%
.[grepl("returns_from", x = .$variable2,
ignore.case = T),]
# months
m6v12_returns$ret_months <- NA
m6v12_returns$ret_months[grepl("6_Months", m6v12_returns$variable2)] <- "After 6 Months"
m6v12_returns$ret_months[grepl("12_Months", m6v12_returns$variable2)] <- "After 12 Months"
# race
m6v12_returns <- m6v12_returns #%>% .[is.na(.$hisp),]
m6v12_returns$race2 <- NA
m6v12_returns$race2[grepl("White", m6v12_returns$names)] <- "White"
m6v12_returns$race2[grepl("Black", m6v12_returns$names)] <- "Black"
m6v12_returns$race2[grepl("All HH", m6v12_returns$names)] <- "All HH"
m6v12_returns$race2[grepl("Latino", m6v12_returns$names) &
grepl("Non-", m6v12_returns$names)] <- "Non-Hispanic"
m6v12_returns$race2[grepl("Latino", m6v12_returns$names) &
is.na(m6v12_returns$race2)] <- "Hispanic"
m6v12_returns$race2[is.na(m6v12_returns$race2)] <- "Other"
m6v12_returns <- m6v12_returns[!grepl("HISP",m6v12_returns$names),]
m6v12_returns <- m6v12_returns %>%
group_by(ret_months, race2, variable2) %>%
summarise(t18 = sum(FY18),
t19 = sum(FY19),
t20 = sum(FY20),
t21 = sum(FY21),
t22 = sum(FY22))
m6v12_returns$t18_19 <- (m6v12_returns$t19 - m6v12_returns$t18) / m6v12_returns$t18
m6v12_returns$t19_20 <- (m6v12_returns$t20 - m6v12_returns$t19) / m6v12_returns$t19
m6v12_returns$t20_21 <- (m6v12_returns$t21 - m6v12_returns$t20) / m6v12_returns$t20
m6v12_returns$t21_22 <- (m6v12_returns$t22 - m6v12_returns$t21) / m6v12_returns$t21
the.chart_hips <- NA
the.chart <- rbind(cbind(melt(as.data.table(m6v12_returns[,c(1:3,4:8)]),
id.vars = c("ret_months", "race2", "variable2"),
variable.factor = F),
data.frame(chart = "A")),
cbind(melt(as.data.table(m6v12_returns[,c(1:3,9:12)]),
id.vars = c("ret_months", "race2", "variable2"),
variable.factor = F),
data.frame(chart = "B"))) %>%
as_tibble() %>%
.[.$chart == "A",]
the.chart$race_f <- factor(the.chart$race2,
levels = c("All HH", "Hispanic",
"Non-Hispanic",
"Black", "White", "Other"))
the.chart$variable2 <- gsub("_within.*Months", "",the.chart$variable2)
as_tibble(the.chart)$variable2 %>% unique()
the.chart$ret_months_f <- factor(the.chart$ret_months,
levels = c("After 6 Months",
"After 12 Months"))
# ggplot(data = the.chart,
# aes(x = as.numeric(factor(variable)), y = value, color = race2)) +
# geom_line() +
# geom_point()+
# facet_grid(variable2~ret_months_f, scales = "free_y") +
# scale_x_continuous(labels = unique(the.chart$variable))+
# scale_y_continuous(labels = scales::comma)+
# theme(legend.position = "bottom", legend.direction = "vertical")+
# labs(title = "Returns to Homelessness after Exit")
#
# ggplot(data = the.chart[the.chart$variable2 == "Returns_From_All_Exit_Types",],
# aes(x = ret_months_f, y = value, fill = ret_months_f) ) +
# #geom_line() +
# geom_col(color = "black")+
# facet_grid(race2~variable, scales = "free_y") +
# scale_x_discrete()+
# scale_y_continuous(labels = scales::comma)+
# theme(legend.position = "none", legend.direction = "vertical",
# axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))+
# labs(title = "Returns to Homelessness after Exit")
ggplot(data = the.chart,#[the.chart$variable2 == "Returns_From_All_Exit_Types",],
aes(x = variable,
group = ret_months_f, y = value, color = ret_months_f) ) +
geom_point(size = 4) +
geom_line(#color = "black"#,
#position = "dodge"
size = 1)+
facet_grid(race_f~variable2, scales = "free_y") +
scale_x_discrete(name = "year")+
scale_y_continuous(labels = scales::comma)+
theme(legend.position = "bottom", legend.direction = "vertical",
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
strip.text.y = element_text(angle = 0))+
labs(title = "Returns to Homelessness after Exit")+
scale_color_discrete(name = "Returned After (Months)")
# year-to-year,
y2y <- all.cons_intersect %>%
.[grepl(pattern = "returns",
x=.$variable2,
ignore.case = T),] %>%
#.[is.na(.$hisp),]%>%
.[colnames(.) %in% c("variable2", "names", "hisp",
"FY18", "FY19", "FY20", "FY21", "FY22")] %>%
#"FY18_to_FY19", "FY19_to_FY20",
#"FY20_to_FY21", "FY21_to_FY22")] %>%
as.data.table() %>%
melt(., id.vars = c("variable2", "names", "hisp"))
y2y$year_int <- as.numeric(gsub(pattern = "\\D", "", y2y$variable)) + 2000
as_tibble(y2y)
if(all(y2y$year_int > 2100)){
temp <- data.frame(year_int = unique(y2y$year_int),
year_order = order(unique(y2y$year_int)))
for(i in 1:nrow(temp)){
y2y$year_int[y2y$year_int == temp$year_int[i]] <- temp$year_order[i]
}
}
y2y$year_int
y2y$race2 <- NA
y2y$race2[grepl("white", y2y$names, ignore.case = T)] <- "White"
y2y$race2[grepl("black", y2y$names, ignore.case = T)] <- "Black"
y2y$race2[!grepl("white|black|latino|all hh", y2y$names, ignore.case = T)] <- "Other (Median Value)"
y2y <- y2y[!is.na(y2y$race2),]
y2y %>%
as_tibble() %>%
group_by(variable2, names, year_int) %>%
summarise(avg_val = median(value, na.rm = T)) %>%
ggplot(data = .,#[!is.na(y2y$race2),],
aes(x = year_int, y = avg_val, color = variable2)) +
geom_point(size =3)+
geom_smooth(se = F, size = 1)+
facet_wrap(names~., scales = "free_y")+
theme(legend.position = "bottom",
legend.direction = "vertical")+
scale_x_continuous(name = "Year")+
scale_y_continuous(name = "Returns from Exit",
labels = scales::comma)+
labs(title = "Returns by Race and Ethnicity, 2018-2022")
y2y$eth_TF <- ifelse(is.na(y2y$hisp),F,T)
y2y$race_maj.min <- ifelse(grepl("white|black",
y2y$race2, ignore.case = T),
"White or black", "other")
y2y$eth_type <- ifelse(is.na(y2y$hisp), "N/A", NA)
y2y$eth_type[is.na(y2y$eth_type)] <- ifelse(grepl("non-hisp",
y2y$names[is.na(y2y$eth_type)],
ignore.case = T),
"Non-Hispanic", "Hispanic")
y2y %>%
.[.$variable2 == unique(.$variable2)[1],] %>%
as_tibble() %>%
group_by(variable2, names, year_int, race2, eth_TF,eth_type,
race_maj.min) %>%
summarise(avg_val = median(value, na.rm = T)) %>%
ggplot(data = .,#[!is.na(y2y$race2),],
aes(x = year_int, y = avg_val, color = names)) +
geom_point(size =3)+
geom_smooth(se = F, size = 1)+
facet_wrap(variable2+eth_type + race2~., scales = "free_y")+
theme(legend.position = "bottom",
legend.direction = "horizontal")+
scale_x_continuous(name = "Year")+
scale_y_continuous(name = "Returns from Exit",
labels = scales::comma)+
labs(title = "Returns by Race and Ethnicity, 2018-2022")
# pct_change_each_year,
y2y <- all.cons_intersect %>%
.[grepl(pattern = "exits",
x=.$variable2,
ignore.case = T),] %>%
.[is.na(.$hisp),]%>%
.[colnames(.) %in% c("variable2", "names", "hisp",
#"FY18", "FY19", "FY20", "FY21", "FY22_half")] %>%
"FY18_to_FY19", "FY19_to_FY20",
"FY20_to_FY21", "FY21_to_FY22")] %>%
as.data.table() %>%
melt(., id.vars = c("variable2", "names", "hisp"))
y2y$year_int <- as.numeric(gsub(pattern = "\\D", "", y2y$variable)) + 2000
if(all(y2y$year_int > 2100)){
temp <- data.frame(year_int = unique(y2y$year_int),
year_order = order(unique(y2y$year_int)))
for(i in 1:nrow(temp)){
y2y$year_int[y2y$year_int == temp$year_int[i]] <- temp$year_order[i]
}
}
y2y$year_int
y2y$race2 <- NA
y2y$race2[grepl("white", y2y$names, ignore.case = T)] <- "White"
y2y$race2[grepl("black", y2y$names, ignore.case = T)] <- "Black"
y2y$race2[!grepl("white|black|latino|all hh", y2y$names, ignore.case = T)] <- "Other (Median Value)"
y2y <- y2y[!is.na(y2y$race2),]
y2y %>%
as_tibble() %>%
group_by(variable2, race2, year_int) %>%
summarise(avg_val = median(value, na.rm = T)) %>%
ggplot(data = .,#[!is.na(y2y$race2),],
aes(x = year_int, y = avg_val, color = race2)) +
geom_point(size =3)+
geom_smooth(se = F, size = 1)+
facet_wrap(variable2~., scales = "free_x")+
theme(legend.position = "bottom")+
scale_x_continuous(name = "Year",
labels = paste("FY",
c("18-19","19-20","20-21","21-22"),
sep = ""))+
scale_y_continuous(name = "Percent Change",
labels = scales::percent)+
labs(title = "Percent Change in Returns by Major Race Groups, 2018-2022")
# pct_share_by_race
psr <- all.cons_eth %>%
.[grepl(pattern = "returns",
x=.$variable2,
ignore.case = T),] %>%
#.[is.na(.$hisp),]%>%
.[colnames(.) %in% c("variable2", "names", "hisp",
"FY18_pct_share", "FY19_pct_share",
"FY20_pct_share", "FY21_pct_share",
"FY22_pct_share")] %>%
#"FY18", "FY19", "FY20", "FY21", "FY22_half")] %>%
# "FY18_to_FY19", "FY19_to_FY20",
# "FY20_to_FY21", "FY21_to_FY22")] %>%
as.data.table() %>%
melt(., id.vars = c("variable2", "names", "hisp"))
psr$year_int <- as.numeric(gsub(pattern = "\\D", "", psr$variable)) + 2000
if(all(psr$year_int > 2100)){
temp <- data.frame(year_int = unique(psr$year_int),
year_order = order(unique(psr$year_int)))
for(i in 1:nrow(temp)){
psr$year_int[psr$year_int == temp$year_int[i]] <- temp$year_order[i]
}
}
psr$year_int
psr$race2 <- NA
psr$race2[grepl("white", psr$names, ignore.case = T)] <- "White"
#psr$race2[grepl("black", psr$names, ignore.case = T)] <- "Black"
psr$race2[!grepl("white|latino|all hh", psr$names, ignore.case = T)] <- "Other (Median Value)"
#psr <- psr[!is.na(psr$race2),]
psr %>%
.[grepl("latino", .$names, ignore.case = T),] %>%
as_tibble() %>%
group_by(variable2, names, year_int) %>%
summarise(avg_val = mean(value, na.rm = T)) %>%
ggplot(data = .,#[!is.na(psr$race2),],
aes(x = year_int, y = avg_val, color = variable2)) +
geom_point(size =3)+
geom_smooth(se = F, size = 1)+
facet_wrap(names~., scales = "free")+
theme(legend.position = "bottom",
legend.direction = "vertical")+
scale_x_continuous(name = "Year")+
scale_y_continuous(name = "Percent Change",
labels = scales::percent,
limits = c(NA,NA))+
labs(title = "Percent Change in Returns by Ethnicity, 2018-2022")
# PIT----
pit.data <- read_tsv(I("geo race 2017_people experiencing homelessness by race (PIT) 2018_people experiencing homelessness by race (PIT) 2019_people experiencing homelessness by race (PIT) 2020_people experiencing homelessness by race (PIT) 2021_people experiencing homelessness by race (PIT) 2022_people experiencing homelessness by race (PIT)
NC hispanic 518 411 460 320
NC non hispanic 8,444 8,903 8,820 6,822
NC asian pacific islander 54 50 50 53
NC black 4,553 4,832 4,757 4,254
NC native american alaskan 190 94 93 75
NC white 3,745 3,992 4,060 2,527
NC other multi racial 420 346 320 233
NC-503 hispanic 160 121 156 94
NC-503 non hispanic 2,894 2,868 3,029 1,426
NC-503 asian pacific islander 15 19 19 16
NC-503 black 1,139 1,112 1,205 651
NC-503 native american alaskan 120 47 51 30