R/afford.R
In timathomas/neighborhood: Neighborhood analysis functions
Defines functions
afford
Documented in
afford
#' @title Affordable market location
#' @description The \code{afford} function identifies where different income groups can afford to live based on local market value while accounting for the region's count of households of the given income group.
#' @param state Study state.
#' @param counties Study county or counties.
#' @param ami_limit Specified area median income, or AMI, limit of interest such as 3. or .5 AMI.
#' @param year Study year.
#' @param geometry Download spatial data.
#' @param ... Other keyword arguments
#' @return Returns a spatial file
#' @examples \dontrun{
#' bay5 <- afford("06", c("001", "013", "041", "075", "081"), .5, 2021)<br>
#' wasatch5 <- afford("49", c('057', '011', '035', '049'), .5, 2019, geometry = TRUE)
#' }
#' @export
afford <- function(
state = "state",
counties = "counties",
ami_limit = "ami_limit",
year = 2021,
geometry = FALSE,
...
){
closest <- function(x = "x", limits = "limits") {
limits[which.min(abs(limits - x))]
}
income <-
tidycensus::get_acs(geography = "county",
table = "B19001",
state = state,
year = year,
cache_table = TRUE) %>%
dplyr::filter(GEOID %in% paste0(state, counties)) %>%
dplyr::left_join(tidycensus::load_variables(year, "acs5", cache = TRUE), by = c("variable" = "name"))
med_inc <-
tidycensus::get_acs(geography = "tract",
variables = "B19013_001",
state = state,
county = counties,
year = year,
cache_table = TRUE)
acsvars <- tidycensus::load_variables(year, "acs5", cache = TRUE)
price <- dplyr::bind_rows(
tidycensus::get_acs(geography = "tract",
variables = c("B25085_001", "B25085_002", "B25085_003", "B25085_004", "B25085_005",
"B25085_006", "B25085_007", "B25085_008", "B25085_009", "B25085_010",
"B25085_011", "B25085_012", "B25085_013", "B25085_014", "B25085_015",
"B25085_016", "B25085_017", "B25085_018", "B25085_019", "B25085_020",
"B25085_021", "B25085_022", "B25085_023", "B25085_024"),
state = state,
county = counties,
year = year,
cache_table = TRUE),
tidycensus::get_acs(geography = "tract",
variables = c("B25085_025", "B25085_026", "B25085_027"),
state = state,
county = counties,
year = year,
cache_table = TRUE)) %>%
dplyr::left_join(acsvars, by = c("variable" = "name")) %>%
dplyr::arrange(GEOID, variable)
value <- dplyr::bind_rows(
tidycensus::get_acs(geography = "tract",
variables = c("B25075_001", "B25075_002", "B25075_003", "B25075_004", "B25075_005",
"B25075_006", "B25075_007", "B25075_008", "B25075_009", "B25075_010",
"B25075_011", "B25075_012", "B25075_013", "B25075_014", "B25075_015",
"B25075_016", "B25075_017", "B25075_018", "B25075_019", "B25075_020",
"B25075_021", "B25075_022", "B25075_023", "B25075_024"),
state = state,
county = counties,
year = year,
cache_table = TRUE),
tidycensus::get_acs(geography = "tract",
variables = c("B25075_025", "B25075_026", "B25075_027"),
state = state,
county = counties,
year = year,
cache_table = TRUE)) %>%
dplyr::left_join(acsvars, by = c("variable" = "name")) %>%
dplyr::arrange(GEOID, variable)
rent <- dplyr::bind_rows(
tidycensus::get_acs(geography = "tract",
variables = c("B25063_002", "B25063_003", "B25063_004", "B25063_005",
"B25063_006", "B25063_007", "B25063_008", "B25063_009", "B25063_010",
"B25063_011", "B25063_012", "B25063_013", "B25063_014", "B25063_015",
"B25063_016", "B25063_017", "B25063_018", "B25063_019", "B25063_020",
"B25063_021", "B25063_022", "B25063_023", "B25063_024", "B25063_025"),
state = state,
county = counties,
year = year,
cache_table = TRUE),
tidycensus::get_acs(geography = "tract",
variables = "B25063_026",
state = state,
county = counties,
year = year,
cache_table = TRUE)) %>%
dplyr::left_join(acsvars, by = c("variable" = "name")) %>%
dplyr::arrange(GEOID, variable)
income_limit <- c(0, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000,
50000, 60000, 75000, 100000, 125000, 150000, 200000, Inf)
income$limit <- rep(income_limit, times = nrow(income)/17)
price_limit <- c(0, 10000, 15000, 20000, 25000, 30000, 35000, 40000,
50000, 60000, 70000, 80000, 90000, 100000, 125000, 150000, 175000, 200000,
250000, 300000, 400000, 500000, 750000, 1000000, 1500000, 2000000, Inf)
price$limit <- rep(price_limit, times = nrow(price)/27)
value$limit <- rep(price_limit, times = nrow(price)/27)
rent_limit <- c(0, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800,
900, 1000, 1250, 1500, 2000, 2500, 3000, 3500, Inf)
rent$limit <- rep(rent_limit, time = nrow(rent)/25)
ami <- stats::median(med_inc$estimate, na.rm = TRUE)
price$income_limit <- price$limit*0.188
value$income_limit <- value$limit*0.188
rent$income_limit <- (rent$limit/0.3)*12
price_counts <-
price %>%
dplyr::filter(income_limit <= closest(ami_limit*ami, income_limit) &
income_limit > 0) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize(tr_own_accessible = sum(estimate)) %>%
dplyr::left_join(
price %>%
dplyr::filter(income_limit == 0) %>%
dplyr::select(GEOID, tr_own_total = estimate)
)
value_counts <-
value %>%
dplyr::filter(income_limit <= closest(ami_limit*ami, income_limit) &
income_limit > 0) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize(tr_own_accessible = sum(estimate)) %>%
dplyr::left_join(
value %>%
dplyr::filter(income_limit == 0) %>%
dplyr::select(GEOID, tr_own_total = estimate)
)
rent_counts <-
rent %>%
dplyr::filter(income_limit <= closest(ami_limit*ami, income_limit) &
income_limit > 0) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize(tr_rent_accessible = sum(estimate)) %>%
dplyr::left_join(
rent %>%
dplyr::filter(income_limit == 0) %>%
dplyr::select(GEOID, tr_rent_total = estimate)
)
total_pop <- sum(income %>%
dplyr::filter(limit == 0) %>%
dplyr::select(estimate))
class_pop <- sum(income %>%
dplyr::filter(limit <= closest(ami_limit*ami, income_limit) & income_limit > 0) %>%
dplyr::select(estimate))
class_prop <- class_pop/total_pop
tract_counts <-
dplyr::bind_rows(price_counts, value_counts) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize_all(~sum(.)) %>%
dplyr::mutate(
tr_own_supply = tr_own_accessible/tr_own_total,
tr_own_ratio = tr_own_supply/class_prop,
tr_own_rate = (tr_own_accessible/class_pop)*100000) %>%
dplyr::left_join(
rent_counts %>%
dplyr::mutate(
tr_rent_supply = tr_rent_accessible/tr_rent_total,
tr_rent_ratio = tr_rent_supply/class_prop,
tr_rent_rate = (tr_rent_accessible/class_pop)*100000),
by = "GEOID") %>%
dplyr::mutate(
reg_total_pop = total_pop,
reg_class_pop = class_pop,
ami_limit = ami_limit)
# max_rent <- max(tract_counts$tr_rent_rate, na.rm = TRUE)/100000
# max_own <- max(tract_counts$tr_own_rate, na.rm = TRUE)/100000
tract_counts <-
tract_counts %>%
dplyr::mutate(
rent_jenks_cat =
factor(
dplyr::case_when(
tr_rent_rate <= 100 ~ "Less than 0.1%",
tr_rent_rate > 100 & tr_rent_rate <= 200 ~ "0.1% to 0.2%",
tr_rent_rate > 200 ~
paste0(
"0.2% to ",
scales::percent(max(tr_rent_rate, na.rm = TRUE)/100000, accuracy = .1)),
),
levels = c(
"Less than 0.1%",
"0.1% to 0.2%",
paste0("0.2% to ", scales::percent(max(tr_rent_rate, na.rm = TRUE)/100000, accuracy = .1))
)
),
own_jenks_cat =
factor(
dplyr::case_when(
tr_own_rate <= 100 ~ "Less than 0.1%",
tr_own_rate > 100 & tr_own_rate <= 200 ~ "0.1% to 0.2%",
tr_own_rate > 200 ~
paste0(
"0.2% to ",
scales::percent(max(tr_own_rate, na.rm = TRUE)/100000, accuracy = .1)
),
),
levels = c(
"Less than 0.1%",
"0.1% to 0.2%",
paste0("0.2% to ", scales::percent(max(tr_own_rate, na.rm = TRUE)/100000, accuracy = .1))
)
),
popup =
stringr::str_c(
"<b>Tract: ", GEOID, "</b>",
"<br>",
"Tract rental units: ", scales::comma(tr_rent_total, accuracy = 1),
"<br>",
"Rentals affordable to ", scales::percent(ami_limit), " AMI: ", scales::comma(tr_rent_accessible, accuracy = 1), " (", scales::percent(tr_rent_supply, accuracy = 1), ")",
"<br>",
scales::percent(tr_rent_rate/100000, accuracy = .1), " of the region's ", scales::percent(ami_limit, accuracy = 1), " AMI households served",
"<br>",
"<br>",
"Tract owned homes: ", scales::comma(tr_own_total, accuracy = 1),
"<br>",
"Homes affordable to ", scales::percent(ami_limit), " AMI: ", scales::comma(tr_own_accessible, accuracy = 1), " (", scales::percent(tr_own_supply, accuracy = 1), ")",
"<br>",
scales::percent(tr_own_rate/100000, accuracy = .1), " of the region's ", scales::percent(ami_limit, accuracy = 1), " AMI households served",
"<br>",
"<br>",
"Region household count: ", scales::comma(reg_total_pop, accuracy = 1), "<br>",
"Region Households at ", scales::percent(ami_limit), " AMI: ", scales::comma(reg_class_pop, accuracy = 1)
)
)
if(geometry == TRUE){
print("Return sf dataframe")
tigris::tracts(state = state, county = counties, cb = TRUE, year = year) %>% dplyr::left_join(tract_counts) %>% sf::st_transform(crs = 4326)
} else {
print("Return dataframe")
return(tract_counts)
}
}
timathomas/neighborhood documentation built on Feb. 17, 2024, 11:44 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions afford
Documented in afford
#' @title Affordable market location
#' @description The \code{afford} function identifies where different income groups can afford to live based on local market value while accounting for the region's count of households of the given income group.
#' @param state Study state.
#' @param counties Study county or counties.
#' @param ami_limit Specified area median income, or AMI, limit of interest such as 3. or .5 AMI.
#' @param year Study year.
#' @param geometry Download spatial data.
#' @param ... Other keyword arguments
#' @return Returns a spatial file
#' @examples \dontrun{
#' bay5 <- afford("06", c("001", "013", "041", "075", "081"), .5, 2021)<br>
#' wasatch5 <- afford("49", c('057', '011', '035', '049'), .5, 2019, geometry = TRUE)
#' }
#' @export
afford <- function(
state = "state",
counties = "counties",
ami_limit = "ami_limit",
year = 2021,
geometry = FALSE,
...
){
closest <- function(x = "x", limits = "limits") {
limits[which.min(abs(limits - x))]
}
income <-
tidycensus::get_acs(geography = "county",
table = "B19001",
state = state,
year = year,
cache_table = TRUE) %>%
dplyr::filter(GEOID %in% paste0(state, counties)) %>%
dplyr::left_join(tidycensus::load_variables(year, "acs5", cache = TRUE), by = c("variable" = "name"))
med_inc <-
tidycensus::get_acs(geography = "tract",
variables = "B19013_001",
state = state,
county = counties,
year = year,
cache_table = TRUE)
acsvars <- tidycensus::load_variables(year, "acs5", cache = TRUE)
price <- dplyr::bind_rows(
tidycensus::get_acs(geography = "tract",
variables = c("B25085_001", "B25085_002", "B25085_003", "B25085_004", "B25085_005",
"B25085_006", "B25085_007", "B25085_008", "B25085_009", "B25085_010",
"B25085_011", "B25085_012", "B25085_013", "B25085_014", "B25085_015",
"B25085_016", "B25085_017", "B25085_018", "B25085_019", "B25085_020",
"B25085_021", "B25085_022", "B25085_023", "B25085_024"),
state = state,
county = counties,
year = year,
cache_table = TRUE),
tidycensus::get_acs(geography = "tract",
variables = c("B25085_025", "B25085_026", "B25085_027"),
state = state,
county = counties,
year = year,
cache_table = TRUE)) %>%
dplyr::left_join(acsvars, by = c("variable" = "name")) %>%
dplyr::arrange(GEOID, variable)
value <- dplyr::bind_rows(
tidycensus::get_acs(geography = "tract",
variables = c("B25075_001", "B25075_002", "B25075_003", "B25075_004", "B25075_005",
"B25075_006", "B25075_007", "B25075_008", "B25075_009", "B25075_010",
"B25075_011", "B25075_012", "B25075_013", "B25075_014", "B25075_015",
"B25075_016", "B25075_017", "B25075_018", "B25075_019", "B25075_020",
"B25075_021", "B25075_022", "B25075_023", "B25075_024"),
state = state,
county = counties,
year = year,
cache_table = TRUE),
tidycensus::get_acs(geography = "tract",
variables = c("B25075_025", "B25075_026", "B25075_027"),
state = state,
county = counties,
year = year,
cache_table = TRUE)) %>%
dplyr::left_join(acsvars, by = c("variable" = "name")) %>%
dplyr::arrange(GEOID, variable)
rent <- dplyr::bind_rows(
tidycensus::get_acs(geography = "tract",
variables = c("B25063_002", "B25063_003", "B25063_004", "B25063_005",
"B25063_006", "B25063_007", "B25063_008", "B25063_009", "B25063_010",
"B25063_011", "B25063_012", "B25063_013", "B25063_014", "B25063_015",
"B25063_016", "B25063_017", "B25063_018", "B25063_019", "B25063_020",
"B25063_021", "B25063_022", "B25063_023", "B25063_024", "B25063_025"),
state = state,
county = counties,
year = year,
cache_table = TRUE),
tidycensus::get_acs(geography = "tract",
variables = "B25063_026",
state = state,
county = counties,
year = year,
cache_table = TRUE)) %>%
dplyr::left_join(acsvars, by = c("variable" = "name")) %>%
dplyr::arrange(GEOID, variable)
income_limit <- c(0, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000,
50000, 60000, 75000, 100000, 125000, 150000, 200000, Inf)
income$limit <- rep(income_limit, times = nrow(income)/17)
price_limit <- c(0, 10000, 15000, 20000, 25000, 30000, 35000, 40000,
50000, 60000, 70000, 80000, 90000, 100000, 125000, 150000, 175000, 200000,
250000, 300000, 400000, 500000, 750000, 1000000, 1500000, 2000000, Inf)
price$limit <- rep(price_limit, times = nrow(price)/27)
value$limit <- rep(price_limit, times = nrow(price)/27)
rent_limit <- c(0, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800,
900, 1000, 1250, 1500, 2000, 2500, 3000, 3500, Inf)
rent$limit <- rep(rent_limit, time = nrow(rent)/25)
ami <- stats::median(med_inc$estimate, na.rm = TRUE)
price$income_limit <- price$limit*0.188
value$income_limit <- value$limit*0.188
rent$income_limit <- (rent$limit/0.3)*12
price_counts <-
price %>%
dplyr::filter(income_limit <= closest(ami_limit*ami, income_limit) &
income_limit > 0) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize(tr_own_accessible = sum(estimate)) %>%
dplyr::left_join(
price %>%
dplyr::filter(income_limit == 0) %>%
dplyr::select(GEOID, tr_own_total = estimate)
)
value_counts <-
value %>%
dplyr::filter(income_limit <= closest(ami_limit*ami, income_limit) &
income_limit > 0) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize(tr_own_accessible = sum(estimate)) %>%
dplyr::left_join(
value %>%
dplyr::filter(income_limit == 0) %>%
dplyr::select(GEOID, tr_own_total = estimate)
)
rent_counts <-
rent %>%
dplyr::filter(income_limit <= closest(ami_limit*ami, income_limit) &
income_limit > 0) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize(tr_rent_accessible = sum(estimate)) %>%
dplyr::left_join(
rent %>%
dplyr::filter(income_limit == 0) %>%
dplyr::select(GEOID, tr_rent_total = estimate)
)
total_pop <- sum(income %>%
dplyr::filter(limit == 0) %>%
dplyr::select(estimate))
class_pop <- sum(income %>%
dplyr::filter(limit <= closest(ami_limit*ami, income_limit) & income_limit > 0) %>%
dplyr::select(estimate))
class_prop <- class_pop/total_pop
tract_counts <-
dplyr::bind_rows(price_counts, value_counts) %>%
dplyr::group_by(GEOID) %>%
dplyr::summarize_all(~sum(.)) %>%
dplyr::mutate(
tr_own_supply = tr_own_accessible/tr_own_total,
tr_own_ratio = tr_own_supply/class_prop,
tr_own_rate = (tr_own_accessible/class_pop)*100000) %>%
dplyr::left_join(
rent_counts %>%
dplyr::mutate(
tr_rent_supply = tr_rent_accessible/tr_rent_total,
tr_rent_ratio = tr_rent_supply/class_prop,
tr_rent_rate = (tr_rent_accessible/class_pop)*100000),
by = "GEOID") %>%
dplyr::mutate(
reg_total_pop = total_pop,
reg_class_pop = class_pop,
ami_limit = ami_limit)
# max_rent <- max(tract_counts$tr_rent_rate, na.rm = TRUE)/100000
# max_own <- max(tract_counts$tr_own_rate, na.rm = TRUE)/100000
tract_counts <-
tract_counts %>%
dplyr::mutate(
rent_jenks_cat =
factor(
dplyr::case_when(
tr_rent_rate <= 100 ~ "Less than 0.1%",
tr_rent_rate > 100 & tr_rent_rate <= 200 ~ "0.1% to 0.2%",
tr_rent_rate > 200 ~
paste0(
"0.2% to ",
scales::percent(max(tr_rent_rate, na.rm = TRUE)/100000, accuracy = .1)),
),
levels = c(
"Less than 0.1%",
"0.1% to 0.2%",
paste0("0.2% to ", scales::percent(max(tr_rent_rate, na.rm = TRUE)/100000, accuracy = .1))
)
),
own_jenks_cat =
factor(
dplyr::case_when(
tr_own_rate <= 100 ~ "Less than 0.1%",
tr_own_rate > 100 & tr_own_rate <= 200 ~ "0.1% to 0.2%",
tr_own_rate > 200 ~
paste0(
"0.2% to ",
scales::percent(max(tr_own_rate, na.rm = TRUE)/100000, accuracy = .1)
),
),
levels = c(
"Less than 0.1%",
"0.1% to 0.2%",
paste0("0.2% to ", scales::percent(max(tr_own_rate, na.rm = TRUE)/100000, accuracy = .1))
)
),
popup =
stringr::str_c(
"<b>Tract: ", GEOID, "</b>",
"<br>",
"Tract rental units: ", scales::comma(tr_rent_total, accuracy = 1),
"<br>",
"Rentals affordable to ", scales::percent(ami_limit), " AMI: ", scales::comma(tr_rent_accessible, accuracy = 1), " (", scales::percent(tr_rent_supply, accuracy = 1), ")",
"<br>",
scales::percent(tr_rent_rate/100000, accuracy = .1), " of the region's ", scales::percent(ami_limit, accuracy = 1), " AMI households served",
"<br>",
"<br>",
"Tract owned homes: ", scales::comma(tr_own_total, accuracy = 1),
"<br>",
"Homes affordable to ", scales::percent(ami_limit), " AMI: ", scales::comma(tr_own_accessible, accuracy = 1), " (", scales::percent(tr_own_supply, accuracy = 1), ")",
"<br>",
scales::percent(tr_own_rate/100000, accuracy = .1), " of the region's ", scales::percent(ami_limit, accuracy = 1), " AMI households served",
"<br>",
"<br>",
"Region household count: ", scales::comma(reg_total_pop, accuracy = 1), "<br>",
"Region Households at ", scales::percent(ami_limit), " AMI: ", scales::comma(reg_class_pop, accuracy = 1)
)
)
if(geometry == TRUE){
print("Return sf dataframe")
tigris::tracts(state = state, county = counties, cb = TRUE, year = year) %>% dplyr::left_join(tract_counts) %>% sf::st_transform(crs = 4326)
} else {
print("Return dataframe")
return(tract_counts)
}
}
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