Nothing
R/powell_wiley.R
In ndi: Neighborhood Deprivation Indices
Defines functions
powell_wiley
Documented in
powell_wiley
#' Neighborhood Deprivation Index based on Andrews _et al._ (2020) and Slotman _et al._ (2022)
#'
#' Compute the aspatial Neighborhood Deprivation Index (Powell-Wiley).
#'
#' @param geo Character string specifying the geography of the data either census tracts \code{geo = "tract"} (the default) or counties \code{geo = "county"}.
#' @param year Numeric. The year to compute the estimate. The default is 2020, and the years 2010 onward are currently available.
#' @param imp Logical. If TRUE, will impute missing census characteristics within the internal \code{\link[psych]{principal}} using median values of variables. If FALSE (the default), will not impute.
#' @param quiet Logical. If TRUE, will display messages about potential missing census information, standardized Cronbach's alpha, and proportion of variance explained by principal component analysis. The default is FALSE.
#' @param round_output Logical. If TRUE, will round the output of raw census and NDI values from the \code{\link[tidycensus]{get_acs}} at one and four significant digits, respectively. The default is FALSE.
#' @param df Optional. Pass a pre-formatted \code{'dataframe'} or \code{'tibble'} with the desired variables through the function. Bypasses the data obtained by \code{\link[tidycensus]{get_acs}}. The default is NULL. See Details below.
#' @param ... Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics
#'
#' @details This function will compute the aspatial Neighborhood Deprivation Index (NDI) of U.S. census tracts or counties for a specified geographical referent (e.g., US-standardized) based on Andrews _et al._ (2020) \doi{10.1080/17445647.2020.1750066} and Slotman _et al._ (2022) \doi{10.1016/j.dib.2022.108002}.
#'
#' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for computation involving a factor analysis with the \code{\link[psych]{principal}} function. The yearly estimates are available in 2010 and after when all census characteristics became available. The thirteen characteristics chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x} are:
#' \itemize{
#' \item **MedHHInc (B19013)**: median household income (dollars)
#' \item **PctRecvIDR (B19054)**: percent of households receiving dividends, interest, or rental income
#' \item **PctPubAsst (B19058)**: percent of households receiving public assistance
#' \item **MedHomeVal (B25077)**: median home value (dollars)
#' \item **PctMgmtBusScArti (C24060)**: percent in a management, business, science, or arts occupation
#' \item **PctFemHeadKids (B11005)**: percent of households that are female headed with any children under 18 years
#' \item **PctOwnerOcc (DP04)**: percent of housing units that are owner occupied
#' \item **PctNoPhone (DP04)**: percent of households without a telephone
#' \item **PctNComPlm (DP04)**: percent of households without complete plumbing facilities
#' \item **PctEducHSPlus (S1501)**: percent with a high school degree or higher (population 25 years and over)
#' \item **PctEducBchPlus (S1501)**: percent with a college degree or higher (population 25 years and over)
#' \item **PctFamBelowPov (S1702)**: percent of families with incomes below the poverty level
#' \item **PctUnempl (S2301)**: percent unemployed
#' }
#'
#' Use the internal \code{state} and \code{county} arguments within the \code{\link[tidycensus]{get_acs}} function to specify the referent for standardizing the NDI (Powell-Wiley) values. For example, if all U.S. states are specified for the \code{state} argument, then the output would be a U.S.-standardized index. Please note: the NDI (Powell-Wiley) values will not exactly match (but will highly correlate with) those found in Andrews _et al._ (2020) \doi{10.1080/17445647.2020.1750066} and Slotman _et al._ (2022) \doi{10.1016/j.dib.2022.108002} because the two studies used a different statistical platform (i.e., SPSS and SAS, respectively) that intrinsically calculate the principal component analysis differently from R.
#'
#' The categorical NDI (Powell-Wiley) values are population-weighted quintiles of the continuous NDI (Powell-Wiley) values.
#'
#' Check if the proportion of variance explained by the first principal component is high (more than 0.5).
#'
#' Users can bypass \code{\link[tidycensus]{get_acs}} by specifying a pre-formatted data frame or tibble using the \code{df} argument. This function will compute an index using the first component of a principal component analysis (PCA) with a Promax (oblique) rotation and a minimum Eigenvalue of 1, omitting variables with absolute loading score < 0.4. The recommended structure of the data frame or tibble is an ID (e.g., GEOID) in the first feature (column), an estimate of the total population in the second feature (column), followed by the variables of interest (in any order) and no additional information (e.g., omit state or county names from the \code{df} argument input).
#'
#' @return An object of class 'list'. This is a named list with the following components:
#'
#' \describe{
#' \item{\code{ndi}}{An object of class 'tbl' for the GEOID, name, NDI continuous, NDI quintiles, and raw census values of specified census geographies.}
#' \item{\code{pca}}{An object of class 'principal', returns the output of \code{\link[psych]{principal}} used to compute the NDI values.}
#' \item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute NDI.}
#' \item{\code{cronbach}}{An object of class 'character' or 'numeric' for the results of the Cronbach's alpha calculation. If only one factor is computed, a message is returned. If more than one factor is computed, Cronbach's alpha is calculated and should check that it is >0.7 for respectable internal consistency between factors.}
#' }
#'
#' @import dplyr
#' @importFrom MASS ginv
#' @importFrom psych alpha principal
#' @importFrom stats complete.cases cor cov2cor loadings median promax quantile sd
#' @importFrom stringr str_trim
#' @importFrom tidycensus get_acs
#' @importFrom tidyr pivot_longer separate
#' @export
#'
#' @seealso \code{\link[tidycensus]{get_acs}} for additional arguments for geographic referent selection (i.e., \code{state} and \code{county}).
#'
#' @examples
#'
#' powell_wiley(df = DCtracts2020[ , -c(3:10)])
#'
#' \dontrun{
#' # Wrapped in \dontrun{} because these examples require a Census API key.
#'
#' # Tract-level metric (2020)
#' powell_wiley(geo = "tract", state = "GA", year = 2020)
#'
#' # Impute NDI for tracts (2020) with missing census information (median values)
#' powell_wiley(state = "tract", "GA", year = 2020, imp = TRUE)
#'
#' # County-level metric (2020)
#' powell_wiley(geo = "county", state = "GA", year = 2020)
#'
#' }
#'
powell_wiley <- function(geo = "tract", year = 2020, imp = FALSE, quiet = FALSE, round_output = FALSE, df = NULL, ...) {
# Check arguments
if (!is.null(df) & !inherits(df, c("tbl_df", "tbl", "data.frame"))) { stop("'df' must be class 'data.frame' or 'tbl'") }
if (is.null(df)) {
# Check additional arguments
match.arg(geo, choices = c("county", "tract"))
stopifnot(is.numeric(year), year >= 2010) # all variables available 2010 onward
# Select census variables
vars <- c(MedHHInc = "B19013_001",
PctRecvIDR_num = "B19054_002", PctRecvIDR_den = "B19054_001",
PctPubAsst_num = "B19058_002", PctPubAsst_den = "B19058_001",
MedHomeVal = "B25077_001",
PctMgmtBusScArti_num = "C24060_002", PctMgmtBusScArti_den = "C24060_001",
PctFemHeadKids_num1 = "B11005_007", PctFemHeadKids_num2 = "B11005_010",
PctFemHeadKids_den = "B11005_001",
PctOwnerOcc = "DP04_0046P",
PctNoPhone = "DP04_0075P",
PctNComPlmb = "DP04_0073P",
PctEduc_num25upHS = "S1501_C01_009",
PctEduc_num25upSC = "S1501_C01_010",
PctEduc_num25upAD = "S1501_C01_011",
PctEduc_num25upBD = "S1501_C01_012",
PctEduc_num25upGD = "S1501_C01_013",
PctEduc_den25up = "S1501_C01_006",
PctFamBelowPov = "S1702_C02_001",
PctUnempl = "S2301_C04_001",
TotalPopulation = "B01001_001")
# Updated census variable definition(s)
if (year < 2015){ vars <- c(vars[-13], PctNoPhone = "DP04_0074P") }
# Acquire NDI variables
ndi_data <- suppressMessages(suppressWarnings(tidycensus::get_acs(geography = geo,
year = year,
output = "wide",
variables = vars, ...)))
if (geo == "tract") {
ndi_data <- ndi_data %>%
tidyr::separate(NAME, into = c("tract", "county", "state"), sep = ",") %>%
dplyr::mutate(tract = gsub("[^0-9\\.]","", tract))
} else {
ndi_data <- ndi_data %>% tidyr::separate(NAME, into = c("county", "state"), sep = ",")
}
ndi_data <- ndi_data %>%
dplyr::mutate(MedHHInc = MedHHIncE,
PctRecvIDR = PctRecvIDR_numE / PctRecvIDR_denE * 100,
PctPubAsst = PctPubAsst_numE / PctPubAsst_denE * 100,
MedHomeVal = MedHomeValE,
PctMgmtBusScArti = PctMgmtBusScArti_numE / PctMgmtBusScArti_denE * 100,
PctFemHeadKids = (PctFemHeadKids_num1E + PctFemHeadKids_num2E) / PctFemHeadKids_denE * 100,
PctOwnerOcc = PctOwnerOccE,
PctNoPhone = PctNoPhoneE,
PctNComPlmb = PctNComPlmbE,
PctEducHSPlus = (PctEduc_num25upHSE + PctEduc_num25upSCE + PctEduc_num25upADE +
PctEduc_num25upBDE + PctEduc_num25upGDE) / PctEduc_den25upE * 100,
PctEducBchPlus = (PctEduc_num25upBDE + PctEduc_num25upGDE) / PctEduc_den25upE * 100,
PctFamBelowPov = PctFamBelowPovE,
PctUnempl = PctUnemplE,
TotalPop = TotalPopulationE) %>%
# Log transform median household income and median home value
# Reverse code percentages so that higher values represent more deprivation
# Round percentages to 1 decimal place
dplyr::mutate(logMedHHInc = log(MedHHInc),
logMedHomeVal = log(MedHomeVal),
PctNoIDR = 100 - PctRecvIDR,
PctWorkClass = 100 - PctMgmtBusScArti,
PctNotOwnerOcc = 100 - PctOwnerOcc,
PctEducLTHS = 100 - PctEducHSPlus,
PctEducLTBch = 100 - PctEducBchPlus) %>%
# Z-standardize the percentages
dplyr::mutate(PctNoIDRZ = scale(PctNoIDR),
PctPubAsstZ = scale(PctPubAsst),
PctWorkClassZ = scale(PctWorkClass),
PctFemHeadKidsZ = scale(PctFemHeadKids),
PctNotOwnerOccZ = scale(PctNotOwnerOcc),
PctNoPhoneZ = scale(PctNoPhone),
PctNComPlmbZ = scale(PctNComPlmb),
PctEducLTHSZ = scale(PctEducLTHS),
PctEducLTBchZ = scale(PctEducLTBch),
PctFamBelowPovZ = scale(PctFamBelowPov),
PctUnemplZ = scale(PctUnempl))
# generate NDI
ndi_data_pca <- ndi_data %>%
dplyr::select(logMedHHInc, PctNoIDRZ, PctPubAsstZ, logMedHomeVal, PctWorkClassZ,
PctFemHeadKidsZ, PctNotOwnerOccZ, PctNoPhoneZ, PctNComPlmbZ, PctEducLTHSZ,
PctEducLTBchZ, PctFamBelowPovZ, PctUnemplZ)
} else {
# If inputing pre-formatted data:
colnames(df)[1:2] <- c("GEOID", "TotalPop") # rename first and second features (columns) with name to match above
ndi_data <- dplyr::as_tibble(df)
ndi_data_pca <- ndi_data[ , -c(1:2)] # omits the first two features (columns) typically an ID (e.g., GEOID or FIPS) and TotalPop
}
# Run a factor analysis using Promax (oblique) rotation and a minimum Eigenvalue of 1
nfa <- eigen(stats::cor(ndi_data_pca, use = "complete.obs"))
nfa <- sum(nfa$values > 1) # count of factors with a minimum Eigenvalue of 1
fit <- psych::principal(ndi_data_pca,
nfactors = nfa,
rotate = "none")
fit_rotate <- stats::promax(stats::loadings(fit), m = 3)
# Calculate the factors using only variables with an absolute loading score > 0.4 for the first factor
## If number of factors > 2, use structure matrix, else pattern matrix
if (nfa > 1) {
P_mat <- matrix(stats::loadings(fit_rotate), nrow = 13, ncol = nfa)
# Structure matrix (S_mat) from under-the-hood of the psych::principal() function
rot.mat <- fit_rotate$rotmat # rotation matrix
ui <- solve(rot.mat)
Phi <- cov2cor(ui %*% t(ui)) # interfactor correlation
S_mat <- P_mat %*% Phi # pattern matrix multiplied by interfactor correlation
} else {
P_mat <- matrix(fit_rotate, nrow = 13, ncol = 1)
Phi <- 1
S_mat <- P_mat
}
## Variable correlation matrix (R_mat)
R_mat <- as.matrix(cor(ndi_data_pca[complete.cases(ndi_data_pca), ]))
## standardized score coefficients or weight matrix (B_mat)
B_mat <- solve(R_mat, S_mat)
# Additional PCA Information
fit_rotate$rotation <- "promax"
fit_rotate$Phi <- Phi
fit_rotate$Structure <- S_mat
if (nfa > 1) {
fit_rotate$communality <- rowSums(P_mat^2)
} else {
fit_rotate$communality <- P_mat^2
}
fit_rotate$uniqueness <- diag(R_mat) - fit_rotate$communality
if (nfa > 1) {
vx <- colSums(P_mat^2)
} else {
vx <- sum(P_mat^2)
}
vtotal <- sum(fit_rotate$communality + fit_rotate$uniqueness)
vx <- diag(Phi %*% t(P_mat) %*% P_mat)
names(vx) <- colnames(loadings)
varex <- rbind(`SS loadings` = vx)
varex <- rbind(varex, `Proportion Var` = vx/vtotal)
if (nfa > 1) {
varex <- rbind(varex, `Cumulative Var` = cumsum(vx/vtotal))
varex <- rbind(varex, `Proportion Explained` = vx/sum(vx))
varex <- rbind(varex, `Cumulative Proportion` = cumsum(vx/sum(vx)))
}
fit_rotate$Vaccounted <- varex
if (imp == TRUE) {
ndi_data_scrs <- as.matrix(ndi_data_pca)
miss <- which(is.na(ndi_data_scrs), arr.ind = TRUE)
item.med <- apply(ndi_data_scrs, 2, stats::median, na.rm = TRUE)
ndi_data_scrs[miss] <- item.med[miss[, 2]]
} else {
ndi_data_scrs <- ndi_data_pca
}
scrs <- as.matrix(scale(ndi_data_scrs[complete.cases(ndi_data_scrs), abs(S_mat[ , 1]) > 0.4 ])) %*% B_mat[abs(S_mat[ , 1]) > 0.4, 1]
ndi_data_NA <- ndi_data[complete.cases(ndi_data_scrs), ]
ndi_data_NA$NDI <- c(scrs)
ndi_data_NDI <- dplyr::left_join(ndi_data[ , c("GEOID", "TotalPop")], ndi_data_NA[ , c("GEOID", "NDI")], by = "GEOID")
# Calculate Cronbach's alpha correlation coefficient among the factors and verify values are above 0.7.
if (nfa == 1) {
crnbch <- "Only one factor with minimum Eigenvalue of 1. Cannot calculate Cronbach's alpha."
} else {
cronbach <- suppressMessages(psych::alpha(ndi_data_pca[ , abs(S_mat[ , 1]) > 0.4 ], check.keys = TRUE, na.rm = TRUE, warnings = FALSE))
crnbch <- cronbach$total$std.alpha
}
# Warning for missingness of census characteristics
missingYN <- ndi_data_pca %>%
tidyr::pivot_longer(cols = dplyr::everything(),
names_to = "variable",
values_to = "val") %>%
dplyr::group_by(variable) %>%
dplyr::summarise(total = dplyr::n(),
n_missing = sum(is.na(val)),
percent_missing = paste0(round(mean(is.na(val)) * 100, 2), " %"))
if (quiet == FALSE) {
# Warning for missing census data
if (sum(missingYN$n_missing) > 0) {
message("Warning: Missing census data")
}
# Warning for Cronbach's alpha < 0.7
if (cronbach$total$std.alpha < 0.7) {
message("Warning: Cronbach's alpha correlation coefficient among the factors is less than 0.7.")
}
# Warning for proportion of variance explained by FA1
if (fit_rotate$Vaccounted[2] < 0.50) {
message("Warning: The proportion of variance explained by PC1 is less than 0.50.")
}
}
# NDI quintiles weighted by tract population
NDIQuint <- ndi_data_NDI %>%
dplyr::mutate(NDIQuint = cut(NDI*log(TotalPop),
breaks = stats::quantile(NDI*log(TotalPop),
probs = c(0, 0.2, 0.4, 0.6, 0.8, 1),
na.rm = TRUE),
labels = c("1-Least deprivation", "2-BelowAvg deprivation",
"3-Average deprivation","4-AboveAvg deprivation",
"5-Most deprivation"),
include.lowest = TRUE),
NDIQuint = factor(replace(as.character(NDIQuint),
is.na(NDIQuint) | is.infinite(NDIQuint),
"9-NDI not avail"),
c(levels(NDIQuint), "9-NDI not avail"))) %>%
dplyr::select(NDI, NDIQuint)
if (is.null(df)) {
# Format output
if (round_output == TRUE) {
ndi <- cbind(ndi_data, NDIQuint) %>%
dplyr::mutate(PctRecvIDR = round(PctRecvIDR, digits = 1),
PctPubAsst = round(PctPubAsst, digits = 1),
PctMgmtBusScArti = round(PctMgmtBusScArti, digits = 1),
PctFemHeadKids = round(PctFemHeadKids, digits = 1),
PctOwnerOcc = round(PctOwnerOcc, digits = 1),
PctNoPhone = round(PctNoPhone, digits = 1),
PctNComPlmb = round(PctNComPlmb, digits = 1),
PctEducHSPlus = round(PctEducHSPlus, digits = 1),
PctEducBchPlus = round(PctEducBchPlus, digits = 1),
PctFamBelowPov = round(PctFamBelowPov, digits = 1),
PctUnempl = round(PctUnempl, digits = 1))
} else {
ndi <- cbind(ndi_data, NDIQuint)
}
if (geo == "tract") {
ndi <- ndi %>%
dplyr::select(GEOID,
state,
county,
tract,
NDI, NDIQuint,
MedHHInc, PctRecvIDR, PctPubAsst, MedHomeVal, PctMgmtBusScArti,
PctFemHeadKids,PctOwnerOcc, PctNoPhone, PctNComPlmb, PctEducHSPlus,
PctEducBchPlus, PctFamBelowPov, PctUnempl, TotalPop)
} else {
ndi <- ndi %>%
dplyr::select(GEOID,
state,
county,
NDI, NDIQuint,
MedHHInc, PctRecvIDR, PctPubAsst, MedHomeVal, PctMgmtBusScArti,
PctFemHeadKids,PctOwnerOcc, PctNoPhone, PctNComPlmb, PctEducHSPlus,
PctEducBchPlus, PctFamBelowPov, PctUnempl, TotalPop)
}
ndi <- ndi %>%
dplyr::mutate(state = stringr::str_trim(state),
county = stringr::str_trim(county)) %>%
dplyr::arrange(GEOID) %>%
dplyr::as_tibble()
} else {
ndi <- cbind(df[ , 1], NDIQuint, df[ , 2:ncol(df)])
ndi <- dplyr::as_tibble(ndi[order(ndi[ , 1]), ])
}
out <- list(ndi = ndi,
pca = fit_rotate,
missing = missingYN,
cronbach = crnbch)
return(out)
}
Try the ndi package in your browser
Any scripts or data that you put into this service are public.
ndi documentation built on May 29, 2024, 9:56 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 powell_wiley
Documented in powell_wiley
#' Neighborhood Deprivation Index based on Andrews _et al._ (2020) and Slotman _et al._ (2022)
#'
#' Compute the aspatial Neighborhood Deprivation Index (Powell-Wiley).
#'
#' @param geo Character string specifying the geography of the data either census tracts \code{geo = "tract"} (the default) or counties \code{geo = "county"}.
#' @param year Numeric. The year to compute the estimate. The default is 2020, and the years 2010 onward are currently available.
#' @param imp Logical. If TRUE, will impute missing census characteristics within the internal \code{\link[psych]{principal}} using median values of variables. If FALSE (the default), will not impute.
#' @param quiet Logical. If TRUE, will display messages about potential missing census information, standardized Cronbach's alpha, and proportion of variance explained by principal component analysis. The default is FALSE.
#' @param round_output Logical. If TRUE, will round the output of raw census and NDI values from the \code{\link[tidycensus]{get_acs}} at one and four significant digits, respectively. The default is FALSE.
#' @param df Optional. Pass a pre-formatted \code{'dataframe'} or \code{'tibble'} with the desired variables through the function. Bypasses the data obtained by \code{\link[tidycensus]{get_acs}}. The default is NULL. See Details below.
#' @param ... Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics
#'
#' @details This function will compute the aspatial Neighborhood Deprivation Index (NDI) of U.S. census tracts or counties for a specified geographical referent (e.g., US-standardized) based on Andrews _et al._ (2020) \doi{10.1080/17445647.2020.1750066} and Slotman _et al._ (2022) \doi{10.1016/j.dib.2022.108002}.
#'
#' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for computation involving a factor analysis with the \code{\link[psych]{principal}} function. The yearly estimates are available in 2010 and after when all census characteristics became available. The thirteen characteristics chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x} are:
#' \itemize{
#' \item **MedHHInc (B19013)**: median household income (dollars)
#' \item **PctRecvIDR (B19054)**: percent of households receiving dividends, interest, or rental income
#' \item **PctPubAsst (B19058)**: percent of households receiving public assistance
#' \item **MedHomeVal (B25077)**: median home value (dollars)
#' \item **PctMgmtBusScArti (C24060)**: percent in a management, business, science, or arts occupation
#' \item **PctFemHeadKids (B11005)**: percent of households that are female headed with any children under 18 years
#' \item **PctOwnerOcc (DP04)**: percent of housing units that are owner occupied
#' \item **PctNoPhone (DP04)**: percent of households without a telephone
#' \item **PctNComPlm (DP04)**: percent of households without complete plumbing facilities
#' \item **PctEducHSPlus (S1501)**: percent with a high school degree or higher (population 25 years and over)
#' \item **PctEducBchPlus (S1501)**: percent with a college degree or higher (population 25 years and over)
#' \item **PctFamBelowPov (S1702)**: percent of families with incomes below the poverty level
#' \item **PctUnempl (S2301)**: percent unemployed
#' }
#'
#' Use the internal \code{state} and \code{county} arguments within the \code{\link[tidycensus]{get_acs}} function to specify the referent for standardizing the NDI (Powell-Wiley) values. For example, if all U.S. states are specified for the \code{state} argument, then the output would be a U.S.-standardized index. Please note: the NDI (Powell-Wiley) values will not exactly match (but will highly correlate with) those found in Andrews _et al._ (2020) \doi{10.1080/17445647.2020.1750066} and Slotman _et al._ (2022) \doi{10.1016/j.dib.2022.108002} because the two studies used a different statistical platform (i.e., SPSS and SAS, respectively) that intrinsically calculate the principal component analysis differently from R.
#'
#' The categorical NDI (Powell-Wiley) values are population-weighted quintiles of the continuous NDI (Powell-Wiley) values.
#'
#' Check if the proportion of variance explained by the first principal component is high (more than 0.5).
#'
#' Users can bypass \code{\link[tidycensus]{get_acs}} by specifying a pre-formatted data frame or tibble using the \code{df} argument. This function will compute an index using the first component of a principal component analysis (PCA) with a Promax (oblique) rotation and a minimum Eigenvalue of 1, omitting variables with absolute loading score < 0.4. The recommended structure of the data frame or tibble is an ID (e.g., GEOID) in the first feature (column), an estimate of the total population in the second feature (column), followed by the variables of interest (in any order) and no additional information (e.g., omit state or county names from the \code{df} argument input).
#'
#' @return An object of class 'list'. This is a named list with the following components:
#'
#' \describe{
#' \item{\code{ndi}}{An object of class 'tbl' for the GEOID, name, NDI continuous, NDI quintiles, and raw census values of specified census geographies.}
#' \item{\code{pca}}{An object of class 'principal', returns the output of \code{\link[psych]{principal}} used to compute the NDI values.}
#' \item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute NDI.}
#' \item{\code{cronbach}}{An object of class 'character' or 'numeric' for the results of the Cronbach's alpha calculation. If only one factor is computed, a message is returned. If more than one factor is computed, Cronbach's alpha is calculated and should check that it is >0.7 for respectable internal consistency between factors.}
#' }
#'
#' @import dplyr
#' @importFrom MASS ginv
#' @importFrom psych alpha principal
#' @importFrom stats complete.cases cor cov2cor loadings median promax quantile sd
#' @importFrom stringr str_trim
#' @importFrom tidycensus get_acs
#' @importFrom tidyr pivot_longer separate
#' @export
#'
#' @seealso \code{\link[tidycensus]{get_acs}} for additional arguments for geographic referent selection (i.e., \code{state} and \code{county}).
#'
#' @examples
#'
#' powell_wiley(df = DCtracts2020[ , -c(3:10)])
#'
#' \dontrun{
#' # Wrapped in \dontrun{} because these examples require a Census API key.
#'
#' # Tract-level metric (2020)
#' powell_wiley(geo = "tract", state = "GA", year = 2020)
#'
#' # Impute NDI for tracts (2020) with missing census information (median values)
#' powell_wiley(state = "tract", "GA", year = 2020, imp = TRUE)
#'
#' # County-level metric (2020)
#' powell_wiley(geo = "county", state = "GA", year = 2020)
#'
#' }
#'
powell_wiley <- function(geo = "tract", year = 2020, imp = FALSE, quiet = FALSE, round_output = FALSE, df = NULL, ...) {
# Check arguments
if (!is.null(df) & !inherits(df, c("tbl_df", "tbl", "data.frame"))) { stop("'df' must be class 'data.frame' or 'tbl'") }
if (is.null(df)) {
# Check additional arguments
match.arg(geo, choices = c("county", "tract"))
stopifnot(is.numeric(year), year >= 2010) # all variables available 2010 onward
# Select census variables
vars <- c(MedHHInc = "B19013_001",
PctRecvIDR_num = "B19054_002", PctRecvIDR_den = "B19054_001",
PctPubAsst_num = "B19058_002", PctPubAsst_den = "B19058_001",
MedHomeVal = "B25077_001",
PctMgmtBusScArti_num = "C24060_002", PctMgmtBusScArti_den = "C24060_001",
PctFemHeadKids_num1 = "B11005_007", PctFemHeadKids_num2 = "B11005_010",
PctFemHeadKids_den = "B11005_001",
PctOwnerOcc = "DP04_0046P",
PctNoPhone = "DP04_0075P",
PctNComPlmb = "DP04_0073P",
PctEduc_num25upHS = "S1501_C01_009",
PctEduc_num25upSC = "S1501_C01_010",
PctEduc_num25upAD = "S1501_C01_011",
PctEduc_num25upBD = "S1501_C01_012",
PctEduc_num25upGD = "S1501_C01_013",
PctEduc_den25up = "S1501_C01_006",
PctFamBelowPov = "S1702_C02_001",
PctUnempl = "S2301_C04_001",
TotalPopulation = "B01001_001")
# Updated census variable definition(s)
if (year < 2015){ vars <- c(vars[-13], PctNoPhone = "DP04_0074P") }
# Acquire NDI variables
ndi_data <- suppressMessages(suppressWarnings(tidycensus::get_acs(geography = geo,
year = year,
output = "wide",
variables = vars, ...)))
if (geo == "tract") {
ndi_data <- ndi_data %>%
tidyr::separate(NAME, into = c("tract", "county", "state"), sep = ",") %>%
dplyr::mutate(tract = gsub("[^0-9\\.]","", tract))
} else {
ndi_data <- ndi_data %>% tidyr::separate(NAME, into = c("county", "state"), sep = ",")
}
ndi_data <- ndi_data %>%
dplyr::mutate(MedHHInc = MedHHIncE,
PctRecvIDR = PctRecvIDR_numE / PctRecvIDR_denE * 100,
PctPubAsst = PctPubAsst_numE / PctPubAsst_denE * 100,
MedHomeVal = MedHomeValE,
PctMgmtBusScArti = PctMgmtBusScArti_numE / PctMgmtBusScArti_denE * 100,
PctFemHeadKids = (PctFemHeadKids_num1E + PctFemHeadKids_num2E) / PctFemHeadKids_denE * 100,
PctOwnerOcc = PctOwnerOccE,
PctNoPhone = PctNoPhoneE,
PctNComPlmb = PctNComPlmbE,
PctEducHSPlus = (PctEduc_num25upHSE + PctEduc_num25upSCE + PctEduc_num25upADE +
PctEduc_num25upBDE + PctEduc_num25upGDE) / PctEduc_den25upE * 100,
PctEducBchPlus = (PctEduc_num25upBDE + PctEduc_num25upGDE) / PctEduc_den25upE * 100,
PctFamBelowPov = PctFamBelowPovE,
PctUnempl = PctUnemplE,
TotalPop = TotalPopulationE) %>%
# Log transform median household income and median home value
# Reverse code percentages so that higher values represent more deprivation
# Round percentages to 1 decimal place
dplyr::mutate(logMedHHInc = log(MedHHInc),
logMedHomeVal = log(MedHomeVal),
PctNoIDR = 100 - PctRecvIDR,
PctWorkClass = 100 - PctMgmtBusScArti,
PctNotOwnerOcc = 100 - PctOwnerOcc,
PctEducLTHS = 100 - PctEducHSPlus,
PctEducLTBch = 100 - PctEducBchPlus) %>%
# Z-standardize the percentages
dplyr::mutate(PctNoIDRZ = scale(PctNoIDR),
PctPubAsstZ = scale(PctPubAsst),
PctWorkClassZ = scale(PctWorkClass),
PctFemHeadKidsZ = scale(PctFemHeadKids),
PctNotOwnerOccZ = scale(PctNotOwnerOcc),
PctNoPhoneZ = scale(PctNoPhone),
PctNComPlmbZ = scale(PctNComPlmb),
PctEducLTHSZ = scale(PctEducLTHS),
PctEducLTBchZ = scale(PctEducLTBch),
PctFamBelowPovZ = scale(PctFamBelowPov),
PctUnemplZ = scale(PctUnempl))
# generate NDI
ndi_data_pca <- ndi_data %>%
dplyr::select(logMedHHInc, PctNoIDRZ, PctPubAsstZ, logMedHomeVal, PctWorkClassZ,
PctFemHeadKidsZ, PctNotOwnerOccZ, PctNoPhoneZ, PctNComPlmbZ, PctEducLTHSZ,
PctEducLTBchZ, PctFamBelowPovZ, PctUnemplZ)
} else {
# If inputing pre-formatted data:
colnames(df)[1:2] <- c("GEOID", "TotalPop") # rename first and second features (columns) with name to match above
ndi_data <- dplyr::as_tibble(df)
ndi_data_pca <- ndi_data[ , -c(1:2)] # omits the first two features (columns) typically an ID (e.g., GEOID or FIPS) and TotalPop
}
# Run a factor analysis using Promax (oblique) rotation and a minimum Eigenvalue of 1
nfa <- eigen(stats::cor(ndi_data_pca, use = "complete.obs"))
nfa <- sum(nfa$values > 1) # count of factors with a minimum Eigenvalue of 1
fit <- psych::principal(ndi_data_pca,
nfactors = nfa,
rotate = "none")
fit_rotate <- stats::promax(stats::loadings(fit), m = 3)
# Calculate the factors using only variables with an absolute loading score > 0.4 for the first factor
## If number of factors > 2, use structure matrix, else pattern matrix
if (nfa > 1) {
P_mat <- matrix(stats::loadings(fit_rotate), nrow = 13, ncol = nfa)
# Structure matrix (S_mat) from under-the-hood of the psych::principal() function
rot.mat <- fit_rotate$rotmat # rotation matrix
ui <- solve(rot.mat)
Phi <- cov2cor(ui %*% t(ui)) # interfactor correlation
S_mat <- P_mat %*% Phi # pattern matrix multiplied by interfactor correlation
} else {
P_mat <- matrix(fit_rotate, nrow = 13, ncol = 1)
Phi <- 1
S_mat <- P_mat
}
## Variable correlation matrix (R_mat)
R_mat <- as.matrix(cor(ndi_data_pca[complete.cases(ndi_data_pca), ]))
## standardized score coefficients or weight matrix (B_mat)
B_mat <- solve(R_mat, S_mat)
# Additional PCA Information
fit_rotate$rotation <- "promax"
fit_rotate$Phi <- Phi
fit_rotate$Structure <- S_mat
if (nfa > 1) {
fit_rotate$communality <- rowSums(P_mat^2)
} else {
fit_rotate$communality <- P_mat^2
}
fit_rotate$uniqueness <- diag(R_mat) - fit_rotate$communality
if (nfa > 1) {
vx <- colSums(P_mat^2)
} else {
vx <- sum(P_mat^2)
}
vtotal <- sum(fit_rotate$communality + fit_rotate$uniqueness)
vx <- diag(Phi %*% t(P_mat) %*% P_mat)
names(vx) <- colnames(loadings)
varex <- rbind(`SS loadings` = vx)
varex <- rbind(varex, `Proportion Var` = vx/vtotal)
if (nfa > 1) {
varex <- rbind(varex, `Cumulative Var` = cumsum(vx/vtotal))
varex <- rbind(varex, `Proportion Explained` = vx/sum(vx))
varex <- rbind(varex, `Cumulative Proportion` = cumsum(vx/sum(vx)))
}
fit_rotate$Vaccounted <- varex
if (imp == TRUE) {
ndi_data_scrs <- as.matrix(ndi_data_pca)
miss <- which(is.na(ndi_data_scrs), arr.ind = TRUE)
item.med <- apply(ndi_data_scrs, 2, stats::median, na.rm = TRUE)
ndi_data_scrs[miss] <- item.med[miss[, 2]]
} else {
ndi_data_scrs <- ndi_data_pca
}
scrs <- as.matrix(scale(ndi_data_scrs[complete.cases(ndi_data_scrs), abs(S_mat[ , 1]) > 0.4 ])) %*% B_mat[abs(S_mat[ , 1]) > 0.4, 1]
ndi_data_NA <- ndi_data[complete.cases(ndi_data_scrs), ]
ndi_data_NA$NDI <- c(scrs)
ndi_data_NDI <- dplyr::left_join(ndi_data[ , c("GEOID", "TotalPop")], ndi_data_NA[ , c("GEOID", "NDI")], by = "GEOID")
# Calculate Cronbach's alpha correlation coefficient among the factors and verify values are above 0.7.
if (nfa == 1) {
crnbch <- "Only one factor with minimum Eigenvalue of 1. Cannot calculate Cronbach's alpha."
} else {
cronbach <- suppressMessages(psych::alpha(ndi_data_pca[ , abs(S_mat[ , 1]) > 0.4 ], check.keys = TRUE, na.rm = TRUE, warnings = FALSE))
crnbch <- cronbach$total$std.alpha
}
# Warning for missingness of census characteristics
missingYN <- ndi_data_pca %>%
tidyr::pivot_longer(cols = dplyr::everything(),
names_to = "variable",
values_to = "val") %>%
dplyr::group_by(variable) %>%
dplyr::summarise(total = dplyr::n(),
n_missing = sum(is.na(val)),
percent_missing = paste0(round(mean(is.na(val)) * 100, 2), " %"))
if (quiet == FALSE) {
# Warning for missing census data
if (sum(missingYN$n_missing) > 0) {
message("Warning: Missing census data")
}
# Warning for Cronbach's alpha < 0.7
if (cronbach$total$std.alpha < 0.7) {
message("Warning: Cronbach's alpha correlation coefficient among the factors is less than 0.7.")
}
# Warning for proportion of variance explained by FA1
if (fit_rotate$Vaccounted[2] < 0.50) {
message("Warning: The proportion of variance explained by PC1 is less than 0.50.")
}
}
# NDI quintiles weighted by tract population
NDIQuint <- ndi_data_NDI %>%
dplyr::mutate(NDIQuint = cut(NDI*log(TotalPop),
breaks = stats::quantile(NDI*log(TotalPop),
probs = c(0, 0.2, 0.4, 0.6, 0.8, 1),
na.rm = TRUE),
labels = c("1-Least deprivation", "2-BelowAvg deprivation",
"3-Average deprivation","4-AboveAvg deprivation",
"5-Most deprivation"),
include.lowest = TRUE),
NDIQuint = factor(replace(as.character(NDIQuint),
is.na(NDIQuint) | is.infinite(NDIQuint),
"9-NDI not avail"),
c(levels(NDIQuint), "9-NDI not avail"))) %>%
dplyr::select(NDI, NDIQuint)
if (is.null(df)) {
# Format output
if (round_output == TRUE) {
ndi <- cbind(ndi_data, NDIQuint) %>%
dplyr::mutate(PctRecvIDR = round(PctRecvIDR, digits = 1),
PctPubAsst = round(PctPubAsst, digits = 1),
PctMgmtBusScArti = round(PctMgmtBusScArti, digits = 1),
PctFemHeadKids = round(PctFemHeadKids, digits = 1),
PctOwnerOcc = round(PctOwnerOcc, digits = 1),
PctNoPhone = round(PctNoPhone, digits = 1),
PctNComPlmb = round(PctNComPlmb, digits = 1),
PctEducHSPlus = round(PctEducHSPlus, digits = 1),
PctEducBchPlus = round(PctEducBchPlus, digits = 1),
PctFamBelowPov = round(PctFamBelowPov, digits = 1),
PctUnempl = round(PctUnempl, digits = 1))
} else {
ndi <- cbind(ndi_data, NDIQuint)
}
if (geo == "tract") {
ndi <- ndi %>%
dplyr::select(GEOID,
state,
county,
tract,
NDI, NDIQuint,
MedHHInc, PctRecvIDR, PctPubAsst, MedHomeVal, PctMgmtBusScArti,
PctFemHeadKids,PctOwnerOcc, PctNoPhone, PctNComPlmb, PctEducHSPlus,
PctEducBchPlus, PctFamBelowPov, PctUnempl, TotalPop)
} else {
ndi <- ndi %>%
dplyr::select(GEOID,
state,
county,
NDI, NDIQuint,
MedHHInc, PctRecvIDR, PctPubAsst, MedHomeVal, PctMgmtBusScArti,
PctFemHeadKids,PctOwnerOcc, PctNoPhone, PctNComPlmb, PctEducHSPlus,
PctEducBchPlus, PctFamBelowPov, PctUnempl, TotalPop)
}
ndi <- ndi %>%
dplyr::mutate(state = stringr::str_trim(state),
county = stringr::str_trim(county)) %>%
dplyr::arrange(GEOID) %>%
dplyr::as_tibble()
} else {
ndi <- cbind(df[ , 1], NDIQuint, df[ , 2:ncol(df)])
ndi <- dplyr::as_tibble(ndi[order(ndi[ , 1]), ])
}
out <- list(ndi = ndi,
pca = fit_rotate,
missing = missingYN,
cronbach = crnbch)
return(out)
}
Try the ndi package in your browser
Any scripts or data that you put into this service are public.
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.