NDI

Using the NDI Package

The NDI Package was created by Ian D. Buller. It includes several depravation indices. Here we will use the NDI Deprivation Index.

First, load packages

#|warning: false


library(waywiser)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(sfdep)
library(spdep)

Loading required package: sp

Loading required package: spData

To access larger datasets in this package, install the spDataLarge
package with: `install.packages('spDataLarge',
repos='https://nowosad.github.io/drat/', type='source')`

Loading required package: sf

Linking to GEOS 3.9.1, GDAL 3.4.3, PROJ 7.2.1; sf_use_s2() is TRUE

library(gridExtra)

Attaching package: 'gridExtra'

The following object is masked from 'package:dplyr':

    combine

library(grid)
library(ndi)

Welcome to {ndi} version 0.1.2
> help("ndi") # for documentation
> citation("ndi") # for how to cite

library(ggplot2)
library(sf)
library(tidycensus) # a dependency for the "ndi"" package
library(tigris) # a dependency for the "ndi"" package

To enable caching of data, set `options(tigris_use_cache = TRUE)`
in your R script or .Rprofile.

options(scipen=999)

Second, call the ndi values and load geometries

# Compute the NDI (Messer) values (2016-2020 5-year ACS) for TX census tracts
census_api_key("42539e850e81857ea4d3a8219088cab9544e88bb")
TX2020messer <- ndi::messer(state = "TX", year = 2020)

# Obtain the 2020 census tracts from the "tigris" package
tract2020TX <- tigris::tracts(state = "TX", year = 2020, cb = TRUE)

# Join the NDI (Messer) values to the census tract geometry
TX2020messer <- merge(tract2020TX, TX2020messer$ndi, by = "GEOID")

TX2020messer

Third, visualize the index

# Visualize the NDI (Messer) values  for TX, U.S.A., counties
## Continuous Index
ggplot2::ggplot() + 
  ggplot2::geom_sf(data = TX2020messer, 
                   ggplot2::aes(fill = NDI),
                   size = 0.20,
                   color = "white") +
  ggplot2::theme_minimal() + 
  ggplot2::scale_fill_viridis_c() +
  ggplot2::labs(fill = "Index (Continuous)",
                caption = "Source: U.S. Census ACS 2020 5 Year estimates") +
  ggplot2::ggtitle("Neighborhood Deprivation Index (Messer)",
                   subtitle = "TX counties as the referent")

Alternative option: Visualize with quartiles

## Categorical Index

### Rename "9-NDI not avail" level as NA for plotting
TX2020messer$NDIQuartNA <- factor(replace(as.character(TX2020messer$NDIQuart), 
                                            TX2020messer$NDIQuart == "9-NDI not avail", NA),
                                         c(levels(TX2020messer$NDIQuart)[-5], NA))

plot2<-ggplot2::ggplot() + 
  ggplot2::geom_sf(data = TX2020messer, 
                   ggplot2::aes(fill = NDIQuartNA),
                   size = 0.20,
                   color = "white") +
  ggplot2::theme_minimal() + 
  ggplot2::scale_fill_viridis_d(guide = ggplot2::guide_legend(reverse = TRUE),
                                na.value = "grey80") +
  ggplot2::labs(fill = "Index (Categorical)",
                caption = "Source: U.S. Census ACS 2020 5-Year estimates") +
  ggplot2::ggtitle("Neighborhood Deprivation Index (Messer) Quartiles",
                   subtitle = "TX counties as the referent") 
plot2

# ggplotly(plot2, tooltip = "text")