census10to20

library(sdc.census10to20)
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(readr)
library(tigris)
#> To enable caching of data, set `options(tigris_use_cache = TRUE)`
#> in your R script or .Rprofile.

library(ggplot2)
library(sf)
#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE

load data

data(va_hdcttr_vdh_2017_2021_material_deprivation_index,
  package = "sdc.census10to20"
)
data <- df

filter to years and region_type that need to be redistributed

data <- data %>% filter(year <= 2019)
data <- data %>% filter(region_type == "tract")

use the standardize function

this function produces both standardized and original values (see the measure variable)

standardized_data <- standardize_all(data)
#> Warning in convert_2010_to_2020_bounds(temp): data contains missing or NULL values. the value of any new tract that
#>       overlaps with a NULL value will be coerced to NULL.
#>       If this is an issue, we recommend manual insertion of values based on
#>       your contextual specifications.
#> Warning in convert_2010_to_2020_bounds(temp): data contains missing or NULL values. the value of any new tract that
#>       overlaps with a NULL value will be coerced to NULL.
#>       If this is an issue, we recommend manual insertion of values based on
#>       your contextual specifications.
#> Warning in convert_2010_to_2020_bounds(temp): data contains missing or NULL values. the value of any new tract that
#>       overlaps with a NULL value will be coerced to NULL.
#>       If this is an issue, we recommend manual insertion of values based on
#>       your contextual specifications.

producing two maps with standardized and original index values for the year 2019

standardized_data <- standardized_data %>% filter(year == 2019)
standardized_data_std <- standardized_data %>%
  filter(measure == "material_deprivation_indicator_geo20")
standardized_data_org <- standardized_data %>%
  filter(measure == 'material_deprivation_indicator_geo10')

getting tract shape files for VA

virginia_tracts_2010 <- tracts(state = "51", year = 2010, cb = TRUE)
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virginia_tracts_2020 <- tracts(state = "51", year = 2020, cb = TRUE)
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virginia_tracts_2010$geoid <- substring(virginia_tracts_2010$GEO_ID, 10)
virginia_tracts_2020$geoid <- virginia_tracts_2020$GEOID

merging the standardized and original files with shapefiles

standardized_data_std <- merge(
  virginia_tracts_2020,
  standardized_data_std,
  by = "geoid",
  all.x = TRUE
)

standardized_data_org <- merge(
  virginia_tracts_2010,
  standardized_data_org,
  by = "geoid",
  all = TRUE
)

making standaardized map

my_plot_std <- ggplot(data = standardized_data_std) +
  geom_sf(aes(fill = value)) +
  scale_fill_gradient2(
    low = "white",
    high = "red",
    midpoint = median(standardized_data_std$value, na.rm = TRUE),
    space = "Lab",
    na.value = "grey50",
    limits = c(0, 0.5)
  ) +
  labs(title = 'Townsend Index - Standardized', fill = "Value") +
  theme_minimal()
# ggsave(
#   "standardized.png",
#   plot = my_plot,
#   width = 10,
#   height = 8,
#   dpi = 300
# )

making original map

my_plot_org <- ggplot(data = standardized_data_org) +
  geom_sf(aes(fill = value)) +
  scale_fill_gradient2(
    low = "white",
    high = "red",
    midpoint = median(standardized_data_org$value, na.rm = TRUE),
    space = "Lab",
    na.value = "grey50",
    limits = c(0, 0.5)
  ) +
  labs(title = 'Townsend Index - Original', fill = "Value") +
  theme_minimal()
# ggsave(
#   "original.png",
#   plot = my_plot,
#   width = 10,
#   height = 8,
#   dpi = 300
# )

par(mfrow=c(2,1))
plot(my_plot_std)

plot(my_plot_org)