source('2_process/src/data_utils.R')
p2_targets <- list(
# Confirming raw data matches `p1_unc_stats` from SB
tar_target(p2_unc_agg_summary,
p1_unc_stats |>
group_by(dimension, determinant) |>
summarize(across(c(contains('related'),
contains('unknown'),
contains('significant'),
contains('direction')),
list(total = ~sum(.x, na.rm=TRUE)))) |>
mutate(evidence_val = pos_related_total + neg_related_total +
unrelated_total + unk_direction_total)
),
tar_target(p2_unc_agg_ind_summary,
p1_unc_stats |>
group_by(dimension, determinant, indicator) |>
summarize(across(c(contains('related'),
contains('unknown'),
contains('significant'),
contains('direction')),
list(total = ~sum(.x, na.rm=TRUE)))) |>
mutate(evidence_val = pos_related_total + neg_related_total +
unrelated_total + unk_direction_total)
),
# Based on metadata:
# Amt of evidence: Small = total_studies < 5; Medium = total_studies 5-9; Large,total_studies = > 9
# Amt of agreement: Low = < 50% of models; Medium = >50% & <74% of models; High = >74% of models; NA if the level of agreement could not be calculated as indicator was measured only once.
# Dimension and determinant level
tar_target(p2_top_trend_stats,
p2_unc_agg_summary |>
dplyr::select(dimension, determinant, #indicator,
pos_related_total, neg_related_total, unrelated_total,
unk_direction_total) |>
#pivot_longer(!c(dimension,determinant)) |>
group_by(#dimension,
determinant) |>
# for each determinant find the maximum % of studies in agreement
# across the significance categories.
#slice_max(value) |>
# rename(sig_name = name, sig_value = value)
mutate(sig_value = pmax(pos_related_total, neg_related_total, unrelated_total, unk_direction_total))
),
tar_target(p2_top_trend_ind_stats,
p2_unc_agg_ind_summary |>
dplyr::select(dimension, determinant, indicator,
pos_related_total, neg_related_total, unrelated_total,
unk_direction_total) |>
pivot_longer(!c(dimension,determinant, indicator)) |>
group_by(dimension, determinant, indicator) |>
slice_max(value) |>
rename(sig_name = name, sig_value = value)
),
# Join `p2_unc_agg_summary` to top trends to get percentages of agreement and evidence for determinant and nested dimension
tar_target(`p2_unc_agg_summary_csv`,
p2_unc_agg_summary |>
left_join(p2_top_trend_stats) |>
# level of agreement is the max percent of studies in agreement
dplyr::mutate(level_agreement = 100*(sig_value/evidence_val),
evidence_bin = case_when(
evidence_val < 5 ~ "Small",
between(evidence_val, 5, 9) ~ "Medium",
evidence_val >= 10 ~ "Large"),
agreement_bin = case_when(
level_agreement < 50 ~ "Low",
between(level_agreement, 51, 74) ~ "Medium",
level_agreement > 74 ~ "High")) |>
# distinct(determinant, .keep_all = TRUE) |>
readr::write_csv('public/determinant_uncertainty.csv')
),
# commented out for now so we don't overwrite spanish names
#tar_target(p2_unc_determinant_json,
# read_csv(p2_unc_agg_summary_csv) |>
# toJSON(pretty = TRUE) |>
# write("public/determinant_uncertainty.json")
# ),
tar_target(`p2_unc_agg_summary_ind_csv`,
p2_unc_agg_ind_summary |>
left_join(p2_top_trend_ind_stats) |>
# level of agreement is the max percent of studies in agreement
dplyr::mutate(level_agreement = 100*(sig_value/evidence_val),
evidence_bin = case_when(
evidence_val < 5 ~ "Small",
between(evidence_val, 5, 9) ~ "Medium",
evidence_val >= 10 ~ "Large"),
agreement_bin = case_when(
level_agreement < 50 ~ "Low",
between(level_agreement, 51, 74) ~ "Medium",
level_agreement > 74 ~ "High")) |>
distinct(indicator, .keep_all = TRUE) |>
dplyr::select(dimension, determinant, indicator, evidence_val, evidence_bin, level_agreement) |>
readr::write_csv('public/indicator_uncertainty.csv')
),
tar_target(p2_indicators,
p1_unc_stats |>
distinct(dimension, determinant, indicator)
),
# Process census data for variables of interest
# B01003_001 = Total Population
# B19013_001 = Median Household Income in the Past 12 Months (in 2022 Inflation-Adjusted Dollars)
# B02001_003 = Estimate!!Total:!!Black or African American alone
# B03001_003 = Estimate!!Total:!!Hispanic or Latino:
# B01001_002 = Estimate!!Total:!!Male:
# B01001_026 = Estimate!!Total:!!Female:
tar_target(p2_census_acs5_layers,
list("B01003_001", "B19013_001", "B02001_003",
"B03001_003", "B01001_002", "B01001_026")
),
tar_target(p2_census_acs5_data,
get_census_data(geography = 'county',
variable = p2_census_acs5_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = FALSE),
pattern = map(p2_census_acs5_layers),
iteration = "list"
),
tar_target(p2_tot_pop,
p2_census_acs5_data[[1]] |>
st_drop_geometry() |>
rename(tot_pop = estimate)),
# Add % of total population col to each census layer
tar_target(p2_perc_census_acs5_layers_sf,
process_perc(tot_var = p2_census_acs5_data,
tot_pop = p2_tot_pop),
pattern = map(p2_census_acs5_data),
iteration = "list"),
# Disaggregated census data
# The subject tables include the following geographies: nation, all states (including DC and Puerto Rico), all metropolitan areas, all congressional districts, all counties, all places and all tracts. Subject tables provide an overview of the estimates available in a particular topic. The data are presented as both counts and percentages. There are over 66,000 variables in this dataset.
# More info here: https://api.census.gov/data/2019/acs/acs5.html
# load_variables(2022, "acs5/subject", cache = TRUE)
# Age related variables
# S0101_C02_022 = Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!Under 18 years
# S0101_C02_023 = Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!18 to 24 years
# S0101_C02_024 = Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!15 to 44 years
# S0101_C02_028 = Estimate!!Percent!!Total population!!SELECTED AGE CATEGORIES!!60 years and over
tar_target(p2_census_acs5sub_age_layers,
c("S0101_C02_022", "S0101_C02_023", "S0101_C02_024", "S0101_C02_028")),
tar_target(p2_census_acs5sub_age_data,
get_census_data(geography = 'county',
variable = p2_census_acs5sub_age_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = TRUE),
pattern = map(p2_census_acs5sub_age_layers),
iteration = "list"),
# income related variables
# S1901_C01_014 = Estimate!!Households!!PERCENT ALLOCATED!!Household income in the past 12 months
tar_target(p2_census_acs5sub_income_layers,
c("S1901_C01_014")),
tar_target(p2_census_acs5sub_income_data,
get_census_data(geography = 'county',
variable = p2_census_acs5sub_income_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = TRUE),
pattern = map(p2_census_acs5sub_income_layers),
iteration = "list"),
# education related variables
# S1501_C01_003 = Estimate!!Total!!AGE BY EDUCATIONAL ATTAINMENT!!Population 18 to 24 years!!High school graduate (includes equivalency)
# S1501_C01_009 = Estimate!!Total!!AGE BY EDUCATIONAL ATTAINMENT!!Population 25 years and over!!High school graduate (includes equivalency)
tar_target(p2_census_acs5sub_education_layers,
c("S1501_C01_003", "S1501_C01_009")),
tar_target(p2_census_acs5sub_education_data,
get_census_data(geography = 'county',
variable = p2_census_acs5sub_education_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = FALSE),
pattern = map(p2_census_acs5sub_education_layers),
iteration = "list"),
# household and rent related variables
# B25010_001 = Estimate!!Average household size --!!Total:Average Household Size of Occupied Housing Units by Tenure
# B25064_001 = Estimate!!Median gross rent
tar_target(p2_census_acs5_household_layers,
c("B25010_001", "B25064_001")),
tar_target(p2_census_acs5sub_household_data,
get_census_data(geography = 'county',
variable = p2_census_acs5_household_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = FALSE),
pattern = map(p2_census_acs5_household_layers),
iteration = "list"),
# percent households variable
# DP04_0002P = Percent!!HOUSING OCCUPANCY!!Total housing units!!Occupied housing units
# this does not have geometry, so we will join using tigris::counties()
tar_target(p2_census_acs5profile_household_layers,
c("DP04_0002P")),
tar_target(p2_census_acs5profile_household_data,
get_acs(geography = "county",
variables = p2_census_acs5profile_household_layers,
year = 2022,
survey = "acs5") |>
mutate(state_name = sub(".*, ", "", NAME)) |>
filter(state_name %in% p1_census_states)),
tar_target(p2_counties_sf,
tigris::counties(cb = TRUE) |>
st_transform(crs = p1_proj) |>
ms_simplify(keep = 0.2)),
# Join counties spatial to households dataframe
tar_target(p2_census_acs5profile_household_sf,
p2_counties_sf |>
inner_join(p2_census_acs5profile_household_data, by = "GEOID")),
# Median Household Income in the Past 12 Months (in 2022 Inflation-Adjusted Dollars) for white only, Black or African American Alone, American Indian and Alaska Native Alone, Asian Alone, Native Hawaiian and Other Pacific Islander Alone, Hispanic or Latino
tar_target(p2_census_acs5_income_by_race_layers,
c("B19013A_001", "B19013B_001", "B19013C_001", "B19013D_001", "B19013E_001", "B19013I_001")),
tar_target(p2_census_acs5sub_income_by_race_data,
get_census_data(geography = 'county',
variable = p2_census_acs5_income_by_race_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = FALSE),
pattern = map(p2_census_acs5_income_by_race_layers),
iteration = "list"),
# Disability status
# S1810_C03_001: Estimate!!Percent with a disability!!Total civilian noninstitutionalized population
# S1810_C02_001: Estimate!!With a disability!!Total civilian noninstitutionalized population
tar_target(p2_census_acs5_disability_layers,
c("S1810_C03_001", "S1810_C02_001")),
tar_target(p2_census_acs5sub_disability_data,
get_census_data(geography = 'county',
variable = p2_census_acs5_disability_layers,
states = p1_census_states,
year = 2022,
proj = p1_proj,
survey_var = "acs5",
percent_rename = FALSE),
pattern = map(p2_census_acs5_disability_layers),
iteration = "list"),
# process population density raster data
tar_target(p2_conus_sf,
fetch_conus_sf()),
tar_target(p2_conus_sf_proj,
p2_conus_sf |>
st_transform(p1_proj)),
tar_target(p2_conus_inner,
rmapshaper::ms_innerlines(p2_conus_sf_proj)),
tar_target(p2_pop_density_processed,
process_pop_dens_raster(in_raster = p1_pop_density_raster_tif, #proj = p1_proj,
conus = p2_conus_sf, conus_proj = p2_conus_sf_proj,
outfile_path = "2_process/out/pop_density.tif"),
format = "file"),
# process impervious surfaces raster data
tar_target(p2_imp_surf_processed,
process_imp_surf(in_raster = p1_imp_surf_tif, conus_proj = p2_conus_sf_proj,
outfile_path = "2_process/out/imp_surfaces.tif"),
format = "file")
)