Vietnam regional analysis
Emil O. W. Kirkegaard
What
This is an R notebook for the study:
- Kirkegaard, E. O. W. & Pesta, B. Regional inequality in Vietnam and its relationship to cognitive ability and demographics. Journal of to be determined.
Start
options(digits = 2)
library(pacman)
p_load(kirkegaard, readxl, sp, lavaan, lavaan.survey, rms)
#having issues with rgdal?
#apt install libproj-dev libgdal-dev libgdal1i
#https://stackoverflow.com/questions/15248815/rgdal-package-installation
#https://stackoverflow.com/questions/30790036/error-istruegpclibpermitstatus-is-not-true
#replacement of describe()
#not sure about why this bug exists, it works fine when done manually
describe = function(...) {
y = psych::describe(...)
class(y) = "data.frame"
y
}Data
#cognitive
cog = readxl::read_xlsx("data/2004-worldbank table.xlsx")
#remove Vietnam as a whole
#Ha Tay no longer exists, so we delete it
#https://en.wikipedia.org/wiki/H%C3%A0_T%C3%A2y_Province
cog = cog[-c(3, 62), ]
#social
social = readxl::read_xlsx("data/all_province.xlsx") %>% df_legalize_names()
#fix names so they match exactly
social$province_name = social$province_name %>% str_replace(" city", "")
#more social
#files from Vietnamese official statsbank
#they are messy and were partially reformated by hand first
#get sheets names
social_data_sheets = readxl::excel_sheets("data/combined_statbank.xlsx")
#get each sheet
social2 = map_df(social_data_sheets, function(sheet) {
# browser()
#read file with the right sheet
d = readxl::read_xlsx("data/combined_statbank.xlsx", sheet = sheet)
#get the variable
var = colnames(d)[1]
#clean name
var = str_replace(var, "by province.*", "") %>%
str_trim()
#remove header rows
headers = d[1:2, ]
d = d[-c(1:2), ]
#exclude wrong units
#tricky, must be done by name match
#we can exclude the obviously wrong ones
#then approximate match the remainder
#the obvious ones are the regions (superordinate units) and the one province that was merged into Hanoi
#remove the city part like before
#and double spaces
colnames(d)[1] = "province"
d$province %<>% str_replace(" [Cc]ity", "") %>% str_replace_all("\\s+", " ")
exclude = c(
"Red River Delta",
"Northern midlands and mountain areas",
"Northern Central area and Central coastal area",
"North Central area and Central coastal area",
"Central Highlands",
"South East",
"Mekong River Delta",
"Ha Tay"
)
d = d %>% filter(!province %in% exclude)
#error if more units than possible
if (nrow(d) > 63) browser()
#average across years
d2 = rowMeans(d[, -1], na.rm = T)
y = cbind(
province_name = d[[1]],
value = d2,
var = var %>% str_legalize()
)
# colnames(y)[2] = var %>% str_legalize()
y %>% as_data_frame()
})
#switch to ISO
#names are messy!!!
social2$ISO = pu_translate(social2$province_name, superunit = "VNM")## No exact match: Thua Thien-Hue## No exact match: Ba Ria - Vung Tau## No exact match: Thua Thien-Hue## No exact match: Ba Ria - Vung Tau## No exact match: Thua Thien-Hue## No exact match: Ba Ria - Vung Tau## No exact match: Thua Thien-Hue## No exact match: Ba Ria - Vung Tau## No exact match: Thua Thien-Hue## No exact match: Ba Ria - Vung Tau## No exact match: Thua Thien-Hue## No exact match: Ba Ria - Vung Tau## No exact match: Thua Thien - Hue## No exact match: Ba Ria - Vung Tau## Best fuzzy match found: Thua Thien-Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00## Best fuzzy match found: Thua Thien-Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00## Best fuzzy match found: Thua Thien-Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00## Best fuzzy match found: Thua Thien-Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00## Best fuzzy match found: Thua Thien-Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00## Best fuzzy match found: Thua Thien-Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00## Best fuzzy match found: Thua Thien - Hue -> Thua Thien–Hue with distance 3.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00social2$province_name = NULL
#spread
social2 %<>% spread(var, value)
#enforce unique ISO
assert_that(!any(duplicated(social2$ISO)))## [1] TRUE#income data
income = readxl::read_xls("data/income.xls", sheet = 2)
income$ISO = pu_translate(income$province, superunit = "VNM")## No exact match: Thua Thien - Hue## No exact match: Ho Chi Minh City## No exact match: Ba Ria-Vung Tau## Best fuzzy match found: Thua Thien - Hue -> Thua Thien–Hue with distance 3.00## Best fuzzy match found: Ho Chi Minh City -> Ho Chi Minh with distance 5.00## Best fuzzy match found: Ba Ria-Vung Tau -> Ba Ria–Vung Tau with distance 1.00income$province = NULL
assert_that(!any(duplicated(income$ISO)))## [1] TRUE#demographics
demos = readxl::read_xlsx("data/demographics.xlsx")
#enforce consistency
assert_that(all(apply(demos[-c(1)], 1, function(x) {
x[1] >= sum(x[-1], na.rm=T)
})))## [1] TRUEpop_included_ethnics = apply(demos[-c(1)], 1, function(x) {
sum(x[-1], na.rm=T)
}) %>% sum()
print(pop_included_ethnics / sum(demos$total), 4)## [1] 0.9645#mismatches?
setdiff(social$province_name, cog$Province)## [1] "Dien Bien" "Dak Nong" "Hau Giang"setdiff(cog$Province, social$province_name)## character(0)#get ISO and join
social$ISO = pu_translate(social$province_name)## No exact match: Thua Thien Hue## No exact match: Ba Ria - Vung Tau## Best fuzzy match found: Thua Thien Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00cog$ISO = pu_translate(cog$Province)## No exact match: Thua Thien Hue## No exact match: Ba Ria - Vung Tau## Best fuzzy match found: Thua Thien Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00demos$ISO = pu_translate(demos$province)## No exact match: Thua Thien Hue## No exact match: Ba Ria - Vung Tau## Best fuzzy match found: Thua Thien Hue -> Thua Thien–Hue with distance 1.00## Best fuzzy match found: Ba Ria - Vung Tau -> Ba Ria–Vung Tau with distance 3.00#join
d = full_join(social, cog, by = "ISO")
d = full_join(d, demos, by = "ISO")
d = full_join(d, social2, by = "ISO")
d = full_join(d, income, by = "ISO")
#geometric data for plotting
geo = read_rds("data/geo63.rds")
#translate ISO-2 to custom ISO-3
geo$ISO = geo$ISO.1 %>% str_replace("VN", "VNM")
#join density into main
d = full_join(d, geo %>% as_data_frame() %>% select(ISO, Density), by = "ISO")
#rename / create
d$CA = d$`Mean(PRD500)`
d$CA_ise = 1 / d$SE
d$pop = d$Population_N %>% str_replace_all(",", "") %>% as.integer()
d$pop_sqrt = d$pop %>% sqrt()
#ethnic fractions
ethnics = names(demos)[-c(1, 2, ncol(demos))]
for (ethnic in ethnics) {
#any missing should be 0
d[[ethnic]] = if_else(is.na(d[[ethnic]]), true = 0, false = d[[ethnic]])
#get fraction of total and same
d[str_c(ethnic, "_frac")] = d[[ethnic]] / d$total
}
d$other_frac = 1 - apply(d %>% select(ends_with("_frac")), 1, sum)
d$other_frac %>% describe()#Kinh
print(sum(d$kinh) / sum(d$total), digits = 4)## [1] 0.8573#prepare geodata
#https://github.com/tidyverse/ggplot2/wiki/plotting-polygon-shapefiles
geo@data$id = rownames(geo@data)
geo_points = fortify(geo, region = "id")
geo_df = inner_join(geo_points, geo@data, by = "id")
#centroid-ish for each province
province_meta = geo_df %>%
group_by(ISO) %>%
dplyr::summarize(
lat = mean(range(lat)),
long = mean(range(long)),
name = unique(NAME_2)
)
#move geopos data to main
#join bc order may differ
d = full_join(d, province_meta %>% select(-name))## Joining, by = "ISO"Descriptives
#CA
describe(d$CA)#ethnics
describe(d[ethnics + "_frac"])S factor
#impute some missing data for statbank data
#we impute zero for investment based on the plausible assumption that no data here means no investment recorded
#we also transform it to per capita basis
d$Foreign_direct_investment_projects_licensed_in_2016 %<>% plyr::mapvalues(NA, 0) %>% as.numeric()
#per capita
d$Foreign_direct_investment_projects_licensed_in_2016_per_capita = d$Foreign_direct_investment_projects_licensed_in_2016 / d$pop
#S vars
S_vars = c("Poverty_GSO_WB_poverty_headcount_pct",
"Main_employment_agriculture_pct",
"Main_employment_wage_work_pct",
"Main_light_source_electricity_pct",
"Sanitation_indoor_flush_toilet_pct",
"Water_indoor_tap_public_tap_or_well_pct",
"Secondary_school_attendance_Lower_11_15_years_pct",
"Secondary_school_attendance_Upper_16_18_years_pct",
"Stunting_total",
"Foreign_direct_investment_projects_licensed_in_2016_per_capita",
"Index_of_Industrial_production",
"Infant_mortality_rate",
"Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population",
"Under_five_mortality_rate",
"monthly_income")
#collect, impute
S_data = d[S_vars] %>% map_df(as.numeric)
S_data_orig = S_data
#much more missing data?
miss_by_var(S_data_orig)## Poverty_GSO_WB_poverty_headcount_pct
## 0
## Main_employment_agriculture_pct
## 0
## Main_employment_wage_work_pct
## 0
## Main_light_source_electricity_pct
## 0
## Sanitation_indoor_flush_toilet_pct
## 0
## Water_indoor_tap_public_tap_or_well_pct
## 0
## Secondary_school_attendance_Lower_11_15_years_pct
## 0
## Secondary_school_attendance_Upper_16_18_years_pct
## 0
## Stunting_total
## 0
## Foreign_direct_investment_projects_licensed_in_2016_per_capita
## 0
## Index_of_Industrial_production
## 0
## Infant_mortality_rate
## 0
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population
## 1
## Under_five_mortality_rate
## 0
## monthly_income
## 3miss_plot(S_data_orig)
miss_amount(S_data_orig)## cases with missing data vars with missing data cells with missing data
## 0.0600 0.1300 0.0042#impute
S_data = miss_impute(S_data)
#ranks
S_data_ranks = df_rank(S_data)
#control for pop density
S_data_density = df_residualize(cbind(S_data, density = d$Density), resid.vars = "density", return.resid.vars = F, weights = d$pop_sqrt)
#cors
wtd.cors(S_data)## Poverty_GSO_WB_poverty_headcount_pct
## Poverty_GSO_WB_poverty_headcount_pct 1.000
## Main_employment_agriculture_pct 0.750
## Main_employment_wage_work_pct -0.687
## Main_light_source_electricity_pct -0.820
## Sanitation_indoor_flush_toilet_pct -0.572
## Water_indoor_tap_public_tap_or_well_pct -0.669
## Secondary_school_attendance_Lower_11_15_years_pct -0.473
## Secondary_school_attendance_Upper_16_18_years_pct -0.440
## Stunting_total 0.761
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.469
## Index_of_Industrial_production 0.053
## Infant_mortality_rate 0.850
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.552
## Under_five_mortality_rate 0.848
## monthly_income -0.492
## Main_employment_agriculture_pct
## Poverty_GSO_WB_poverty_headcount_pct 0.750
## Main_employment_agriculture_pct 1.000
## Main_employment_wage_work_pct -0.979
## Main_light_source_electricity_pct -0.522
## Sanitation_indoor_flush_toilet_pct -0.871
## Water_indoor_tap_public_tap_or_well_pct -0.781
## Secondary_school_attendance_Lower_11_15_years_pct -0.063
## Secondary_school_attendance_Upper_16_18_years_pct -0.119
## Stunting_total 0.745
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.537
## Index_of_Industrial_production 0.072
## Infant_mortality_rate 0.639
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.695
## Under_five_mortality_rate 0.631
## monthly_income -0.702
## Main_employment_wage_work_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.687
## Main_employment_agriculture_pct -0.979
## Main_employment_wage_work_pct 1.000
## Main_light_source_electricity_pct 0.464
## Sanitation_indoor_flush_toilet_pct 0.857
## Water_indoor_tap_public_tap_or_well_pct 0.738
## Secondary_school_attendance_Lower_11_15_years_pct 0.038
## Secondary_school_attendance_Upper_16_18_years_pct 0.075
## Stunting_total -0.720
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.591
## Index_of_Industrial_production -0.045
## Infant_mortality_rate -0.591
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.613
## Under_five_mortality_rate -0.582
## monthly_income 0.698
## Main_light_source_electricity_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.820
## Main_employment_agriculture_pct -0.522
## Main_employment_wage_work_pct 0.464
## Main_light_source_electricity_pct 1.000
## Sanitation_indoor_flush_toilet_pct 0.394
## Water_indoor_tap_public_tap_or_well_pct 0.444
## Secondary_school_attendance_Lower_11_15_years_pct 0.601
## Secondary_school_attendance_Upper_16_18_years_pct 0.509
## Stunting_total -0.578
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.357
## Index_of_Industrial_production -0.082
## Infant_mortality_rate -0.690
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.357
## Under_five_mortality_rate -0.692
## monthly_income 0.224
## Sanitation_indoor_flush_toilet_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.572
## Main_employment_agriculture_pct -0.871
## Main_employment_wage_work_pct 0.857
## Main_light_source_electricity_pct 0.394
## Sanitation_indoor_flush_toilet_pct 1.000
## Water_indoor_tap_public_tap_or_well_pct 0.639
## Secondary_school_attendance_Lower_11_15_years_pct 0.109
## Secondary_school_attendance_Upper_16_18_years_pct 0.176
## Stunting_total -0.653
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.370
## Index_of_Industrial_production -0.087
## Infant_mortality_rate -0.445
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.636
## Under_five_mortality_rate -0.437
## monthly_income 0.728
## Water_indoor_tap_public_tap_or_well_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.669
## Main_employment_agriculture_pct -0.781
## Main_employment_wage_work_pct 0.738
## Main_light_source_electricity_pct 0.444
## Sanitation_indoor_flush_toilet_pct 0.639
## Water_indoor_tap_public_tap_or_well_pct 1.000
## Secondary_school_attendance_Lower_11_15_years_pct -0.034
## Secondary_school_attendance_Upper_16_18_years_pct 0.020
## Stunting_total -0.681
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.408
## Index_of_Industrial_production -0.022
## Infant_mortality_rate -0.644
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.647
## Under_five_mortality_rate -0.638
## monthly_income 0.572
## Secondary_school_attendance_Lower_11_15_years_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.47282
## Main_employment_agriculture_pct -0.06318
## Main_employment_wage_work_pct 0.03810
## Main_light_source_electricity_pct 0.60068
## Sanitation_indoor_flush_toilet_pct 0.10910
## Water_indoor_tap_public_tap_or_well_pct -0.03408
## Secondary_school_attendance_Lower_11_15_years_pct 1.00000
## Secondary_school_attendance_Upper_16_18_years_pct 0.93300
## Stunting_total -0.33705
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.27264
## Index_of_Industrial_production 0.02792
## Infant_mortality_rate -0.31666
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.00029
## Under_five_mortality_rate -0.32039
## monthly_income -0.05256
## Secondary_school_attendance_Upper_16_18_years_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.440
## Main_employment_agriculture_pct -0.119
## Main_employment_wage_work_pct 0.075
## Main_light_source_electricity_pct 0.509
## Sanitation_indoor_flush_toilet_pct 0.176
## Water_indoor_tap_public_tap_or_well_pct 0.020
## Secondary_school_attendance_Lower_11_15_years_pct 0.933
## Secondary_school_attendance_Upper_16_18_years_pct 1.000
## Stunting_total -0.349
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.203
## Index_of_Industrial_production 0.041
## Infant_mortality_rate -0.252
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.174
## Under_five_mortality_rate -0.255
## monthly_income -0.034
## Stunting_total
## Poverty_GSO_WB_poverty_headcount_pct 0.7613
## Main_employment_agriculture_pct 0.7449
## Main_employment_wage_work_pct -0.7201
## Main_light_source_electricity_pct -0.5776
## Sanitation_indoor_flush_toilet_pct -0.6526
## Water_indoor_tap_public_tap_or_well_pct -0.6813
## Secondary_school_attendance_Lower_11_15_years_pct -0.3370
## Secondary_school_attendance_Upper_16_18_years_pct -0.3494
## Stunting_total 1.0000
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.4063
## Index_of_Industrial_production 0.0062
## Infant_mortality_rate 0.7826
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.5051
## Under_five_mortality_rate 0.7767
## monthly_income -0.6642
## Foreign_direct_investment_projects_licensed_in_2016_per_capita
## Poverty_GSO_WB_poverty_headcount_pct -0.469
## Main_employment_agriculture_pct -0.537
## Main_employment_wage_work_pct 0.591
## Main_light_source_electricity_pct 0.357
## Sanitation_indoor_flush_toilet_pct 0.370
## Water_indoor_tap_public_tap_or_well_pct 0.408
## Secondary_school_attendance_Lower_11_15_years_pct 0.273
## Secondary_school_attendance_Upper_16_18_years_pct 0.203
## Stunting_total -0.406
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1.000
## Index_of_Industrial_production 0.027
## Infant_mortality_rate -0.411
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.186
## Under_five_mortality_rate -0.405
## monthly_income 0.340
## Index_of_Industrial_production
## Poverty_GSO_WB_poverty_headcount_pct 0.0532
## Main_employment_agriculture_pct 0.0722
## Main_employment_wage_work_pct -0.0448
## Main_light_source_electricity_pct -0.0822
## Sanitation_indoor_flush_toilet_pct -0.0869
## Water_indoor_tap_public_tap_or_well_pct -0.0217
## Secondary_school_attendance_Lower_11_15_years_pct 0.0279
## Secondary_school_attendance_Upper_16_18_years_pct 0.0412
## Stunting_total 0.0062
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.0273
## Index_of_Industrial_production 1.0000
## Infant_mortality_rate 0.1076
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.0582
## Under_five_mortality_rate 0.1137
## monthly_income -0.1157
## Infant_mortality_rate
## Poverty_GSO_WB_poverty_headcount_pct 0.85
## Main_employment_agriculture_pct 0.64
## Main_employment_wage_work_pct -0.59
## Main_light_source_electricity_pct -0.69
## Sanitation_indoor_flush_toilet_pct -0.45
## Water_indoor_tap_public_tap_or_well_pct -0.64
## Secondary_school_attendance_Lower_11_15_years_pct -0.32
## Secondary_school_attendance_Upper_16_18_years_pct -0.25
## Stunting_total 0.78
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.41
## Index_of_Industrial_production 0.11
## Infant_mortality_rate 1.00
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.35
## Under_five_mortality_rate 1.00
## monthly_income -0.47
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population
## Poverty_GSO_WB_poverty_headcount_pct 0.55229
## Main_employment_agriculture_pct 0.69509
## Main_employment_wage_work_pct -0.61262
## Main_light_source_electricity_pct -0.35723
## Sanitation_indoor_flush_toilet_pct -0.63586
## Water_indoor_tap_public_tap_or_well_pct -0.64739
## Secondary_school_attendance_Lower_11_15_years_pct 0.00029
## Secondary_school_attendance_Upper_16_18_years_pct -0.17385
## Stunting_total 0.50514
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.18629
## Index_of_Industrial_production -0.05820
## Infant_mortality_rate 0.35278
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1.00000
## Under_five_mortality_rate 0.34483
## monthly_income -0.54551
## Under_five_mortality_rate
## Poverty_GSO_WB_poverty_headcount_pct 0.85
## Main_employment_agriculture_pct 0.63
## Main_employment_wage_work_pct -0.58
## Main_light_source_electricity_pct -0.69
## Sanitation_indoor_flush_toilet_pct -0.44
## Water_indoor_tap_public_tap_or_well_pct -0.64
## Secondary_school_attendance_Lower_11_15_years_pct -0.32
## Secondary_school_attendance_Upper_16_18_years_pct -0.25
## Stunting_total 0.78
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.40
## Index_of_Industrial_production 0.11
## Infant_mortality_rate 1.00
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.34
## Under_five_mortality_rate 1.00
## monthly_income -0.46
## monthly_income
## Poverty_GSO_WB_poverty_headcount_pct -0.492
## Main_employment_agriculture_pct -0.702
## Main_employment_wage_work_pct 0.698
## Main_light_source_electricity_pct 0.224
## Sanitation_indoor_flush_toilet_pct 0.728
## Water_indoor_tap_public_tap_or_well_pct 0.572
## Secondary_school_attendance_Lower_11_15_years_pct -0.053
## Secondary_school_attendance_Upper_16_18_years_pct -0.034
## Stunting_total -0.664
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.340
## Index_of_Industrial_production -0.116
## Infant_mortality_rate -0.469
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.546
## Under_five_mortality_rate -0.459
## monthly_income 1.000wtd.cors(S_data_ranks)## Poverty_GSO_WB_poverty_headcount_pct
## Poverty_GSO_WB_poverty_headcount_pct 1.00
## Main_employment_agriculture_pct 0.83
## Main_employment_wage_work_pct -0.82
## Main_light_source_electricity_pct -0.70
## Sanitation_indoor_flush_toilet_pct -0.64
## Water_indoor_tap_public_tap_or_well_pct -0.67
## Secondary_school_attendance_Lower_11_15_years_pct -0.42
## Secondary_school_attendance_Upper_16_18_years_pct -0.42
## Stunting_total 0.81
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.73
## Index_of_Industrial_production -0.17
## Infant_mortality_rate 0.85
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.49
## Under_five_mortality_rate 0.85
## monthly_income -0.58
## Main_employment_agriculture_pct
## Poverty_GSO_WB_poverty_headcount_pct 0.83
## Main_employment_agriculture_pct 1.00
## Main_employment_wage_work_pct -0.98
## Main_light_source_electricity_pct -0.50
## Sanitation_indoor_flush_toilet_pct -0.83
## Water_indoor_tap_public_tap_or_well_pct -0.79
## Secondary_school_attendance_Lower_11_15_years_pct -0.07
## Secondary_school_attendance_Upper_16_18_years_pct -0.14
## Stunting_total 0.73
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.56
## Index_of_Industrial_production -0.18
## Infant_mortality_rate 0.76
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.75
## Under_five_mortality_rate 0.76
## monthly_income -0.68
## Main_employment_wage_work_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.821
## Main_employment_agriculture_pct -0.984
## Main_employment_wage_work_pct 1.000
## Main_light_source_electricity_pct 0.503
## Sanitation_indoor_flush_toilet_pct 0.800
## Water_indoor_tap_public_tap_or_well_pct 0.746
## Secondary_school_attendance_Lower_11_15_years_pct 0.084
## Secondary_school_attendance_Upper_16_18_years_pct 0.144
## Stunting_total -0.718
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.598
## Index_of_Industrial_production 0.216
## Infant_mortality_rate -0.742
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.725
## Under_five_mortality_rate -0.741
## monthly_income 0.663
## Main_light_source_electricity_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.70
## Main_employment_agriculture_pct -0.50
## Main_employment_wage_work_pct 0.50
## Main_light_source_electricity_pct 1.00
## Sanitation_indoor_flush_toilet_pct 0.34
## Water_indoor_tap_public_tap_or_well_pct 0.43
## Secondary_school_attendance_Lower_11_15_years_pct 0.75
## Secondary_school_attendance_Upper_16_18_years_pct 0.73
## Stunting_total -0.58
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.69
## Index_of_Industrial_production 0.25
## Infant_mortality_rate -0.51
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.30
## Under_five_mortality_rate -0.51
## monthly_income 0.11
## Sanitation_indoor_flush_toilet_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.64
## Main_employment_agriculture_pct -0.83
## Main_employment_wage_work_pct 0.80
## Main_light_source_electricity_pct 0.34
## Sanitation_indoor_flush_toilet_pct 1.00
## Water_indoor_tap_public_tap_or_well_pct 0.65
## Secondary_school_attendance_Lower_11_15_years_pct 0.05
## Secondary_school_attendance_Upper_16_18_years_pct 0.13
## Stunting_total -0.58
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.30
## Index_of_Industrial_production 0.06
## Infant_mortality_rate -0.53
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.70
## Under_five_mortality_rate -0.53
## monthly_income 0.69
## Water_indoor_tap_public_tap_or_well_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.668
## Main_employment_agriculture_pct -0.789
## Main_employment_wage_work_pct 0.746
## Main_light_source_electricity_pct 0.434
## Sanitation_indoor_flush_toilet_pct 0.645
## Water_indoor_tap_public_tap_or_well_pct 1.000
## Secondary_school_attendance_Lower_11_15_years_pct 0.039
## Secondary_school_attendance_Upper_16_18_years_pct 0.086
## Stunting_total -0.672
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.446
## Index_of_Industrial_production 0.217
## Infant_mortality_rate -0.720
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.680
## Under_five_mortality_rate -0.719
## monthly_income 0.606
## Secondary_school_attendance_Lower_11_15_years_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.417
## Main_employment_agriculture_pct -0.070
## Main_employment_wage_work_pct 0.084
## Main_light_source_electricity_pct 0.746
## Sanitation_indoor_flush_toilet_pct 0.050
## Water_indoor_tap_public_tap_or_well_pct 0.039
## Secondary_school_attendance_Lower_11_15_years_pct 1.000
## Secondary_school_attendance_Upper_16_18_years_pct 0.948
## Stunting_total -0.299
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.463
## Index_of_Industrial_production 0.182
## Infant_mortality_rate -0.167
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.014
## Under_five_mortality_rate -0.167
## monthly_income -0.124
## Secondary_school_attendance_Upper_16_18_years_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.419
## Main_employment_agriculture_pct -0.137
## Main_employment_wage_work_pct 0.144
## Main_light_source_electricity_pct 0.732
## Sanitation_indoor_flush_toilet_pct 0.130
## Water_indoor_tap_public_tap_or_well_pct 0.086
## Secondary_school_attendance_Lower_11_15_years_pct 0.948
## Secondary_school_attendance_Upper_16_18_years_pct 1.000
## Stunting_total -0.280
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.427
## Index_of_Industrial_production 0.159
## Infant_mortality_rate -0.132
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.164
## Under_five_mortality_rate -0.131
## monthly_income -0.084
## Stunting_total
## Poverty_GSO_WB_poverty_headcount_pct 0.81
## Main_employment_agriculture_pct 0.73
## Main_employment_wage_work_pct -0.72
## Main_light_source_electricity_pct -0.58
## Sanitation_indoor_flush_toilet_pct -0.58
## Water_indoor_tap_public_tap_or_well_pct -0.67
## Secondary_school_attendance_Lower_11_15_years_pct -0.30
## Secondary_school_attendance_Upper_16_18_years_pct -0.28
## Stunting_total 1.00
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.57
## Index_of_Industrial_production -0.17
## Infant_mortality_rate 0.81
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.46
## Under_five_mortality_rate 0.81
## monthly_income -0.57
## Foreign_direct_investment_projects_licensed_in_2016_per_capita
## Poverty_GSO_WB_poverty_headcount_pct -0.73
## Main_employment_agriculture_pct -0.56
## Main_employment_wage_work_pct 0.60
## Main_light_source_electricity_pct 0.69
## Sanitation_indoor_flush_toilet_pct 0.30
## Water_indoor_tap_public_tap_or_well_pct 0.45
## Secondary_school_attendance_Lower_11_15_years_pct 0.46
## Secondary_school_attendance_Upper_16_18_years_pct 0.43
## Stunting_total -0.57
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1.00
## Index_of_Industrial_production 0.33
## Infant_mortality_rate -0.59
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.24
## Under_five_mortality_rate -0.59
## monthly_income 0.28
## Index_of_Industrial_production
## Poverty_GSO_WB_poverty_headcount_pct -0.17
## Main_employment_agriculture_pct -0.18
## Main_employment_wage_work_pct 0.22
## Main_light_source_electricity_pct 0.25
## Sanitation_indoor_flush_toilet_pct 0.06
## Water_indoor_tap_public_tap_or_well_pct 0.22
## Secondary_school_attendance_Lower_11_15_years_pct 0.18
## Secondary_school_attendance_Upper_16_18_years_pct 0.16
## Stunting_total -0.17
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.33
## Index_of_Industrial_production 1.00
## Infant_mortality_rate -0.15
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.17
## Under_five_mortality_rate -0.15
## monthly_income -0.04
## Infant_mortality_rate
## Poverty_GSO_WB_poverty_headcount_pct 0.85
## Main_employment_agriculture_pct 0.76
## Main_employment_wage_work_pct -0.74
## Main_light_source_electricity_pct -0.51
## Sanitation_indoor_flush_toilet_pct -0.53
## Water_indoor_tap_public_tap_or_well_pct -0.72
## Secondary_school_attendance_Lower_11_15_years_pct -0.17
## Secondary_school_attendance_Upper_16_18_years_pct -0.13
## Stunting_total 0.81
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.59
## Index_of_Industrial_production -0.15
## Infant_mortality_rate 1.00
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.46
## Under_five_mortality_rate 1.00
## monthly_income -0.63
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population
## Poverty_GSO_WB_poverty_headcount_pct 0.486
## Main_employment_agriculture_pct 0.746
## Main_employment_wage_work_pct -0.725
## Main_light_source_electricity_pct -0.298
## Sanitation_indoor_flush_toilet_pct -0.701
## Water_indoor_tap_public_tap_or_well_pct -0.680
## Secondary_school_attendance_Lower_11_15_years_pct -0.014
## Secondary_school_attendance_Upper_16_18_years_pct -0.164
## Stunting_total 0.461
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.245
## Index_of_Industrial_production -0.167
## Infant_mortality_rate 0.456
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1.000
## Under_five_mortality_rate 0.455
## monthly_income -0.536
## Under_five_mortality_rate
## Poverty_GSO_WB_poverty_headcount_pct 0.85
## Main_employment_agriculture_pct 0.76
## Main_employment_wage_work_pct -0.74
## Main_light_source_electricity_pct -0.51
## Sanitation_indoor_flush_toilet_pct -0.53
## Water_indoor_tap_public_tap_or_well_pct -0.72
## Secondary_school_attendance_Lower_11_15_years_pct -0.17
## Secondary_school_attendance_Upper_16_18_years_pct -0.13
## Stunting_total 0.81
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.59
## Index_of_Industrial_production -0.15
## Infant_mortality_rate 1.00
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.45
## Under_five_mortality_rate 1.00
## monthly_income -0.63
## monthly_income
## Poverty_GSO_WB_poverty_headcount_pct -0.576
## Main_employment_agriculture_pct -0.680
## Main_employment_wage_work_pct 0.663
## Main_light_source_electricity_pct 0.113
## Sanitation_indoor_flush_toilet_pct 0.692
## Water_indoor_tap_public_tap_or_well_pct 0.606
## Secondary_school_attendance_Lower_11_15_years_pct -0.124
## Secondary_school_attendance_Upper_16_18_years_pct -0.084
## Stunting_total -0.574
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.278
## Index_of_Industrial_production -0.040
## Infant_mortality_rate -0.635
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.536
## Under_five_mortality_rate -0.634
## monthly_income 1.000wtd.cors(S_data_density)## Poverty_GSO_WB_poverty_headcount_pct
## Poverty_GSO_WB_poverty_headcount_pct 1.00
## Main_employment_agriculture_pct 0.65
## Main_employment_wage_work_pct -0.56
## Main_light_source_electricity_pct -0.79
## Sanitation_indoor_flush_toilet_pct -0.42
## Water_indoor_tap_public_tap_or_well_pct -0.56
## Secondary_school_attendance_Lower_11_15_years_pct -0.37
## Secondary_school_attendance_Upper_16_18_years_pct -0.31
## Stunting_total 0.65
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.35
## Index_of_Industrial_production 0.04
## Infant_mortality_rate 0.81
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.42
## Under_five_mortality_rate 0.81
## monthly_income -0.21
## Main_employment_agriculture_pct
## Poverty_GSO_WB_poverty_headcount_pct 0.646
## Main_employment_agriculture_pct 1.000
## Main_employment_wage_work_pct -0.970
## Main_light_source_electricity_pct -0.401
## Sanitation_indoor_flush_toilet_pct -0.827
## Water_indoor_tap_public_tap_or_well_pct -0.711
## Secondary_school_attendance_Lower_11_15_years_pct 0.160
## Secondary_school_attendance_Upper_16_18_years_pct 0.117
## Stunting_total 0.610
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.424
## Index_of_Industrial_production 0.064
## Infant_mortality_rate 0.514
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.613
## Under_five_mortality_rate 0.507
## monthly_income -0.539
## Main_employment_wage_work_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.559
## Main_employment_agriculture_pct -0.970
## Main_employment_wage_work_pct 1.000
## Main_light_source_electricity_pct 0.327
## Sanitation_indoor_flush_toilet_pct 0.811
## Water_indoor_tap_public_tap_or_well_pct 0.652
## Secondary_school_attendance_Lower_11_15_years_pct -0.188
## Secondary_school_attendance_Upper_16_18_years_pct -0.169
## Stunting_total -0.575
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.493
## Index_of_Industrial_production -0.031
## Infant_mortality_rate -0.449
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.507
## Under_five_mortality_rate -0.441
## monthly_income 0.543
## Main_light_source_electricity_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.790
## Main_employment_agriculture_pct -0.401
## Main_employment_wage_work_pct 0.327
## Main_light_source_electricity_pct 1.000
## Sanitation_indoor_flush_toilet_pct 0.247
## Water_indoor_tap_public_tap_or_well_pct 0.325
## Secondary_school_attendance_Lower_11_15_years_pct 0.543
## Secondary_school_attendance_Upper_16_18_years_pct 0.427
## Stunting_total -0.466
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.254
## Index_of_Industrial_production -0.074
## Infant_mortality_rate -0.631
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.230
## Under_five_mortality_rate -0.635
## monthly_income -0.058
## Sanitation_indoor_flush_toilet_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.416
## Main_employment_agriculture_pct -0.827
## Main_employment_wage_work_pct 0.811
## Main_light_source_electricity_pct 0.247
## Sanitation_indoor_flush_toilet_pct 1.000
## Water_indoor_tap_public_tap_or_well_pct 0.533
## Secondary_school_attendance_Lower_11_15_years_pct -0.074
## Secondary_school_attendance_Upper_16_18_years_pct -0.015
## Stunting_total -0.514
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.225
## Index_of_Industrial_production -0.080
## Infant_mortality_rate -0.273
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.547
## Under_five_mortality_rate -0.267
## monthly_income 0.633
## Water_indoor_tap_public_tap_or_well_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.5633
## Main_employment_agriculture_pct -0.7105
## Main_employment_wage_work_pct 0.6524
## Main_light_source_electricity_pct 0.3254
## Sanitation_indoor_flush_toilet_pct 0.5327
## Water_indoor_tap_public_tap_or_well_pct 1.0000
## Secondary_school_attendance_Lower_11_15_years_pct -0.2351
## Secondary_school_attendance_Upper_16_18_years_pct -0.1952
## Stunting_total -0.5641
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.2830
## Index_of_Industrial_production -0.0061
## Infant_mortality_rate -0.5439
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.5612
## Under_five_mortality_rate -0.5389
## monthly_income 0.3853
## Secondary_school_attendance_Lower_11_15_years_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.370
## Main_employment_agriculture_pct 0.160
## Main_employment_wage_work_pct -0.188
## Main_light_source_electricity_pct 0.543
## Sanitation_indoor_flush_toilet_pct -0.074
## Water_indoor_tap_public_tap_or_well_pct -0.235
## Secondary_school_attendance_Lower_11_15_years_pct 1.000
## Secondary_school_attendance_Upper_16_18_years_pct 0.924
## Stunting_total -0.119
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.166
## Index_of_Industrial_production 0.043
## Infant_mortality_rate -0.193
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.175
## Under_five_mortality_rate -0.200
## monthly_income -0.398
## Secondary_school_attendance_Upper_16_18_years_pct
## Poverty_GSO_WB_poverty_headcount_pct -0.308
## Main_employment_agriculture_pct 0.117
## Main_employment_wage_work_pct -0.169
## Main_light_source_electricity_pct 0.427
## Sanitation_indoor_flush_toilet_pct -0.015
## Water_indoor_tap_public_tap_or_well_pct -0.195
## Secondary_school_attendance_Lower_11_15_years_pct 0.924
## Secondary_school_attendance_Upper_16_18_years_pct 1.000
## Stunting_total -0.096
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.070
## Index_of_Industrial_production 0.059
## Infant_mortality_rate -0.095
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.011
## Under_five_mortality_rate -0.102
## monthly_income -0.410
## Stunting_total
## Poverty_GSO_WB_poverty_headcount_pct 0.646
## Main_employment_agriculture_pct 0.610
## Main_employment_wage_work_pct -0.575
## Main_light_source_electricity_pct -0.466
## Sanitation_indoor_flush_toilet_pct -0.514
## Water_indoor_tap_public_tap_or_well_pct -0.564
## Secondary_school_attendance_Lower_11_15_years_pct -0.119
## Secondary_school_attendance_Upper_16_18_years_pct -0.096
## Stunting_total 1.000
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.197
## Index_of_Industrial_production -0.029
## Infant_mortality_rate 0.725
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.317
## Under_five_mortality_rate 0.723
## monthly_income -0.358
## Foreign_direct_investment_projects_licensed_in_2016_per_capita
## Poverty_GSO_WB_poverty_headcount_pct -0.350
## Main_employment_agriculture_pct -0.424
## Main_employment_wage_work_pct 0.493
## Main_light_source_electricity_pct 0.254
## Sanitation_indoor_flush_toilet_pct 0.225
## Water_indoor_tap_public_tap_or_well_pct 0.283
## Secondary_school_attendance_Lower_11_15_years_pct 0.166
## Secondary_school_attendance_Upper_16_18_years_pct 0.070
## Stunting_total -0.197
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1.000
## Index_of_Industrial_production 0.044
## Infant_mortality_rate -0.292
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.030
## Under_five_mortality_rate -0.288
## monthly_income 0.114
## Index_of_Industrial_production
## Poverty_GSO_WB_poverty_headcount_pct 0.0402
## Main_employment_agriculture_pct 0.0636
## Main_employment_wage_work_pct -0.0314
## Main_light_source_electricity_pct -0.0735
## Sanitation_indoor_flush_toilet_pct -0.0802
## Water_indoor_tap_public_tap_or_well_pct -0.0061
## Secondary_school_attendance_Lower_11_15_years_pct 0.0431
## Secondary_school_attendance_Upper_16_18_years_pct 0.0595
## Stunting_total -0.0290
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.0440
## Index_of_Industrial_production 1.0000
## Infant_mortality_rate 0.1019
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.0801
## Under_five_mortality_rate 0.1085
## monthly_income -0.1209
## Infant_mortality_rate
## Poverty_GSO_WB_poverty_headcount_pct 0.807
## Main_employment_agriculture_pct 0.514
## Main_employment_wage_work_pct -0.449
## Main_light_source_electricity_pct -0.631
## Sanitation_indoor_flush_toilet_pct -0.273
## Water_indoor_tap_public_tap_or_well_pct -0.544
## Secondary_school_attendance_Lower_11_15_years_pct -0.193
## Secondary_school_attendance_Upper_16_18_years_pct -0.095
## Stunting_total 0.725
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.292
## Index_of_Industrial_production 0.102
## Infant_mortality_rate 1.000
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.190
## Under_five_mortality_rate 1.000
## monthly_income -0.213
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population
## Poverty_GSO_WB_poverty_headcount_pct 0.424
## Main_employment_agriculture_pct 0.613
## Main_employment_wage_work_pct -0.507
## Main_light_source_electricity_pct -0.230
## Sanitation_indoor_flush_toilet_pct -0.547
## Water_indoor_tap_public_tap_or_well_pct -0.561
## Secondary_school_attendance_Lower_11_15_years_pct 0.175
## Secondary_school_attendance_Upper_16_18_years_pct -0.011
## Stunting_total 0.317
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.030
## Index_of_Industrial_production -0.080
## Infant_mortality_rate 0.190
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1.000
## Under_five_mortality_rate 0.184
## monthly_income -0.391
## Under_five_mortality_rate
## Poverty_GSO_WB_poverty_headcount_pct 0.81
## Main_employment_agriculture_pct 0.51
## Main_employment_wage_work_pct -0.44
## Main_light_source_electricity_pct -0.63
## Sanitation_indoor_flush_toilet_pct -0.27
## Water_indoor_tap_public_tap_or_well_pct -0.54
## Secondary_school_attendance_Lower_11_15_years_pct -0.20
## Secondary_school_attendance_Upper_16_18_years_pct -0.10
## Stunting_total 0.72
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.29
## Index_of_Industrial_production 0.11
## Infant_mortality_rate 1.00
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.18
## Under_five_mortality_rate 1.00
## monthly_income -0.21
## monthly_income
## Poverty_GSO_WB_poverty_headcount_pct -0.206
## Main_employment_agriculture_pct -0.539
## Main_employment_wage_work_pct 0.543
## Main_light_source_electricity_pct -0.058
## Sanitation_indoor_flush_toilet_pct 0.633
## Water_indoor_tap_public_tap_or_well_pct 0.385
## Secondary_school_attendance_Lower_11_15_years_pct -0.398
## Secondary_school_attendance_Upper_16_18_years_pct -0.410
## Stunting_total -0.358
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.114
## Index_of_Industrial_production -0.121
## Infant_mortality_rate -0.213
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.391
## Under_five_mortality_rate -0.206
## monthly_income 1.000#factor analysis
S_list = list(
standard = fa(S_data),
ranks = fa(S_data_ranks),
weights = fa(S_data, weight = d$pop_sqrt),
ctrl_density = fa(S_data_density)
)
#print produces an error, so we have to print with try
try(print(S_list))## $standard
## Factor Analysis using method = minres
## Call: fa(r = S_data)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Poverty_GSO_WB_poverty_headcount_pct 0.91
## Main_employment_agriculture_pct 0.92
## Main_employment_wage_work_pct -0.87
## Main_light_source_electricity_pct -0.68
## Sanitation_indoor_flush_toilet_pct -0.77
## Water_indoor_tap_public_tap_or_well_pct -0.79
## Secondary_school_attendance_Lower_11_15_years_pct -0.30
## Secondary_school_attendance_Upper_16_18_years_pct -0.31
## Stunting_total 0.87
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.52
## Index_of_Industrial_production 0.06
## Infant_mortality_rate 0.82
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.62
## Under_five_mortality_rate 0.82
## monthly_income -0.67
## h2
## Poverty_GSO_WB_poverty_headcount_pct 0.8262
## Main_employment_agriculture_pct 0.8411
## Main_employment_wage_work_pct 0.7555
## Main_light_source_electricity_pct 0.4691
## Sanitation_indoor_flush_toilet_pct 0.5872
## Water_indoor_tap_public_tap_or_well_pct 0.6163
## Secondary_school_attendance_Lower_11_15_years_pct 0.0879
## Secondary_school_attendance_Upper_16_18_years_pct 0.0932
## Stunting_total 0.7644
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.2752
## Index_of_Industrial_production 0.0042
## Infant_mortality_rate 0.6786
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.3893
## Under_five_mortality_rate 0.6675
## monthly_income 0.4436
## u2
## Poverty_GSO_WB_poverty_headcount_pct 0.17
## Main_employment_agriculture_pct 0.16
## Main_employment_wage_work_pct 0.24
## Main_light_source_electricity_pct 0.53
## Sanitation_indoor_flush_toilet_pct 0.41
## Water_indoor_tap_public_tap_or_well_pct 0.38
## Secondary_school_attendance_Lower_11_15_years_pct 0.91
## Secondary_school_attendance_Upper_16_18_years_pct 0.91
## Stunting_total 0.24
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.72
## Index_of_Industrial_production 1.00
## Infant_mortality_rate 0.32
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.61
## Under_five_mortality_rate 0.33
## monthly_income 0.56
## com
## Poverty_GSO_WB_poverty_headcount_pct 1
## Main_employment_agriculture_pct 1
## Main_employment_wage_work_pct 1
## Main_light_source_electricity_pct 1
## Sanitation_indoor_flush_toilet_pct 1
## Water_indoor_tap_public_tap_or_well_pct 1
## Secondary_school_attendance_Lower_11_15_years_pct 1
## Secondary_school_attendance_Upper_16_18_years_pct 1
## Stunting_total 1
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1
## Index_of_Industrial_production 1
## Infant_mortality_rate 1
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1
## Under_five_mortality_rate 1
## monthly_income 1
##
## MR1
## SS loadings 7.5
## Proportion Var 0.5
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 105 and the objective function was 26 with Chi Square of 1469
## The degrees of freedom for the model are 90 and the objective function was 17
##
## The root mean square of the residuals (RMSR) is 0.15
## The df corrected root mean square of the residuals is 0.16
##
## The harmonic number of observations is 63 with the empirical chi square 307 with prob < 1.5e-25
## The total number of observations was 63 with Likelihood Chi Square = 932 with prob < 3.6e-140
##
## Tucker Lewis Index of factoring reliability = 0.27
## RMSEA index = 0.41 and the 90 % confidence intervals are 0.37 NA
## BIC = 560
## Fit based upon off diagonal values = 0.91
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.99
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.97
##
## $ranks
## Factor Analysis using method = minres
## Call: fa(r = S_data_ranks)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Poverty_GSO_WB_poverty_headcount_pct -0.94
## Main_employment_agriculture_pct -0.93
## Main_employment_wage_work_pct 0.92
## Main_light_source_electricity_pct 0.63
## Sanitation_indoor_flush_toilet_pct 0.74
## Water_indoor_tap_public_tap_or_well_pct 0.80
## Secondary_school_attendance_Lower_11_15_years_pct 0.28
## Secondary_school_attendance_Upper_16_18_years_pct 0.30
## Stunting_total -0.85
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.66
## Index_of_Industrial_production 0.22
## Infant_mortality_rate -0.87
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.65
## Under_five_mortality_rate -0.87
## monthly_income 0.65
## h2
## Poverty_GSO_WB_poverty_headcount_pct 0.879
## Main_employment_agriculture_pct 0.870
## Main_employment_wage_work_pct 0.844
## Main_light_source_electricity_pct 0.402
## Sanitation_indoor_flush_toilet_pct 0.541
## Water_indoor_tap_public_tap_or_well_pct 0.642
## Secondary_school_attendance_Lower_11_15_years_pct 0.077
## Secondary_school_attendance_Upper_16_18_years_pct 0.092
## Stunting_total 0.719
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.439
## Index_of_Industrial_production 0.047
## Infant_mortality_rate 0.765
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.418
## Under_five_mortality_rate 0.764
## monthly_income 0.428
## u2
## Poverty_GSO_WB_poverty_headcount_pct 0.12
## Main_employment_agriculture_pct 0.13
## Main_employment_wage_work_pct 0.16
## Main_light_source_electricity_pct 0.60
## Sanitation_indoor_flush_toilet_pct 0.46
## Water_indoor_tap_public_tap_or_well_pct 0.36
## Secondary_school_attendance_Lower_11_15_years_pct 0.92
## Secondary_school_attendance_Upper_16_18_years_pct 0.91
## Stunting_total 0.28
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.56
## Index_of_Industrial_production 0.95
## Infant_mortality_rate 0.23
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.58
## Under_five_mortality_rate 0.24
## monthly_income 0.57
## com
## Poverty_GSO_WB_poverty_headcount_pct 1
## Main_employment_agriculture_pct 1
## Main_employment_wage_work_pct 1
## Main_light_source_electricity_pct 1
## Sanitation_indoor_flush_toilet_pct 1
## Water_indoor_tap_public_tap_or_well_pct 1
## Secondary_school_attendance_Lower_11_15_years_pct 1
## Secondary_school_attendance_Upper_16_18_years_pct 1
## Stunting_total 1
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1
## Index_of_Industrial_production 1
## Infant_mortality_rate 1
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1
## Under_five_mortality_rate 1
## monthly_income 1
##
## MR1
## SS loadings 7.93
## Proportion Var 0.53
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 105 and the objective function was 28 with Chi Square of 1562
## The degrees of freedom for the model are 90 and the objective function was 17
##
## The root mean square of the residuals (RMSR) is 0.17
## The df corrected root mean square of the residuals is 0.18
##
## The harmonic number of observations is 63 with the empirical chi square 363 with prob < 1.7e-34
## The total number of observations was 63 with Likelihood Chi Square = 942 with prob < 5e-142
##
## Tucker Lewis Index of factoring reliability = 0.31
## RMSEA index = 0.41 and the 90 % confidence intervals are 0.37 NA
## BIC = 569
## Fit based upon off diagonal values = 0.91
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.99
## Multiple R square of scores with factors 0.98
## Minimum correlation of possible factor scores 0.96
##
## $weights
## Factor Analysis using method = minres
## Call: fa(r = S_data, weight = d$pop_sqrt)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Poverty_GSO_WB_poverty_headcount_pct 0.89
## Main_employment_agriculture_pct 0.93
## Main_employment_wage_work_pct -0.89
## Main_light_source_electricity_pct -0.64
## Sanitation_indoor_flush_toilet_pct -0.80
## Water_indoor_tap_public_tap_or_well_pct -0.80
## Secondary_school_attendance_Lower_11_15_years_pct -0.26
## Secondary_school_attendance_Upper_16_18_years_pct -0.28
## Stunting_total 0.89
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.53
## Index_of_Industrial_production 0.05
## Infant_mortality_rate 0.81
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.67
## Under_five_mortality_rate 0.80
## monthly_income -0.72
## h2
## Poverty_GSO_WB_poverty_headcount_pct 0.7949
## Main_employment_agriculture_pct 0.8592
## Main_employment_wage_work_pct 0.7870
## Main_light_source_electricity_pct 0.4105
## Sanitation_indoor_flush_toilet_pct 0.6440
## Water_indoor_tap_public_tap_or_well_pct 0.6418
## Secondary_school_attendance_Lower_11_15_years_pct 0.0691
## Secondary_school_attendance_Upper_16_18_years_pct 0.0799
## Stunting_total 0.7879
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.2784
## Index_of_Industrial_production 0.0026
## Infant_mortality_rate 0.6547
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.4533
## Under_five_mortality_rate 0.6394
## monthly_income 0.5167
## u2
## Poverty_GSO_WB_poverty_headcount_pct 0.21
## Main_employment_agriculture_pct 0.14
## Main_employment_wage_work_pct 0.21
## Main_light_source_electricity_pct 0.59
## Sanitation_indoor_flush_toilet_pct 0.36
## Water_indoor_tap_public_tap_or_well_pct 0.36
## Secondary_school_attendance_Lower_11_15_years_pct 0.93
## Secondary_school_attendance_Upper_16_18_years_pct 0.92
## Stunting_total 0.21
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.72
## Index_of_Industrial_production 1.00
## Infant_mortality_rate 0.35
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.55
## Under_five_mortality_rate 0.36
## monthly_income 0.48
## com
## Poverty_GSO_WB_poverty_headcount_pct 1
## Main_employment_agriculture_pct 1
## Main_employment_wage_work_pct 1
## Main_light_source_electricity_pct 1
## Sanitation_indoor_flush_toilet_pct 1
## Water_indoor_tap_public_tap_or_well_pct 1
## Secondary_school_attendance_Lower_11_15_years_pct 1
## Secondary_school_attendance_Upper_16_18_years_pct 1
## Stunting_total 1
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1
## Index_of_Industrial_production 1
## Infant_mortality_rate 1
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1
## Under_five_mortality_rate 1
## monthly_income 1
##
## MR1
## SS loadings 7.62
## Proportion Var 0.51
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 105 and the objective function was 27 with Chi Square of 1515
## The degrees of freedom for the model are 90 and the objective function was 17
##
## The root mean square of the residuals (RMSR) is 0.16
## The df corrected root mean square of the residuals is 0.17
##
## The harmonic number of observations is 63 with the empirical chi square 319 with prob < 2.7e-27
## The total number of observations was 63 with Likelihood Chi Square = 955 with prob < 1.2e-144
##
## Tucker Lewis Index of factoring reliability = 0.28
## RMSEA index = 0.42 and the 90 % confidence intervals are 0.37 NA
## BIC = 582
## Fit based upon off diagonal values = 0.91
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.99
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.98
##
## $ctrl_density
## Factor Analysis using method = minres
## Call: fa(r = S_data_density)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Poverty_GSO_WB_poverty_headcount_pct 0.84
## Main_employment_agriculture_pct 0.90
## Main_employment_wage_work_pct -0.83
## Main_light_source_electricity_pct -0.58
## Sanitation_indoor_flush_toilet_pct -0.69
## Water_indoor_tap_public_tap_or_well_pct -0.74
## Secondary_school_attendance_Lower_11_15_years_pct -0.05
## Secondary_school_attendance_Upper_16_18_years_pct -0.04
## Stunting_total 0.77
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.39
## Index_of_Industrial_production 0.06
## Infant_mortality_rate 0.77
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.52
## Under_five_mortality_rate 0.76
## monthly_income -0.45
## h2
## Poverty_GSO_WB_poverty_headcount_pct 0.7072
## Main_employment_agriculture_pct 0.8128
## Main_employment_wage_work_pct 0.6908
## Main_light_source_electricity_pct 0.3386
## Sanitation_indoor_flush_toilet_pct 0.4723
## Water_indoor_tap_public_tap_or_well_pct 0.5540
## Secondary_school_attendance_Lower_11_15_years_pct 0.0028
## Secondary_school_attendance_Upper_16_18_years_pct 0.0014
## Stunting_total 0.5908
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.1522
## Index_of_Industrial_production 0.0034
## Infant_mortality_rate 0.5868
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.2703
## Under_five_mortality_rate 0.5787
## monthly_income 0.2054
## u2
## Poverty_GSO_WB_poverty_headcount_pct 0.29
## Main_employment_agriculture_pct 0.19
## Main_employment_wage_work_pct 0.31
## Main_light_source_electricity_pct 0.66
## Sanitation_indoor_flush_toilet_pct 0.53
## Water_indoor_tap_public_tap_or_well_pct 0.45
## Secondary_school_attendance_Lower_11_15_years_pct 1.00
## Secondary_school_attendance_Upper_16_18_years_pct 1.00
## Stunting_total 0.41
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.85
## Index_of_Industrial_production 1.00
## Infant_mortality_rate 0.41
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.73
## Under_five_mortality_rate 0.42
## monthly_income 0.79
## com
## Poverty_GSO_WB_poverty_headcount_pct 1
## Main_employment_agriculture_pct 1
## Main_employment_wage_work_pct 1
## Main_light_source_electricity_pct 1
## Sanitation_indoor_flush_toilet_pct 1
## Water_indoor_tap_public_tap_or_well_pct 1
## Secondary_school_attendance_Lower_11_15_years_pct 1
## Secondary_school_attendance_Upper_16_18_years_pct 1
## Stunting_total 1
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 1
## Index_of_Industrial_production 1
## Infant_mortality_rate 1
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 1
## Under_five_mortality_rate 1
## monthly_income 1
##
## MR1
## SS loadings 6.0
## Proportion Var 0.4
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 105 and the objective function was 24 with Chi Square of 1325
## The degrees of freedom for the model are 90 and the objective function was 17
##
## The root mean square of the residuals (RMSR) is 0.19
## The df corrected root mean square of the residuals is 0.21
##
## The harmonic number of observations is 63 with the empirical chi square 494 with prob < 5.6e-57
## The total number of observations was 63 with Likelihood Chi Square = 957 with prob < 5.3e-145
##
## Tucker Lewis Index of factoring reliability = 0.16
## RMSEA index = 0.42 and the 90 % confidence intervals are 0.37 NA
## BIC = 584
## Fit based upon off diagonal values = 0.8
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.99
## Multiple R square of scores with factors 0.98
## Minimum correlation of possible factor scores 0.95#plot
fa_plot_loadings(S_list, reverse_vector = c(-1, 1, -1, -1)) +
scale_y_continuous(limits = c(-1, 1))
GG_save("figs/S_loadings_compare.png")
#check for method variance
fa_mv = fa_all_methods(S_data, weights = d$sqrt_pop)## 1 out of 40 - regression_minres## Saving results from regression_minres## 2 out of 40 - Thurstone_minres## Saving results from Thurstone_minres## 3 out of 40 - tenBerge_minres## Saving results from tenBerge_minres## 4 out of 40 - Anderson_minres## Skipping Anderson_minres due to extraction error## 5 out of 40 - Bartlett_minres## Saving results from Bartlett_minres## 6 out of 40 - regression_ols## Saving results from regression_ols## 7 out of 40 - Thurstone_ols## Saving results from Thurstone_ols## 8 out of 40 - tenBerge_ols## Saving results from tenBerge_ols## 9 out of 40 - Anderson_ols## Skipping Anderson_ols due to extraction error## 10 out of 40 - Bartlett_ols## Saving results from Bartlett_ols## 11 out of 40 - regression_wls## Saving results from regression_wls## 12 out of 40 - Thurstone_wls## Saving results from Thurstone_wls## 13 out of 40 - tenBerge_wls## Saving results from tenBerge_wls## 14 out of 40 - Anderson_wls## Skipping Anderson_wls due to extraction error## 15 out of 40 - Bartlett_wls## Saving results from Bartlett_wls## 16 out of 40 - regression_gls## Saving results from regression_gls## 17 out of 40 - Thurstone_gls## Saving results from Thurstone_gls## 18 out of 40 - tenBerge_gls## Saving results from tenBerge_gls## 19 out of 40 - Anderson_gls## Skipping Anderson_gls due to extraction error## 20 out of 40 - Bartlett_gls## Saving results from Bartlett_gls## 21 out of 40 - regression_pa## Saving results from regression_pa## 22 out of 40 - Thurstone_pa## Saving results from Thurstone_pa## 23 out of 40 - tenBerge_pa## Saving results from tenBerge_pa## 24 out of 40 - Anderson_pa## Skipping Anderson_pa due to extraction error## 25 out of 40 - Bartlett_pa## Saving results from Bartlett_pa## 26 out of 40 - regression_ml## Saving results from regression_ml## 27 out of 40 - Thurstone_ml## Saving results from Thurstone_ml## 28 out of 40 - tenBerge_ml## Saving results from tenBerge_ml## 29 out of 40 - Anderson_ml## Skipping Anderson_ml due to extraction error## 30 out of 40 - Bartlett_ml## Saving results from Bartlett_ml## 31 out of 40 - regression_minchi## Saving results from regression_minchi## 32 out of 40 - Thurstone_minchi## Saving results from Thurstone_minchi## 33 out of 40 - tenBerge_minchi## Saving results from tenBerge_minchi## 34 out of 40 - Anderson_minchi## Skipping Anderson_minchi due to extraction error## 35 out of 40 - Bartlett_minchi## Saving results from Bartlett_minchi## 36 out of 40 - regression_minrank## Saving results from regression_minrank## 37 out of 40 - Thurstone_minrank## Saving results from Thurstone_minrank## 38 out of 40 - tenBerge_minrank## Saving results from tenBerge_minrank## 39 out of 40 - Anderson_minrank## Skipping Anderson_minrank due to extraction error## 40 out of 40 - Bartlett_minrank## Saving results from Bartlett_minrank#examine cors of scores
fa_mv$scores %>% wtd.cors()## regression_minres Thurstone_minres tenBerge_minres
## regression_minres 1.00 1.00 1.00
## Thurstone_minres 1.00 1.00 1.00
## tenBerge_minres 1.00 1.00 1.00
## Bartlett_minres 0.99 0.99 0.99
## regression_ols 1.00 1.00 1.00
## Thurstone_ols 1.00 1.00 1.00
## tenBerge_ols 1.00 1.00 1.00
## Bartlett_ols 0.99 0.99 0.99
## regression_wls 0.99 0.99 0.99
## Thurstone_wls 0.99 0.99 0.99
## tenBerge_wls 0.99 0.99 0.99
## Bartlett_wls 0.99 0.99 0.99
## regression_gls 0.99 0.99 0.99
## Thurstone_gls 0.99 0.99 0.99
## tenBerge_gls 0.99 0.99 0.99
## Bartlett_gls 0.99 0.99 0.99
## regression_pa 1.00 1.00 1.00
## Thurstone_pa 1.00 1.00 1.00
## tenBerge_pa 1.00 1.00 1.00
## Bartlett_pa 0.99 0.99 0.99
## regression_ml -0.93 -0.93 -0.93
## Thurstone_ml -0.93 -0.93 -0.93
## tenBerge_ml -0.93 -0.93 -0.93
## Bartlett_ml -0.93 -0.93 -0.93
## regression_minchi 0.90 0.90 0.90
## Thurstone_minchi 0.90 0.90 0.90
## tenBerge_minchi 0.90 0.90 0.90
## Bartlett_minchi 0.88 0.88 0.88
## regression_minrank 0.99 0.99 0.99
## Thurstone_minrank 0.99 0.99 0.99
## tenBerge_minrank 0.99 0.99 0.99
## Bartlett_minrank 0.99 0.99 0.99
## Bartlett_minres regression_ols Thurstone_ols
## regression_minres 0.99 1.00 1.00
## Thurstone_minres 0.99 1.00 1.00
## tenBerge_minres 0.99 1.00 1.00
## Bartlett_minres 1.00 0.99 0.99
## regression_ols 0.99 1.00 1.00
## Thurstone_ols 0.99 1.00 1.00
## tenBerge_ols 0.99 1.00 1.00
## Bartlett_ols 1.00 0.99 0.99
## regression_wls 1.00 0.99 0.99
## Thurstone_wls 1.00 0.99 0.99
## tenBerge_wls 1.00 0.99 0.99
## Bartlett_wls 1.00 0.99 0.99
## regression_gls 1.00 0.99 0.99
## Thurstone_gls 1.00 0.99 0.99
## tenBerge_gls 1.00 0.99 0.99
## Bartlett_gls 1.00 0.99 0.99
## regression_pa 0.99 1.00 1.00
## Thurstone_pa 0.99 1.00 1.00
## tenBerge_pa 0.99 1.00 1.00
## Bartlett_pa 1.00 0.99 0.99
## regression_ml -0.94 -0.93 -0.93
## Thurstone_ml -0.94 -0.93 -0.93
## tenBerge_ml -0.94 -0.93 -0.93
## Bartlett_ml -0.94 -0.93 -0.93
## regression_minchi 0.91 0.90 0.90
## Thurstone_minchi 0.91 0.90 0.90
## tenBerge_minchi 0.91 0.90 0.90
## Bartlett_minchi 0.89 0.88 0.88
## regression_minrank 1.00 0.99 0.99
## Thurstone_minrank 1.00 0.99 0.99
## tenBerge_minrank 1.00 0.99 0.99
## Bartlett_minrank 1.00 0.99 0.99
## tenBerge_ols Bartlett_ols regression_wls Thurstone_wls
## regression_minres 1.00 0.99 0.99 0.99
## Thurstone_minres 1.00 0.99 0.99 0.99
## tenBerge_minres 1.00 0.99 0.99 0.99
## Bartlett_minres 0.99 1.00 1.00 1.00
## regression_ols 1.00 0.99 0.99 0.99
## Thurstone_ols 1.00 0.99 0.99 0.99
## tenBerge_ols 1.00 0.99 0.99 0.99
## Bartlett_ols 0.99 1.00 1.00 1.00
## regression_wls 0.99 1.00 1.00 1.00
## Thurstone_wls 0.99 1.00 1.00 1.00
## tenBerge_wls 0.99 1.00 1.00 1.00
## Bartlett_wls 0.99 1.00 1.00 1.00
## regression_gls 0.99 1.00 1.00 1.00
## Thurstone_gls 0.99 1.00 1.00 1.00
## tenBerge_gls 0.99 1.00 1.00 1.00
## Bartlett_gls 0.99 1.00 1.00 1.00
## regression_pa 1.00 0.99 0.99 0.99
## Thurstone_pa 1.00 0.99 0.99 0.99
## tenBerge_pa 1.00 0.99 0.99 0.99
## Bartlett_pa 0.99 1.00 1.00 1.00
## regression_ml -0.93 -0.94 -0.93 -0.93
## Thurstone_ml -0.93 -0.94 -0.93 -0.93
## tenBerge_ml -0.93 -0.94 -0.93 -0.93
## Bartlett_ml -0.93 -0.94 -0.93 -0.93
## regression_minchi 0.90 0.91 0.92 0.92
## Thurstone_minchi 0.90 0.91 0.92 0.92
## tenBerge_minchi 0.90 0.91 0.92 0.92
## Bartlett_minchi 0.88 0.89 0.89 0.89
## regression_minrank 0.99 1.00 1.00 1.00
## Thurstone_minrank 0.99 1.00 1.00 1.00
## tenBerge_minrank 0.99 1.00 1.00 1.00
## Bartlett_minrank 0.99 1.00 1.00 1.00
## tenBerge_wls Bartlett_wls regression_gls Thurstone_gls
## regression_minres 0.99 0.99 0.99 0.99
## Thurstone_minres 0.99 0.99 0.99 0.99
## tenBerge_minres 0.99 0.99 0.99 0.99
## Bartlett_minres 1.00 1.00 1.00 1.00
## regression_ols 0.99 0.99 0.99 0.99
## Thurstone_ols 0.99 0.99 0.99 0.99
## tenBerge_ols 0.99 0.99 0.99 0.99
## Bartlett_ols 1.00 1.00 1.00 1.00
## regression_wls 1.00 1.00 1.00 1.00
## Thurstone_wls 1.00 1.00 1.00 1.00
## tenBerge_wls 1.00 1.00 1.00 1.00
## Bartlett_wls 1.00 1.00 1.00 1.00
## regression_gls 1.00 1.00 1.00 1.00
## Thurstone_gls 1.00 1.00 1.00 1.00
## tenBerge_gls 1.00 1.00 1.00 1.00
## Bartlett_gls 1.00 1.00 1.00 1.00
## regression_pa 0.99 0.99 0.99 0.99
## Thurstone_pa 0.99 0.99 0.99 0.99
## tenBerge_pa 0.99 0.99 0.99 0.99
## Bartlett_pa 1.00 1.00 1.00 1.00
## regression_ml -0.93 -0.93 -0.93 -0.93
## Thurstone_ml -0.93 -0.93 -0.93 -0.93
## tenBerge_ml -0.93 -0.93 -0.93 -0.93
## Bartlett_ml -0.93 -0.93 -0.93 -0.93
## regression_minchi 0.92 0.92 0.92 0.92
## Thurstone_minchi 0.92 0.92 0.92 0.92
## tenBerge_minchi 0.92 0.92 0.92 0.92
## Bartlett_minchi 0.89 0.89 0.89 0.89
## regression_minrank 1.00 1.00 1.00 1.00
## Thurstone_minrank 1.00 1.00 1.00 1.00
## tenBerge_minrank 1.00 1.00 1.00 1.00
## Bartlett_minrank 1.00 1.00 1.00 1.00
## tenBerge_gls Bartlett_gls regression_pa Thurstone_pa
## regression_minres 0.99 0.99 1.00 1.00
## Thurstone_minres 0.99 0.99 1.00 1.00
## tenBerge_minres 0.99 0.99 1.00 1.00
## Bartlett_minres 1.00 1.00 0.99 0.99
## regression_ols 0.99 0.99 1.00 1.00
## Thurstone_ols 0.99 0.99 1.00 1.00
## tenBerge_ols 0.99 0.99 1.00 1.00
## Bartlett_ols 1.00 1.00 0.99 0.99
## regression_wls 1.00 1.00 0.99 0.99
## Thurstone_wls 1.00 1.00 0.99 0.99
## tenBerge_wls 1.00 1.00 0.99 0.99
## Bartlett_wls 1.00 1.00 0.99 0.99
## regression_gls 1.00 1.00 0.99 0.99
## Thurstone_gls 1.00 1.00 0.99 0.99
## tenBerge_gls 1.00 1.00 0.99 0.99
## Bartlett_gls 1.00 1.00 0.99 0.99
## regression_pa 0.99 0.99 1.00 1.00
## Thurstone_pa 0.99 0.99 1.00 1.00
## tenBerge_pa 0.99 0.99 1.00 1.00
## Bartlett_pa 1.00 1.00 0.99 0.99
## regression_ml -0.93 -0.93 -0.93 -0.93
## Thurstone_ml -0.93 -0.93 -0.93 -0.93
## tenBerge_ml -0.93 -0.93 -0.93 -0.93
## Bartlett_ml -0.93 -0.93 -0.93 -0.93
## regression_minchi 0.92 0.92 0.90 0.90
## Thurstone_minchi 0.92 0.92 0.90 0.90
## tenBerge_minchi 0.92 0.92 0.90 0.90
## Bartlett_minchi 0.89 0.89 0.88 0.88
## regression_minrank 1.00 1.00 0.99 0.99
## Thurstone_minrank 1.00 1.00 0.99 0.99
## tenBerge_minrank 1.00 1.00 0.99 0.99
## Bartlett_minrank 1.00 1.00 0.99 0.99
## tenBerge_pa Bartlett_pa regression_ml Thurstone_ml
## regression_minres 1.00 0.99 -0.93 -0.93
## Thurstone_minres 1.00 0.99 -0.93 -0.93
## tenBerge_minres 1.00 0.99 -0.93 -0.93
## Bartlett_minres 0.99 1.00 -0.94 -0.94
## regression_ols 1.00 0.99 -0.93 -0.93
## Thurstone_ols 1.00 0.99 -0.93 -0.93
## tenBerge_ols 1.00 0.99 -0.93 -0.93
## Bartlett_ols 0.99 1.00 -0.94 -0.94
## regression_wls 0.99 1.00 -0.93 -0.93
## Thurstone_wls 0.99 1.00 -0.93 -0.93
## tenBerge_wls 0.99 1.00 -0.93 -0.93
## Bartlett_wls 0.99 1.00 -0.93 -0.93
## regression_gls 0.99 1.00 -0.93 -0.93
## Thurstone_gls 0.99 1.00 -0.93 -0.93
## tenBerge_gls 0.99 1.00 -0.93 -0.93
## Bartlett_gls 0.99 1.00 -0.93 -0.93
## regression_pa 1.00 0.99 -0.93 -0.93
## Thurstone_pa 1.00 0.99 -0.93 -0.93
## tenBerge_pa 1.00 0.99 -0.93 -0.93
## Bartlett_pa 0.99 1.00 -0.94 -0.94
## regression_ml -0.93 -0.94 1.00 1.00
## Thurstone_ml -0.93 -0.94 1.00 1.00
## tenBerge_ml -0.93 -0.94 1.00 1.00
## Bartlett_ml -0.93 -0.94 1.00 1.00
## regression_minchi 0.90 0.91 -0.76 -0.76
## Thurstone_minchi 0.90 0.91 -0.76 -0.76
## tenBerge_minchi 0.90 0.91 -0.76 -0.76
## Bartlett_minchi 0.88 0.89 -0.71 -0.71
## regression_minrank 0.99 1.00 -0.92 -0.92
## Thurstone_minrank 0.99 1.00 -0.92 -0.92
## tenBerge_minrank 0.99 1.00 -0.92 -0.92
## Bartlett_minrank 0.99 1.00 -0.93 -0.93
## tenBerge_ml Bartlett_ml regression_minchi
## regression_minres -0.93 -0.93 0.90
## Thurstone_minres -0.93 -0.93 0.90
## tenBerge_minres -0.93 -0.93 0.90
## Bartlett_minres -0.94 -0.94 0.91
## regression_ols -0.93 -0.93 0.90
## Thurstone_ols -0.93 -0.93 0.90
## tenBerge_ols -0.93 -0.93 0.90
## Bartlett_ols -0.94 -0.94 0.91
## regression_wls -0.93 -0.93 0.92
## Thurstone_wls -0.93 -0.93 0.92
## tenBerge_wls -0.93 -0.93 0.92
## Bartlett_wls -0.93 -0.93 0.92
## regression_gls -0.93 -0.93 0.92
## Thurstone_gls -0.93 -0.93 0.92
## tenBerge_gls -0.93 -0.93 0.92
## Bartlett_gls -0.93 -0.93 0.92
## regression_pa -0.93 -0.93 0.90
## Thurstone_pa -0.93 -0.93 0.90
## tenBerge_pa -0.93 -0.93 0.90
## Bartlett_pa -0.94 -0.94 0.91
## regression_ml 1.00 1.00 -0.76
## Thurstone_ml 1.00 1.00 -0.76
## tenBerge_ml 1.00 1.00 -0.76
## Bartlett_ml 1.00 1.00 -0.76
## regression_minchi -0.76 -0.76 1.00
## Thurstone_minchi -0.76 -0.76 1.00
## tenBerge_minchi -0.76 -0.76 1.00
## Bartlett_minchi -0.71 -0.71 1.00
## regression_minrank -0.92 -0.92 0.92
## Thurstone_minrank -0.92 -0.92 0.92
## tenBerge_minrank -0.92 -0.92 0.92
## Bartlett_minrank -0.93 -0.93 0.92
## Thurstone_minchi tenBerge_minchi Bartlett_minchi
## regression_minres 0.90 0.90 0.88
## Thurstone_minres 0.90 0.90 0.88
## tenBerge_minres 0.90 0.90 0.88
## Bartlett_minres 0.91 0.91 0.89
## regression_ols 0.90 0.90 0.88
## Thurstone_ols 0.90 0.90 0.88
## tenBerge_ols 0.90 0.90 0.88
## Bartlett_ols 0.91 0.91 0.89
## regression_wls 0.92 0.92 0.89
## Thurstone_wls 0.92 0.92 0.89
## tenBerge_wls 0.92 0.92 0.89
## Bartlett_wls 0.92 0.92 0.89
## regression_gls 0.92 0.92 0.89
## Thurstone_gls 0.92 0.92 0.89
## tenBerge_gls 0.92 0.92 0.89
## Bartlett_gls 0.92 0.92 0.89
## regression_pa 0.90 0.90 0.88
## Thurstone_pa 0.90 0.90 0.88
## tenBerge_pa 0.90 0.90 0.88
## Bartlett_pa 0.91 0.91 0.89
## regression_ml -0.76 -0.76 -0.71
## Thurstone_ml -0.76 -0.76 -0.71
## tenBerge_ml -0.76 -0.76 -0.71
## Bartlett_ml -0.76 -0.76 -0.71
## regression_minchi 1.00 1.00 1.00
## Thurstone_minchi 1.00 1.00 1.00
## tenBerge_minchi 1.00 1.00 1.00
## Bartlett_minchi 1.00 1.00 1.00
## regression_minrank 0.92 0.92 0.89
## Thurstone_minrank 0.92 0.92 0.89
## tenBerge_minrank 0.92 0.92 0.89
## Bartlett_minrank 0.92 0.92 0.89
## regression_minrank Thurstone_minrank tenBerge_minrank
## regression_minres 0.99 0.99 0.99
## Thurstone_minres 0.99 0.99 0.99
## tenBerge_minres 0.99 0.99 0.99
## Bartlett_minres 1.00 1.00 1.00
## regression_ols 0.99 0.99 0.99
## Thurstone_ols 0.99 0.99 0.99
## tenBerge_ols 0.99 0.99 0.99
## Bartlett_ols 1.00 1.00 1.00
## regression_wls 1.00 1.00 1.00
## Thurstone_wls 1.00 1.00 1.00
## tenBerge_wls 1.00 1.00 1.00
## Bartlett_wls 1.00 1.00 1.00
## regression_gls 1.00 1.00 1.00
## Thurstone_gls 1.00 1.00 1.00
## tenBerge_gls 1.00 1.00 1.00
## Bartlett_gls 1.00 1.00 1.00
## regression_pa 0.99 0.99 0.99
## Thurstone_pa 0.99 0.99 0.99
## tenBerge_pa 0.99 0.99 0.99
## Bartlett_pa 1.00 1.00 1.00
## regression_ml -0.92 -0.92 -0.92
## Thurstone_ml -0.92 -0.92 -0.92
## tenBerge_ml -0.92 -0.92 -0.92
## Bartlett_ml -0.92 -0.92 -0.92
## regression_minchi 0.92 0.92 0.92
## Thurstone_minchi 0.92 0.92 0.92
## tenBerge_minchi 0.92 0.92 0.92
## Bartlett_minchi 0.89 0.89 0.89
## regression_minrank 1.00 1.00 1.00
## Thurstone_minrank 1.00 1.00 1.00
## tenBerge_minrank 1.00 1.00 1.00
## Bartlett_minrank 1.00 1.00 1.00
## Bartlett_minrank
## regression_minres 0.99
## Thurstone_minres 0.99
## tenBerge_minres 0.99
## Bartlett_minres 1.00
## regression_ols 0.99
## Thurstone_ols 0.99
## tenBerge_ols 0.99
## Bartlett_ols 1.00
## regression_wls 1.00
## Thurstone_wls 1.00
## tenBerge_wls 1.00
## Bartlett_wls 1.00
## regression_gls 1.00
## Thurstone_gls 1.00
## tenBerge_gls 1.00
## Bartlett_gls 1.00
## regression_pa 0.99
## Thurstone_pa 0.99
## tenBerge_pa 0.99
## Bartlett_pa 1.00
## regression_ml -0.93
## Thurstone_ml -0.93
## tenBerge_ml -0.93
## Bartlett_ml -0.93
## regression_minchi 0.92
## Thurstone_minchi 0.92
## tenBerge_minchi 0.92
## Bartlett_minchi 0.89
## regression_minrank 1.00
## Thurstone_minrank 1.00
## tenBerge_minrank 1.00
## Bartlett_minrank 1.00fa_mv$scores %>% wtd.cors() %>% MAT_half() %>% GG_denhist()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#save scores + reverse
d$S_fa = S_list$weights$scores %>% as.vector() %>% `*`(-1)#subdatasets
list_group_factors = list(
economy = S_data[c("monthly_income", "Poverty_GSO_WB_poverty_headcount_pct")],
health = S_data[c("Stunting_total", "Infant_mortality_rate", "Under_five_mortality_rate")],
education = S_data[c("Secondary_school_attendance_Upper_16_18_years_pct", "Secondary_school_attendance_Lower_11_15_years_pct")],
employment = S_data[c("Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population")]
)
#loop over, factor analyze, extract factor scores, return list of data frames
#then merge to one, then do a final factor analysis
second_order_scores = list_group_factors %>% imap_dfr(function(ds, name) {
# browser()
data_frame(
score = fa(ds) %>% .$scores %>% as.vector(),
factor = name,
province = d$ISO
)
}) %>% spread(factor, score)## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.#higher order S
S_ho = fa(second_order_scores[-1])## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully#mean Z
average_z = function(x, reverse = NULL, messages = F) {
#standardize
x = x %>% df_standardize(messages = messages)
#reverse if needed
for (v in reverse) {
x[[v]] = x[[v]] * -1
}
rowMeans(x, na.rm = T)
}
#more robust approach
#average Z scores within class
second_order_scores2 = list(
economy = S_data[c("monthly_income", "Poverty_GSO_WB_poverty_headcount_pct")] %>% average_z(2),
health = S_data[c("Stunting_total", "Infant_mortality_rate", "Under_five_mortality_rate")] %>% average_z(1:3),
education = S_data[c("Secondary_school_attendance_Upper_16_18_years_pct", "Secondary_school_attendance_Lower_11_15_years_pct")] %>% average_z(),
employment = S_data[c("Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population")] %>% average_z(1)
) %>% as_data_frame()
#factor analysis
S_ho2 = fa(second_order_scores2)## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully#robust S
second_order_scores2$S_uw = average_z(second_order_scores2)
#ordinary
second_order_scores2$S_fa = d$S_fa
second_order_scores2$CA = d$CA
#wtd
wtd.cors(second_order_scores2, weight = d$pop_sqrt)## economy health education employment S_uw S_fa CA
## economy 1.00 0.83 0.21 0.69 0.90 0.93 0.37
## health 0.83 1.00 0.30 0.51 0.86 0.89 0.28
## education 0.21 0.30 1.00 0.11 0.53 0.25 0.70
## employment 0.69 0.51 0.11 1.00 0.76 0.70 0.26
## S_uw 0.90 0.86 0.53 0.76 1.00 0.91 0.53
## S_fa 0.93 0.89 0.25 0.70 0.91 1.00 0.38
## CA 0.37 0.28 0.70 0.26 0.53 0.38 1.00#combine and save to main
d$S = average_z(data.frame(d$S_fa, second_order_scores2$S_uw))
#save domains to main as well
d$health = second_order_scores2$health
d$economy = second_order_scores2$economy
d$education = second_order_scores2$education
d$employment = second_order_scores2$employmentScatterplots
#CA x S
#weights = pop sqrt
GG_scatter(d, "CA", "S", weights = "pop_sqrt", case_names = "province_name")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.
GG_save("figs/CA_S.png")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.#inverse standard error of CA
GG_scatter(d, "CA", "S", weights = "CA_ise", case_names = "province_name")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.
#no weights
GG_scatter(d, "CA", "S", case_names = "province_name")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.
#Jensen's method
#tricky because we need the empirical loadings, not the ones from the factor analysis
#so we calculate these manually
S_loadings_cors = cbind(S_data, S = d$S) %>% wtd.cors(weight = d$pop_sqrt) %>% .[, ncol(S_data) + 1] %>% .[-length(.)]
S_criterion_cors = cbind(S_data, CA = d$CA) %>% wtd.cors(weight = d$pop_sqrt) %>% .[, ncol(S_data) + 1] %>% .[-length(.)]
S_criterion_cors2 = cbind(S_data, Kinh = d$kinh_frac) %>% wtd.cors(weight = d$pop_sqrt) %>% .[, ncol(S_data) + 1] %>% .[-length(.)]
#apply reversing
for (i in seq_along(S_loadings_cors)) {
#is negative?
if (S_loadings_cors[i] < 0) {
S_loadings_cors[i] = S_loadings_cors[i] * -1
S_criterion_cors[i] = S_criterion_cors[i] * -1
S_criterion_cors2[i] = S_criterion_cors2[i] * -1
}
}
#CA Jensen
Jensen_df = data_frame(
loading = S_loadings_cors,
CA_cor = S_criterion_cors,
Kinh_cor = S_criterion_cors2,
var = names(S_data)
)
Jensen_df %>%
GG_scatter("loading", "CA_cor", case_names = "var") +
scale_y_continuous("r (cognitive ability x S indicator)")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.
GG_save("figs/CA_S_Jensen.png")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.Jensen_df %>%
GG_scatter("loading", "Kinh_cor", case_names = "var") +
scale_y_continuous("r (Kinh% x S indicator)")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.
GG_save("figs/Kinh_S_Jensen.png")## Don't know how to automatically pick scale for object of type NULL. Defaulting to continuous.Ethnics
#cors
eth_cors_wtd = wtd.cors(d[c("CA", "S", names(list_group_factors), ethnics + "_frac", "lat", "long")], weight = d$pop_sqrt)
eth_cors = wtd.cors(d[c("CA", "S", names(list_group_factors), ethnics + "_frac", "lat", "long")])
combine_upperlower(eth_cors_wtd, eth_cors) %>% write_clipboard()## X
## 1
## 2 0.41
## 3 0.31
## 4 0.21
## 5 0.66
## 6 0.22
## 7 0.38
## 8 -0.27
## 9 -0.03
## 10 -0.11
## 11 -0.42
## 12 -0.15
## 13 -0.23
## 14 -0.07
## 15 -0.13
## 16 0.34
## 17 0.08
## 18 0.47
## 19
## 20 0.93
## 21 0.88
## 22 0.42
## 23 0.71
## 24 0.81
## 25 -0.47
## 26 -0.45
## 27 -0.18
## 28 0.05
## 29 0.34
## 30 -0.35
## 31 -0.64
## 32 -0.60
## 33 -0.43
## 34 0.35
## 35 0.37
## 36 0.94
## 37
## 38 0.82
## 39 0.24
## 40 0.64
## 41 0.73
## 42 -0.41
## 43 -0.46
## 44 -0.19
## 45 0.10
## 46 0.47
## 47 -0.28
## 48 -0.61
## 49 -0.56
## 50 -0.55
## 51 0.31
## 52 0.28
## 53 0.90
## 54 0.83
## 55
## 56 0.33
## 57 0.42
## 58 0.71
## 59 -0.29
## 60 -0.44
## 61 -0.03
## 62 0.13
## 63 0.29
## 64 -0.21
## 65 -0.65
## 66 -0.52
## 67 -0.38
## 68 0.19
## 69 0.70
## 70 0.40
## 71 0.21
## 72 0.30
## 73
## 74 0.09
## 75 0.40
## 76 -0.10
## 77 -0.24
## 78 0.03
## 79 -0.41
## 80 -0.29
## 81 -0.02
## 82 -0.38
## 83 -0.26
## 84 0.43
## 85 0.32
## 86 0.26
## 87 0.75
## 88 0.69
## 89 0.51
## 90 0.11
## 91
## 92 0.62
## 93 -0.62
## 94 -0.22
## 95 -0.30
## 96 0.23
## 97 0.37
## 98 -0.47
## 99 -0.36
## 100 -0.54
## 101 -0.50
## 102 0.30
## 103 0.38
## 104 0.74
## 105 0.63
## 106 0.70
## 107 0.38
## 108 0.55
## 109
## 110 -0.67
## 111 -0.49
## 112 -0.25
## 113 -0.03
## 114 0.04
## 115 -0.54
## 116 -0.70
## 117 -0.73
## 118 -0.45
## 119 0.37
## 120 -0.25
## 121 -0.43
## 122 -0.36
## 123 -0.32
## 124 -0.12
## 125 -0.52
## 126 -0.67
## 127
## 128 -0.07
## 129 -0.03
## 130 -0.10
## 131 -0.06
## 132 0.74
## 133 0.32
## 134 0.78
## 135 0.41
## 136 -0.15
## 137 -0.06
## 138 -0.41
## 139 -0.40
## 140 -0.40
## 141 -0.19
## 142 -0.25
## 143 -0.50
## 144 -0.05
## 145
## 146 0.10
## 147 -0.08
## 148 -0.10
## 149 -0.07
## 150 0.61
## 151 0.15
## 152 0.29
## 153 -0.49
## 154 -0.11
## 155 -0.19
## 156 -0.20
## 157 -0.07
## 158 0.02
## 159 -0.31
## 160 -0.28
## 161 -0.02
## 162 0.13
## 163
## 164 -0.06
## 165 -0.10
## 166 -0.06
## 167 -0.02
## 168 0.00
## 169 0.18
## 170 -0.15
## 171 -0.44
## 172 0.00
## 173 0.04
## 174 0.09
## 175 -0.44
## 176 0.21
## 177 -0.07
## 178 -0.09
## 179 -0.07
## 180 -0.06
## 181
## 182 0.45
## 183 -0.08
## 184 -0.10
## 185 -0.12
## 186 -0.36
## 187 -0.11
## 188 -0.04
## 189 0.48
## 190 0.61
## 191 0.40
## 192 -0.22
## 193 0.46
## 194 0.05
## 195 -0.07
## 196 -0.11
## 197 -0.10
## 198 0.35
## 199
## 200 -0.06
## 201 -0.06
## 202 -0.08
## 203 -0.43
## 204 -0.01
## 205 -0.21
## 206 -0.32
## 207 -0.25
## 208 -0.23
## 209 -0.03
## 210 -0.41
## 211 -0.54
## 212 0.76
## 213 -0.06
## 214 -0.05
## 215 -0.07
## 216 -0.06
## 217
## 218 0.17
## 219 0.38
## 220 0.30
## 221 -0.01
## 222 -0.10
## 223 -0.57
## 224 -0.51
## 225 -0.61
## 226 -0.33
## 227 -0.34
## 228 -0.70
## 229 0.36
## 230 0.57
## 231 0.00
## 232 -0.09
## 233 -0.07
## 234 0.18
## 235
## 236 0.64
## 237 0.42
## 238 -0.55
## 239 -0.13
## 240 -0.53
## 241 -0.47
## 242 -0.51
## 243 -0.23
## 244 -0.46
## 245 -0.71
## 246 0.79
## 247 0.14
## 248 0.01
## 249 -0.10
## 250 -0.08
## 251 0.38
## 252 0.66
## 253
## 254 0.50
## 255 -0.38
## 256 0.37
## 257 -0.36
## 258 -0.49
## 259 -0.34
## 260 0.51
## 261 -0.44
## 262 -0.37
## 263 0.36
## 264 0.27
## 265 0.20
## 266 -0.35
## 267 -0.45
## 268 0.26
## 269 0.36
## 270 0.44
## 271
## 272 -0.37
## 273 0.10
## 274 0.27
## 275 0.23
## 276 0.13
## 277 0.27
## 278 0.25
## 279 0.31
## 280 -0.13
## 281 -0.45
## 282 -0.17
## 283 -0.13
## 284 0.03
## 285 0.01
## 286 -0.47
## 287 -0.33
## 288 -0.32
## 289#CA and each indicator
eth_cors2_wtd = wtd.cors(d[c("CA", names(S_data), "Total_fertility_rate", "Population_growth_rate", "lat", "long")], weight = d$pop_sqrt)
eth_cors2 = wtd.cors(d[c("CA", names(S_data), "Total_fertility_rate", "Population_growth_rate", "lat", "long")])
combine_upperlower(eth_cors2_wtd, eth_cors2)## CA
## CA NA
## Poverty_GSO_WB_poverty_headcount_pct -0.369
## Main_employment_agriculture_pct -0.234
## Main_employment_wage_work_pct 0.240
## Main_light_source_electricity_pct 0.338
## Sanitation_indoor_flush_toilet_pct 0.301
## Water_indoor_tap_public_tap_or_well_pct 0.141
## Secondary_school_attendance_Lower_11_15_years_pct 0.627
## Secondary_school_attendance_Upper_16_18_years_pct 0.661
## Stunting_total -0.271
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.331
## Index_of_Industrial_production 0.173
## Infant_mortality_rate -0.158
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.235
## Under_five_mortality_rate -0.155
## monthly_income 0.166
## Total_fertility_rate 0.144
## Population_growth_rate 0.265
## lat 0.343
## long 0.067
## Poverty_GSO_WB_poverty_headcount_pct
## CA -0.418
## Poverty_GSO_WB_poverty_headcount_pct NA
## Main_employment_agriculture_pct 0.750
## Main_employment_wage_work_pct -0.687
## Main_light_source_electricity_pct -0.820
## Sanitation_indoor_flush_toilet_pct -0.572
## Water_indoor_tap_public_tap_or_well_pct -0.669
## Secondary_school_attendance_Lower_11_15_years_pct -0.473
## Secondary_school_attendance_Upper_16_18_years_pct -0.440
## Stunting_total 0.761
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.469
## Index_of_Industrial_production 0.053
## Infant_mortality_rate 0.850
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.555
## Under_five_mortality_rate 0.848
## monthly_income -0.500
## Total_fertility_rate 0.579
## Population_growth_rate 0.193
## lat 0.437
## long -0.422
## Main_employment_agriculture_pct
## CA -0.276
## Poverty_GSO_WB_poverty_headcount_pct 0.744
## Main_employment_agriculture_pct NA
## Main_employment_wage_work_pct -0.979
## Main_light_source_electricity_pct -0.522
## Sanitation_indoor_flush_toilet_pct -0.871
## Water_indoor_tap_public_tap_or_well_pct -0.781
## Secondary_school_attendance_Lower_11_15_years_pct -0.063
## Secondary_school_attendance_Upper_16_18_years_pct -0.119
## Stunting_total 0.745
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.535
## Index_of_Industrial_production 0.072
## Infant_mortality_rate 0.639
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.706
## Under_five_mortality_rate 0.631
## monthly_income -0.707
## Total_fertility_rate 0.650
## Population_growth_rate -0.112
## lat 0.538
## long -0.365
## Main_employment_wage_work_pct
## CA 0.285
## Poverty_GSO_WB_poverty_headcount_pct -0.689
## Main_employment_agriculture_pct -0.981
## Main_employment_wage_work_pct NA
## Main_light_source_electricity_pct 0.464
## Sanitation_indoor_flush_toilet_pct 0.857
## Water_indoor_tap_public_tap_or_well_pct 0.738
## Secondary_school_attendance_Lower_11_15_years_pct 0.038
## Secondary_school_attendance_Upper_16_18_years_pct 0.075
## Stunting_total -0.720
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.589
## Index_of_Industrial_production -0.045
## Infant_mortality_rate -0.591
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.630
## Under_five_mortality_rate -0.582
## monthly_income 0.702
## Total_fertility_rate -0.628
## Population_growth_rate 0.202
## lat -0.482
## long 0.327
## Main_light_source_electricity_pct
## CA 0.417
## Poverty_GSO_WB_poverty_headcount_pct -0.808
## Main_employment_agriculture_pct -0.476
## Main_employment_wage_work_pct 0.430
## Main_light_source_electricity_pct NA
## Sanitation_indoor_flush_toilet_pct 0.394
## Water_indoor_tap_public_tap_or_well_pct 0.444
## Secondary_school_attendance_Lower_11_15_years_pct 0.601
## Secondary_school_attendance_Upper_16_18_years_pct 0.509
## Stunting_total -0.578
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.358
## Index_of_Industrial_production -0.082
## Infant_mortality_rate -0.690
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.361
## Under_five_mortality_rate -0.692
## monthly_income 0.215
## Total_fertility_rate -0.351
## Population_growth_rate -0.281
## lat -0.185
## long 0.459
## Sanitation_indoor_flush_toilet_pct
## CA 0.342
## Poverty_GSO_WB_poverty_headcount_pct -0.582
## Main_employment_agriculture_pct -0.893
## Main_employment_wage_work_pct 0.880
## Main_light_source_electricity_pct 0.365
## Sanitation_indoor_flush_toilet_pct NA
## Water_indoor_tap_public_tap_or_well_pct 0.639
## Secondary_school_attendance_Lower_11_15_years_pct 0.109
## Secondary_school_attendance_Upper_16_18_years_pct 0.176
## Stunting_total -0.653
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.367
## Index_of_Industrial_production -0.087
## Infant_mortality_rate -0.445
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.641
## Under_five_mortality_rate -0.437
## monthly_income 0.730
## Total_fertility_rate -0.519
## Population_growth_rate 0.282
## lat -0.463
## long 0.466
## Water_indoor_tap_public_tap_or_well_pct
## CA 0.1817
## Poverty_GSO_WB_poverty_headcount_pct -0.6734
## Main_employment_agriculture_pct -0.7928
## Main_employment_wage_work_pct 0.7563
## Main_light_source_electricity_pct 0.4223
## Sanitation_indoor_flush_toilet_pct 0.6718
## Water_indoor_tap_public_tap_or_well_pct NA
## Secondary_school_attendance_Lower_11_15_years_pct -0.0341
## Secondary_school_attendance_Upper_16_18_years_pct 0.0197
## Stunting_total -0.6813
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.4058
## Index_of_Industrial_production -0.0217
## Infant_mortality_rate -0.6442
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.6468
## Under_five_mortality_rate -0.6382
## monthly_income 0.5698
## Total_fertility_rate -0.6327
## Population_growth_rate 0.0099
## lat -0.5123
## long 0.1957
## Secondary_school_attendance_Lower_11_15_years_pct
## CA 0.675
## Poverty_GSO_WB_poverty_headcount_pct -0.441
## Main_employment_agriculture_pct -0.033
## Main_employment_wage_work_pct 0.018
## Main_light_source_electricity_pct 0.602
## Sanitation_indoor_flush_toilet_pct 0.087
## Water_indoor_tap_public_tap_or_well_pct -0.043
## Secondary_school_attendance_Lower_11_15_years_pct NA
## Secondary_school_attendance_Upper_16_18_years_pct 0.933
## Stunting_total -0.337
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.276
## Index_of_Industrial_production 0.028
## Infant_mortality_rate -0.317
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.017
## Under_five_mortality_rate -0.320
## monthly_income -0.063
## Total_fertility_rate 0.187
## Population_growth_rate -0.092
## lat 0.428
## long 0.280
## Secondary_school_attendance_Upper_16_18_years_pct
## CA 0.705
## Poverty_GSO_WB_poverty_headcount_pct -0.422
## Main_employment_agriculture_pct -0.092
## Main_employment_wage_work_pct 0.059
## Main_light_source_electricity_pct 0.523
## Sanitation_indoor_flush_toilet_pct 0.150
## Water_indoor_tap_public_tap_or_well_pct 0.013
## Secondary_school_attendance_Lower_11_15_years_pct 0.942
## Secondary_school_attendance_Upper_16_18_years_pct NA
## Stunting_total -0.349
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.208
## Index_of_Industrial_production 0.041
## Infant_mortality_rate -0.252
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.195
## Under_five_mortality_rate -0.255
## monthly_income -0.038
## Total_fertility_rate 0.249
## Population_growth_rate -0.110
## lat 0.415
## long 0.316
## Stunting_total
## CA -0.3603
## Poverty_GSO_WB_poverty_headcount_pct 0.7422
## Main_employment_agriculture_pct 0.7828
## Main_employment_wage_work_pct -0.7683
## Main_light_source_electricity_pct -0.5380
## Sanitation_indoor_flush_toilet_pct -0.7244
## Water_indoor_tap_public_tap_or_well_pct -0.7012
## Secondary_school_attendance_Lower_11_15_years_pct -0.3062
## Secondary_school_attendance_Upper_16_18_years_pct -0.3353
## Stunting_total NA
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.4082
## Index_of_Industrial_production 0.0062
## Infant_mortality_rate 0.7826
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.5037
## Under_five_mortality_rate 0.7767
## monthly_income -0.6665
## Total_fertility_rate 0.6112
## Population_growth_rate 0.1225
## lat 0.2751
## long -0.1607
## Foreign_direct_investment_projects_licensed_in_2016_per_capita
## CA 0.344
## Poverty_GSO_WB_poverty_headcount_pct -0.479
## Main_employment_agriculture_pct -0.538
## Main_employment_wage_work_pct 0.585
## Main_light_source_electricity_pct 0.359
## Sanitation_indoor_flush_toilet_pct 0.397
## Water_indoor_tap_public_tap_or_well_pct 0.424
## Secondary_school_attendance_Lower_11_15_years_pct 0.272
## Secondary_school_attendance_Upper_16_18_years_pct 0.205
## Stunting_total -0.403
## Foreign_direct_investment_projects_licensed_in_2016_per_capita NA
## Index_of_Industrial_production 0.027
## Infant_mortality_rate -0.409
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.197
## Under_five_mortality_rate -0.404
## monthly_income 0.334
## Total_fertility_rate -0.299
## Population_growth_rate 0.331
## lat -0.023
## long 0.140
## Index_of_Industrial_production
## CA 0.1310
## Poverty_GSO_WB_poverty_headcount_pct 0.0241
## Main_employment_agriculture_pct 0.0834
## Main_employment_wage_work_pct -0.0591
## Main_light_source_electricity_pct -0.0185
## Sanitation_indoor_flush_toilet_pct -0.0943
## Water_indoor_tap_public_tap_or_well_pct -0.0249
## Secondary_school_attendance_Lower_11_15_years_pct 0.0762
## Secondary_school_attendance_Upper_16_18_years_pct 0.0711
## Stunting_total 0.0063
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.0310
## Index_of_Industrial_production NA
## Infant_mortality_rate 0.1076
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.0637
## Under_five_mortality_rate 0.1137
## monthly_income -0.1183
## Total_fertility_rate 0.1518
## Population_growth_rate 0.1754
## lat 0.1599
## long -0.1479
## Infant_mortality_rate
## CA -0.204
## Poverty_GSO_WB_poverty_headcount_pct 0.852
## Main_employment_agriculture_pct 0.644
## Main_employment_wage_work_pct -0.600
## Main_light_source_electricity_pct -0.672
## Sanitation_indoor_flush_toilet_pct -0.467
## Water_indoor_tap_public_tap_or_well_pct -0.653
## Secondary_school_attendance_Lower_11_15_years_pct -0.278
## Secondary_school_attendance_Upper_16_18_years_pct -0.225
## Stunting_total 0.734
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.418
## Index_of_Industrial_production 0.061
## Infant_mortality_rate NA
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.348
## Under_five_mortality_rate 1.000
## monthly_income -0.473
## Total_fertility_rate 0.723
## Population_growth_rate 0.308
## lat 0.403
## long -0.208
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population
## CA -0.2717
## Poverty_GSO_WB_poverty_headcount_pct 0.5731
## Main_employment_agriculture_pct 0.7296
## Main_employment_wage_work_pct -0.6597
## Main_light_source_electricity_pct -0.3433
## Sanitation_indoor_flush_toilet_pct -0.6834
## Water_indoor_tap_public_tap_or_well_pct -0.6715
## Secondary_school_attendance_Lower_11_15_years_pct -0.0355
## Secondary_school_attendance_Upper_16_18_years_pct -0.2156
## Stunting_total 0.5976
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.2139
## Index_of_Industrial_production -0.0078
## Infant_mortality_rate 0.3961
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population NA
## Under_five_mortality_rate 0.3401
## monthly_income -0.5389
## Total_fertility_rate 0.3566
## Population_growth_rate 0.0396
## lat 0.4967
## long -0.3348
## Under_five_mortality_rate
## CA -0.199
## Poverty_GSO_WB_poverty_headcount_pct 0.850
## Main_employment_agriculture_pct 0.633
## Main_employment_wage_work_pct -0.588
## Main_light_source_electricity_pct -0.675
## Sanitation_indoor_flush_toilet_pct -0.455
## Water_indoor_tap_public_tap_or_well_pct -0.645
## Secondary_school_attendance_Lower_11_15_years_pct -0.282
## Secondary_school_attendance_Upper_16_18_years_pct -0.227
## Stunting_total 0.724
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.412
## Index_of_Industrial_production 0.065
## Infant_mortality_rate 1.000
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.386
## Under_five_mortality_rate NA
## monthly_income -0.462
## Total_fertility_rate 0.718
## Population_growth_rate 0.316
## lat 0.399
## long -0.211
## monthly_income
## CA 0.2540
## Poverty_GSO_WB_poverty_headcount_pct -0.5066
## Main_employment_agriculture_pct -0.7621
## Main_employment_wage_work_pct 0.7581
## Main_light_source_electricity_pct 0.2143
## Sanitation_indoor_flush_toilet_pct 0.8007
## Water_indoor_tap_public_tap_or_well_pct 0.6073
## Secondary_school_attendance_Lower_11_15_years_pct -0.0334
## Secondary_school_attendance_Upper_16_18_years_pct 0.0059
## Stunting_total -0.7604
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.3338
## Index_of_Industrial_production -0.1160
## Infant_mortality_rate -0.4741
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.6202
## Under_five_mortality_rate -0.4594
## monthly_income NA
## Total_fertility_rate -0.6407
## Population_growth_rate 0.2622
## lat -0.5224
## long 0.1517
## Total_fertility_rate
## CA 0.093
## Poverty_GSO_WB_poverty_headcount_pct 0.567
## Main_employment_agriculture_pct 0.694
## Main_employment_wage_work_pct -0.676
## Main_light_source_electricity_pct -0.298
## Sanitation_indoor_flush_toilet_pct -0.585
## Water_indoor_tap_public_tap_or_well_pct -0.653
## Secondary_school_attendance_Lower_11_15_years_pct 0.240
## Secondary_school_attendance_Upper_16_18_years_pct 0.274
## Stunting_total 0.623
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.306
## Index_of_Industrial_production 0.130
## Infant_mortality_rate 0.698
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.437
## Under_five_mortality_rate 0.691
## monthly_income -0.662
## Total_fertility_rate NA
## Population_growth_rate 0.162
## lat 0.697
## long -0.244
## Population_growth_rate
## CA 0.3232
## Poverty_GSO_WB_poverty_headcount_pct 0.0536
## Main_employment_agriculture_pct -0.2811
## Main_employment_wage_work_pct 0.3593
## Main_light_source_electricity_pct -0.1686
## Sanitation_indoor_flush_toilet_pct 0.4226
## Water_indoor_tap_public_tap_or_well_pct 0.1732
## Secondary_school_attendance_Lower_11_15_years_pct -0.0198
## Secondary_school_attendance_Upper_16_18_years_pct -0.0393
## Stunting_total -0.0960
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.4396
## Index_of_Industrial_production 0.1340
## Infant_mortality_rate 0.1512
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.0772
## Under_five_mortality_rate 0.1619
## monthly_income 0.3980
## Total_fertility_rate 0.0009
## Population_growth_rate NA
## lat 0.0964
## long 0.0142
## lat
## CA 0.3761
## Poverty_GSO_WB_poverty_headcount_pct 0.3697
## Main_employment_agriculture_pct 0.5054
## Main_employment_wage_work_pct -0.4538
## Main_light_source_electricity_pct -0.0905
## Sanitation_indoor_flush_toilet_pct -0.4461
## Water_indoor_tap_public_tap_or_well_pct -0.4696
## Secondary_school_attendance_Lower_11_15_years_pct 0.5128
## Secondary_school_attendance_Upper_16_18_years_pct 0.4985
## Stunting_total 0.2370
## Foreign_direct_investment_projects_licensed_in_2016_per_capita -0.0039
## Index_of_Industrial_production 0.1640
## Infant_mortality_rate 0.3747
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population 0.4365
## Under_five_mortality_rate 0.3694
## monthly_income -0.4663
## Total_fertility_rate 0.7002
## Population_growth_rate 0.0447
## lat NA
## long -0.3845
## long
## CA 0.100
## Poverty_GSO_WB_poverty_headcount_pct -0.366
## Main_employment_agriculture_pct -0.324
## Main_employment_wage_work_pct 0.295
## Main_light_source_electricity_pct 0.399
## Sanitation_indoor_flush_toilet_pct 0.433
## Water_indoor_tap_public_tap_or_well_pct 0.163
## Secondary_school_attendance_Lower_11_15_years_pct 0.241
## Secondary_school_attendance_Upper_16_18_years_pct 0.276
## Stunting_total -0.138
## Foreign_direct_investment_projects_licensed_in_2016_per_capita 0.148
## Index_of_Industrial_production -0.108
## Infant_mortality_rate -0.146
## Percentage_of_employed_workers_at_15_years_of_age_and_above_among_population -0.313
## Under_five_mortality_rate -0.148
## monthly_income 0.144
## Total_fertility_rate -0.223
## Population_growth_rate 0.069
## lat -0.343
## long NA#plot all ethnicities
for (ethnic in ethnics) {
#CA
ca_plot = GG_scatter(d, ethnic + "_frac", "CA", weights = "pop_sqrt") +
scale_x_continuous(str_to_upper_initial(ethnic) + " %", labels = scales::percent)
#S
s_plot = GG_scatter(d, ethnic + "_frac", "S", weights = "pop_sqrt") +
scale_x_continuous(str_to_upper_initial(ethnic) + " %", labels = scales::percent)
#save
GG_save(filename = sprintf("figs/CA~%s.png", str_to_upper_initial(ethnic)), plot = ca_plot)
GG_save(filename = sprintf("figs/S~%s.png", str_to_upper_initial(ethnic)), plot = s_plot)
print(ca_plot)
print(s_plot)
}
Maps
#join data
geo_df2 = left_join(geo_df, d %>% select(ISO, CA, S, ethnics + "_frac"), by = "ISO")
#maps
ggplot(geo_df2, aes(long, lat)) +
geom_polygon(aes(fill = Density, group = group)) +
# ggrepel::geom_text_repel(data = province_meta, aes(label = name), color = "green", size = 2.5, ) +
geom_path(aes(group = group), size = .1, color = "white") +
scale_fill_continuous("Population density\n(persons/km sq.)") +
xlab("Longitude") + ylab("Latitude") +
theme_bw()
GG_save("maps/map_density.png")
ggplot(geo_df2) +
aes(long, lat, group = group, fill = CA) +
geom_polygon() +
geom_path(aes(group = group), size = .1, color = "white") +
scale_fill_continuous("Cognitive ability") +
xlab("Longitude") + ylab("Latitude") +
theme_bw()
GG_save("maps/map_ca.png")
ggplot(geo_df2) +
aes(long, lat, group = group, fill = S) +
geom_path(aes(group = group), size = .1, color = "white") +
geom_polygon() +
xlab("Longitude") + ylab("Latitude") +
theme_bw()
GG_save("maps/map_s.png")
#Kinh
ggplot(geo_df2) +
aes(long, lat, group = group, fill = kinh_frac) +
geom_path(aes(group = group), size = .1, color = "white") +
geom_polygon() +
theme_bw() +
xlab("Longitude") + ylab("Latitude") +
scale_fill_continuous("Kinh %\n(main ethnic group)", labels = scales::percent, high = "blue", low = "green")
GG_save("maps/map_kinh.png")
#Hoa
ggplot(geo_df2) +
aes(long, lat, group = group, fill = hoa_frac) +
geom_path(aes(group = group), size = .1, color = "white") +
geom_polygon() +
theme_bw() +
xlab("Longitude") + ylab("Latitude") +
scale_fill_continuous("Hoa % (Chinese)", labels = scales::percent, high = "blue", low = "green")
GG_save("maps/map_hoa.png")
#remaining ethnicities
for (ethnic in setdiff(ethnics, c("kinh", "hoa"))) {
ggplot(geo_df2) +
aes_string("long", "lat", group = "group", fill = ethnic + "_frac") +
geom_path(aes(group = group), size = .1, color = "white") +
geom_polygon() +
theme_bw() +
xlab("Longitude") + ylab("Latitude") +
scale_fill_continuous(ethnic + " %", labels = scales::percent, high = "blue", low = "green")
GG_save(sprintf("maps/map_%s.png", ethnic))
}Multivariate
#path model
path1 = "
S ~ CA + kinh_frac
CA ~ kinh_frac"
#fit
path1_fit = lavaan::sem(path1, data = d, std.ov = T)
# fitmeasures(path1_fit) #nothing to see
parameterestimates(path1_fit, standardized = T)#weights
path1_fit_wt = lavaan.survey(path1_fit,
survey.design = survey::svydesign(id=~Unique_Identifier,
weights = d$pop_sqrt,
data = d))## Warning in lavData(data = data, group = group, cluster = cluster, ov.names
## = ov.names, : lavaan WARNING: std.ov argument is ignored if only sample
## statistics are provided.parameterestimates(path1_fit_wt, standardized = T)#standard MR
ols(S ~ kinh_frac + CA, data = d, weights = d$pop_sqrt)## Frequencies of Missing Values Due to Each Variable
## S kinh_frac CA (weights)
## 0 0 3 0
##
## Linear Regression Model
##
## ols(formula = S ~ kinh_frac + CA, data = d, weights = d$pop_sqrt)
##
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 60 LR chi2 50.73 R2 0.571
## sigma22.5228 d.f. 2 R2 adj 0.556
## d.f. 57 Pr(> chi2) 0.0000 g 0.896
##
## Residuals
##
## Min 1Q Median 3Q Max
## -1.0568 -0.4351 -0.1753 0.1971 2.1466
##
##
## Coef S.E. t Pr(>|t|)
## Intercept -5.1525 1.2658 -4.07 0.0001
## kinh_frac 2.6247 0.3836 6.84 <0.0001
## CA 0.0064 0.0027 2.38 0.0207
## lm(S ~ kinh_frac + CA, data = d, weights = d$pop_sqrt) %>% MOD_summary()## Warning in sqrt(.): NaNs produced##
## ---- Model summary ----
## Model coefficients
## Beta SE CI_lower CI_upper
## kinh_frac 0.76 0.112 0.541 0.99
## CA 0.23 0.097 0.037 0.42
##
##
## Model meta-data
## outcome N df R2 R2-adj. R2-cv
## 1 S 60 57 0.57 0.56 NA
##
##
## Etas from analysis of variance
## Eta Eta_partial
## kinh_frac NaN 1
## CA NaN 1Data output
write_rds(d, "data/data_out.rds")
write_csv(d, "data/data_out.csv", na = "")