Skew in Village Pop. in India With Implications for Optimal Strategy to Reach Out

shrug_2011_vd.R

library(readr)
shrug <- read_csv("/Users/soodoku/Downloads/shrug-vd11-csv/pc11_vd_clean_shrid.csv")

# Take out missing from village pop (just 1) and sort
t_pop <- shrug$pc11_vd_t_p[!is.na(shrug$pc11_vd_t_p)]
sorted_values <- sort(t_pop, decreasing = TRUE)

# We have like 570k villages. 50k villages have ...
sum(sorted_values[1:50000])/sum(sorted_values) 
# [1] 0.3720554

# Let's do it more systematically
percentiles <- seq(10, 100, by = 10)
proportions <- length(percentiles)

for (i in seq_along(percentiles)) {
  index <- round(length(sorted_values) * (percentiles[i]/100))
  percentile_sum <- sum(sorted_values[1:index])
  proportions[i] <- percentile_sum / sum(t_pop)
}

# Combine percentiles and proportions into a data frame for easier interpretation
result <- data.frame(Percentile = percentiles, Proportion = proportions)

# Print the result
print(result)