Assign NA value to neighbor polygon with largest pop

assign_NA_to_neighbor.R

```
library(sf)
library(geobr)


# sample spatial data
df <- read_state()

# create random pop and cases
df$pop <- sample(1:27, 27, replace = T)
df$cases <- sample(1:10, 27, replace = T)

# impute a couple NAs
df$pop[7] <- NA
df$pop[10] <- NA



# list polygons with NA pop
na_ids <- rownames(df)[which(is.na(df$pop))] %>% as.numeric()


for (i in na_ids){
  
# find neighbor polygons
nei_index <- st_intersects( df[i,] , df)
nei_index <- unlist(nei_index, use.names=FALSE)
nei_poly <- df[nei_index,]

                                                                                
# find neighbor polygon with largest pop
target_poly <- rownames(nei_poly)[which(nei_poly$pop == max(nei_poly$pop, na.rm=T) )][1] %>% as.numeric()

# get cases from problematic polygon and assign them to target_poly
df$cases[target_poly] <- df$cases[target_poly] + df$cases[i]


# update cases in problematic polygon to zero
df$cases[i] <- 0
df$pop[i] <- 0
}


summary(df$pop)
summary(df$cases)
```

fixNA_weekly.R

library(sf)
library(geobr)


# sample spatial data
states_sf <- read_state()

# create panel with 2 weeks
df <- rbind(states_sf, states_sf, states_sf)
df$week <- c(rep(1, 27), rep(2, 27), rep(3, 27))



# create random pop and cases
set.seed(0)
df$pop <- sample(20:100, 27, replace = T) %>% rep(.,3)
df$cases <- sample(0:19, nrow(df), replace = T)

# impute a couple NAs
df$pop[1] <- NA
df$pop[28] <- NA



# list polygons with NA pop
# na_ids <- rownames(df)[which(is.na(df$pop))] %>% as.numeric()
na_ids <- df$code_state[which(is.na(df$pop))] %>% as.numeric() %>% unique()

# list weeks when there is at list one NA pop
na_weeks <- df$week[which(is.na(df$pop))] %>% as.numeric() %>% unique()


for (i in na_ids){

# find neighbor polygons
nei_index <- st_intersects( subset(df, code_state==i)[1,] , df[1:length(unique(df$code_state)),] )
nei_index <- unlist(nei_index, use.names=FALSE)
nei_poly <- df[nei_index,]
                                                                                
# find neighbor polygon with largest pop
target_poly <- rownames(nei_poly)[which(nei_poly$pop == max(nei_poly$pop, na.rm=T) )][1] %>% as.numeric()

# Assign cases in each week
for (j in na_weeks){
  # get cases from problematic polygon and assign them to target_poly
  df$cases[which(df$week==j)][target_poly] <- df$cases[which(df$week==j)][target_poly] + df$cases[which(df$week==j & df$code_state==i)]
  
  # update cases in problematic polygon to zero
  df$cases[which(df$week==j & df$code_state==i)] <- 0
  }
}

summary(df$pop)
summary(df$cases)