How to group twice? - r

Question

I'd like to know how to group twice in a data set. I must answer the following question: "For each state, which municipalities have the lowest and the highest infections and death rates?". This question is part of a homework (https://github.com/umbertomig/intro-prob-stat-FGV/blob/master/assignments/hw6.Rmd) and I don't know how to do it. I've tried to use top_n, but I am not sure if this is the best way.
I wanted to generate a data set in which, for each state, there were four municipalities (two with the highest rates of infection and death from coronavirus and two with the smallest). This is what a have done so far:
library(tidyverse)
brazilcorona <- read.csv("https://raw.githubusercontent.com/umbertomig/intro-prob-stat-FGV/master/data sets/brazilcorona.csv")
brazilcorona_hl_rates <- select(brazilcorona, (estado:emAcompanhamentoNovos)) %>%
filter(data >= "2020-05-15") %>%
subset(!(coduf == 76)) %>%
mutate(av_inf = (casosAcumulado/populacaoTCU2019)*100000,
av_dth = (obitosAcumulado/populacaoTCU2019)*100000)
brazilcorona_hilow_rates <- brazilcorona_hl_rates %>%
group_by(estado) %>%
summarize(top_dth = top_n(1, av_dth))

Answer 1

In my example, I find two cities per state with the maximum and minimum values for the column "obitosAcumulado", but to solve your problem you should simply change it to the column containing the information you want to extract the information from.
rm(list=ls())
brazilcorona <- read.csv("https://raw.githubusercontent.com/umbertomig/intro-prob-stat-FGV/master/datasets/brazilcorona.csv")
#
#remove NA's from municipios
brazilcorona<-brazilcorona[!is.na(brazilcorona$municipio),]
#here I am gonna use the column "obitosAcumulado" but you should use the one you want
brazilcorona$obitosAcumulado<-as.numeric(brazilcorona$obitosAcumulado)
states<-as.list(unique(brazilcorona$estado))
result<-lapply(states,FUN=function(x){
df<-brazilcorona[brazilcorona$estado==x,]
df<-df[order(df$obitosAcumulado,decreasing = T),]
return(c(paste(x),as.character(df[1:2,"municipio"]),
as.character(df[(nrow(df)-1):nrow(df),"municipio"])))})
I hope it helps you...