Google maps GPS locations due to COVID in Brazil

gistfile1.R

rm(list=ls())
library(data.table)
library(dplyr)
library(ggplot2)
library(viridis)
library(stringr)
library(patchwork)

google <- data.table::fread("data-raw/Global_Mobility_Report_google.csv")
google <- google[country_region %in% "Brazil",]
j_list <- c("retail_and_recreation","grocery_and_pharmacy","parks","transit_stations","workplaces","residential")
for(j in j_list){ # j = j_list[1]
  state <- stringr::str_remove_all(unique(google$sub_region_1),"State of ");state <- state[-1]; 
  
  df <- lapply(state, function(i){ # i = state[1]
    #vec <- unlist(google[,6]) %>% t() %>%  as.vector()
    google1 <- google[sub_region_1 %like% i,]
    google2 <- data.table::data.table("mode" = rep(names(google1)[6:11],each = nrow(google1)),
                                      "data" = rep(1:63,6),
                                      "dia_mes" = gsub("2020-","",rep(google1$date,6)),
                                      "rate" = c(google1[,retail_and_recreation_percent_change_from_baseline],
                                                 google1[,grocery_and_pharmacy_percent_change_from_baseline],
                                                 google1[,parks_percent_change_from_baseline],
                                                 google1[,transit_stations_percent_change_from_baseline],
                                                 google1[,workplaces_percent_change_from_baseline],
                                                 google1[,residential_percent_change_from_baseline]),
                                      "state" = i)
    return(google2)
  }) %>% data.table::rbindlist()
  
  df[,mode:= gsub("_percent_change_from_baseline","",mode)]
  df1 <- df[mode %in% j,]
  df2 <- df1
  df2 <- df2[,sum(rate),by = state][order(V1),]
  p1 <- ggplot(df1, aes( data,state)) + 
    geom_tile(aes(fill = rate),colour = "white") +
    viridis::scale_fill_viridis(option = "A",direction = -1) +  
    scale_x_continuous(breaks = seq(1,63,2) - 0.5,
                       labels = df1$dia_mes[seq(1,63,2)])+ 
    guides(fill=guide_legend(title="Percent change \n from baseline")) +
    labs(title = paste0(stringr::str_to_title(j)," destination"),
         x = NULL, y = "State") +
    theme_bw() + theme_minimal() + 
    theme(axis.text.x = element_text(angle = 90, hjust = 0,size=8),
          axis.text.y = element_text(angle = 0, hjust = 1,size=8),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank()
    )+
    coord_cartesian(xlim = c(df1$data[3], df1[.N,data]-2))
  
  p1
  p2 <- ggplot(df1, aes(data,rate,
                        group = data, fill = rate)) + 
    geom_boxplot() + 
    viridis::scale_fill_viridis(option = "A",direction = -1)  + 
    labs(x = NULL, 
         y = "Percentage change \n from baseline") + 
    scale_x_continuous(breaks = seq(1,63,2) - 0.5,
                       labels = df1$dia_mes[seq(1,63,2)]) + 
    theme(axis.text.x = element_text(angle = 90, hjust = 0,size=8)) +
    coord_cartesian(xlim = c(df1$data[3], df1[.N,data]-2))
  #p2
  #theme_bw() + 
  #theme_minimal() 
  message(j)
  #p2
  pf <- p1/p2
  ggsave(paste0("plot/",j,".jpg"),
         units = "cm",scale = 2.50,
         width = 10,
         height = 8)
}