Cleaning script on mortality by region and state
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Regiao,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019
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Norte,47561,50670,50330,52787,54274,54857,55872,56731,60967,62993,65425,67789,70666,71595,74518,77944,80105,82983,84409,85686
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Nordeste,228410,238256,248980,253019,256614,254544,256139,262193,273873,280476,284635,301596,305746,316415,319748,337713,347095,352045,343969,352801
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Sudeste,463948,465471,470221,479735,487492,475801,493850,495877,504984,515214,534495,541518,543383,554513,562401,573965,595573,593692,598138,616243
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Sul,152476,151629,154987,157625,163070,159922,163388,169004,169646,175573,179428,184658,183528,189231,188514,191172,202593,197793,203734,206086
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Centro-Oeste,54291,55466,58289,59174,62623,61703,62442,64019,67537,68832,72964,74937,77843,78720,81858,83381,84408,86150,86469,88985
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## Building tables to be used in dashboard
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library(flexdashboard)
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library(WDI)
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library(ggplot2)
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library(dplyr)
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library(DT)
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library(plotly)
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library(data.table)
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library(dygraphs)
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library(knitr)
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library(tidyr)
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## Mortalidade geral: casos totais e mortalidade por 1000 habitantes
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cols = paste0("2000":"2019")
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d <- fread("../data/obitos_UF_2000_2019.csv", header = TRUE) |>
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pivot_longer(cols = all_of(cols),
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names_to = "Ano",
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values_to = "Obitos")
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d1 <- fread("../data/pop_UF_2000_2020.csv", header = TRUE) |>
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select(-`2020`) |>
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pivot_longer(cols = all_of(cols),
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names_to = "Ano",
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values_to = "População")
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d3 <- left_join(d, d1, by = c("UF", "Ano")) |>
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mutate(Mortalidade = (Obitos/População) * 1000)
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d4 <- fread("../data/obitos_reg_2000_2020.csv", header = TRUE) |>
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pivot_longer(cols = all_of(cols),
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names_to = "Ano",
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values_to = "Obitos")
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d5 <- fread("../data/pop_reg_2000_2020.csv", header = TRUE) |>
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pivot_longer(cols = all_of(cols),
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names_to = "Ano",
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values_to = "População")
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d6 <- left_join(d4, d5, by = c("Regiao", "Ano")) |>
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mutate(Mortalidade = (Obitos/População) * 1000) |>
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rename(Região = "Regiao")
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