Adding dashboard with mortality rate by causes - CID 10

README.org

@@ -7,6 +7,8 @@
 
 - [[./script/db.R][Usando RSQlite para salvar dados de maior tamanho: exemplo]]
 - [[./script/mortal_anos.R][Descrição básica do indicador "mortalidade geral" da população brasileira 2010]]
+- [[./script/save_rds.R][Comprimindo tabela em formato rds no R]]
+- [[./script/mort_CID10.Rmd][Compilado mortalidade por cem mil habitantes CID 10]]
 
 ** Dados
 
@@ -16,4 +18,8 @@ Saúde do Brasil.
 
 - [[https://opendatasus.saude.gov.br/dataset/sim-1979-2019][Dados de mortalidade 1979-2019]]
 - [[https://opendatasus.saude.gov.br/dataset/sim-2020-2021][Dados de mortalidade 2021]]
+- [[./data/mortalidade_CID_10.csv][Compilado dados mortalidade por causas - CID 10]]
+- [[./data/pop_reg_2000_2020.csv][Compilado dados população brasileira]]
+- [[https://diaad.s3.sa-east-1.amazonaws.com/sim/Mortalidade_Geral+-+Estrutura.pdf
+][Dicionário de dados mortalidade SIM]]
 - [[https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SP.DYN.CDRT.IN][Definição do indicador "mortalidade geral" - Banco Mundial]]

data/mortalidade_CID_10.csv

@@ -0,0 +1,20 @@
+CID 10,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019
+I.   Infecciosas e parasitarias,44515,45032,45175,46533,46067,46628,46508,45945,47295,47010,48823,49175,49608,52058,52174,55022,57188,54874,54679,56666
+II.  Neoplasias,120517,125348,129923,134691,140801,147418,155796,161491,167677,172255,178990,184384,191577,196954,201968,209780,215217,221821,227920,235301
+III. Sangue hemat e transt imunit,4800,5240,5217,5354,4978,4999,5496,5719,5825,6011,6284,6344,6358,6388,6108,6506,6878,6622,6601,7068
+IV.  Endocrinas nutricionais e metablicas,47281,47800,49222,51190,53134,53983,58904,61860,64631,66984,70276,73929,72495,74726,73972,76235,78075,79662,81365,83485
+V.   Transtornos mentais e comportamentais,6139,6655,7011,7356,8158,8931,10256,10948,11852,11861,12759,13725,12641,13052,12480,12558,12674,12858,13697,14526
+VI.  Sistema nervoso,11575,12296,12857,13750,15156,16384,19166,20413,21609,23018,25303,26948,28712,30300,32381,34721,36870,38786,41035,45235
+VII. Olho e anexos,10,12,11,21,21,13,28,26,39,23,31,23,38,15,18,21,20,19,21,23
+VIII. Ouvido e da apofise mastoide,133,129,115,120,119,112,145,118,125,125,125,150,139,143,157,147,173,179,169,206
+IX.  Aparelho circulatorio,260603,263417,267496,274068,285543,283927,302817,308466,317797,320074,326371,335213,333295,339672,340284,349642,362091,358882,357770,364132
+X.   Aparelho respiratorio,88370,90288,94754,97656,102168,97397,102866,104498,104989,114539,119114,126693,127204,137832,139045,149541,158041,155620,155191,162005
+XI. Aparelho digestivo,43029,44393,45797,46894,48661,50097,51924,53724,55272,56202,58061,59707,60509,61934,62763,64202,66044,66052,67316,68770
+XII. Pele e tecido subcutaneo,1652,1825,1932,1977,1886,2014,2466,2475,2642,2979,3225,3395,3722,3919,4300,4970,5874,6100,6273,7152
+XIII. Sist osteomuscular e tec conjuntivo,2478,2606,2885,3001,3002,3084,3597,3789,4094,4216,4541,4488,4607,5001,5325,5385,5787,5912,6153,6506
+XIV. Aparelho geniturinario,13370,14350,15167,15858,17094,18365,17421,18301,19790,22489,24519,26317,27975,29709,32510,36549,39367,40470,43428,47566
+XV.  Gravidez parto e puerperio,1646,1587,1650,1597,1672,1661,1637,1615,1691,1884,1728,1680,1647,1787,1889,1896,1814,1874,1862,1726
+XVI. Algumas afec originadas no periodo perinatal,36618,34274,33136,32040,31011,29799,28336,26898,26080,25367,23723,23579,23069,22745,22482,22162,21049,21458,20764,20354
+XVII. Malf cong deformid e anomalias cromossomicas,9804,9520,9733,10143,10210,9927,10397,10262,10502,10360,10196,10543,10622,10752,11050,10989,10882,10995,11156,11308
+XVIII. Sint sinais e achad anorm ex clin e laborat,135749,135766,134176,133434,126922,104455,85543,80244,79161,78994,79622,78363,74935,71804,71191,71713,75869,71822,70505,74972
+XX.  Causas externas,118397,120954,126550,126657,127470,127633,128388,131032,135936,138697,143256,145842,152013,151683,156942,152136,155861,158657,150814,142800

data/pop_reg_2000_2020.csv

@@ -0,0 +1,6 @@
+Regiao,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019
+Norte,12399633,13245084,13504599,13784881,14373260,14698878,15022060,14648122,15142684,15385707,15865678,16095187,16347807,17013559,17261983,17504446,17740418,17936201,18182253,18430980
+Nordeste,46768451,48331186,48845112,49352225,50427274,51019091,51609027,51535782,53088499,53591197,53078137,53501859,53907144,55794707,56186190,56560081,56915936,57254159,56760780,57071654
+Sudeste,70758097,73470763,74447456,75391969,77374720,78472017,79561095,77873342,80187717,80915332,80353724,80975616,81565983,84465570,85115623,85745520,86356952,86949714,87711946,88371433
+Sul,24738865,25453264,25734253,26025091,26635629,26973511,27308863,26733877,27497970,27719118,27384815,27562433,27731644,28795762,29016114,29230180,29439773,29644948,29754036,29975984
+Centro-Oeste,11447472,11885529,12101540,12317271,12770141,13020767,13269517,13223393,13695944,13895375,14050340,14244192,14423952,14993191,15219608,15442232,15660988,15875907,16085885,16297074

script/db.R

@@ -34,7 +34,7 @@ theme_set(theme_bw())
 #' R canot handle data larger than RAM. Therefore, how "big" some data is, is
 #' relative to the memory and processing capacity of the machines.
 #'
-#' These popultaion mortality data are larger than the RAM memory of most commom
+#' These population mortality data are larger than the RAM memory of most commom
 #' personal computers (4 to 16 Gigabytes).
 #'
 #' Thus, it would be appropriate to look for smart strategies to deal with this data.
@@ -44,13 +44,13 @@ theme_set(theme_bw())
 #'
 #'
 #' - Download the data in csv format, and reduce the size of each file
-#'  - You may use 'rds' files in R for this purposes
+#'  - You may use 'rds' files in R for this purpose
 #'  - Check "?saveRDS" help for more information
-#' - Work with pieces of data, extracting only columns or rows you are intrested
+#' - Work with pieces of data, extracting only columns or rows you are interested
 #' in
 #' - Explore a database solution
-#'  - There are multiple resources to word with SQL and NoSQL databases inR
-#'  - Take a look to RPostgreSQL package for SQL integration
+#'  - There are multiple resources to work with SQL and NoSQL databases in R
+#'  - Take a look at RPostgreSQL package for SQL integration
 #'  - Check the "mongolite" package for NoSQL integration
 #'
 #' Learn about SQLite database can be an option to make a first approach to
@@ -61,7 +61,7 @@ theme_set(theme_bw())
 #' [@wiley2020advanced]
 #'
 #'
-#' And, take a look to this:
+#' And, don't forget to take a look at this:
 
 ?saveRDS
 
@@ -102,7 +102,7 @@ mortdb <- dbConnect(RSQLite::SQLite(), "mort_db.sqlite")
 #'
 #'------------------------------------------------------------------------------
 #'
-#' Download the data, then load the table to be written in the workspace and use #' the following to include it in the database.
+#' Download the data. Then load the table to be written in the workspace.
 #'
 #' I saved the data as 'rds' to reduce the object weight.
 #'
@@ -130,6 +130,9 @@ head(names(d))
 #' Please note that the data has been updated since the build of this script, thus
 #' some colum names and type differ between tables
 
+
+#' The following code is used to include the data on the database:
+
 dbWriteTable(mortdb, "Mortalidade_Br_2010", d)
 
 #'------------------------------------------------------------------------------

script/mort_CID10.Rmd

@@ -0,0 +1,129 @@
+---
+title: "Mortalidade população brasileira 2000-2019"
+subtitle: "Categorizada por capítulo CID 10"
+author: "José"
+date: "2021-04-22"
+output:
+  flexdashboard::flex_dashboard:
+    orientation: rows
+    vertical_layout: fill
+    source_code: embed
+    social: menu
+runtime: shiny
+---
+
+```{r setup, include=FALSE}
+library(flexdashboard)
+library(WDI)
+library(ggplot2)
+library(data.table)
+library(DT)
+library(plotly)
+library(dygraphs)
+library(knitr)
+```
+
+Óbitos categorizados por Capítulo CID 10 e por 100000 habitantes
+
+```{r }
+ob <- fread("../data/mortalidade_CID_10.csv",
+            skip = 0,
+            header = TRUE)
+
+names(ob)
+
+cols = names(ob)[2:21]
+
+b = melt(ob,
+         id="CID 10",
+         measure = cols,
+         variable.name = "Ano",
+         value.name = "Obitos")
+
+
+b[, Ano :=  gsub('X', "", b$Ano)]
+
+str(b)
+```
+
+População
+
+```{r }
+p <- fread("../data/pop_reg_2000_2020.csv",
+           skip = 0,
+           header = TRUE)
+
+names(p)
+
+str(p)
+
+
+po = melt(p,
+          id = "Regiao",
+          measure = cols,
+          variable.name = "Ano",
+          value.name = "População")
+
+po = po[, .("População" = sum(`População`)), by = Ano]
+
+str(po)
+```
+
+Unindo tabelas
+
+```{r }
+ci = po[b, on = 'Ano']
+
+names(ci)
+
+ci[, Mortalidade := (Obitos/`População`) * 100000 ]
+
+str(ci)
+```
+
+Tabela
+=======================================================================
+
+```{r }
+datatable(ci , filter = 'top') |>
+    formatRound('Mortalidade', 1)
+```
+
+Causas comuns
+=======================================================================
+
+```{r }
+
+ci2 <- ci[`CID 10` == "II.  Neoplasias" |
+        `CID 10` == "IX.  Aparelho circulatorio" |
+        `CID 10` == "X.   Aparelho respiratorio" |
+        `CID 10` == "XX.  Causas externas" |
+        `CID 10` == "XVIII. Sint sinais e achad anorm ex clin e laborat", ]
+
+
+p1 = ggplot(ci2, aes(Ano, Mortalidade, group = `CID 10`)) +
+    geom_point() +
+    geom_path(aes(col = `CID 10`), size = 1) +
+    theme_minimal() +
+    theme(legend.position = c(0.85, 0.7)) +
+    labs(x = "Ano", y = "Mortalidade x 100000 habitantes")
+```
+
+```{r }
+plotly::ggplotly(p1)
+```
+
+Todas as causas
+=======================================================================
+
+```{r }
+p2 = ggplot(ci, aes(Ano, Mortalidade, group = `CID 10`)) +
+    geom_point() +
+    geom_path(aes(col = `CID 10`), size = 1) +
+    theme_minimal() +
+    labs(x = "Ano", y = "Mortalidade x 100000 habitantes")
+```
+
+```{r }
+ggplotly(p2)
+```