microdatasus

https://rfsaldanha.github.io/microdatasus/
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microdatasus is an R package for downloading, reading, and preprocessing health microdata from...
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### Example: process_sinan_leishmaniose_tegumentar on sample data

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinan_leishmaniose_tegumentar.html

Example call using the sample dataset sinan_leishmaniose_tegumentar_sample. The output shows a tibble with 100 rows and 79 columns, where supported codes are replaced with descriptive values.

```R
process_sinan_leishmaniose_tegumentar(sinan_leishmaniose_tegumentar_sample)
#> # A tibble: 100 × 79
#>    TP_NOT    ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA
#>    <chr>     <chr>     <chr>      <chr>   <chr>  <chr>     <chr>      <chr>     
#>  1 Individu… B551      2023-09-21 202338  2023   Espírito… 320265     NA        
#>  2 Individu… B551      2023-07-19 202329  2023   Espírito… 320115     NA        
#>  3 Individu… B551      2023-03-17 202311  2023   Espírito… 320450     NA        
#>  4 Individu… B551      2023-02-17 202307  2023   Espírito… 320370     NA        
#>  5 Individu… B551      2023-12-26 202352  2023   Espírito… 320450     NA        
#>  6 Individu… B551      2023-10-11 202341  2023   Espírito… 320450     NA        
#>  7 Individu… B551      2023-09-25 202339  2023   Espírito… 320313     NA        
#>  8 Individu… B551      2023-07-25 202330  2023   Espírito… 320360     NA        
#>  9 Individu… B551      2023-02-02 202305  2023   Espírito… 320450     NA        
#> 10 Individu… B551      2023-05-11 202319  2023   Espírito… 320450     NA        
#> # ℹ 90 more rows
#> # ℹ 71 more variables: ID_UNIDADE <chr>, DT_DIAG <chr>, SEM_DIAG <chr>,
#> #   ANO_NASC <chr>, NU_IDADE_N <chr>, CS_SEXO <chr>, CS_GESTANT <chr>,
#> #   CS_RACA <chr>, CS_ESCOL_N <chr>, SG_UF <chr>, ID_MN_RESI <chr>,
#> #   ID_RG_RESI <chr>, ID_PAIS <chr>, NDUPLIC_N <chr>, DT_DIGITA <chr>,
#> #   DT_TRANSUS <chr>, DT_TRANSDM <chr>, DT_TRANSSM <chr>, DT_TRANSRM <chr>,
#> #   DT_TRANSRS <chr>, DT_TRANSSE <chr>, NU_LOTE_V <chr>, NU_LOTE_H <chr>, …
```

--------------------------------

### fetch_datasus() examples for SIM-DO and SIH-RD

Source: https://rfsaldanha.github.io/microdatasus/reference/fetch_datasus.html

Example usage of fetch_datasus() for SIM-DO and SIH-RD systems. The first call downloads SIM-DO data for Acre in 2014 with selected columns; the second downloads SIH-RD data for Acre and Roraima from January to February 2014. The example is wrapped in if (FALSE) to avoid execution during package checks.

```r
if (FALSE) { # interactive() && curl::has_internet()
sim <- fetch_datasus(
  year_start = 2014,
  year_end = 2014,
  uf = "AC",
  information_system = "SIM-DO",
  vars = c("CODMUNRES", "DTOBITO", "CAUSABAS")
)

sih <- fetch_datasus(
  year_start = 2014,
  month_start = 1,
  year_end = 2014,
  month_end = 2,
  uf = c("AC", "RR"),
  information_system = "SIH-RD"
)
}
```

--------------------------------

### Install microdatasus from GitHub

Source: https://rfsaldanha.github.io/microdatasus/index.html

Install the development version of the microdatasus package using the remotes package.

```R
install.packages("remotes")
remotes::install_github("rfsaldanha/microdatasus", ref = "dev")
```

--------------------------------

### Example of process_sinasc with sample data

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinasc.html

Demonstrates the output of processing a SINASC sample dataset.

```R
process_sinasc(sinasc_sample)
#> # A tibble: 100 × 69
#>    contador ORIGEM CODESTAB CODMUNNASC LOCNASC  IDADEMAE ESTCIVMAE        ESCMAE
#>    <chr>    <chr>  <chr>    <chr>      <chr>    <chr>    <chr>            <chr> 
#>  1 1        1      5618347  110020     Hospital 29       União consensual 4 a 7…
#>  2 2        1      5618347  110020     Hospital 14       União consensual 4 a 7…
#>  3 3        1      4001303  110020     Hospital 30       Casada           4 a 7…
#>  4 4        1      5618347  110020     Hospital 23       Solteira         8 a 1…
#>  5 5        1      5618347  110020     Hospital 30       Casada           8 a 1…
#>  6 6        1      3970442  110020     Hospital 25       União consensual 8 a 1…
#>  7 7        1      5701929  120001     Hospital 21       União consensual 1 a 3…
#>  8 8        1      5701929  120001     Hospital 16       União consensual 8 a 1…
#>  9 9        1      5701929  120001     Hospital 25       NA               8 a 1…
#> 10 10       1      5701929  120001     Hospital 25       Solteira         4 a 7…
#> # ℹ 90 more rows
#> # ℹ 61 more variables: QTDFILVIVO <chr>, QTDFILMORT <chr>, CODMUNRES <chr>,
#> #   GESTACAO <chr>, GRAVIDEZ <chr>, PARTO <chr>, CONSULTAS <chr>, DTNASC <chr>,
#> #   HORANASC <chr>, SEXO <chr>, APGAR1 <chr>, APGAR5 <chr>, RACACOR <chr>,
#> #   PESO <chr>, IDANOMAL <chr>, DTCADASTRO <chr>, CODANOMAL <chr>,
#> #   NUMEROLOTE <chr>, VERSAOSIST <chr>, DTRECEBIM <chr>, DIFDATA <chr>,
#> #   DTRECORIGA <chr>, NATURALMAE <chr>, CODMUNNATU <chr>, CODUFNATU <chr>, …
```

--------------------------------

### process_sim example: recode sim_do_sample

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sim.html

Example call to process_sim() on the sim_do_sample dataset. The output is a tibble with 100 rows and 101 columns, showing recoded values such as 'Não Fetal' for TIPOBITO and 'Masc…'/'Femi…' for SEXO. The displayed output is truncated with 90 more rows and 92 more variables.

```r
process_sim(sim_do_sample)
#> # A tibble: 100 × 101
#>    CONTADOR ORIGEM TIPOBITO  DTOBITO    HORAOBITO CODMUNNATU DTNASC  IDADE SEXO 
#>    <chr>    <chr>  <chr>     <chr>      <chr>     <chr>      <chr>   <chr> <chr>
#>  1 1        1      Não Fetal 2016-01-01 NA        120033     1988-1… 427   Masc…
#>  2 2        1      Não Fetal 2016-01-01 NA        120030     2002-1… 413   Femi…
#>  3 3        1      Não Fetal 2016-01-01 NA        120030     1985-1… 430   Masc…
#>  4 4        1      Não Fetal 2016-01-01 2100      NA         1959-1… 456   Masc…
#>  5 5        1      Não Fetal 2016-01-01 2050      120040     2014-1… 401   Femi…
#>  6 6        1      Não Fetal 2016-01-01 2040      120050     1974-0… 441   Masc…
#>  7 7        1      Não Fetal 2016-01-01 0530      120040     1970-0… 445   Masc…
#>  8 8        1      Não Fetal 2016-01-01 0230      130350     1978-0… 437   Masc…
#>  9 9        1      Não Fetal 2016-01-01 0600      120040     1989-0… 426   Masc…
#> 10 10       1      Não Fetal 2016-01-01 0450      120060     1958-0… 457   Femi…
#> # ℹ 90 more rows
#> # ℹ 92 more variables: RACACOR <chr>, ESTCIV <chr>, ESC <chr>, ESC2010 <chr>,
#> #   SERIESCFAL <chr>, CODMUNRES <chr>, LOCOCOR <chr>, CODESTAB <chr>,
#> #   ESTABDESCR <chr>, CODMUNOCOR <chr>, IDADEMAE <chr>, ESCMAE <chr>,
#> #   ESCMAE2010 <chr>, SERIESCMAE <chr>, QTDFILVIVO <chr>, QTDFILMORT <chr>,
#> #   GRAVIDEZ <chr>, SEMAGESTAC <chr>, GESTACAO <chr>, PARTO <chr>,
#> #   OBITOPARTO <chr>, PESO <chr>, TPMORTEOCO <chr>, OBITOGRAV <chr>, …
```

--------------------------------

### process_sih() example with output

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sih.html

Example call processing the sample data frame sih_rd_sample, followed by the resulting tibble output. The output shows the first 10 rows and 121 columns, with character columns and municipality data added.

```r
process_sih(sih_rd_sample)
```

```r
#> # A tibble: 100 × 121
#>    UF_ZI  ANO_CMPT MES_CMPT ESPEC CGC_HOSP     N_AIH IDENT CEP   MUNIC_RES NASC 
#>    <chr>  <chr>    <chr>    <chr> <chr>        <chr> <chr> <chr> <chr>     <chr>
#>  1 120000 2016     6        03    04034526001… 1216… Prin… 6994… 120050    1934…
#>  2 120000 2016     6        03    04034526001… 1216… Prin… 6994… 120050    1982…
#>  3 120000 2016     6        03    04034526001… 1216… Prin… 6994… 120050    1973…
#>  4 120000 2016     6        03    04034526001… 1216… Prin… 6994… 120050    1938…
#>  5 120000 2016     6        03    04034526001… 1216… Prin… 6994… 120050    1943…
#>  6 120000 2016     6        03    04034526001… 1216… Prin… 6994… 120050    1947…
#>  7 120000 2016     6        01    04034526002… 1216… Prin… 6998… 120020    2006…
#>  8 120000 2016     6        01    04034526002… 1216… Prin… 6998… 120020    1978…
#>  9 120000 2016     6        02    04034526001… 1216… Prin… 6994… 120050    1993…
#> 10 120000 2016     6        02    04034526001… 1216… Prin… 6994… 120050    1992…
#> # ℹ 90 more rows
#> # ℹ 111 more variables: SEXO <chr>, UTI_MES_IN <chr>, UTI_MES_AN <chr>,
#> #   UTI_MES_AL <chr>, UTI_MES_TO <chr>, MARCA_UTI <chr>, UTI_INT_IN <chr>,
#> #   UTI_INT_AN <chr>, UTI_INT_AL <chr>, UTI_INT_TO <chr>, DIAR_ACOM <chr>,
#> #   QT_DIARIAS <chr>, PROC_SOLIC <chr>, PROC_REA <chr>, VAL_SH <chr>,
#> #   VAL_SP <chr>, VAL_SADT <chr>, VAL_RN <chr>, VAL_ACOMP <chr>,
#> #   VAL_ORTP <chr>, VAL_SANGUE <chr>, VAL_SADTSR <chr>, VAL_TRANSP <chr>, …
```

--------------------------------

### Process CNES-ST establishment sample

Source: https://rfsaldanha.github.io/microdatasus/reference/process_cnes.html

Example call using the cnes_st_sample dataset with information_system = "CNES-ST". The output is a tibble with 100 rows and 209 columns, showing recoded values and added municipality data.

```r
process_cnes(cnes_st_sample, information_system = "CNES-ST")
#> # A tibble: 100 × 209
#>    CNES    CODUFMUN COD_CEP  CPF_CNPJ     PF_PJ NIV_DEP CNPJ_MAN COD_IR REGSAUDE
#>    <chr>   <chr>    <chr>    <chr>        <chr> <chr>   <chr>    <chr>  <chr>   
#>  1 2002043 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  2 2002159 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  3 3006166 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  4 3382745 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  5 3393984 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  6 3638685 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  7 5403669 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#>  8 5701929 120001   69945000 00000000000… Pess… Mantida 4034526… NA     NA      
#>  9 6514669 120001   69945000 00000000000… Pess… Mantida 8430673… NA     NA      
#> 10 7026641 120001   69945000 00000000000… Pess… Mantida 4034526… NA     NA      
#> # ℹ 90 more rows
#> # ℹ 200 more variables: MICR_REG <chr>, DISTRSAN <chr>, DISTRADM <chr>,
#> #   VINC_SUS <chr>, TPGESTAO <chr>, ESFERA_A <chr>, RETENCAO <chr>,
#> #   ATIVIDAD <chr>, NATUREZA <chr>, CLIENTEL <chr>, TP_UNID <chr>,
#> #   TURNO_AT <chr>, NIV_HIER <chr>, TP_PREST <chr>, CO_BANCO <chr>,
#> #   CO_AGENC <chr>, C_CORREN <chr>, CONTRATM <chr>, DT_PUBLM <chr>,
#> #   CONTRATE <chr>, DT_PUBLE <chr>, ALVARA <chr>, DT_EXPED <chr>, …
```

--------------------------------

### Process CNES-PF professional sample

Source: https://rfsaldanha.github.io/microdatasus/reference/process_cnes.html

Example call using the cnes_pf_sample dataset with information_system = "CNES-PF". The output is a tibble with 100 rows and 48 columns, showing the different layout for professional records.

```r
process_cnes(cnes_pf_sample, information_system = "CNES-PF")
#> # A tibble: 100 × 48
#>    CNES    CODUFMUN REGSAUDE MICR_REG DISTRSAN DISTRADM TPGESTAO  PF_PJ CPF_CNPJ
#>    <chr>   <chr>    <chr>    <chr>    <chr>    <chr>    <chr>     <chr> <chr>   
#>  1 2002043 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  2 2002159 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  3 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  4 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  5 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  6 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  7 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  8 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#>  9 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#> 10 3006166 120001   NA       NA       NA       NA       Municipal Pess… 0       
#> # ℹ 90 more rows
#> # ℹ 39 more variables: NIV_DEP <chr>, CNPJ_MAN <chr>, ESFERA_A <chr>,
#> #   ATIVIDAD <chr>, RETENCAO <chr>, NATUREZA <chr>, CLIENTEL <chr>,
#> #   TP_UNID <chr>, TURNO_AT <chr>, NIV_HIER <chr>, TERCEIRO <chr>,
#> #   CPFUNICO <chr>, CBO <chr>, CBOUNICO <chr>, NOMEPROF <chr>, CNS_PROF <chr>,
#> #   CONSELHO <chr>, REGISTRO <chr>, VINCULAC <chr>, VINCUL_C <chr>,
#> #   VINCUL_A <chr>, VINCUL_N <chr>, PROF_SUS <chr>, PROFNSUS <chr>, …
```

--------------------------------

### process_sinan_malaria() example with sample data

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinan_malaria.html

Example call to `process_sinan_malaria()` on the sample dataset `sinan_malaria_sample`. The output is a tibble with 100 rows and 55 columns, all character type, with recoded descriptive values for supported fields.

```r
process_sinan_malaria(sinan_malaria_sample)
#> # A tibble: 100 × 55
#>    TP_NOT    ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA
#>    <chr>     <chr>     <chr>      <chr>   <chr>  <chr>     <chr>      <chr>     
#>  1 Individu… B54       2016-01-01 201552  2016   São Paulo 355030     1331      
#>  2 Individu… B54       2016-01-02 201552  2016   Bahia     292740     1380      
#>  3 Individu… B54       2016-01-02 201552  2016   Bahia     291072     1388      
#>  4 Individu… B54       2016-01-02 201552  2016   Espírito… 320130     1510      
#>  5 Individu… B54       2016-01-02 201552  2016   Bahia     292860     1380      
#>  6 Individu… B54       2016-01-03 201601  2016   Paraíba   250750     1377      
#>  7 Individu… B54       2016-01-04 201601  2016   Paraná    410830     1363      
#>  8 Individu… B54       2016-01-04 201601  2016   Goiás     520870     1779      
#>  9 Individu… B54       2016-01-04 201601  2016   Rio Gran… 430510     1608      
#> 10 Individu… B54       2016-01-04 201601  2016   São Paulo 350950     1342      
#> # ℹ 90 more rows
#> # ℹ 47 more variables: ID_UNIDADE <chr>, DT_SIN_PRI <chr>, SEM_PRI <chr>,
#> #   ANO_NASC <chr>, NU_IDADE_N <chr>, CS_SEXO <chr>, CS_GESTANT <chr>,
#> #   CS_RACA <chr>, CS_ESCOL_N <chr>, SG_UF <chr>, ID_MN_RESI <chr>,
#> #   ID_RG_RESI <chr>, ID_PAIS <chr>, DT_INVEST <chr>, ID_OCUPA_N <chr>,
#> #   CLASSI_FIN <chr>, AT_ATIVIDA <chr>, AT_LAMINA <chr>, AT_SINTOMA <chr>,
#> #   TPAUTOCTO <chr>, COUFINF <chr>, COPAISINF <chr>, COMUNINF <chr>, …
```

--------------------------------

### Example: process_sinan_dengue(sinan_dengue_sample) with output

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinan_dengue.html

Example call to `process_sinan_dengue()` on the sample dataset `sinan_dengue_sample`. The output is a tibble with 100 rows and 79 columns, showing recoded values such as "Individu…" for TP_NOT and "Pernambu…" for SG_UF_NOT. The remaining columns are character vectors, as indicated by the `<chr>` type annotations.

```r
process_sinan_dengue(sinan_dengue_sample)
#> # A tibble: 100 × 79
#>    TP_NOT    ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA
#>    <chr>     <chr>     <chr>      <chr>   <chr>  <chr>     <chr>      <chr>     
#>  1 Individu… A90       2010-01-26 201004  2010   Pernambu… 260190     1499      
#>  2 Individu… A90       2010-01-26 201004  2010   Rio de J… 330455     NA        
#>  3 Individu… A90       2010-01-11 201002  2010   Tocantins 170950     NA        
#>  4 Individu… A90       2010-01-22 201003  2010   Tocantins 170950     NA        
#>  5 Individu… A90       2010-01-25 201004  2010   Minas Ge… 314330     1473      
#>  6 Individu… A90       2010-01-25 201004  2010   Minas Ge… 314330     1473      
#>  7 Individu… A90       2010-01-29 201004  2010   Minas Ge… 314330     1473      
#>  8 Individu… A90       2010-01-21 201003  2010   Minas Ge… 314330     1473      
#>  9 Individu… A90       2010-01-25 201004  2010   Minas Ge… 314330     1473      
#> 10 Individu… A90       2010-01-25 201004  2010   Minas Ge… 314330     1473      
#> # ℹ 90 more rows
#> # ℹ 71 more variables: ID_UNIDADE <chr>, DT_SIN_PRI <chr>, SEM_PRI <chr>,
#> #   NU_IDADE_N <chr>, CS_SEXO <chr>, CS_GESTANT <chr>, CS_RACA <chr>,
#> #   CS_ESCOL_N <chr>, SG_UF <chr>, ID_MN_RESI <chr>, ID_RG_RESI <chr>,
#> #   ID_PAIS <chr>, NDUPLIC_N <chr>, DT_DIGITA <chr>, CS_FLXRET <chr>,
#> #   FLXRECEBI <chr>, MIGRADO_W <chr>, DT_INVEST <chr>, ID_OCUPA_N <chr>,
#> #   DT_SORO <chr>, RESUL_SORO <chr>, DT_NS1 <chr>, RESUL_NS1 <chr>, …
```

--------------------------------

### Install stable version from CRAN

Source: https://rfsaldanha.github.io/microdatasus/index.html

Use this command to install the stable release of the microdatasus package from the CRAN repository.

```R
install.packages("microdatasus")
```

--------------------------------

### process_sinan_chagas() example with output

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinan_chagas.html

Example usage of process_sinan_chagas() on the sample dataset sinan_chagas_sample. The output is a tibble with 100 rows and 113 columns, showing recoded values such as 'Individu…' for TP_NOT and 'B571' for ID_AGRAVO, with many columns truncated in the display.

```r
process_sinan_chagas(sinan_chagas_sample)
#> # A tibble: 100 × 113
#>    TP_NOT    ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA
#>    <chr>     <chr>     <chr>      <chr>   <chr>  <chr>     <chr>      <chr>     
#>  1 Individu… B571      2023-05-05 202318  2023   Bahia     292740     1380      
#>  2 Individu… B571      2023-11-30 202348  2023   Pará      150210     1496      
#>  3 Individu… B571      2023-03-07 202310  2023   Pará      150150     1484      
#>  4 Individu… B571      2023-02-28 202309  2023   Pará      150140     1484      
#>  5 Individu… B571      2023-06-12 202324  2023   Pará      150140     1484      
#>  6 Individu… B571      2023-03-28 202313  2023   Pará      150210     1496      
#>  7 Individu… B571      2023-07-03 202327  2023   Pará      150442     1484      
#>  8 Individu… B571      2023-10-03 202340  2023   Rio de J… 330455     NA        
#>  9 Individu… B571      2023-01-31 202305  2023   Pará      150210     1496      
#> 10 Individu… B571      2023-07-02 202327  2023   Ceará     231290     1521      
#> # ℹ 90 more rows
#> # ℹ 105 more variables: ID_UNIDADE <chr>, DT_SIN_PRI <chr>, SEM_PRI <chr>,
#> #   ANO_NASC <chr>, NU_IDADE_N <chr>, CS_SEXO <chr>, CS_GESTANT <chr>,
#> #   CS_RACA <chr>, CS_ESCOL_N <chr>, SG_UF <chr>, ID_MN_RESI <chr>,
#> #   ID_RG_RESI <chr>, ID_PAIS <chr>, NDUPLIC_N <chr>, DT_INVEST <chr>,
#> #   ID_OCUPA_N <chr>, ANT_UF_1 <chr>, MUN_1 <chr>, ANT_UF_2 <chr>, MUN_2 <chr>,
#> #   ANT_UF_3 <chr>, MUN_3 <chr>, PRESENCA <chr>, PARASITO <chr>, …
```

--------------------------------

### fetch_datasus annual query for SIM-DO

Source: https://rfsaldanha.github.io/microdatasus/articles/download-e-rastreabilidade.html

Use for annual systems (SIM, SINASC, SINAN) where only years are required. The example fetches SIM-DO data for Acre from 2020 to 2022. Note that SIM-DO and SINASC files are organized by UF; special SIM subsets and SINAN diseases are national and ignore the uf argument with a warning.

```r
sim_raw <- fetch_datasus(
  year_start = 2020,
  year_end = 2022,
  uf = "AC",
  information_system = "SIM-DO"
)
```

--------------------------------

### Example: process_sinan_leishmaniose_visceral on sample data

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinan_leishmaniose_visceral.html

Example usage of `process_sinan_leishmaniose_visceral()` on the sample dataset `sinan_leishmaniose_visceral_sample`. The output is a tibble with 100 rows and 81 columns, showing recoded values such as 'Individu…' for TP_NOT and state names like 'Rondônia' and 'Maranhão'. The tibble contains character columns, and the output is truncated with 90 more rows and 73 more variables.

```r
process_sinan_leishmaniose_visceral(sinan_leishmaniose_visceral_sample)
#> # A tibble: 100 × 81
#>    TP_NOT    ID_AGRAVO DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA
#>    <chr>     <chr>     <chr>      <chr>   <chr>  <chr>     <chr>      <chr>     
#>  1 Individu… B550      2023-09-06 202336  2023   Rondônia  110028     1482      
#>  2 Individu… B550      2023-01-06 202301  2023   Maranhão  210530     1440      
#>  3 Individu… B550      2023-05-08 202319  2023   Maranhão  210530     1440      
#>  4 Individu… B550      2023-06-09 202323  2023   Maranhão  210530     1440      
#>  5 Individu… B550      2023-07-04 202327  2023   Maranhão  210530     1440      
#>  6 Individu… B550      2023-04-12 202315  2023   Roraima   140010     NA        
#>  7 Individu… B550      2023-04-05 202314  2023   Roraima   140010     NA        
#>  8 Individu… B550      2023-07-24 202330  2023   Minas Ge… 316860     1465      
#>  9 Individu… B550      2023-01-29 202305  2023   Roraima   140010     NA        
#> 10 Individu… B550      2023-02-12 202307  2023   Roraima   140040     NA        
#> # ℹ 90 more rows
#> # ℹ 73 more variables: ID_UNIDADE <chr>, DT_SIN_PRI <chr>, SEM_PRI <chr>,
#> #   ANO_NASC <chr>, NU_IDADE_N <chr>, CS_SEXO <chr>, CS_GESTANT <chr>,
#> #   CS_RACA <chr>, CS_ESCOL_N <chr>, SG_UF <chr>, ID_MN_RESI <chr>,
#> #   ID_RG_RESI <chr>, ID_PAIS <chr>, NDUPLIC_N <chr>, DT_DIGITA <chr>,
#> #   CS_FLXRET <chr>, FLXRECEBI <chr>, MIGRADO_W <chr>, DT_INVEST <chr>,
#> #   ID_OCUPA_N <chr>, FEBRE <chr>, FRAQUEZA <chr>, EDEMA <chr>, EMAGRA <chr>, …
```

--------------------------------

### process_sinan_zika() example with sinan_zika_sample

Source: https://rfsaldanha.github.io/microdatasus/reference/process_sinan_zika.html

Example call to `process_sinan_zika()` using the built-in `sinan_zika_sample` dataset. The output is a tibble with 100 rows and 43 columns, showing recoded values such as 'Individu…' for `TP_NOT` and 'Tocantins' for `SG_UF_NOT`. The tibble includes many additional character columns (e.g., `ID_REGIONA`, `DT_SIN_PRI`, `SEM_PRI`) that are not shown in the preview.

```r
process_sinan_zika(sinan_zika_sample)
#> # A tibble: 100 × 43
#>    TP_NOT    ID_AGRAVO CS_SUSPEIT DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP
#>    <chr>     <chr>     <chr>      <chr>      <chr>   <chr>  <chr>     <chr>     
#>  1 Individu… A928      NA         2016-01-01 201552  2016   Tocantins 170230    
#>  2 Individu… A928      NA         2016-01-01 201552  2016   Bahia     292040    
#>  3 Individu… A928      NA         2016-01-01 201552  2016   Bahia     292040    
#>  4 Individu… A928      NA         2016-01-01 201552  2016   Bahia     293240    
#>  5 Individu… A928      NA         2016-01-01 201552  2016   Espírito… 320530    
#>  6 Individu… A928      NA         2016-01-01 201552  2016   Espírito… 320530    
#>  7 Individu… A928      NA         2016-01-01 201552  2016   Espírito… 320530    
#>  8 Individu… A928      NA         2016-01-01 201552  2016   Espírito… 320530    
#>  9 Individu… A928      NA         2016-01-01 201552  2016   Pará      150613    
#> 10 Individu… A928      NA         2016-01-01 201552  2016   Pará      150613    
#> # ℹ 90 more rows
#> # ℹ 35 more variables: ID_REGIONA <chr>, DT_SIN_PRI <chr>, SEM_PRI <chr>,
#> #   NU_IDADE_N <chr>, CS_SEXO <chr>, CS_GESTANT <chr>, CS_RACA <chr>,
#> #   CS_ESCOL_N <chr>, SG_UF <chr>, ID_MN_RESI <chr>, ID_RG_RESI <chr>,
#> #   ID_PAIS <chr>, NDUPLIC_N <chr>, IN_VINCULA <chr>, DT_INVEST <chr>,
#> #   ID_OCUPA_N <chr>, CLASSI_FIN <chr>, CRITERIO <chr>, TPAUTOCTO <chr>,
#> #   COUFINF <chr>, COPAISINF <chr>, COMUNINF <chr>, DOENCA_TRA <chr>, …
```

--------------------------------

### Handle partial success with stop_on_error = FALSE

Source: https://rfsaldanha.github.io/microdatasus/articles/download-e-rastreabilidade.html

With `stop_on_error = FALSE`, valid files are used and failures are collected into a single diagnostic. The return is `NULL` when no file can be obtained, so always check before processing or persisting; the example shows the required `if (!is.null(dados))` guard before calling `process_sih()`.

```R
dados <- fetch_datasus(
  year_start = 2023,
  month_start = 1,
  year_end = 2023,
  month_end = 12,
  uf = c("AC", "RO"),
  information_system = "SIH-RD",
  stop_on_error = FALSE
)
```

```R
if (!is.null(dados)) {
  dados <- process_sih(dados)
}
```

--------------------------------

### Baixar múltiplas UFs com fetch_datasus

Source: https://rfsaldanha.github.io/microdatasus/articles/FAQ.html

Baixa dados de múltiplas UFs em uma única chamada. Use `uf = "all"` para todas as UFs, mas não combine com códigos individuais. Em sistemas nacionais como SINAN, `uf` é ignorado com alerta; filtre a variável territorial depois do download.

```r
dados <- fetch_datasus(
  year_start = 2023,
  month_start = 1,
  year_end = 2023,
  month_end = 1,
  uf = c("AC", "RO", "AM"),
  information_system = "CNES-ST"
)
```

--------------------------------

### fetch_datasus com track_source = TRUE

Source: https://rfsaldanha.github.io/microdatasus/articles/FAQ.html

Usa track_source = TRUE para adicionar a coluna source indicando o arquivo de origem de cada linha. A coluna é preservada quando vars é usado e não sobrescreve uma coluna source já existente no DBC.

```R
dados <- fetch_datasus(
  year_start = 2023,
  month_start = 1,
  year_end = 2023,
  month_end = 2,
  uf = c("AC", "RO"),
  information_system = "SIH-RD",
  track_source = TRUE
)

unique(dados$source)

```

--------------------------------

### Converter colunas após process_sim()

Source: https://rfsaldanha.github.io/microdatasus/articles/FAQ.html

Converte colunas processadas para os tipos necessários após process_sim(). As funções process_*() retornam um tibble de colunas character; converta somente o que a análise exige, depois de inspecionar códigos especiais, ausências e mudanças de layout.

```R
sim <- process_sim(sim_raw)

sim$DTOBITO <- as.Date(sim$DTOBITO)
sim$IDADEanos <- as.integer(sim$IDADEanos)

```

--------------------------------

### Ler DBC com tipos inferidos (as_character = FALSE)

Source: https://rfsaldanha.github.io/microdatasus/articles/FAQ.html

Lê um arquivo DBC mantendo os tipos inferidos do DBF. Use `as_character = FALSE` para preservar os tipos originais.

```r
dados <- read_dbc("arquivo.dbc", as_character = FALSE)
```

--------------------------------

### Fluxo básico: baixar e processar SIM-DO com conversão de data

Source: https://rfsaldanha.github.io/microdatasus/articles/exemplos.html

Exemplo do fluxo básico: baixa dados de mortalidade (SIM-DO) do Acre em 2022, processa com process_sim() e converte a coluna DTOBITO para Date. Inclui o argumento track_source = TRUE.

```R
sim_raw <- fetch_datasus(
  year_start = 2022,
  year_end = 2022,
  uf = "AC",
  information_system = "SIM-DO",
  vars = c("CODMUNRES", "DTOBITO", "CAUSABAS"),
  track_source = TRUE
)

sim <- process_sim(sim_raw)
sim$DTOBITO <- as.Date(sim$DTOBITO)
```

--------------------------------

### Download and process CNES professionals (CNES-PF)

Source: https://rfsaldanha.github.io/microdatasus/articles/exemplos.html

Downloads CNES-PF data for Pará (PA) in October 2021 and processes it with the default nomes parameter. This example is for professionals data.

```R
cnes_pf_raw <- fetch_datasus(
  year_start = 2021,
  month_start = 10,
  year_end = 2021,
  month_end = 10,
  uf = "PA",
  information_system = "CNES-PF"
)

cnes_pf <- process_cnes(
  cnes_pf_raw,
  information_system = "CNES-PF"
)
```

--------------------------------

### fetch_datasus com stop_on_error = FALSE

Source: https://rfsaldanha.github.io/microdatasus/articles/FAQ.html

Chama fetch_datasus com stop_on_error = FALSE (padrão) para retornar arquivos válidos e resumir falhas ao final. Se nenhum arquivo for lido, o retorno é NULL, e o exemplo verifica isso com is.null().

```R
dados <- fetch_datasus(
  year_start = 2023,
  month_start = 1,
  year_end = 2023,
  month_end = 12,
  uf = c("AC", "RO"),
  information_system = "SIH-RD",
  stop_on_error = FALSE
)

if (is.null(dados)) {
  message("Nenhum arquivo foi obtido.")
}

```

--------------------------------

### fetch_datasus monthly query for SIH-RD

Source: https://rfsaldanha.github.io/microdatasus/articles/download-e-rastreabilidade.html

Use for monthly systems (SIH, SIA, CNES) where both month_start and month_end are required, even for a single month. The example fetches SIH-RD data for Minas Gerais from December 2023 to February 2024. The interval can cross years.

```r
sih_raw <- fetch_datasus(
  year_start = 2023,
  month_start = 12,
  year_end = 2024,
  month_end = 2,
  uf = "MG",
  information_system = "SIH-RD"
)
```
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