Chilean Municipalities Information System Wrapper
This R package allows easy SINIM (https://sinim.gov.cl) data retrieval what have advantages over the site:
- When you work with multiple variables or years it will be very useful for rapid analyses.
- Fast ploting directly from data source using the included geometries.
- Data download with or without monetary correction using a switch.
library(dplyr)
library(sinimr)
library(sf)
library(tmap)
varcode <- 882
var <- get_sinim(varcode, 2018,
region = 13,
truevalue = T,
geometry = T,
auc = T,
unit = "limites")
gran_santiago_plot <- tm_shape(var) +
tm_fill(col = "value",
palette = "BuPu",
border.col = "white",
border.alpha = 0.5,
lwd=1,
style = "jenks",
title = get_sinim_var_name(varcode))+
tm_text("municipality", size = 0.4, style="jenks") +
tm_legend(legend.position = c("left", "top"), legend.title.size = 1, legend.text.size = 0.6) +
tm_compass(type = "8star", position = c(.85, .80)) +
tm_scale_bar(breaks = c(0, 10), text.size = 0.75, position = c("right", "bottom")) +
tm_credits("Fuente: Sistema Nacional de Información Municipal (SINIM), SUBDERE, Ministerio del Interior.", position=c("left", "bottom"), size=0.55)+
tm_layout(legend.width=1,
inner.margins = c(0.1, 0.1, 0.10, 0.1),
legend.format = list(text.separator = "a",
fun = mm)) +
tm_borders(col = 'black')
gran_santiago_plot
FONDECYT Regular 2016 Nº 1161417, ¿Quién es responsable del desarrollo local? Una geografía política del neoestructuralismo en “comunas de exportación” (Comisión Nacional de Investigación Científica y Tecnológica).
When querying the API, please be respectful of the resources required to provide this data. Please retain the results for each request to avoid repeated requests for duplicate information.
install.packages("devtools")
devtools::install_github("robsalasco/sinimr")sinimR comes with a small set of functions to deliver the content of
SINIM’s webpage. To get a first glance of the categories of information
what are available please use the get_sinim_cats() command.
library(sinimr)
get_sinim_cats()
#> $`-`
#> variable code
#> 1 * SIM FIMU 539
#>
#> $`01. ADMINISTRACION Y FINANZAS MUNICIPALES`
#> variable code
#> 1 A.1. PRESUPUESTO INICIAL Y VIGENTE MUNICIPAL (M$) 517
#> 2 A. INGRESOS MUNICIPALES (M$) 21
#> 3 B. INGRESOS MUNICIPALES (%) 191
#> 4 C. GASTOS MUNICIPALES (M$) 22
#> 5 D. GASTOS MUNICIPALES (%) 172
#> 6 E. GASTOS EN PERSONAL 169
#> 7 F. TRANSFERENCIAS E INVERSION 170
#> 8 G. SERVICIOS BASICOS Y GENERALES 370
#> 9 I. TRANSFERENCIAS Y COMPENSACIONES SUBDERE 485
#> 10 J. FONDO COMÚN MUNICIPAL (FCM) 508
#> 11 K. GESTION MUNICIPAL 486
#> 12 L. 24
#> 13 M. 506
#>
#> $`02. RECURSOS HUMANOS MUNICIPAL`
#> variable code
#> 1 A. PERSONAL DE PLANTA 381
#> 2 B. PERSONAL A CONTRATA 382
#> 3 C. HONORARIOS 383
#> 4 D. OTROS INDICADORES 384
#>
#> $`03. EDUCACION MUNICIPAL`
#> variable code
#> 1 A. ANTECEDENTES GENERALES DE EDUCACION 38
#> 2 B. ASISTENCIA Y MATRÍCULAS EN EDUCACION 32
#> 3 C. RESULTADOS PAES 33
#> 4 D. INGRESOS EN EDUCACION MUNICIPAL 35
#> 5 E. GASTOS EN EDUCACION MUNICIPAL 36
#> 6 F. RECURSOS HUMANOS EN SECTOR EDUCACION 34
#> 7 G. ESTABLECIMIENTOS DE EDUCACION MUNICIPAL 379
#>
#> $`04. SALUD MUNICIPAL`
#> variable code
#> 1 A. ANTECEDENTES GENERALES DE SALUD 30
#> 2 B. COBERTURA EN SALUD MUNICIPAL 25
#> 3 C. INGRESOS EN SALUD MUNICIPAL 26
#> 4 D. GASTOS EN SALUD MUNICIPAL 28
#> 5 E. RED ASISTENCIAL SALUD 31
#> 6 F. RECURSOS HUMANOS EN SALUD 362
#>
#> $`05. SOCIAL Y COMUNITARIA`
#> variable code
#> 1 A. INFORMACION ENCUESTA CASEN 47
#> 2 B. RED SOCIAL (SUBSIDIOS Y PENSIONES) 44
#> 3 C. INTERMEDIACION LABORAL 43
#> 4 D. ORGANIZACIONES COMUNITARIAS 46
#> 5 E. BECAS 377
#> 6 F. PARTICIPACIÓN CIUDADANA 510
#> 7 G. DISCAPACIDAD 512
#> 8 H. PREVENCIÓN DEL DELITO Y SEGURIDAD 511
#> 9 L. RSH - ANTECEDENTES GENERALES 560
#> 10 N.RSH - NIVEL INGRESOS - VULNERABILIDAD 561
#> 11 O.RSH - CARACTERISTICAS ENCUESTADOS 562
#> 12 P. INE - TIPO DE HOGARES 563
#> 13 RETIROS 10% 565
#>
#> $`06. DESARROLLO Y GESTION TERRITORIAL`
#> variable code
#> 1 A. CARACTERISTICAS TERRITORIALES 39
#> 2 B. SERVICIOS BASICOS A LA COMUNIDAD 41
#> 3 C. INFRAESTRUCTURA 40
#> 4 D. CATASTRO PREDIOS Y VALORACION CATASTRAL 300
#> 5 E. AREAS VERDES 376
#> 6 F. PLAN DE DESARROLLO COMUNAL (PLADECO) 304
#> 7 G. PLAN REGULADOR COMUNAL 42
#> 8 H. CULTURA 587
#>
#> $`07. CARACTERIZACION COMUNAL`
#> variable code
#> 1 A. GEOGRAFICO ADMINISTRATIVA 49
#> 2 B. POBLACION 50
#>
#> $`08. GENERO`
#> variable code
#> 1 DOTACION MUNICIPAL HOMBRES Y MUJERES 262
#>
#> $`09. CEMENTERIO`
#> variable code
#> 1 1. INFORMACION GENERAL 516
#> 2 A. INGRESOS CEMENTERIO (M$) 456
#> 3 B. GASTOS CEMENTERIO (M$) 457Every category have a bunch of variables associated. Use the CODE number
and the get_sinim_vars() function to get them.
get_sinim_vars(517)
#> variable unit code
#> 1 Presupuesto Inicial Sector Municipal M$ 4210
#> 2 Presupuesto Inicial Gastos Municipales M$ 4211
#> 3 Presupuesto Vigente Sector Municipal M$ 4212
#> 4 Presupuesto Vigente Gastos Municpiales M$ 4213
#> 5 Presupuesto Vigente Saldo Inicial de Caja Sector Municipal M$ 4226
#> 6 Presupuesto Inicial Saldo Inicial de Caja sector Municipal M$ 4441Finally, to obtain the data across municipalities use the code column and specify a year.
head(get_sinim(c(4210, 4211), 2015))
#> code municipality year variable value
#> 1 1101 IQUIQUE 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 47727846
#> 2 1107 ALTO HOSPICIO 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 13878279
#> 3 1401 POZO ALMONTE 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 10393011
#> 4 1402 CAMIÑA 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 1784932
#> 5 1403 COLCHANE 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 2880967
#> 6 1404 HUARA 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 4418315By default the values are in miles de millones but it can be
disabled using the truevalue = T switch.
head(get_sinim(c(4210, 4211), 2015, truevalue = T))
#> code municipality year variable value
#> 1 1101 IQUIQUE 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 47727846000
#> 2 1107 ALTO HOSPICIO 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 13878279000
#> 3 1401 POZO ALMONTE 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 10393011000
#> 4 1402 CAMIÑA 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 1784932000
#> 5 1403 COLCHANE 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 2880967000
#> 6 1404 HUARA 2015 PRESUPUESTO INICIAL GASTOS MUNICIPALES 4418315000You can get multiple years too! use the command get_sinim() and add
more years as in the example.
head(get_sinim(880, 2015:2017))
#> code municipality year variable value
#> 1 1101 IQUIQUE 2017 INGRESOS POR FONDO COMÚN MUNICIPAL 4654846
#> 2 1107 ALTO HOSPICIO 2017 INGRESOS POR FONDO COMÚN MUNICIPAL 9821923
#> 3 1401 POZO ALMONTE 2017 INGRESOS POR FONDO COMÚN MUNICIPAL 2555329
#> 4 1402 CAMIÑA 2017 INGRESOS POR FONDO COMÚN MUNICIPAL 2200987
#> 5 1403 COLCHANE 2017 INGRESOS POR FONDO COMÚN MUNICIPAL 1564349
#> 6 1404 HUARA 2017 INGRESOS POR FONDO COMÚN MUNICIPAL 1903874The geometries are available in long format using the geometry=T
argument. By default it uses the comunal geographies but the
limite urbano censal is also available. The switches are
unit="comunas" and unit="limites". Note: Using limites not all
features are available because some comunas are not related to urban
zones. As shown in the example below you can obtain multiple years and
variables in long format.
head(get_sinim(882, 2015:2017, geometry=T))
#> old-style crs object detected; please recreate object with a recent sf::st_crs()
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -70.20924 ymin: -21.43871 xmax: -69.81985 ymax: -20.04854
#> Geodetic CRS: WGS 84
#> code municipality year variable value
#> 1 1101 IQUIQUE 2016 INGRESOS PROPIOS (IPP Y FCM) 49563214
#> 2 1101 IQUIQUE 2015 INGRESOS PROPIOS (IPP Y FCM) 47580403
#> 3 1101 IQUIQUE 2017 INGRESOS PROPIOS (IPP Y FCM) 51320111
#> 4 1107 ALTO HOSPICIO 2017 INGRESOS PROPIOS (IPP Y FCM) 15396829
#> 5 1107 ALTO HOSPICIO 2016 INGRESOS PROPIOS (IPP Y FCM) 14763918
#> 6 1107 ALTO HOSPICIO 2015 INGRESOS PROPIOS (IPP Y FCM) 14475961
#> geometry
#> 1 MULTIPOLYGON (((-70.09894 -...
#> 2 MULTIPOLYGON (((-70.09894 -...
#> 3 MULTIPOLYGON (((-70.09894 -...
#> 4 MULTIPOLYGON (((-70.1095 -2...
#> 5 MULTIPOLYGON (((-70.1095 -2...
#> 6 MULTIPOLYGON (((-70.1095 -2...Another interesting feature is the possibility to subset by different
contexts. e.g if you want the comunas of Antofagasta region this command
is available. The command works with or without the presence of the
geometry switch and other switches are avaiblable too region,
provincia and comuna all working with codes.
head(get_sinim(882, 2015:2017, geometry=T, region=2))
#> old-style crs object detected; please recreate object with a recent sf::st_crs()
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -70.62886 ymin: -25.40293 xmax: -68.0676 ymax: -22.4254
#> Geodetic CRS: WGS 84
#> code municipality year variable value
#> 1 2101 ANTOFAGASTA 2015 INGRESOS PROPIOS (IPP Y FCM) 68261771
#> 2 2101 ANTOFAGASTA 2017 INGRESOS PROPIOS (IPP Y FCM) 72372118
#> 3 2101 ANTOFAGASTA 2016 INGRESOS PROPIOS (IPP Y FCM) 72746340
#> 4 2102 MEJILLONES 2016 INGRESOS PROPIOS (IPP Y FCM) 9512153
#> 5 2102 MEJILLONES 2015 INGRESOS PROPIOS (IPP Y FCM) 8654511
#> 6 2102 MEJILLONES 2017 INGRESOS PROPIOS (IPP Y FCM) NA
#> geometry
#> 1 MULTIPOLYGON (((-70.60654 -...
#> 2 MULTIPOLYGON (((-70.60654 -...
#> 3 MULTIPOLYGON (((-70.60654 -...
#> 4 MULTIPOLYGON (((-69.97002 -...
#> 5 MULTIPOLYGON (((-69.97002 -...
#> 6 MULTIPOLYGON (((-69.97002 -...You can get a subset too
head(get_sinim(882, 2015:2017, geometry=T, region=c(2,3)))
#> old-style crs object detected; please recreate object with a recent sf::st_crs()
#> Simple feature collection with 6 features and 5 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: -70.62886 ymin: -25.40293 xmax: -68.0676 ymax: -22.4254
#> Geodetic CRS: WGS 84
#> code municipality year variable value
#> 1 2101 ANTOFAGASTA 2016 INGRESOS PROPIOS (IPP Y FCM) 72746340
#> 2 2101 ANTOFAGASTA 2015 INGRESOS PROPIOS (IPP Y FCM) 68261771
#> 3 2101 ANTOFAGASTA 2017 INGRESOS PROPIOS (IPP Y FCM) 72372118
#> 4 2102 MEJILLONES 2016 INGRESOS PROPIOS (IPP Y FCM) 9512153
#> 5 2102 MEJILLONES 2015 INGRESOS PROPIOS (IPP Y FCM) 8654511
#> 6 2102 MEJILLONES 2017 INGRESOS PROPIOS (IPP Y FCM) NA
#> geometry
#> 1 MULTIPOLYGON (((-70.60654 -...
#> 2 MULTIPOLYGON (((-70.60654 -...
#> 3 MULTIPOLYGON (((-70.60654 -...
#> 4 MULTIPOLYGON (((-69.97002 -...
#> 5 MULTIPOLYGON (((-69.97002 -...
#> 6 MULTIPOLYGON (((-69.97002 -...But where obtain the codes? a database is provided and you can filter it using the standard R functions.
head(id_geo_census)
#> code municipality code.reg nom.reg code.prov nom.prov auc
#> 1 1101 IQUIQUE 1 TARAPACA 11 IQUIQUE 1
#> 2 1107 ALTO HOSPICIO 1 TARAPACA 11 IQUIQUE 1
#> 3 1401 POZO ALMONTE 1 TARAPACA 14 TAMARUGAL 0
#> 4 1402 CAMIÑA 1 TARAPACA 14 TAMARUGAL 0
#> 5 1403 COLCHANE 1 TARAPACA 14 TAMARUGAL 0
#> 6 1404 HUARA 1 TARAPACA 14 TAMARUGAL 0Related to variables if you don’t know what are you looking for use
search_sinim_vars()to get search results based on variable
descriptions, names and groups.
search_sinim_vars("cementerio")
#> code
#> 450 4140
#> 451 4141
#> 452 4406
#> 453 4407
#> variable
#> 450 Ingresos Cementerio (Ingreso Total Percibido)
#> 451 Gastos Cementerio (Gasto Total Devengado)
#> 452 ¿La Municipalidad o Corporación administra Cementerio?
#> 453 Si la Municipalidad o Corporación administra Cementerio, indique si tiene presupuesto propio. SI = presupuesto propio o independiente.
#> description
#> 450 Ingreso total percibido del sector Cementerio (clasificador presupuestario)
#> 451 Gastos total devengado sector Cementerio (clasificador presupuestario).
#> 452 Indica si la Municipalidad o Corporación administra o no Cementerio Municipal, ya sea con presupuesto propio o asociado a otro sector de la municipalidad o corporación.
#> 453 Indica si administra un presupuesto independiente o anexo a otro sector de la municipalidad, como Salud, Municipalidad, etc.
#> area subarea unit
#> 450 09. CEMENTERIO A. INGRESOS CEMENTERIO (M$) M$
#> 451 09. CEMENTERIO B. GASTOS CEMENTERIO (M$) M$
#> 452 09. CEMENTERIO 1. INFORMACION GENERAL S-N
#> 453 09. CEMENTERIO 1. INFORMACION GENERAL S-NSINIM (Sistema Nacional de Información Municipal) by default applies a
monetary correction to show current values of variables. The original
values provided by municipalities are available using the moncorr = F
switch. And if you want geographical identifiers like region or
provincia you can apply them using idgeo = T switch.
library(tmap)
library(dplyr)
library(stringr)
library(sinimr)
library(sf)
data_sinim <- get_sinim(var = c(3954,4174,880,1226,4251,4173),
year = 2018,
region = 13,
geometry = T,
truevalue = T,
auc = T,
unit = "limites")
gran_santiago_plot <- tm_shape(data_sinim) +
tm_fill(col = "value",
palette = "BuPu",
border.col = "white",
border.alpha = 0.5,
lwd=1,
style = "jenks",
title = "variable")+
tm_text("municipality", size = 0.4) +
tm_style("white", frame = T, legend.title.size = 1, legend.width=1) +
tm_layout(inner.margins = c(0.01, 0.1, 0.1, 0.01),
outer.margins = c(0.01, 0.01, 0.01, 0.01),
design.mode=F,
legend.format = list(text.separator = "a",
fun = mm))+
tm_borders(col = 'black') +
tm_facets(by="variable", ncol = 2)
#> Warning: argument design.mode not used anymore. Please use the stand-alone
#> function tmap_design_mode() to set the design mode globally.
gran_santiago_plot
library(dplyr)
library(sinimr)
library(sf)
library(tmap)
var <- get_sinim(c(880, 882, 1226),
2016:2018,
region = 13,
truevalue = T,
geometry = T,
auc = T,
unit = "limites")
gran_santiago_plot <- tm_shape(var) +
tm_fill("value",
palette="BuPu",
border.col = "white",
style = "jenks",
border.alpha = 0.5,
lwd=1) +
tm_text("municipality", size = 0.4) +
tm_legend(legend.position = c("left", "top")) +
tm_layout(legend.width=0.09,
inner.margins = c(0.01, 0.1, 0.1, 0.01),
outer.margins = c(0.01, 0.01, 0.01, 0.01),
legend.format = list(text.separator = "a",
fun = mm)) +
tm_facets(by=c("year","variable"),) +
tm_borders(col = 'black')
gran_santiago_plot
library(sf)
library(dplyr)
library(geofacet)
library(sinimr)
library(ggplot2)
library(zoo)
library(scales)
library(ggpubr)
data <- get_sinim(882, 2002:2018,
region = 13,
moncorr = F,
truevalue = T,
auc = T)
data$year <- as.numeric(as.character(data$year))
data$year <- as.Date(as.yearmon(data$year, "1-%y"))
reg13 <- geogrid::read_polygons("https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson")
#> Warning in geogrid::read_polygons("https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson"): 'geogrid::read_polygons' is deprecated.
#> Use 'sf::st_read' instead.
#> See help("Deprecated")
#> Reading layer `R13' from data source
#> `https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson'
#> using driver `GeoJSON'
#> Simple feature collection with 37 features and 8 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -70.84306 ymin: -33.65716 xmax: -70.43015 ymax: -33.31069
#> Geodetic CRS: WGS 84
grd <- grid_auto(reg13, seed = 1, names = "NOM_COMUNA", codes = "COMUNA")
#grid_preview(grd, label = "name_NOM_COMUNA")
#grid_design(grd, label = "name_NOM_COMUNA")
ggplot(data, aes(year, value, group=1)) +
geom_line(color = "steelblue") +
facet_geo(~ municipality, grid = grd, scales = "free_y")+
scale_x_date() +
scale_y_continuous(labels = dollar_format(suffix = "", prefix = "$", big.mark = ".", decimal.mark=","))+
theme_bw()
citation("sinimr")
#> To cite package 'sinimr' in publications use:
#>
#> Salas R (2025). _sinimr: Chilean Municipalities Information System
#> Wrapper_. R package version 0.3.5,
#> <https://github.com/robsalasco/sinimr>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {sinimr: Chilean Municipalities Information System Wrapper},
#> author = {Roberto Salas},
#> year = {2025},
#> note = {R package version 0.3.5},
#> url = {https://github.com/robsalasco/sinimr},
#> }- Sistema Nacional de Información Municipal (SINIM), SUBDERE, Ministerio del Interior.