`, etc.) that has more than one element. Defaults to 1 (for ``).
76 | getTopLevel: function($scope) {
77 | for (var i = 1; i <= 6; i++) {
78 | var $headings = this.findOrFilter($scope, 'h' + i);
79 | if ($headings.length > 1) {
80 | return i;
81 | }
82 | }
83 |
84 | return 1;
85 | },
86 |
87 | // returns the elements for the top level, and the next below it
88 | getHeadings: function($scope, topLevel) {
89 | var topSelector = 'h' + topLevel;
90 |
91 | var secondaryLevel = topLevel + 1;
92 | var secondarySelector = 'h' + secondaryLevel;
93 |
94 | return this.findOrFilter($scope, topSelector + ',' + secondarySelector);
95 | },
96 |
97 | getNavLevel: function(el) {
98 | return parseInt(el.tagName.charAt(1), 10);
99 | },
100 |
101 | populateNav: function($topContext, topLevel, $headings) {
102 | var $context = $topContext;
103 | var $prevNav;
104 |
105 | var helpers = this;
106 | $headings.each(function(i, el) {
107 | var $newNav = helpers.generateNavItem(el);
108 | var navLevel = helpers.getNavLevel(el);
109 |
110 | // determine the proper $context
111 | if (navLevel === topLevel) {
112 | // use top level
113 | $context = $topContext;
114 | } else if ($prevNav && $context === $topContext) {
115 | // create a new level of the tree and switch to it
116 | $context = helpers.createChildNavList($prevNav);
117 | } // else use the current $context
118 |
119 | $context.append($newNav);
120 |
121 | $prevNav = $newNav;
122 | });
123 | },
124 |
125 | parseOps: function(arg) {
126 | var opts;
127 | if (arg.jquery) {
128 | opts = {
129 | $nav: arg
130 | };
131 | } else {
132 | opts = arg;
133 | }
134 | opts.$scope = opts.$scope || $(document.body);
135 | return opts;
136 | }
137 | },
138 |
139 | // accepts a jQuery object, or an options object
140 | init: function(opts) {
141 | opts = this.helpers.parseOps(opts);
142 |
143 | // ensure that the data attribute is in place for styling
144 | opts.$nav.attr('data-toggle', 'toc');
145 |
146 | var $topContext = this.helpers.createChildNavList(opts.$nav);
147 | var topLevel = this.helpers.getTopLevel(opts.$scope);
148 | var $headings = this.helpers.getHeadings(opts.$scope, topLevel);
149 | this.helpers.populateNav($topContext, topLevel, $headings);
150 | }
151 | };
152 |
153 | $(function() {
154 | $('nav[data-toggle="toc"]').each(function(i, el) {
155 | var $nav = $(el);
156 | Toc.init($nav);
157 | });
158 | });
159 | })();
160 |
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1 |
2 | Changelog • sinimR
6 |
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9 |
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43 |
44 |
45 |
46 | Changelog
47 | Source: NEWS.md
48 |
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9 | R library for SINIM — sinimr • sinimR
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117 | Access SINIM data and view it as data frames.
118 |
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/docs/authors.html:
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1 |
2 | Authors and Citation • sinimR
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8 |
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99 |
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/docs/LICENSE-text.html:
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1 |
2 | License • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | License
47 |
48 |
49 | The MIT License (MIT)
50 |
51 | Copyright (c) 2018 Roberto Salas
52 |
53 | Permission is hereby granted, free of charge, to any person obtaining a copy
54 | of this software and associated documentation files (the "Software"), to deal
55 | in the Software without restriction, including without limitation the rights
56 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
57 | copies of the Software, and to permit persons to whom the Software is
58 | furnished to do so, subject to the following conditions:
59 |
60 | The above copyright notice and this permission notice shall be included in
61 | all copies or substantial portions of the Software.
62 |
63 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
64 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
65 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
66 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
67 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
68 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
69 | THE SOFTWARE.
70 |
71 |
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/docs/reference/census_geometry_comunas.html:
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1 |
2 | Chilean municipalities polygons as simple features — census_geometry_comunas • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | Chilean municipalities polygons as simple features
47 | Source: R/data.R
48 | census_geometry_comunas.Rd
49 |
50 |
51 |
52 | An sf object containing the chilean municipalities.
53 |
54 |
55 |
56 | census_geometry_comunas
57 |
58 |
59 |
60 | Format
61 | A data frame with 345 rows and 2 variables:
- code
62 | municipality code
- geometry
65 | municipality geometry
69 |
70 | Source
71 |
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/docs/reference/census_geometry_limites.html:
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1 |
2 | Chilean municipalities urban boundaries polygons as simple features — census_geometry_limites • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | Chilean municipalities urban boundaries polygons as simple features
47 | Source: R/data.R
48 | census_geometry_limites.Rd
49 |
50 |
51 |
52 | An sf object containing the chilean municipalities urban boundaries.
53 |
54 |
55 |
56 | census_geometry_limites
57 |
58 |
59 |
60 | Format
61 | A data frame with 220 rows and 2 variables:
- code
62 | municipality code
- geometry
65 | municipality geometry
69 |
70 | Source
71 |
72 |
73 |
74 |
75 |
78 |
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81 |
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/docs/reference/idgeocensus.html:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 | Geographical identifier — idgeocensus • sinimR
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55 |
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108 |
109 |
110 |
111 |
112 |
113 |
118 |
119 |
120 |
121 | A dataset containing the geographical units of the
122 | chilean 2017 census.
123 |
124 |
125 |
126 | idgeocensus
127 |
128 | Format
129 |
130 | A data frame with 246 rows and 6 variables:
131 | - CODE.REG
Region code
- NOM.REG
Region name
- CODE.PROV
Provincia code
- NOM.PROV
Provincia name
- CODE
Municipality code
- MUNICIPALITY
Municipality name
138 |
139 | Source
140 |
141 |
142 |
143 |
144 |
145 |
155 |
156 |
157 |
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169 |
170 |
171 |
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174 |
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/docs/reference/id_geo_census.html:
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1 |
2 | Chilean administrative divisions — id_geo_census • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
50 |
51 |
52 | A dataset containing the chilean administrative units.
53 |
54 |
55 |
56 | id_geo_census
57 |
58 |
59 |
60 | Format
61 | A data frame with 345 rows and 7 variables:
- code
62 | municipality code
- municipality
65 | municipality name
- code.reg
68 | region code
- nom.reg
71 | region name
- code.prov
74 | province code
- nom.prov
77 | province name
- auc
80 | yes/no consolidated urban area
84 |
85 | Source
86 |
87 |
88 |
89 |
90 |
93 |
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96 |
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106 |
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108 |
109 |
110 |
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112 |
113 |
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/docs/reference/mm.html:
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1 |
2 | Return a abbreviated value of the "miles de millones" format — mm • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | Return a abbreviated value of the "miles de millones" format
47 | Source: R/mm.R
48 | mm.Rd
49 |
50 |
51 |
52 | Return a abbreviated value of the "miles de millones" format
53 |
54 |
55 |
56 | mm(x)
57 |
58 |
59 |
67 |
68 | Value
69 | A character value in short format
70 |
71 |
72 |
73 | Examples
74 | mm(5807587000)
75 | #> [1] "6 mm$"
76 |
77 |
78 |
79 |
82 |
83 |
84 |
85 |
94 |
95 |
96 |
97 |
98 |
99 |
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101 |
102 |
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/docs/reference/get_sinim_vars.html:
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1 |
2 | List available variables for a category — get_sinim_vars • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | List available variables for a category
47 | Source: R/get_sinim_vars.R
48 | get_sinim_vars.Rd
49 |
50 |
51 |
52 | List available variables for a category
53 |
54 |
55 |
56 | get_sinim_vars(catn)
57 |
58 |
59 |
67 |
68 | Value
69 | data frame with variables and values
70 |
71 |
72 |
73 | Examples
74 | get_sinim_vars(47)
75 | #> variable unit code
76 | #> 1 Indice de Pobreza CASEN (Última Encuesta Vigente) % 744
77 |
78 |
79 |
80 |
83 |
84 |
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101 |
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103 |
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/docs/reference/getsinimvariables.html:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 | List available variables for a category — getsinimvariables • sinimR
10 |
11 |
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15 |
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48 |
49 |
50 |
51 |
52 |
53 |
54 |
106 |
107 |
108 |
109 |
110 |
111 |
112 |
113 | List available variables for a category
114 | Source: R/getsinimvariables.R
115 | getsinimvariables.Rd
116 |
117 |
118 |
119 |
120 | List available variables for a category
121 |
122 |
123 |
124 | getsinimvariables(catn)
125 |
126 | Arguments
127 |
128 |
130 | catn
131 | category number
133 |
134 |
135 | Value
136 |
137 | data frame with variables and values
138 |
139 |
140 | Examples
141 | getsinimvariables(47)#> VARIABLE UNIT CODE
142 | #> 314 Porcentaje de Población en Condiciones de Pobreza , según CASEN % 562
143 | #> 315 Indice de Pobreza CASEN (Última Encuesta Vigente) % 744
144 |
145 |
156 |
157 |
158 |
168 |
169 |
170 |
171 |
172 |
173 |
174 |
175 |
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/docs/reference/get_sinim_var_name.html:
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1 |
2 | Return SINIM variable name using their internal codes — get_sinim_var_name • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | Return SINIM variable name using their internal codes
47 | Source: R/get_sinim_var_name.R
48 | get_sinim_var_name.Rd
49 |
50 |
51 |
52 | Return SINIM variable name using their internal codes
53 |
54 |
55 |
56 | get_sinim_var_name(var)
57 |
58 |
59 |
67 |
68 | Value
69 | A vector of requested variable names
70 |
71 |
72 |
73 | Examples
74 | get_sinim_var_name(880)
75 | #> [1] "INGRESOS POR FONDO COMÚN MUNICIPAL"
76 | get_sinim_var_name(c(880, 882))
77 | #> [1] "INGRESOS POR FONDO COMÚN MUNICIPAL" "INGRESOS PROPIOS (IPP Y FCM)"
78 |
79 |
80 |
81 |
84 |
85 |
86 |
87 |
96 |
97 |
98 |
99 |
100 |
101 |
102 |
103 |
104 |
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/docs/reference/index.html:
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1 |
2 | Package index • sinimR
6 |
7 |
8 |
9 |
40 |
41 |
42 |
43 |
44 |
45 |
46 | Reference
47 |
48 |
49 |
50 | All functions
51 |
52 |
53 |
54 |
55 |
56 | Chilean municipalities polygons as simple features
58 |
59 |
60 | Chilean municipalities urban boundaries polygons as simple features
62 |
63 |
64 | Get a SINIM variable data in a specific year as a data frame
66 |
67 |
68 | Get SINIM data categories
70 |
71 |
72 | Return SINIM variable name using their internal codes
74 |
75 |
76 | List available variables for a category
78 |
79 |
80 | Chilean administrative divisions
82 |
83 |
84 | Return a abbreviated value of the "miles de millones" format
86 |
87 |
88 | Search for a SINIM variable and if no keyword is provided returns all variables
90 |
91 |
94 |
95 |
96 |
97 |
106 |
107 |
108 |
109 |
110 |
111 |
112 |
113 |
114 |
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/R/helpers.R:
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1 | #' @import httr
2 | #' @import XML
3 | #' @importFrom jsonlite fromJSON
4 | #' @importFrom reshape2 melt
5 | #' @importFrom stats complete.cases
6 |
7 | # Helper functions for API calls, data parsing, and variable lookups used throughout the sinimr package.
8 |
9 | # Register global variables to avoid CRAN NOTES about undefined variables in data manipulation code.
10 | utils::globalVariables(c(
11 | "id_geo_census", "code.reg", "code.prov", "code",
12 | "census_geometry_comunas", "census_geometry_limites", "id_subarea"
13 | ))
14 |
15 | # Call the SINIM API with a GET request and return the raw response as a string.
16 | callapi <- function(url) { # nocov start
17 | resp <- httr::GET(url, add_headers("X-Request-Source" = "r"))
18 | stop_for_status(resp, task = "call api")
19 | data <- httr::content(resp, "text", encoding = "UTF-8")
20 | data <- substr(data, 2, nchar(data))
21 |
22 | return(data)
23 | } # nocov end
24 |
25 | # Call the SINIM API with a POST request and return the parsed JSON response.
26 | postapi <- function(url, body) { # nocov start
27 | resp <- httr::POST(
28 | url,
29 | body = body,
30 | add_headers(
31 | "User-Agent" = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3.1 Safari/605.1.15",
32 | "Referer" = "https://datos.sinim.gov.cl/datos_municipales.php",
33 | "Host" = "datos.sinim.gov.cl",
34 | "X-Requested-With" = "XMLHttpRequest",
35 | "Accept-Encoding" = "gzip, deflate, br"
36 | )
37 | )
38 |
39 | stop_for_status(resp, task = "call api")
40 |
41 | data <- jsonlite::fromJSON(content(resp, "text"))
42 | return(data)
43 | } # nocov end
44 |
45 | # Validate and convert a year or vector of years to their index in the supported year list.
46 | getyear <- function(year) { # nocov start
47 | year_list <- c(
48 | 2000,
49 | 2001,
50 | 2002,
51 | 2003,
52 | 2004,
53 | 2005,
54 | 2006,
55 | 2007,
56 | 2008,
57 | 2009,
58 | 2010,
59 | 2011,
60 | 2012,
61 | 2013,
62 | 2014,
63 | 2015,
64 | 2016,
65 | 2017,
66 | 2018,
67 | 2019,
68 | 2020,
69 | 2021,
70 | 2022,
71 | 2023,
72 | 2024
73 | )
74 | if (any(is.na(match(year, year_list)))) {
75 | stop("Year not found in list")
76 | } else {
77 | return(match(year, year_list))
78 | }
79 | } # nocov end
80 |
81 | # Retrieve the internal SINIM variable ID(s) for a given variable name or names.
82 | getid <- function(name) { # nocov start
83 | body <- list("dato_area[]" = "T", "dato_subarea[]" = "T")
84 | resp <-
85 | postapi(
86 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_filtros.php",
87 | body
88 | )
89 | list <- reshape2::melt(sapply(resp, function(b)
90 | b$mtro_datos_nombre))
91 | values <- reshape2::melt(sapply(resp, function(b)
92 | b$id_dato))
93 | list$id <- values$value
94 | colnames(list) <- c("variable", "category", "value")
95 | list$variable <-
96 | as.vector(lapply(as.character(list$variable), function(x)
97 | trimws(x)))
98 | return(list[complete.cases(match(list$variable, name)), 3])
99 | } # nocov end
100 |
101 | # Retrieve the SINIM variable name(s) for a given variable code or codes.
102 | getname <- function(names) { # nocov start
103 | body <- list("dato_area[]" = "T", "dato_subarea[]" = "T")
104 | resp <- postapi(
105 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_filtros.php",
106 | body
107 | )
108 | list <- reshape2::melt(sapply(resp, function(b)
109 | b$mtro_datos_nombre))
110 | values <- reshape2::melt(sapply(resp, function(b)
111 | b$id_dato))
112 | list$id <- values$value
113 | colnames(list) <- c("variable", "category", "value")
114 | list$variable <-
115 | as.vector(lapply(as.character(list$variable), function(x)
116 | trimws(x)))
117 | names.list <- gsub("\\.", "", toupper(unlist(list[which(list$value %in% names), 1])))
118 | return(names.list)
119 | } # nocov end
120 |
121 | # Parse XML data from the SINIM API for the given variables and years, optionally applying monetary correction.
122 | parsexml <- function(var, years, moncorr=T) { # nocov start
123 | yearsn <- getyear(years)
124 | if(moncorr==T){
125 | url <- paste(
126 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_municipales.php?area[]=T&subarea[]=T&variables[]=",
127 | paste(var, collapse = ","), "&periodos[]=", paste(yearsn, collapse = ","), "®iones[]=T&municipios[]=T&corrmon=1",
128 | sep = ""
129 | )
130 | } else {
131 | url <- paste(
132 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_municipales.php?area[]=T&subarea[]=T&variables[]=",
133 | paste(var, collapse = ","), "&periodos[]=", paste(yearsn, collapse = ","), "®iones[]=T&municipios[]=T&corrmon=0",
134 | sep = ""
135 | )
136 | }
137 | data <- XML::xmlParse(callapi(url))
138 | columns <- as.numeric(
139 | XML::xpathApply(
140 | data,
141 | "//tei:Table/@tei:ExpandedColumnCount",
142 | namespaces = c(tei = getDefaultNamespace(data)[[1]]$uri)
143 | )[[1]][[1]],
144 | xmlValue
145 | )
146 | varxml <- XML::xpathSApply(
147 | data,
148 | "//tei:Cell[not(@tei:StyleID)]",
149 | namespaces = c(tei = getDefaultNamespace(data)[[1]]$uri),
150 | xmlValue
151 | )
152 | values <- matrix(varxml,
153 | nrow = length(varxml)/((length(var)*length(yearsn))+2),
154 | ncol = ((length(var)*length(yearsn))+2), byrow = T)
155 | values <- as.data.frame(values, stringsAsFactors = F)
156 | return(values)
157 | } # nocov end
158 |
159 | # Construct column names for variables and years in the format VARIABLE.YEAR.
160 | namesco <- function(x,y){ #nocov start
161 | rep_vars <- rep(getname(x), each=length(y))
162 | rep_years <- rep(sort(y, decreasing = T), length(x))
163 | return(paste(rep_vars, rep_years, sep="."))
164 | } # nocov end
165 |
166 | # Filter geographic codes by region, province, or comuna, with optional AUC filtering.
167 | geofilter <- function(region, provincia, comuna, auc=F) { #nocov start
168 | if (!missing(region)) {
169 | stopifnot(missing(provincia))
170 | stopifnot(missing(comuna))
171 | if(auc==T) {
172 | selection <- subset(id_geo_census, code.reg %in% region &
173 | auc==1)$code
174 | } else {
175 | selection <- subset(id_geo_census, code.reg %in% region)$code
176 | }
177 | return(selection)
178 | } else if (!missing(provincia)) {
179 | stopifnot(missing(region))
180 | stopifnot(missing(comuna))
181 | if(auc==T) {
182 | warning("AUC not available subsetting provincias")
183 | }
184 | selection <- subset(id_geo_census, code.prov %in% provincia)$code
185 | return(selection)
186 | } else if (!missing(comuna)) {
187 | stopifnot(missing(region))
188 | stopifnot(missing(provincia))
189 | if(auc==T) {
190 | warning("AUC not available subsetting comunas")
191 | }
192 | selection <- subset(id_geo_census, code %in% comuna)$code
193 | return(selection)
194 | } else {
195 | return(id_geo_census$code)
196 | }
197 | } # nocov end
198 |
--------------------------------------------------------------------------------
/R/get_sinim.R:
--------------------------------------------------------------------------------
1 | #' Get a SINIM variable data in a specific year as a data frame
2 | #' @param var Variable code
3 | #' @param year Year
4 | #' @param moncorr A logical value indicating the use of monetary correction
5 | #' @param truevalue A logical value indicating the use of converted values
6 | #' @param idgeo A logical value to add provincia and region columns
7 | #' @param geometry A logical value to add geographical features
8 | #' @param region Region subsetting variable
9 | #' @param provincia Provincia subsetting variable
10 | #' @param comuna Comuna subsetting variable
11 | #' @param auc A logical value to retrieve only AUC features
12 | #' @param unit Use "comunas" or "limites"
13 | #' @return data frame for the requested variable over time with optional geometry
14 | #' @export
15 | #' @examples
16 | #' get_sinim(880, 2015)
17 | #' get_sinim(880, 2015:2017)
18 | #' get_sinim(c(880, 882), 2015)
19 | #' get_sinim(c(880, 882), 2015:2017)
20 | #' get_sinim(c(880, 882), 2015, idgeo=TRUE)
21 | #' get_sinim(882, 2015, geometry=TRUE)
22 | #' get_sinim(882, 2015, region="02")
23 | #' @importFrom reshape2 melt
24 | #' @importFrom stats reshape
25 | #' @importFrom stats na.omit
26 | #' @import sf
27 | #'
28 | get_sinim <-
29 | function(var,
30 | year,
31 | moncorr = T,
32 | truevalue = F,
33 | idgeo = F,
34 | geometry = F,
35 | region,
36 | provincia,
37 | comuna,
38 | auc = F,
39 | unit = "comunas") {
40 | stopifnot(is.numeric(var))
41 | stopifnot(is.numeric(year))
42 |
43 | # Download and parse the raw data for the requested variables and years
44 | values <- parsexml(var, year, moncorr = moncorr)
45 |
46 | # Set column names for the parsed data
47 | colnames(values) <-
48 | c("code", "municipality", namesco(var, year))
49 |
50 | # Reshape the data to long format for easier manipulation
51 | datav <- stats::reshape(
52 | values,
53 | idvar = c("code", "municipality"),
54 | varying = namesco(var, year),
55 | direction = "long",
56 | timevar = "year",
57 | times = sort(year, decreasing = T)
58 | )
59 |
60 | # Remove row names for clarity
61 | rownames(datav) <- NULL
62 |
63 | # Replace 'No Recepcionado' and 'No Aplica' with NA
64 | datav <-
65 | as.data.frame(apply(datav, 2, function(x)
66 | gsub("No Recepcionado|No Aplica", NA, x)), stringsAsFactors = FALSE)
67 |
68 | if (geometry == FALSE) {
69 | # Melt the data to have one row per variable per year
70 | datav <- reshape2::melt(
71 | datav,
72 | id = c("code", "municipality", "year"),
73 | value.name = "value",
74 | variable.name = "variable"
75 | )
76 |
77 | datav$variable <- as.character(datav$variable)
78 |
79 | # Identify numeric columns for conversion
80 | t <-
81 | vapply(datav, function(x)
82 | all(grepl("[0-9]+", na.omit(x))), logical(1))
83 |
84 | t[1:4] <- c(TRUE, FALSE, FALSE, FALSE)
85 | datav[t] <- lapply(datav[t], function(x)
86 | (as.numeric(x)))
87 |
88 | # Apply value conversion if requested
89 | if (truevalue == TRUE) {
90 | datav$value <- datav$value * 1000
91 | }
92 |
93 | # Subset by region, provincia, or comuna if provided
94 | if (!missing(region) |
95 | !missing(provincia) |
96 | !missing(comuna)) {
97 | selection <-
98 | geofilter(region = region,
99 | provincia = provincia,
100 | comuna = comuna,
101 | auc)
102 | data.selection <- subset(datav, code %in% selection)
103 |
104 | # Merge with geographic info if requested
105 | if (idgeo == T) {
106 | merged.pretty <-
107 | merge(data.selection,
108 | id_geo_census,
109 | by = c("code", "municipality"))
110 | return(merged.pretty)
111 | } else {
112 | return(data.selection)
113 | }
114 |
115 | } else {
116 | # Merge with geographic info for all data if requested
117 | if (idgeo == T) {
118 | merged.pretty <- merge(datav, id_geo_census, by = "code")
119 | return(merged.pretty)
120 | } else {
121 | return(datav)
122 | }
123 | }
124 | } else {
125 | # Melt the data for geometry output
126 | datav <- reshape2::melt(
127 | datav,
128 | id = c("code", "municipality", "year"),
129 | value.name = "value",
130 | variable.name = "variable"
131 | )
132 |
133 | datav$variable <- as.character(datav$variable)
134 |
135 | # Identify numeric columns for conversion
136 | t <-
137 | vapply(datav, function(x)
138 | all(grepl("[0-9]+", na.omit(x))), logical(1))
139 |
140 | t[1:4] <- c(TRUE, FALSE, FALSE, FALSE)
141 | datav[t] <- lapply(datav[t], function(x)
142 | (as.numeric(x)))
143 |
144 | # Apply value conversion if requested
145 | if (truevalue == TRUE) {
146 | datav$value <- datav$value * 1000
147 | }
148 |
149 | # Subset by region, provincia, or comuna if provided
150 | if (!missing(region) |
151 | !missing(provincia) |
152 | !missing(comuna)) {
153 | selection <-
154 | geofilter(region = region,
155 | provincia = provincia,
156 | comuna = comuna,
157 | auc)
158 |
159 | data.selection <- subset(datav, code %in% selection)
160 |
161 | # Merge with geometry based on the selected unit
162 | if (unit == "comunas") {
163 | merged.geo <-
164 | merge(census_geometry_comunas, data.selection, by = "code")
165 | } else if (unit == "limites") {
166 | merged.geo <-
167 | merge(census_geometry_limites, data.selection, by = "code")
168 | } else {
169 | stop("Unit not valid")
170 | }
171 |
172 | # Merge with geographic info if requested
173 | if (idgeo == T) {
174 | merged.geo.pretty <-
175 | merge(merged.geo,
176 | id_geo_census,
177 | by = c("code", "municipality"))
178 | return(merged.geo.pretty)
179 | } else {
180 | return(merged.geo)
181 | }
182 |
183 | } else {
184 | # Merge with geometry for all data based on the selected unit
185 | if (unit == "comunas") {
186 | merged.geo <-
187 | merge(census_geometry_comunas, datav, by = "code")
188 | } else if (unit == "limites") {
189 | merged.geo <-
190 | merge(census_geometry_limites, datav, by = "code")
191 | } else {
192 | stop("Unit not valid")
193 | }
194 |
195 | # Merge with geographic info if requested
196 | if (idgeo == T) {
197 | merged.geo.pretty <- merge(merged.geo, id_geo_census, by = c("code","municipality"))
198 | return(merged.geo.pretty)
199 | } else {
200 | return(merged.geo)
201 | }
202 | }
203 | }
204 | }
205 |
--------------------------------------------------------------------------------
/docs/pkgdown.css:
--------------------------------------------------------------------------------
1 | /* Sticky footer */
2 |
3 | /**
4 | * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
5 | * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
6 | *
7 | * .Site -> body > .container
8 | * .Site-content -> body > .container .row
9 | * .footer -> footer
10 | *
11 | * Key idea seems to be to ensure that .container and __all its parents__
12 | * have height set to 100%
13 | *
14 | */
15 |
16 | html, body {
17 | height: 100%;
18 | }
19 |
20 | body {
21 | position: relative;
22 | }
23 |
24 | body > .container {
25 | display: flex;
26 | height: 100%;
27 | flex-direction: column;
28 | }
29 |
30 | body > .container .row {
31 | flex: 1 0 auto;
32 | }
33 |
34 | footer {
35 | margin-top: 45px;
36 | padding: 35px 0 36px;
37 | border-top: 1px solid #e5e5e5;
38 | color: #666;
39 | display: flex;
40 | flex-shrink: 0;
41 | }
42 | footer p {
43 | margin-bottom: 0;
44 | }
45 | footer div {
46 | flex: 1;
47 | }
48 | footer .pkgdown {
49 | text-align: right;
50 | }
51 | footer p {
52 | margin-bottom: 0;
53 | }
54 |
55 | img.icon {
56 | float: right;
57 | }
58 |
59 | /* Ensure in-page images don't run outside their container */
60 | .contents img {
61 | max-width: 100%;
62 | height: auto;
63 | }
64 |
65 | /* Fix bug in bootstrap (only seen in firefox) */
66 | summary {
67 | display: list-item;
68 | }
69 |
70 | /* Typographic tweaking ---------------------------------*/
71 |
72 | .contents .page-header {
73 | margin-top: calc(-60px + 1em);
74 | }
75 |
76 | dd {
77 | margin-left: 3em;
78 | }
79 |
80 | /* Section anchors ---------------------------------*/
81 |
82 | a.anchor {
83 | display: none;
84 | margin-left: 5px;
85 | width: 20px;
86 | height: 20px;
87 |
88 | background-image: url(./link.svg);
89 | background-repeat: no-repeat;
90 | background-size: 20px 20px;
91 | background-position: center center;
92 | }
93 |
94 | h1:hover .anchor,
95 | h2:hover .anchor,
96 | h3:hover .anchor,
97 | h4:hover .anchor,
98 | h5:hover .anchor,
99 | h6:hover .anchor {
100 | display: inline-block;
101 | }
102 |
103 | /* Fixes for fixed navbar --------------------------*/
104 |
105 | .contents h1, .contents h2, .contents h3, .contents h4 {
106 | padding-top: 60px;
107 | margin-top: -40px;
108 | }
109 |
110 | /* Navbar submenu --------------------------*/
111 |
112 | .dropdown-submenu {
113 | position: relative;
114 | }
115 |
116 | .dropdown-submenu>.dropdown-menu {
117 | top: 0;
118 | left: 100%;
119 | margin-top: -6px;
120 | margin-left: -1px;
121 | border-radius: 0 6px 6px 6px;
122 | }
123 |
124 | .dropdown-submenu:hover>.dropdown-menu {
125 | display: block;
126 | }
127 |
128 | .dropdown-submenu>a:after {
129 | display: block;
130 | content: " ";
131 | float: right;
132 | width: 0;
133 | height: 0;
134 | border-color: transparent;
135 | border-style: solid;
136 | border-width: 5px 0 5px 5px;
137 | border-left-color: #cccccc;
138 | margin-top: 5px;
139 | margin-right: -10px;
140 | }
141 |
142 | .dropdown-submenu:hover>a:after {
143 | border-left-color: #ffffff;
144 | }
145 |
146 | .dropdown-submenu.pull-left {
147 | float: none;
148 | }
149 |
150 | .dropdown-submenu.pull-left>.dropdown-menu {
151 | left: -100%;
152 | margin-left: 10px;
153 | border-radius: 6px 0 6px 6px;
154 | }
155 |
156 | /* Sidebar --------------------------*/
157 |
158 | #pkgdown-sidebar {
159 | margin-top: 30px;
160 | position: -webkit-sticky;
161 | position: sticky;
162 | top: 70px;
163 | }
164 |
165 | #pkgdown-sidebar h2 {
166 | font-size: 1.5em;
167 | margin-top: 1em;
168 | }
169 |
170 | #pkgdown-sidebar h2:first-child {
171 | margin-top: 0;
172 | }
173 |
174 | #pkgdown-sidebar .list-unstyled li {
175 | margin-bottom: 0.5em;
176 | }
177 |
178 | /* bootstrap-toc tweaks ------------------------------------------------------*/
179 |
180 | /* All levels of nav */
181 |
182 | nav[data-toggle='toc'] .nav > li > a {
183 | padding: 4px 20px 4px 6px;
184 | font-size: 1.5rem;
185 | font-weight: 400;
186 | color: inherit;
187 | }
188 |
189 | nav[data-toggle='toc'] .nav > li > a:hover,
190 | nav[data-toggle='toc'] .nav > li > a:focus {
191 | padding-left: 5px;
192 | color: inherit;
193 | border-left: 1px solid #878787;
194 | }
195 |
196 | nav[data-toggle='toc'] .nav > .active > a,
197 | nav[data-toggle='toc'] .nav > .active:hover > a,
198 | nav[data-toggle='toc'] .nav > .active:focus > a {
199 | padding-left: 5px;
200 | font-size: 1.5rem;
201 | font-weight: 400;
202 | color: inherit;
203 | border-left: 2px solid #878787;
204 | }
205 |
206 | /* Nav: second level (shown on .active) */
207 |
208 | nav[data-toggle='toc'] .nav .nav {
209 | display: none; /* Hide by default, but at >768px, show it */
210 | padding-bottom: 10px;
211 | }
212 |
213 | nav[data-toggle='toc'] .nav .nav > li > a {
214 | padding-left: 16px;
215 | font-size: 1.35rem;
216 | }
217 |
218 | nav[data-toggle='toc'] .nav .nav > li > a:hover,
219 | nav[data-toggle='toc'] .nav .nav > li > a:focus {
220 | padding-left: 15px;
221 | }
222 |
223 | nav[data-toggle='toc'] .nav .nav > .active > a,
224 | nav[data-toggle='toc'] .nav .nav > .active:hover > a,
225 | nav[data-toggle='toc'] .nav .nav > .active:focus > a {
226 | padding-left: 15px;
227 | font-weight: 500;
228 | font-size: 1.35rem;
229 | }
230 |
231 | /* orcid ------------------------------------------------------------------- */
232 |
233 | .orcid {
234 | font-size: 16px;
235 | color: #A6CE39;
236 | /* margins are required by official ORCID trademark and display guidelines */
237 | margin-left:4px;
238 | margin-right:4px;
239 | vertical-align: middle;
240 | }
241 |
242 | /* Reference index & topics ----------------------------------------------- */
243 |
244 | .ref-index th {font-weight: normal;}
245 |
246 | .ref-index td {vertical-align: top; min-width: 100px}
247 | .ref-index .icon {width: 40px;}
248 | .ref-index .alias {width: 40%;}
249 | .ref-index-icons .alias {width: calc(40% - 40px);}
250 | .ref-index .title {width: 60%;}
251 |
252 | .ref-arguments th {text-align: right; padding-right: 10px;}
253 | .ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
254 | .ref-arguments .name {width: 20%;}
255 | .ref-arguments .desc {width: 80%;}
256 |
257 | /* Nice scrolling for wide elements --------------------------------------- */
258 |
259 | table {
260 | display: block;
261 | overflow: auto;
262 | }
263 |
264 | /* Syntax highlighting ---------------------------------------------------- */
265 |
266 | pre, code, pre code {
267 | background-color: #f8f8f8;
268 | color: #333;
269 | }
270 | pre, pre code {
271 | white-space: pre-wrap;
272 | word-break: break-all;
273 | overflow-wrap: break-word;
274 | }
275 |
276 | pre {
277 | border: 1px solid #eee;
278 | }
279 |
280 | pre .img, pre .r-plt {
281 | margin: 5px 0;
282 | }
283 |
284 | pre .img img, pre .r-plt img {
285 | background-color: #fff;
286 | }
287 |
288 | code a, pre a {
289 | color: #375f84;
290 | }
291 |
292 | a.sourceLine:hover {
293 | text-decoration: none;
294 | }
295 |
296 | .fl {color: #1514b5;}
297 | .fu {color: #000000;} /* function */
298 | .ch,.st {color: #036a07;} /* string */
299 | .kw {color: #264D66;} /* keyword */
300 | .co {color: #888888;} /* comment */
301 |
302 | .error {font-weight: bolder;}
303 | .warning {font-weight: bolder;}
304 |
305 | /* Clipboard --------------------------*/
306 |
307 | .hasCopyButton {
308 | position: relative;
309 | }
310 |
311 | .btn-copy-ex {
312 | position: absolute;
313 | right: 0;
314 | top: 0;
315 | visibility: hidden;
316 | }
317 |
318 | .hasCopyButton:hover button.btn-copy-ex {
319 | visibility: visible;
320 | }
321 |
322 | /* headroom.js ------------------------ */
323 |
324 | .headroom {
325 | will-change: transform;
326 | transition: transform 200ms linear;
327 | }
328 | .headroom--pinned {
329 | transform: translateY(0%);
330 | }
331 | .headroom--unpinned {
332 | transform: translateY(-100%);
333 | }
334 |
335 | /* mark.js ----------------------------*/
336 |
337 | mark {
338 | background-color: rgba(255, 255, 51, 0.5);
339 | border-bottom: 2px solid rgba(255, 153, 51, 0.3);
340 | padding: 1px;
341 | }
342 |
343 | /* vertical spacing after htmlwidgets */
344 | .html-widget {
345 | margin-bottom: 10px;
346 | }
347 |
348 | /* fontawesome ------------------------ */
349 |
350 | .fab {
351 | font-family: "Font Awesome 5 Brands" !important;
352 | }
353 |
354 | /* don't display links in code chunks when printing */
355 | /* source: https://stackoverflow.com/a/10781533 */
356 | @media print {
357 | code a:link:after, code a:visited:after {
358 | content: "";
359 | }
360 | }
361 |
362 | /* Section anchors ---------------------------------
363 | Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
364 | */
365 |
366 | div.csl-bib-body { }
367 | div.csl-entry {
368 | clear: both;
369 | }
370 | .hanging-indent div.csl-entry {
371 | margin-left:2em;
372 | text-indent:-2em;
373 | }
374 | div.csl-left-margin {
375 | min-width:2em;
376 | float:left;
377 | }
378 | div.csl-right-inline {
379 | margin-left:2em;
380 | padding-left:1em;
381 | }
382 | div.csl-indent {
383 | margin-left: 2em;
384 | }
385 |
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/docs/reference/getsinimcategories.html:
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9 | Get SINIM data categories — getsinimcategories • sinimR
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112 |
113 | Get SINIM data categories
114 | Source: R/getsinimcategories.R
115 | getsinimcategories.Rd
116 |
117 |
118 |
119 |
120 | Get SINIM data categories
121 |
122 |
123 |
124 | getsinimcategories()
125 |
126 | Value
127 |
128 | Returns a data frame with available data in SINIM's webpage
129 |
130 |
131 | Examples
132 | getsinimcategories()#> $`-`
133 | #> VARIABLE CODE
134 | #> 1 * SIM FIMU 539
135 | #>
136 | #> $`01. ADMINISTRACION Y FINANZAS MUNICIPALES`
137 | #> VARIABLE CODE
138 | #> 1 A.1. PRESUPUESTO INICIAL Y VIGENTE MUNICIPAL (M$) 517
139 | #> 2 A. INGRESOS MUNICIPALES (M$) 21
140 | #> 3 B. INGRESOS MUNICIPALES (%) 191
141 | #> 4 C. GASTOS MUNICIPALES (M$) 22
142 | #> 5 D. GASTOS MUNICIPALES (%) 172
143 | #> 6 E. GASTOS EN PERSONAL 169
144 | #> 7 F. TRANSFERENCIAS E INVERSION 170
145 | #> 8 G. SERVICIOS BASICOS Y GENERALES 370
146 | #> 9 I. TRANSFERENCIAS Y COMPENSACIONES SUBDERE 485
147 | #> 10 J. FONDO COMÚN MUNICIPAL (FCM) 508
148 | #> 11 K. GESTION MUNICIPAL 486
149 | #> 12 L. 24
150 | #> 13 M. 506
151 | #>
152 | #> $`02. RECURSOS HUMANOS MUNICIPAL`
153 | #> VARIABLE CODE
154 | #> 1 A. PERSONAL DE PLANTA 381
155 | #> 2 B. PERSONAL A CONTRATA 382
156 | #> 3 C. HONORARIOS 383
157 | #> 4 D. CÓDIGO DEL TRABAJO 523
158 | #> 5 D. OTROS INDICADORES 384
159 | #>
160 | #> $`03. EDUCACION MUNICIPAL`
161 | #> VARIABLE CODE
162 | #> 1 A. ANTECEDENTES GENERALES DE EDUCACION 38
163 | #> 2 B. ASISTENCIA Y MATRÍCULAS EN EDUCACION 32
164 | #> 3 C. RESULTADOS PSU 33
165 | #> 4 D. INGRESOS EN EDUCACION MUNICIPAL 35
166 | #> 5 E. GASTOS EN EDUCACION MUNICIPAL 36
167 | #> 6 F. RECURSOS HUMANOS EN SECTOR EDUCACION 34
168 | #> 7 G. ESTABLECIMIENTOS DE EDUCACION MUNICIPAL 379
169 | #>
170 | #> $`04. SALUD MUNICIPAL`
171 | #> VARIABLE CODE
172 | #> 1 A. ANTECEDENTES GENERALES DE SALUD 30
173 | #> 2 B. COBERTURA EN SALUD MUNICIPAL 25
174 | #> 3 C. INGRESOS EN SALUD MUNICIPAL 26
175 | #> 4 D. GASTOS EN SALUD MUNICIPAL 28
176 | #> 5 E. RED ASISTENCIAL SALUD 31
177 | #> 6 F. RECURSOS HUMANOS EN SALUD 362
178 | #>
179 | #> $`05. SOCIAL Y COMUNITARIA`
180 | #> VARIABLE CODE
181 | #> 1 A. INFORMACION ENCUESTA CASEN 47
182 | #> 2 B. RED SOCIAL (SUBSIDIOS Y PENSIONES) 44
183 | #> 3 C. INTERMEDIACION LABORAL 43
184 | #> 4 D. ORGANIZACIONES COMUNITARIAS 46
185 | #> 5 E. BECAS 377
186 | #> 6 F. PARTICIPACIÓN CIUDADANA 510
187 | #> 7 G. DISCAPACIDAD 512
188 | #> 8 H. PREVENCIÓN DEL DELITO 511
189 | #>
190 | #> $`06. DESARROLLO Y GESTION TERRITORIAL`
191 | #> VARIABLE CODE
192 | #> 1 A. CARACTERISTICAS TERRITORIALES 39
193 | #> 2 B. SERVICIOS BASICOS A LA COMUNIDAD 41
194 | #> 3 C. INFRAESTRUCTURA 40
195 | #> 4 D. CATASTRO PREDIOS Y VALORACION CATASTRAL 300
196 | #> 5 E. AREAS VERDES 376
197 | #> 6 F. PLAN DE DESARROLLO COMUNAL (PLADECO) 304
198 | #> 7 G. PLAN REGULADOR COMUNAL 42
199 | #>
200 | #> $`07. CARACTERIZACION COMUNAL`
201 | #> VARIABLE CODE
202 | #> 1 A. GEOGRAFICO ADMINISTRATIVA 49
203 | #> 2 B. POBLACION 50
204 | #>
205 | #> $`08. GENERO`
206 | #> VARIABLE CODE
207 | #> 1 A. DOTACION FUNCIONARIA Y PROFESIONAL DE MUJERES 262
208 | #>
209 | #> $`09. CEMENTERIO`
210 | #> VARIABLE CODE
211 | #> 1 1. INFORMACION GENERAL 516
212 | #> 2 A. INGRESOS CEMENTERIO (M$) 456
213 | #> 3 B. GASTOS CEMENTERIO (M$) 457
214 | #>
215 |
216 |
226 |
227 |
228 |
238 |
239 |
240 |
241 |
242 |
243 |
244 |
245 |
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/README.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | output: github_document
3 | ---
4 |
5 | ```{r, echo = FALSE}
6 | knitr::opts_chunk$set(
7 | collapse = TRUE,
8 | comment = "#>",
9 | fig.path = "man/figures/"
10 | )
11 | ```
12 |
13 | [](https://saythanks.io/to/robsalasco)
14 | [](https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=WDDLRUVD344XL¤cy_code=USD&source=url)
15 | [](https://github.com/robsalasco/sinimr/actions)
16 | [](https://app.codecov.io/github/robsalasco/sinimr?branch=master)
17 |
18 | # sinimR 
19 |
20 | Chilean Municipalities Information System Wrapper
21 |
22 | ### What can I do with this?
23 |
24 | This R package allows easy SINIM (https://sinim.gov.cl) data retrieval what have advantages over the site:
25 |
26 | - When you work with multiple variables or years it will be very useful for rapid analyses.
27 | - Fast ploting directly from data source using the included geometries.
28 | - Data download with or without monetary correction using a switch.
29 |
30 | ```{r, message=F, fig.height=6, fig.width=6, fig.retina=2}
31 | library(dplyr)
32 | library(sinimr)
33 | library(sf)
34 | library(tmap)
35 |
36 | varcode <- 882
37 | var <- get_sinim(varcode, 2018,
38 | region = 13,
39 | truevalue = T,
40 | geometry = T,
41 | auc = T,
42 | unit = "limites")
43 |
44 | gran_santiago_plot <- tm_shape(var) +
45 | tm_fill(col = "value",
46 | palette = "BuPu",
47 | border.col = "white",
48 | border.alpha = 0.5,
49 | lwd=1,
50 | style = "jenks",
51 | title = get_sinim_var_name(varcode))+
52 | tm_text("municipality", size = 0.4, style="jenks") +
53 | tm_legend(legend.position = c("left", "top"), legend.title.size = 1, legend.text.size = 0.6) +
54 | tm_compass(type = "8star", position = c(.85, .80)) +
55 | tm_scale_bar(breaks = c(0, 10), text.size = 0.75, position = c("right", "bottom")) +
56 | tm_credits("Fuente: Sistema Nacional de Información Municipal (SINIM), SUBDERE, Ministerio del Interior.", position=c("left", "bottom"), size=0.55)+
57 | tm_layout(legend.width=1,
58 | inner.margins = c(0.1, 0.1, 0.10, 0.1),
59 | legend.format = list(text.separator = "a",
60 | fun = mm)) +
61 | tm_borders(col = 'black')
62 |
63 |
64 | gran_santiago_plot
65 |
66 | ```
67 |
68 | ### Support
69 |
70 | 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).
71 |
72 | ### A note on usage
73 |
74 | 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.
75 |
76 | ### Installation
77 |
78 | ```R
79 | install.packages("devtools")
80 | devtools::install_github("robsalasco/sinimr")
81 | ```
82 |
83 | ### How do I use it?
84 |
85 | 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.
86 |
87 | ```{r}
88 | library(sinimr)
89 | get_sinim_cats()
90 | ```
91 |
92 | Every category have a bunch of variables associated. Use the CODE number and the ```get_sinim_vars()``` function to get them.
93 |
94 | ```{r}
95 | get_sinim_vars(517)
96 | ```
97 |
98 | Finally, to obtain the data across municipalities use the code column and specify a year.
99 |
100 | ```{r}
101 | head(get_sinim(c(4210, 4211), 2015))
102 | ```
103 |
104 | By default the values are in **miles de millones** but it can be disabled using the ```truevalue = T``` switch.
105 |
106 | ```{r}
107 | head(get_sinim(c(4210, 4211), 2015, truevalue = T))
108 | ```
109 |
110 | You can get multiple years too! use the command ```get_sinim()``` and add more years as in the example.
111 |
112 | ```{r}
113 | head(get_sinim(880, 2015:2017))
114 | ```
115 |
116 | The 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.
117 |
118 |
119 | ```{r}
120 | head(get_sinim(882, 2015:2017, geometry=T))
121 | ```
122 |
123 |
124 | 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.
125 |
126 | ```{r}
127 | head(get_sinim(882, 2015:2017, geometry=T, region=2))
128 | ```
129 |
130 | You can get a subset too
131 |
132 | ```{r}
133 | head(get_sinim(882, 2015:2017, geometry=T, region=c(2,3)))
134 | ```
135 |
136 | But where obtain the codes? a database is provided and you can filter it using the standard R functions.
137 |
138 | ```{r}
139 | head(id_geo_census)
140 | ```
141 |
142 | Related 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.
143 |
144 | ```{r}
145 | search_sinim_vars("cementerio")
146 | ```
147 |
148 | ## Advanced usage
149 |
150 | SINIM (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.
151 |
152 | ### Other example plots
153 |
154 | #### Multiple variable faceted plot
155 |
156 | ```{r, message=F, fig.height=8, fig.width=15, fig.retina=2}
157 | library(tmap)
158 | library(dplyr)
159 | library(stringr)
160 | library(sinimr)
161 | library(sf)
162 |
163 | data_sinim <- get_sinim(var = c(3954,4174,880,1226,4251,4173),
164 | year = 2018,
165 | region = 13,
166 | geometry = T,
167 | truevalue = T,
168 | auc = T,
169 | unit = "limites")
170 |
171 | gran_santiago_plot <- tm_shape(data_sinim) +
172 | tm_fill(col = "value",
173 | palette = "BuPu",
174 | border.col = "white",
175 | border.alpha = 0.5,
176 | lwd=1,
177 | style = "jenks",
178 | title = "variable")+
179 | tm_text("municipality", size = 0.4) +
180 | tm_style("white", frame = T, legend.title.size = 1, legend.width=1) +
181 | tm_layout(inner.margins = c(0.01, 0.1, 0.1, 0.01),
182 | outer.margins = c(0.01, 0.01, 0.01, 0.01),
183 | design.mode=F,
184 | legend.format = list(text.separator = "a",
185 | fun = mm))+
186 | tm_borders(col = 'black') +
187 | tm_facets(by="variable", ncol = 2)
188 |
189 | gran_santiago_plot
190 |
191 | ```
192 |
193 | #### A variable in multiple years using facets
194 |
195 | ```{r, message=F, fig.height=10, fig.width=18, fig.retina=2}
196 | library(dplyr)
197 | library(sinimr)
198 | library(sf)
199 | library(tmap)
200 |
201 |
202 | var <- get_sinim(c(880, 882, 1226),
203 | 2016:2018,
204 | region = 13,
205 | truevalue = T,
206 | geometry = T,
207 | auc = T,
208 | unit = "limites")
209 |
210 | gran_santiago_plot <- tm_shape(var) +
211 | tm_fill("value",
212 | palette="BuPu",
213 | border.col = "white",
214 | style = "jenks",
215 | border.alpha = 0.5,
216 | lwd=1) +
217 | tm_text("municipality", size = 0.4) +
218 | tm_legend(legend.position = c("left", "top")) +
219 | tm_layout(legend.width=0.09,
220 | inner.margins = c(0.01, 0.1, 0.1, 0.01),
221 | outer.margins = c(0.01, 0.01, 0.01, 0.01),
222 | legend.format = list(text.separator = "a",
223 | fun = mm)) +
224 | tm_facets(by=c("year","variable"),) +
225 | tm_borders(col = 'black')
226 |
227 | gran_santiago_plot
228 |
229 | ```
230 |
231 | #### Multiple variables and years using geofacet
232 |
233 | ```{r, message=F, fig.height=12, fig.width=24, fig.retina=2}
234 | library(sf)
235 | library(dplyr)
236 | library(geofacet)
237 | library(sinimr)
238 | library(ggplot2)
239 | library(zoo)
240 | library(scales)
241 | library(ggpubr)
242 |
243 | data <- get_sinim(882, 2002:2018,
244 | region = 13,
245 | moncorr = F,
246 | truevalue = T,
247 | auc = T)
248 |
249 | data$year <- as.numeric(as.character(data$year))
250 | data$year <- as.Date(as.yearmon(data$year, "1-%y"))
251 |
252 | reg13 <- geogrid::read_polygons("https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson")
253 | grd <- grid_auto(reg13, seed = 1, names = "NOM_COMUNA", codes = "COMUNA")
254 |
255 | #grid_preview(grd, label = "name_NOM_COMUNA")
256 | #grid_design(grd, label = "name_NOM_COMUNA")
257 |
258 | ggplot(data, aes(year, value, group=1)) +
259 | geom_line(color = "steelblue") +
260 | facet_geo(~ municipality, grid = grd, scales = "free_y")+
261 | scale_x_date() +
262 | scale_y_continuous(labels = dollar_format(suffix = "", prefix = "$", big.mark = ".", decimal.mark=","))+
263 | theme_bw()
264 | ```
265 |
266 |
267 | ### Citation
268 |
269 | ```{r}
270 | citation("sinimr")
271 | ```
272 |
273 | ### References
274 |
275 | - Sistema Nacional de Información Municipal (SINIM), SUBDERE, Ministerio del Interior.
276 |
277 |
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8 | Creating geofacets with sinimR • sinimR
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80 | Creating geofacets with sinimR
81 | Roberto Salas
82 |
83 | 2019-09-27
84 |
85 | Source: vignettes/creating-geofacets.Rmd
86 | creating-geofacets.Rmd
87 |
88 |
89 |
90 |
91 |
92 |
93 |
94 | Description
95 | When you need to get a global overview of a phenomenom it’s difficult to have a plot with all the data. One option to achieve the goal are the use of facets but what if you want an spatial view? geofacets are the answer.
96 | library(sf)
97 | library(dplyr)
98 | library(geofacet)
99 | library(sinimr)
100 | library(ggplot2)
101 | library(zoo)
102 | library(scales)
103 | library(ggpubr)
104 |
105 | data <- get_sinim(882, 2002:2018,
106 | region = 13,
107 | moncorr = F,
108 | truevalue = T,
109 | auc = T)
110 |
111 | data$year <- as.numeric(as.character(data$year))
112 | data$year <- as.Date(as.yearmon(data$year, "1-%y"))
113 |
114 | reg13 <- geogrid::read_polygons("https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson")
115 | grd <- grid_auto(reg13, seed = 1, names = "NOM_COMUNA", codes = "COMUNA")
116 |
117 | #grid_preview(grd, label = "name_NOM_COMUNA")
118 | #grid_design(grd, label = "name_NOM_COMUNA")
119 |
120 | ggplot(data, aes(year, value, group=1)) +
121 | geom_line(color = "steelblue") +
122 | facet_geo(~ municipality, grid = grd, scales = "free_y")+
123 | scale_x_date() +
124 | scale_y_continuous(labels = dollar_format(suffix = "", prefix = "$", big.mark = ".", decimal.mark=","))+
125 | theme_bw()
126 |
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31 | .algolia-autocomplete .ds-dropdown-menu::before {
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35 | .algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
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66 | float: none;
67 | width: auto;
68 | padding: 0;
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70 | }
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73 | float: none;
74 | width: auto;
75 | padding: 0
76 | }
77 |
78 | .algolia-autocomplete .algolia-docsearch-suggestion--content::before {
79 | display: none
80 | }
81 |
82 | .algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
83 | padding-top: .75rem;
84 | margin-top: .75rem;
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137 | color: #FF8C00;
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2 | Get SINIM data categories — get_sinim_cats • sinimR
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52 | Get SINIM data categories
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56 | get_sinim_cats()
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60 | Value
61 | Returns a data frame with available data in SINIM's webpage
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65 | Examples
66 | get_sinim_cats()
67 | #> $`-`
68 | #> variable code
69 | #> 1 * SIM FIMU 539
70 | #>
71 | #> $`01. ADMINISTRACION Y FINANZAS MUNICIPALES`
72 | #> variable code
73 | #> 1 A.1. PRESUPUESTO INICIAL Y VIGENTE MUNICIPAL (M$) 517
74 | #> 2 A. INGRESOS MUNICIPALES (M$) 21
75 | #> 3 B. INGRESOS MUNICIPALES (%) 191
76 | #> 4 C. GASTOS MUNICIPALES (M$) 22
77 | #> 5 D. GASTOS MUNICIPALES (%) 172
78 | #> 6 E. GASTOS EN PERSONAL 169
79 | #> 7 F. TRANSFERENCIAS E INVERSION 170
80 | #> 8 G. SERVICIOS BASICOS Y GENERALES 370
81 | #> 9 I. TRANSFERENCIAS Y COMPENSACIONES SUBDERE 485
82 | #> 10 J. FONDO COMÚN MUNICIPAL (FCM) 508
83 | #> 11 K. GESTION MUNICIPAL 486
84 | #> 12 L. 24
85 | #> 13 M. 506
86 | #>
87 | #> $`02. RECURSOS HUMANOS MUNICIPAL`
88 | #> variable code
89 | #> 1 A. PERSONAL DE PLANTA 381
90 | #> 2 B. PERSONAL A CONTRATA 382
91 | #> 3 C. HONORARIOS 383
92 | #> 4 D. OTROS INDICADORES 384
93 | #>
94 | #> $`03. EDUCACION MUNICIPAL`
95 | #> variable code
96 | #> 1 A. ANTECEDENTES GENERALES DE EDUCACION 38
97 | #> 2 B. ASISTENCIA Y MATRÍCULAS EN EDUCACION 32
98 | #> 3 C. RESULTADOS PAES 33
99 | #> 4 D. INGRESOS EN EDUCACION MUNICIPAL 35
100 | #> 5 E. GASTOS EN EDUCACION MUNICIPAL 36
101 | #> 6 F. RECURSOS HUMANOS EN SECTOR EDUCACION 34
102 | #> 7 G. ESTABLECIMIENTOS DE EDUCACION MUNICIPAL 379
103 | #>
104 | #> $`04. SALUD MUNICIPAL`
105 | #> variable code
106 | #> 1 A. ANTECEDENTES GENERALES DE SALUD 30
107 | #> 2 B. COBERTURA EN SALUD MUNICIPAL 25
108 | #> 3 C. INGRESOS EN SALUD MUNICIPAL 26
109 | #> 4 D. GASTOS EN SALUD MUNICIPAL 28
110 | #> 5 E. RED ASISTENCIAL SALUD 31
111 | #> 6 F. RECURSOS HUMANOS EN SALUD 362
112 | #>
113 | #> $`05. SOCIAL Y COMUNITARIA`
114 | #> variable code
115 | #> 1 A. INFORMACION ENCUESTA CASEN 47
116 | #> 2 B. RED SOCIAL (SUBSIDIOS Y PENSIONES) 44
117 | #> 3 C. INTERMEDIACION LABORAL 43
118 | #> 4 D. ORGANIZACIONES COMUNITARIAS 46
119 | #> 5 E. BECAS 377
120 | #> 6 F. PARTICIPACIÓN CIUDADANA 510
121 | #> 7 G. DISCAPACIDAD 512
122 | #> 8 H. PREVENCIÓN DEL DELITO Y SEGURIDAD 511
123 | #> 9 L. RSH - ANTECEDENTES GENERALES 560
124 | #> 10 N.RSH - NIVEL INGRESOS - VULNERABILIDAD 561
125 | #> 11 O.RSH - CARACTERISTICAS ENCUESTADOS 562
126 | #> 12 P. INE - TIPO DE HOGARES 563
127 | #> 13 RETIROS 10% 565
128 | #>
129 | #> $`06. DESARROLLO Y GESTION TERRITORIAL`
130 | #> variable code
131 | #> 1 A. CARACTERISTICAS TERRITORIALES 39
132 | #> 2 B. SERVICIOS BASICOS A LA COMUNIDAD 41
133 | #> 3 C. INFRAESTRUCTURA 40
134 | #> 4 D. CATASTRO PREDIOS Y VALORACION CATASTRAL 300
135 | #> 5 E. AREAS VERDES 376
136 | #> 6 F. PLAN DE DESARROLLO COMUNAL (PLADECO) 304
137 | #> 7 G. PLAN REGULADOR COMUNAL 42
138 | #> 8 H. CULTURA 587
139 | #>
140 | #> $`07. CARACTERIZACION COMUNAL`
141 | #> variable code
142 | #> 1 A. GEOGRAFICO ADMINISTRATIVA 49
143 | #> 2 B. POBLACION 50
144 | #>
145 | #> $`08. GENERO`
146 | #> variable code
147 | #> 1 DOTACION MUNICIPAL HOMBRES Y MUJERES 262
148 | #>
149 | #> $`09. CEMENTERIO`
150 | #> variable code
151 | #> 1 1. INFORMACION GENERAL 516
152 | #> 2 A. INGRESOS CEMENTERIO (M$) 456
153 | #> 3 B. GASTOS CEMENTERIO (M$) 457
154 | #>
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/docs/reference/census_geometry_comunas.html:
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49 | License
47 |The MIT License (MIT) 50 | 51 | Copyright (c) 2018 Roberto Salas 52 | 53 | Permission is hereby granted, free of charge, to any person obtaining a copy 54 | of this software and associated documentation files (the "Software"), to deal 55 | in the Software without restriction, including without limitation the rights 56 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 57 | copies of the Software, and to permit persons to whom the Software is 58 | furnished to do so, subject to the following conditions: 59 | 60 | The above copyright notice and this permission notice shall be included in 61 | all copies or substantial portions of the Software. 62 | 63 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 64 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 65 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 66 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 67 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 68 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 69 | THE SOFTWARE. 70 |71 | 72 |
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/docs/reference/census_geometry_limites.html:
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45 |
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46 |
49 |
50 |
51 | Chilean municipalities polygons as simple features
47 | Source:R/data.R
48 | census_geometry_comunas.Rd
52 |
54 |
55 | An sf object containing the chilean municipalities.
53 |
56 |
57 |
58 |
59 | census_geometry_comunas
60 |
69 | Format
61 |A data frame with 345 rows and 2 variables:
- code 62 |
municipality code
63 |
64 | - geometry 65 |
municipality geometry
66 |
67 |
68 |
70 |
73 |
74 | Source
71 | 72 |
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/docs/reference/idgeocensus.html:
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50 |
51 | Chilean municipalities urban boundaries polygons as simple features
47 | Source:R/data.R
48 | census_geometry_limites.Rd
52 |
54 |
55 | An sf object containing the chilean municipalities urban boundaries.
53 |
56 |
57 |
58 |
59 | census_geometry_limites
60 |
69 | Format
61 |A data frame with 220 rows and 2 variables:
- code 62 |
municipality code
63 |
64 | - geometry 65 |
municipality geometry
66 |
67 |
68 |
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74 | Source
71 | 72 |
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/docs/reference/id_geo_census.html:
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145 |
155 |
120 |
121 |
125 |
126 | A dataset containing the geographical units of the 122 | chilean 2017 census.
123 | 124 |idgeocensus
127 |
128 | Format
129 | 130 |A data frame with 246 rows and 6 variables:
-
131 |
- CODE.REG
Region code
132 | - NOM.REG
Region name
133 | - CODE.PROV
Provincia code
134 | - NOM.PROV
Provincia name
135 | - CODE
Municipality code
136 | - MUNICIPALITY
Municipality name
137 |
Source
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/docs/reference/mm.html:
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52 |
54 |
55 | A dataset containing the chilean administrative units.
53 |
56 |
57 |
58 |
59 | id_geo_census
60 |
84 | Format
61 |A data frame with 345 rows and 7 variables:
- code 62 |
municipality code
63 |
64 | - municipality 65 |
municipality name
66 |
67 | - code.reg 68 |
region code
69 |
70 | - nom.reg 71 |
region name
72 |
73 | - code.prov 74 |
province code
75 |
76 | - nom.prov 77 |
province name
78 |
79 | - auc 80 |
yes/no consolidated urban area
81 |
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89 | Source
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/docs/reference/get_sinim_vars.html:
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1 |
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84 |
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46 |
49 |
50 |
51 | Return a abbreviated value of the "miles de millones" format
47 | Source:R/mm.R
48 | mm.Rd
52 |
54 |
55 | Return a abbreviated value of the "miles de millones" format
53 |
56 |
57 |
58 |
59 |
67 | mm(x)
68 |
71 |
72 | Value
69 |A character value in short format
70 |
73 |
77 |
78 | Examples
74 |mm(5807587000)
75 | #> [1] "6 mm$"
76 |
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/docs/reference/getsinimvariables.html:
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46 |
49 |
50 |
51 | List available variables for a category
47 | Source:R/get_sinim_vars.R
48 | get_sinim_vars.Rd
52 |
54 |
55 | List available variables for a category
53 |
56 |
57 |
58 |
59 |
67 | get_sinim_vars(catn)
68 |
71 |
72 | Value
69 |data frame with variables and values
70 |
73 |
78 |
79 | Examples
74 |get_sinim_vars(47)
75 | #> variable unit code
76 | #> 1 Indice de Pobreza CASEN (Última Encuesta Vigente) % 744
77 |
53 |
54 |
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/docs/reference/get_sinim_var_name.html:
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112 |
128 |
134 |
135 |
145 |
156 |
113 |
116 |
117 |
118 | List available variables for a category
114 | Source:R/getsinimvariables.R
115 | getsinimvariables.Rd
119 |
120 |
123 |
124 | List available variables for a category
121 | 122 |getsinimvariables(catn)125 | 126 |
Arguments
127 || catn | 131 |category number |
132 |
|---|
Value
136 | 137 |data frame with variables and values
138 | 139 | 140 |Examples
141 |144 |getsinimvariables(47)#> VARIABLE UNIT CODE 142 | #> 314 Porcentaje de Población en Condiciones de Pobreza , según CASEN % 562 143 | #> 315 Indice de Pobreza CASEN (Última Encuesta Vigente) % 744
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/docs/reference/index.html:
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1 |
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45 |
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46 |
49 |
50 |
51 | Return SINIM variable name using their internal codes
47 | Source:R/get_sinim_var_name.R
48 | get_sinim_var_name.Rd
52 |
54 |
55 | Return SINIM variable name using their internal codes
53 |
56 |
57 |
58 |
59 |
67 | get_sinim_var_name(var)
68 |
71 |
72 | Value
69 |A vector of requested variable names
70 |
73 |
79 |
80 | Examples
74 |get_sinim_var_name(880)
75 | #> [1] "INGRESOS POR FONDO COMÚN MUNICIPAL"
76 | get_sinim_var_name(c(880, 882))
77 | #> [1] "INGRESOS POR FONDO COMÚN MUNICIPAL" "INGRESOS PROPIOS (IPP Y FCM)"
78 |
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/R/helpers.R:
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1 | #' @import httr
2 | #' @import XML
3 | #' @importFrom jsonlite fromJSON
4 | #' @importFrom reshape2 melt
5 | #' @importFrom stats complete.cases
6 |
7 | # Helper functions for API calls, data parsing, and variable lookups used throughout the sinimr package.
8 |
9 | # Register global variables to avoid CRAN NOTES about undefined variables in data manipulation code.
10 | utils::globalVariables(c(
11 | "id_geo_census", "code.reg", "code.prov", "code",
12 | "census_geometry_comunas", "census_geometry_limites", "id_subarea"
13 | ))
14 |
15 | # Call the SINIM API with a GET request and return the raw response as a string.
16 | callapi <- function(url) { # nocov start
17 | resp <- httr::GET(url, add_headers("X-Request-Source" = "r"))
18 | stop_for_status(resp, task = "call api")
19 | data <- httr::content(resp, "text", encoding = "UTF-8")
20 | data <- substr(data, 2, nchar(data))
21 |
22 | return(data)
23 | } # nocov end
24 |
25 | # Call the SINIM API with a POST request and return the parsed JSON response.
26 | postapi <- function(url, body) { # nocov start
27 | resp <- httr::POST(
28 | url,
29 | body = body,
30 | add_headers(
31 | "User-Agent" = "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/18.3.1 Safari/605.1.15",
32 | "Referer" = "https://datos.sinim.gov.cl/datos_municipales.php",
33 | "Host" = "datos.sinim.gov.cl",
34 | "X-Requested-With" = "XMLHttpRequest",
35 | "Accept-Encoding" = "gzip, deflate, br"
36 | )
37 | )
38 |
39 | stop_for_status(resp, task = "call api")
40 |
41 | data <- jsonlite::fromJSON(content(resp, "text"))
42 | return(data)
43 | } # nocov end
44 |
45 | # Validate and convert a year or vector of years to their index in the supported year list.
46 | getyear <- function(year) { # nocov start
47 | year_list <- c(
48 | 2000,
49 | 2001,
50 | 2002,
51 | 2003,
52 | 2004,
53 | 2005,
54 | 2006,
55 | 2007,
56 | 2008,
57 | 2009,
58 | 2010,
59 | 2011,
60 | 2012,
61 | 2013,
62 | 2014,
63 | 2015,
64 | 2016,
65 | 2017,
66 | 2018,
67 | 2019,
68 | 2020,
69 | 2021,
70 | 2022,
71 | 2023,
72 | 2024
73 | )
74 | if (any(is.na(match(year, year_list)))) {
75 | stop("Year not found in list")
76 | } else {
77 | return(match(year, year_list))
78 | }
79 | } # nocov end
80 |
81 | # Retrieve the internal SINIM variable ID(s) for a given variable name or names.
82 | getid <- function(name) { # nocov start
83 | body <- list("dato_area[]" = "T", "dato_subarea[]" = "T")
84 | resp <-
85 | postapi(
86 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_filtros.php",
87 | body
88 | )
89 | list <- reshape2::melt(sapply(resp, function(b)
90 | b$mtro_datos_nombre))
91 | values <- reshape2::melt(sapply(resp, function(b)
92 | b$id_dato))
93 | list$id <- values$value
94 | colnames(list) <- c("variable", "category", "value")
95 | list$variable <-
96 | as.vector(lapply(as.character(list$variable), function(x)
97 | trimws(x)))
98 | return(list[complete.cases(match(list$variable, name)), 3])
99 | } # nocov end
100 |
101 | # Retrieve the SINIM variable name(s) for a given variable code or codes.
102 | getname <- function(names) { # nocov start
103 | body <- list("dato_area[]" = "T", "dato_subarea[]" = "T")
104 | resp <- postapi(
105 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_filtros.php",
106 | body
107 | )
108 | list <- reshape2::melt(sapply(resp, function(b)
109 | b$mtro_datos_nombre))
110 | values <- reshape2::melt(sapply(resp, function(b)
111 | b$id_dato))
112 | list$id <- values$value
113 | colnames(list) <- c("variable", "category", "value")
114 | list$variable <-
115 | as.vector(lapply(as.character(list$variable), function(x)
116 | trimws(x)))
117 | names.list <- gsub("\\.", "", toupper(unlist(list[which(list$value %in% names), 1])))
118 | return(names.list)
119 | } # nocov end
120 |
121 | # Parse XML data from the SINIM API for the given variables and years, optionally applying monetary correction.
122 | parsexml <- function(var, years, moncorr=T) { # nocov start
123 | yearsn <- getyear(years)
124 | if(moncorr==T){
125 | url <- paste(
126 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_municipales.php?area[]=T&subarea[]=T&variables[]=",
127 | paste(var, collapse = ","), "&periodos[]=", paste(yearsn, collapse = ","), "®iones[]=T&municipios[]=T&corrmon=1",
128 | sep = ""
129 | )
130 | } else {
131 | url <- paste(
132 | "https://datos.sinim.gov.cl/datos_municipales/obtener_datos_municipales.php?area[]=T&subarea[]=T&variables[]=",
133 | paste(var, collapse = ","), "&periodos[]=", paste(yearsn, collapse = ","), "®iones[]=T&municipios[]=T&corrmon=0",
134 | sep = ""
135 | )
136 | }
137 | data <- XML::xmlParse(callapi(url))
138 | columns <- as.numeric(
139 | XML::xpathApply(
140 | data,
141 | "//tei:Table/@tei:ExpandedColumnCount",
142 | namespaces = c(tei = getDefaultNamespace(data)[[1]]$uri)
143 | )[[1]][[1]],
144 | xmlValue
145 | )
146 | varxml <- XML::xpathSApply(
147 | data,
148 | "//tei:Cell[not(@tei:StyleID)]",
149 | namespaces = c(tei = getDefaultNamespace(data)[[1]]$uri),
150 | xmlValue
151 | )
152 | values <- matrix(varxml,
153 | nrow = length(varxml)/((length(var)*length(yearsn))+2),
154 | ncol = ((length(var)*length(yearsn))+2), byrow = T)
155 | values <- as.data.frame(values, stringsAsFactors = F)
156 | return(values)
157 | } # nocov end
158 |
159 | # Construct column names for variables and years in the format VARIABLE.YEAR.
160 | namesco <- function(x,y){ #nocov start
161 | rep_vars <- rep(getname(x), each=length(y))
162 | rep_years <- rep(sort(y, decreasing = T), length(x))
163 | return(paste(rep_vars, rep_years, sep="."))
164 | } # nocov end
165 |
166 | # Filter geographic codes by region, province, or comuna, with optional AUC filtering.
167 | geofilter <- function(region, provincia, comuna, auc=F) { #nocov start
168 | if (!missing(region)) {
169 | stopifnot(missing(provincia))
170 | stopifnot(missing(comuna))
171 | if(auc==T) {
172 | selection <- subset(id_geo_census, code.reg %in% region &
173 | auc==1)$code
174 | } else {
175 | selection <- subset(id_geo_census, code.reg %in% region)$code
176 | }
177 | return(selection)
178 | } else if (!missing(provincia)) {
179 | stopifnot(missing(region))
180 | stopifnot(missing(comuna))
181 | if(auc==T) {
182 | warning("AUC not available subsetting provincias")
183 | }
184 | selection <- subset(id_geo_census, code.prov %in% provincia)$code
185 | return(selection)
186 | } else if (!missing(comuna)) {
187 | stopifnot(missing(region))
188 | stopifnot(missing(provincia))
189 | if(auc==T) {
190 | warning("AUC not available subsetting comunas")
191 | }
192 | selection <- subset(id_geo_census, code %in% comuna)$code
193 | return(selection)
194 | } else {
195 | return(id_geo_census$code)
196 | }
197 | } # nocov end
198 |
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/R/get_sinim.R:
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1 | #' Get a SINIM variable data in a specific year as a data frame
2 | #' @param var Variable code
3 | #' @param year Year
4 | #' @param moncorr A logical value indicating the use of monetary correction
5 | #' @param truevalue A logical value indicating the use of converted values
6 | #' @param idgeo A logical value to add provincia and region columns
7 | #' @param geometry A logical value to add geographical features
8 | #' @param region Region subsetting variable
9 | #' @param provincia Provincia subsetting variable
10 | #' @param comuna Comuna subsetting variable
11 | #' @param auc A logical value to retrieve only AUC features
12 | #' @param unit Use "comunas" or "limites"
13 | #' @return data frame for the requested variable over time with optional geometry
14 | #' @export
15 | #' @examples
16 | #' get_sinim(880, 2015)
17 | #' get_sinim(880, 2015:2017)
18 | #' get_sinim(c(880, 882), 2015)
19 | #' get_sinim(c(880, 882), 2015:2017)
20 | #' get_sinim(c(880, 882), 2015, idgeo=TRUE)
21 | #' get_sinim(882, 2015, geometry=TRUE)
22 | #' get_sinim(882, 2015, region="02")
23 | #' @importFrom reshape2 melt
24 | #' @importFrom stats reshape
25 | #' @importFrom stats na.omit
26 | #' @import sf
27 | #'
28 | get_sinim <-
29 | function(var,
30 | year,
31 | moncorr = T,
32 | truevalue = F,
33 | idgeo = F,
34 | geometry = F,
35 | region,
36 | provincia,
37 | comuna,
38 | auc = F,
39 | unit = "comunas") {
40 | stopifnot(is.numeric(var))
41 | stopifnot(is.numeric(year))
42 |
43 | # Download and parse the raw data for the requested variables and years
44 | values <- parsexml(var, year, moncorr = moncorr)
45 |
46 | # Set column names for the parsed data
47 | colnames(values) <-
48 | c("code", "municipality", namesco(var, year))
49 |
50 | # Reshape the data to long format for easier manipulation
51 | datav <- stats::reshape(
52 | values,
53 | idvar = c("code", "municipality"),
54 | varying = namesco(var, year),
55 | direction = "long",
56 | timevar = "year",
57 | times = sort(year, decreasing = T)
58 | )
59 |
60 | # Remove row names for clarity
61 | rownames(datav) <- NULL
62 |
63 | # Replace 'No Recepcionado' and 'No Aplica' with NA
64 | datav <-
65 | as.data.frame(apply(datav, 2, function(x)
66 | gsub("No Recepcionado|No Aplica", NA, x)), stringsAsFactors = FALSE)
67 |
68 | if (geometry == FALSE) {
69 | # Melt the data to have one row per variable per year
70 | datav <- reshape2::melt(
71 | datav,
72 | id = c("code", "municipality", "year"),
73 | value.name = "value",
74 | variable.name = "variable"
75 | )
76 |
77 | datav$variable <- as.character(datav$variable)
78 |
79 | # Identify numeric columns for conversion
80 | t <-
81 | vapply(datav, function(x)
82 | all(grepl("[0-9]+", na.omit(x))), logical(1))
83 |
84 | t[1:4] <- c(TRUE, FALSE, FALSE, FALSE)
85 | datav[t] <- lapply(datav[t], function(x)
86 | (as.numeric(x)))
87 |
88 | # Apply value conversion if requested
89 | if (truevalue == TRUE) {
90 | datav$value <- datav$value * 1000
91 | }
92 |
93 | # Subset by region, provincia, or comuna if provided
94 | if (!missing(region) |
95 | !missing(provincia) |
96 | !missing(comuna)) {
97 | selection <-
98 | geofilter(region = region,
99 | provincia = provincia,
100 | comuna = comuna,
101 | auc)
102 | data.selection <- subset(datav, code %in% selection)
103 |
104 | # Merge with geographic info if requested
105 | if (idgeo == T) {
106 | merged.pretty <-
107 | merge(data.selection,
108 | id_geo_census,
109 | by = c("code", "municipality"))
110 | return(merged.pretty)
111 | } else {
112 | return(data.selection)
113 | }
114 |
115 | } else {
116 | # Merge with geographic info for all data if requested
117 | if (idgeo == T) {
118 | merged.pretty <- merge(datav, id_geo_census, by = "code")
119 | return(merged.pretty)
120 | } else {
121 | return(datav)
122 | }
123 | }
124 | } else {
125 | # Melt the data for geometry output
126 | datav <- reshape2::melt(
127 | datav,
128 | id = c("code", "municipality", "year"),
129 | value.name = "value",
130 | variable.name = "variable"
131 | )
132 |
133 | datav$variable <- as.character(datav$variable)
134 |
135 | # Identify numeric columns for conversion
136 | t <-
137 | vapply(datav, function(x)
138 | all(grepl("[0-9]+", na.omit(x))), logical(1))
139 |
140 | t[1:4] <- c(TRUE, FALSE, FALSE, FALSE)
141 | datav[t] <- lapply(datav[t], function(x)
142 | (as.numeric(x)))
143 |
144 | # Apply value conversion if requested
145 | if (truevalue == TRUE) {
146 | datav$value <- datav$value * 1000
147 | }
148 |
149 | # Subset by region, provincia, or comuna if provided
150 | if (!missing(region) |
151 | !missing(provincia) |
152 | !missing(comuna)) {
153 | selection <-
154 | geofilter(region = region,
155 | provincia = provincia,
156 | comuna = comuna,
157 | auc)
158 |
159 | data.selection <- subset(datav, code %in% selection)
160 |
161 | # Merge with geometry based on the selected unit
162 | if (unit == "comunas") {
163 | merged.geo <-
164 | merge(census_geometry_comunas, data.selection, by = "code")
165 | } else if (unit == "limites") {
166 | merged.geo <-
167 | merge(census_geometry_limites, data.selection, by = "code")
168 | } else {
169 | stop("Unit not valid")
170 | }
171 |
172 | # Merge with geographic info if requested
173 | if (idgeo == T) {
174 | merged.geo.pretty <-
175 | merge(merged.geo,
176 | id_geo_census,
177 | by = c("code", "municipality"))
178 | return(merged.geo.pretty)
179 | } else {
180 | return(merged.geo)
181 | }
182 |
183 | } else {
184 | # Merge with geometry for all data based on the selected unit
185 | if (unit == "comunas") {
186 | merged.geo <-
187 | merge(census_geometry_comunas, datav, by = "code")
188 | } else if (unit == "limites") {
189 | merged.geo <-
190 | merge(census_geometry_limites, datav, by = "code")
191 | } else {
192 | stop("Unit not valid")
193 | }
194 |
195 | # Merge with geographic info if requested
196 | if (idgeo == T) {
197 | merged.geo.pretty <- merge(merged.geo, id_geo_census, by = c("code","municipality"))
198 | return(merged.geo.pretty)
199 | } else {
200 | return(merged.geo)
201 | }
202 | }
203 | }
204 | }
205 |
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1 | /* Sticky footer */
2 |
3 | /**
4 | * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/
5 | * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css
6 | *
7 | * .Site -> body > .container
8 | * .Site-content -> body > .container .row
9 | * .footer -> footer
10 | *
11 | * Key idea seems to be to ensure that .container and __all its parents__
12 | * have height set to 100%
13 | *
14 | */
15 |
16 | html, body {
17 | height: 100%;
18 | }
19 |
20 | body {
21 | position: relative;
22 | }
23 |
24 | body > .container {
25 | display: flex;
26 | height: 100%;
27 | flex-direction: column;
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29 |
30 | body > .container .row {
31 | flex: 1 0 auto;
32 | }
33 |
34 | footer {
35 | margin-top: 45px;
36 | padding: 35px 0 36px;
37 | border-top: 1px solid #e5e5e5;
38 | color: #666;
39 | display: flex;
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42 | footer p {
43 | margin-bottom: 0;
44 | }
45 | footer div {
46 | flex: 1;
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49 | text-align: right;
50 | }
51 | footer p {
52 | margin-bottom: 0;
53 | }
54 |
55 | img.icon {
56 | float: right;
57 | }
58 |
59 | /* Ensure in-page images don't run outside their container */
60 | .contents img {
61 | max-width: 100%;
62 | height: auto;
63 | }
64 |
65 | /* Fix bug in bootstrap (only seen in firefox) */
66 | summary {
67 | display: list-item;
68 | }
69 |
70 | /* Typographic tweaking ---------------------------------*/
71 |
72 | .contents .page-header {
73 | margin-top: calc(-60px + 1em);
74 | }
75 |
76 | dd {
77 | margin-left: 3em;
78 | }
79 |
80 | /* Section anchors ---------------------------------*/
81 |
82 | a.anchor {
83 | display: none;
84 | margin-left: 5px;
85 | width: 20px;
86 | height: 20px;
87 |
88 | background-image: url(./link.svg);
89 | background-repeat: no-repeat;
90 | background-size: 20px 20px;
91 | background-position: center center;
92 | }
93 |
94 | h1:hover .anchor,
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101 | }
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105 | .contents h1, .contents h2, .contents h3, .contents h4 {
106 | padding-top: 60px;
107 | margin-top: -40px;
108 | }
109 |
110 | /* Navbar submenu --------------------------*/
111 |
112 | .dropdown-submenu {
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114 | }
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124 | .dropdown-submenu:hover>.dropdown-menu {
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132 | width: 0;
133 | height: 0;
134 | border-color: transparent;
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137 | border-left-color: #cccccc;
138 | margin-top: 5px;
139 | margin-right: -10px;
140 | }
141 |
142 | .dropdown-submenu:hover>a:after {
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144 | }
145 |
146 | .dropdown-submenu.pull-left {
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157 |
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159 | margin-top: 30px;
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165 | #pkgdown-sidebar h2 {
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173 |
174 | #pkgdown-sidebar .list-unstyled li {
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204 | }
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207 |
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209 | display: none; /* Hide by default, but at >768px, show it */
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211 | }
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214 | padding-left: 16px;
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220 | padding-left: 15px;
221 | }
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227 | font-weight: 500;
228 | font-size: 1.35rem;
229 | }
230 |
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232 |
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237 | margin-left:4px;
238 | margin-right:4px;
239 | vertical-align: middle;
240 | }
241 |
242 | /* Reference index & topics ----------------------------------------------- */
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251 |
252 | .ref-arguments th {text-align: right; padding-right: 10px;}
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259 | table {
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265 |
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270 | pre, pre code {
271 | white-space: pre-wrap;
272 | word-break: break-all;
273 | overflow-wrap: break-word;
274 | }
275 |
276 | pre {
277 | border: 1px solid #eee;
278 | }
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280 | pre .img, pre .r-plt {
281 | margin: 5px 0;
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293 | text-decoration: none;
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303 | .warning {font-weight: bolder;}
304 |
305 | /* Clipboard --------------------------*/
306 |
307 | .hasCopyButton {
308 | position: relative;
309 | }
310 |
311 | .btn-copy-ex {
312 | position: absolute;
313 | right: 0;
314 | top: 0;
315 | visibility: hidden;
316 | }
317 |
318 | .hasCopyButton:hover button.btn-copy-ex {
319 | visibility: visible;
320 | }
321 |
322 | /* headroom.js ------------------------ */
323 |
324 | .headroom {
325 | will-change: transform;
326 | transition: transform 200ms linear;
327 | }
328 | .headroom--pinned {
329 | transform: translateY(0%);
330 | }
331 | .headroom--unpinned {
332 | transform: translateY(-100%);
333 | }
334 |
335 | /* mark.js ----------------------------*/
336 |
337 | mark {
338 | background-color: rgba(255, 255, 51, 0.5);
339 | border-bottom: 2px solid rgba(255, 153, 51, 0.3);
340 | padding: 1px;
341 | }
342 |
343 | /* vertical spacing after htmlwidgets */
344 | .html-widget {
345 | margin-bottom: 10px;
346 | }
347 |
348 | /* fontawesome ------------------------ */
349 |
350 | .fab {
351 | font-family: "Font Awesome 5 Brands" !important;
352 | }
353 |
354 | /* don't display links in code chunks when printing */
355 | /* source: https://stackoverflow.com/a/10781533 */
356 | @media print {
357 | code a:link:after, code a:visited:after {
358 | content: "";
359 | }
360 | }
361 |
362 | /* Section anchors ---------------------------------
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364 | */
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366 | div.csl-bib-body { }
367 | div.csl-entry {
368 | clear: both;
369 | }
370 | .hanging-indent div.csl-entry {
371 | margin-left:2em;
372 | text-indent:-2em;
373 | }
374 | div.csl-left-margin {
375 | min-width:2em;
376 | float:left;
377 | }
378 | div.csl-right-inline {
379 | margin-left:2em;
380 | padding-left:1em;
381 | }
382 | div.csl-indent {
383 | margin-left: 2em;
384 | }
385 |
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48 |
49 | Reference
47 |
50 | All functions51 | 52 | |
53 | |
|---|---|
| 54 | 55 | | 56 |Chilean municipalities polygons as simple features |
57 |
| 58 | 59 | | 60 |Chilean municipalities urban boundaries polygons as simple features |
61 |
| 62 | 63 | | 64 |Get a SINIM variable data in a specific year as a data frame |
65 |
| 66 | 67 | | 68 |Get SINIM data categories |
69 |
| 70 | 71 | | 72 |Return SINIM variable name using their internal codes |
73 |
| 74 | 75 | | 76 |List available variables for a category |
77 |
| 78 | 79 | | 80 |Chilean administrative divisions |
81 |
| 82 | 83 | | 84 |Return a abbreviated value of the "miles de millones" format |
85 |
| 86 | 87 | | 88 |Search for a SINIM variable and if no keyword is provided returns all variables |
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/README.Rmd:
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1 | ---
2 | output: github_document
3 | ---
4 |
5 | ```{r, echo = FALSE}
6 | knitr::opts_chunk$set(
7 | collapse = TRUE,
8 | comment = "#>",
9 | fig.path = "man/figures/"
10 | )
11 | ```
12 |
13 | [](https://saythanks.io/to/robsalasco)
14 | [](https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=WDDLRUVD344XL¤cy_code=USD&source=url)
15 | [](https://github.com/robsalasco/sinimr/actions)
16 | [](https://app.codecov.io/github/robsalasco/sinimr?branch=master)
17 |
18 | # sinimR
111 |
227 |
228 |
238 |
112 |
216 |
226 |
113 |
116 |
117 |
118 | Get SINIM data categories
114 | Source:R/getsinimcategories.R
115 | getsinimcategories.Rd
119 |
120 |
123 |
124 | Get SINIM data categories
121 | 122 |getsinimcategories()
125 |
126 | Value
127 | 128 |Returns a data frame with available data in SINIM's webpage
129 | 130 | 131 |Examples
132 |215 |getsinimcategories()#> $`-` 133 | #> VARIABLE CODE 134 | #> 1 * SIM FIMU 539 135 | #> 136 | #> $`01. ADMINISTRACION Y FINANZAS MUNICIPALES` 137 | #> VARIABLE CODE 138 | #> 1 A.1. PRESUPUESTO INICIAL Y VIGENTE MUNICIPAL (M$) 517 139 | #> 2 A. INGRESOS MUNICIPALES (M$) 21 140 | #> 3 B. INGRESOS MUNICIPALES (%) 191 141 | #> 4 C. GASTOS MUNICIPALES (M$) 22 142 | #> 5 D. GASTOS MUNICIPALES (%) 172 143 | #> 6 E. GASTOS EN PERSONAL 169 144 | #> 7 F. TRANSFERENCIAS E INVERSION 170 145 | #> 8 G. SERVICIOS BASICOS Y GENERALES 370 146 | #> 9 I. TRANSFERENCIAS Y COMPENSACIONES SUBDERE 485 147 | #> 10 J. FONDO COMÚN MUNICIPAL (FCM) 508 148 | #> 11 K. GESTION MUNICIPAL 486 149 | #> 12 L. 24 150 | #> 13 M. 506 151 | #> 152 | #> $`02. RECURSOS HUMANOS MUNICIPAL` 153 | #> VARIABLE CODE 154 | #> 1 A. PERSONAL DE PLANTA 381 155 | #> 2 B. PERSONAL A CONTRATA 382 156 | #> 3 C. HONORARIOS 383 157 | #> 4 D. CÓDIGO DEL TRABAJO 523 158 | #> 5 D. OTROS INDICADORES 384 159 | #> 160 | #> $`03. EDUCACION MUNICIPAL` 161 | #> VARIABLE CODE 162 | #> 1 A. ANTECEDENTES GENERALES DE EDUCACION 38 163 | #> 2 B. ASISTENCIA Y MATRÍCULAS EN EDUCACION 32 164 | #> 3 C. RESULTADOS PSU 33 165 | #> 4 D. INGRESOS EN EDUCACION MUNICIPAL 35 166 | #> 5 E. GASTOS EN EDUCACION MUNICIPAL 36 167 | #> 6 F. RECURSOS HUMANOS EN SECTOR EDUCACION 34 168 | #> 7 G. ESTABLECIMIENTOS DE EDUCACION MUNICIPAL 379 169 | #> 170 | #> $`04. SALUD MUNICIPAL` 171 | #> VARIABLE CODE 172 | #> 1 A. ANTECEDENTES GENERALES DE SALUD 30 173 | #> 2 B. COBERTURA EN SALUD MUNICIPAL 25 174 | #> 3 C. INGRESOS EN SALUD MUNICIPAL 26 175 | #> 4 D. GASTOS EN SALUD MUNICIPAL 28 176 | #> 5 E. RED ASISTENCIAL SALUD 31 177 | #> 6 F. RECURSOS HUMANOS EN SALUD 362 178 | #> 179 | #> $`05. SOCIAL Y COMUNITARIA` 180 | #> VARIABLE CODE 181 | #> 1 A. INFORMACION ENCUESTA CASEN 47 182 | #> 2 B. RED SOCIAL (SUBSIDIOS Y PENSIONES) 44 183 | #> 3 C. INTERMEDIACION LABORAL 43 184 | #> 4 D. ORGANIZACIONES COMUNITARIAS 46 185 | #> 5 E. BECAS 377 186 | #> 6 F. PARTICIPACIÓN CIUDADANA 510 187 | #> 7 G. DISCAPACIDAD 512 188 | #> 8 H. PREVENCIÓN DEL DELITO 511 189 | #> 190 | #> $`06. DESARROLLO Y GESTION TERRITORIAL` 191 | #> VARIABLE CODE 192 | #> 1 A. CARACTERISTICAS TERRITORIALES 39 193 | #> 2 B. SERVICIOS BASICOS A LA COMUNIDAD 41 194 | #> 3 C. INFRAESTRUCTURA 40 195 | #> 4 D. CATASTRO PREDIOS Y VALORACION CATASTRAL 300 196 | #> 5 E. AREAS VERDES 376 197 | #> 6 F. PLAN DE DESARROLLO COMUNAL (PLADECO) 304 198 | #> 7 G. PLAN REGULADOR COMUNAL 42 199 | #> 200 | #> $`07. CARACTERIZACION COMUNAL` 201 | #> VARIABLE CODE 202 | #> 1 A. GEOGRAFICO ADMINISTRATIVA 49 203 | #> 2 B. POBLACION 50 204 | #> 205 | #> $`08. GENERO` 206 | #> VARIABLE CODE 207 | #> 1 A. DOTACION FUNCIONARIA Y PROFESIONAL DE MUJERES 262 208 | #> 209 | #> $`09. CEMENTERIO` 210 | #> VARIABLE CODE 211 | #> 1 1. INFORMACION GENERAL 516 212 | #> 2 A. INGRESOS CEMENTERIO (M$) 456 213 | #> 3 B. GASTOS CEMENTERIO (M$) 457 214 | #>
19 |
20 | Chilean Municipalities Information System Wrapper
21 |
22 | ### What can I do with this?
23 |
24 | This R package allows easy SINIM (https://sinim.gov.cl) data retrieval what have advantages over the site:
25 |
26 | - When you work with multiple variables or years it will be very useful for rapid analyses.
27 | - Fast ploting directly from data source using the included geometries.
28 | - Data download with or without monetary correction using a switch.
29 |
30 | ```{r, message=F, fig.height=6, fig.width=6, fig.retina=2}
31 | library(dplyr)
32 | library(sinimr)
33 | library(sf)
34 | library(tmap)
35 |
36 | varcode <- 882
37 | var <- get_sinim(varcode, 2018,
38 | region = 13,
39 | truevalue = T,
40 | geometry = T,
41 | auc = T,
42 | unit = "limites")
43 |
44 | gran_santiago_plot <- tm_shape(var) +
45 | tm_fill(col = "value",
46 | palette = "BuPu",
47 | border.col = "white",
48 | border.alpha = 0.5,
49 | lwd=1,
50 | style = "jenks",
51 | title = get_sinim_var_name(varcode))+
52 | tm_text("municipality", size = 0.4, style="jenks") +
53 | tm_legend(legend.position = c("left", "top"), legend.title.size = 1, legend.text.size = 0.6) +
54 | tm_compass(type = "8star", position = c(.85, .80)) +
55 | tm_scale_bar(breaks = c(0, 10), text.size = 0.75, position = c("right", "bottom")) +
56 | tm_credits("Fuente: Sistema Nacional de Información Municipal (SINIM), SUBDERE, Ministerio del Interior.", position=c("left", "bottom"), size=0.55)+
57 | tm_layout(legend.width=1,
58 | inner.margins = c(0.1, 0.1, 0.10, 0.1),
59 | legend.format = list(text.separator = "a",
60 | fun = mm)) +
61 | tm_borders(col = 'black')
62 |
63 |
64 | gran_santiago_plot
65 |
66 | ```
67 |
68 | ### Support
69 |
70 | 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).
71 |
72 | ### A note on usage
73 |
74 | 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.
75 |
76 | ### Installation
77 |
78 | ```R
79 | install.packages("devtools")
80 | devtools::install_github("robsalasco/sinimr")
81 | ```
82 |
83 | ### How do I use it?
84 |
85 | 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.
86 |
87 | ```{r}
88 | library(sinimr)
89 | get_sinim_cats()
90 | ```
91 |
92 | Every category have a bunch of variables associated. Use the CODE number and the ```get_sinim_vars()``` function to get them.
93 |
94 | ```{r}
95 | get_sinim_vars(517)
96 | ```
97 |
98 | Finally, to obtain the data across municipalities use the code column and specify a year.
99 |
100 | ```{r}
101 | head(get_sinim(c(4210, 4211), 2015))
102 | ```
103 |
104 | By default the values are in **miles de millones** but it can be disabled using the ```truevalue = T``` switch.
105 |
106 | ```{r}
107 | head(get_sinim(c(4210, 4211), 2015, truevalue = T))
108 | ```
109 |
110 | You can get multiple years too! use the command ```get_sinim()``` and add more years as in the example.
111 |
112 | ```{r}
113 | head(get_sinim(880, 2015:2017))
114 | ```
115 |
116 | The 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.
117 |
118 |
119 | ```{r}
120 | head(get_sinim(882, 2015:2017, geometry=T))
121 | ```
122 |
123 |
124 | 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.
125 |
126 | ```{r}
127 | head(get_sinim(882, 2015:2017, geometry=T, region=2))
128 | ```
129 |
130 | You can get a subset too
131 |
132 | ```{r}
133 | head(get_sinim(882, 2015:2017, geometry=T, region=c(2,3)))
134 | ```
135 |
136 | But where obtain the codes? a database is provided and you can filter it using the standard R functions.
137 |
138 | ```{r}
139 | head(id_geo_census)
140 | ```
141 |
142 | Related 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.
143 |
144 | ```{r}
145 | search_sinim_vars("cementerio")
146 | ```
147 |
148 | ## Advanced usage
149 |
150 | SINIM (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.
151 |
152 | ### Other example plots
153 |
154 | #### Multiple variable faceted plot
155 |
156 | ```{r, message=F, fig.height=8, fig.width=15, fig.retina=2}
157 | library(tmap)
158 | library(dplyr)
159 | library(stringr)
160 | library(sinimr)
161 | library(sf)
162 |
163 | data_sinim <- get_sinim(var = c(3954,4174,880,1226,4251,4173),
164 | year = 2018,
165 | region = 13,
166 | geometry = T,
167 | truevalue = T,
168 | auc = T,
169 | unit = "limites")
170 |
171 | gran_santiago_plot <- tm_shape(data_sinim) +
172 | tm_fill(col = "value",
173 | palette = "BuPu",
174 | border.col = "white",
175 | border.alpha = 0.5,
176 | lwd=1,
177 | style = "jenks",
178 | title = "variable")+
179 | tm_text("municipality", size = 0.4) +
180 | tm_style("white", frame = T, legend.title.size = 1, legend.width=1) +
181 | tm_layout(inner.margins = c(0.01, 0.1, 0.1, 0.01),
182 | outer.margins = c(0.01, 0.01, 0.01, 0.01),
183 | design.mode=F,
184 | legend.format = list(text.separator = "a",
185 | fun = mm))+
186 | tm_borders(col = 'black') +
187 | tm_facets(by="variable", ncol = 2)
188 |
189 | gran_santiago_plot
190 |
191 | ```
192 |
193 | #### A variable in multiple years using facets
194 |
195 | ```{r, message=F, fig.height=10, fig.width=18, fig.retina=2}
196 | library(dplyr)
197 | library(sinimr)
198 | library(sf)
199 | library(tmap)
200 |
201 |
202 | var <- get_sinim(c(880, 882, 1226),
203 | 2016:2018,
204 | region = 13,
205 | truevalue = T,
206 | geometry = T,
207 | auc = T,
208 | unit = "limites")
209 |
210 | gran_santiago_plot <- tm_shape(var) +
211 | tm_fill("value",
212 | palette="BuPu",
213 | border.col = "white",
214 | style = "jenks",
215 | border.alpha = 0.5,
216 | lwd=1) +
217 | tm_text("municipality", size = 0.4) +
218 | tm_legend(legend.position = c("left", "top")) +
219 | tm_layout(legend.width=0.09,
220 | inner.margins = c(0.01, 0.1, 0.1, 0.01),
221 | outer.margins = c(0.01, 0.01, 0.01, 0.01),
222 | legend.format = list(text.separator = "a",
223 | fun = mm)) +
224 | tm_facets(by=c("year","variable"),) +
225 | tm_borders(col = 'black')
226 |
227 | gran_santiago_plot
228 |
229 | ```
230 |
231 | #### Multiple variables and years using geofacet
232 |
233 | ```{r, message=F, fig.height=12, fig.width=24, fig.retina=2}
234 | library(sf)
235 | library(dplyr)
236 | library(geofacet)
237 | library(sinimr)
238 | library(ggplot2)
239 | library(zoo)
240 | library(scales)
241 | library(ggpubr)
242 |
243 | data <- get_sinim(882, 2002:2018,
244 | region = 13,
245 | moncorr = F,
246 | truevalue = T,
247 | auc = T)
248 |
249 | data$year <- as.numeric(as.character(data$year))
250 | data$year <- as.Date(as.yearmon(data$year, "1-%y"))
251 |
252 | reg13 <- geogrid::read_polygons("https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson")
253 | grd <- grid_auto(reg13, seed = 1, names = "NOM_COMUNA", codes = "COMUNA")
254 |
255 | #grid_preview(grd, label = "name_NOM_COMUNA")
256 | #grid_design(grd, label = "name_NOM_COMUNA")
257 |
258 | ggplot(data, aes(year, value, group=1)) +
259 | geom_line(color = "steelblue") +
260 | facet_geo(~ municipality, grid = grd, scales = "free_y")+
261 | scale_x_date() +
262 | scale_y_continuous(labels = dollar_format(suffix = "", prefix = "$", big.mark = ".", decimal.mark=","))+
263 | theme_bw()
264 | ```
265 |
266 |
267 | ### Citation
268 |
269 | ```{r}
270 | citation("sinimr")
271 | ```
272 |
273 | ### References
274 |
275 | - Sistema Nacional de Información Municipal (SINIM), SUBDERE, Ministerio del Interior.
276 |
277 |
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92 | Creating geofacets with sinimR
81 |Roberto Salas
82 | 83 |2019-09-27
84 | 85 | Source:vignettes/creating-geofacets.Rmd
86 | creating-geofacets.Rmd
93 |
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127 | 94 | Description
95 |When you need to get a global overview of a phenomenom it’s difficult to have a plot with all the data. One option to achieve the goal are the use of facets but what if you want an spatial view? geofacets are the answer.
96 |library(sf)
97 | library(dplyr)
98 | library(geofacet)
99 | library(sinimr)
100 | library(ggplot2)
101 | library(zoo)
102 | library(scales)
103 | library(ggpubr)
104 |
105 | data <- get_sinim(882, 2002:2018,
106 | region = 13,
107 | moncorr = F,
108 | truevalue = T,
109 | auc = T)
110 |
111 | data$year <- as.numeric(as.character(data$year))
112 | data$year <- as.Date(as.yearmon(data$year, "1-%y"))
113 |
114 | reg13 <- geogrid::read_polygons("https://raw.githubusercontent.com/robsalasco/precenso_2016_geojson_chile/master/Extras/GRAN_SANTIAGO.geojson")
115 | grd <- grid_auto(reg13, seed = 1, names = "NOM_COMUNA", codes = "COMUNA")
116 |
117 | #grid_preview(grd, label = "name_NOM_COMUNA")
118 | #grid_design(grd, label = "name_NOM_COMUNA")
119 |
120 | ggplot(data, aes(year, value, group=1)) +
121 | geom_line(color = "steelblue") +
122 | facet_geo(~ municipality, grid = grd, scales = "free_y")+
123 | scale_x_date() +
124 | scale_y_continuous(labels = dollar_format(suffix = "", prefix = "$", big.mark = ".", decimal.mark=","))+
125 | theme_bw()
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55 | Get SINIM data categories
53 |
56 |
57 |
58 |
59 | get_sinim_cats()
60 |
63 |
64 | Value
61 |Returns a data frame with available data in SINIM's webpage
62 |
65 |
156 |
157 | Examples
66 |get_sinim_cats()
67 | #> $`-`
68 | #> variable code
69 | #> 1 * SIM FIMU 539
70 | #>
71 | #> $`01. ADMINISTRACION Y FINANZAS MUNICIPALES`
72 | #> variable code
73 | #> 1 A.1. PRESUPUESTO INICIAL Y VIGENTE MUNICIPAL (M$) 517
74 | #> 2 A. INGRESOS MUNICIPALES (M$) 21
75 | #> 3 B. INGRESOS MUNICIPALES (%) 191
76 | #> 4 C. GASTOS MUNICIPALES (M$) 22
77 | #> 5 D. GASTOS MUNICIPALES (%) 172
78 | #> 6 E. GASTOS EN PERSONAL 169
79 | #> 7 F. TRANSFERENCIAS E INVERSION 170
80 | #> 8 G. SERVICIOS BASICOS Y GENERALES 370
81 | #> 9 I. TRANSFERENCIAS Y COMPENSACIONES SUBDERE 485
82 | #> 10 J. FONDO COMÚN MUNICIPAL (FCM) 508
83 | #> 11 K. GESTION MUNICIPAL 486
84 | #> 12 L. 24
85 | #> 13 M. 506
86 | #>
87 | #> $`02. RECURSOS HUMANOS MUNICIPAL`
88 | #> variable code
89 | #> 1 A. PERSONAL DE PLANTA 381
90 | #> 2 B. PERSONAL A CONTRATA 382
91 | #> 3 C. HONORARIOS 383
92 | #> 4 D. OTROS INDICADORES 384
93 | #>
94 | #> $`03. EDUCACION MUNICIPAL`
95 | #> variable code
96 | #> 1 A. ANTECEDENTES GENERALES DE EDUCACION 38
97 | #> 2 B. ASISTENCIA Y MATRÍCULAS EN EDUCACION 32
98 | #> 3 C. RESULTADOS PAES 33
99 | #> 4 D. INGRESOS EN EDUCACION MUNICIPAL 35
100 | #> 5 E. GASTOS EN EDUCACION MUNICIPAL 36
101 | #> 6 F. RECURSOS HUMANOS EN SECTOR EDUCACION 34
102 | #> 7 G. ESTABLECIMIENTOS DE EDUCACION MUNICIPAL 379
103 | #>
104 | #> $`04. SALUD MUNICIPAL`
105 | #> variable code
106 | #> 1 A. ANTECEDENTES GENERALES DE SALUD 30
107 | #> 2 B. COBERTURA EN SALUD MUNICIPAL 25
108 | #> 3 C. INGRESOS EN SALUD MUNICIPAL 26
109 | #> 4 D. GASTOS EN SALUD MUNICIPAL 28
110 | #> 5 E. RED ASISTENCIAL SALUD 31
111 | #> 6 F. RECURSOS HUMANOS EN SALUD 362
112 | #>
113 | #> $`05. SOCIAL Y COMUNITARIA`
114 | #> variable code
115 | #> 1 A. INFORMACION ENCUESTA CASEN 47
116 | #> 2 B. RED SOCIAL (SUBSIDIOS Y PENSIONES) 44
117 | #> 3 C. INTERMEDIACION LABORAL 43
118 | #> 4 D. ORGANIZACIONES COMUNITARIAS 46
119 | #> 5 E. BECAS 377
120 | #> 6 F. PARTICIPACIÓN CIUDADANA 510
121 | #> 7 G. DISCAPACIDAD 512
122 | #> 8 H. PREVENCIÓN DEL DELITO Y SEGURIDAD 511
123 | #> 9 L. RSH - ANTECEDENTES GENERALES 560
124 | #> 10 N.RSH - NIVEL INGRESOS - VULNERABILIDAD 561
125 | #> 11 O.RSH - CARACTERISTICAS ENCUESTADOS 562
126 | #> 12 P. INE - TIPO DE HOGARES 563
127 | #> 13 RETIROS 10% 565
128 | #>
129 | #> $`06. DESARROLLO Y GESTION TERRITORIAL`
130 | #> variable code
131 | #> 1 A. CARACTERISTICAS TERRITORIALES 39
132 | #> 2 B. SERVICIOS BASICOS A LA COMUNIDAD 41
133 | #> 3 C. INFRAESTRUCTURA 40
134 | #> 4 D. CATASTRO PREDIOS Y VALORACION CATASTRAL 300
135 | #> 5 E. AREAS VERDES 376
136 | #> 6 F. PLAN DE DESARROLLO COMUNAL (PLADECO) 304
137 | #> 7 G. PLAN REGULADOR COMUNAL 42
138 | #> 8 H. CULTURA 587
139 | #>
140 | #> $`07. CARACTERIZACION COMUNAL`
141 | #> variable code
142 | #> 1 A. GEOGRAFICO ADMINISTRATIVA 49
143 | #> 2 B. POBLACION 50
144 | #>
145 | #> $`08. GENERO`
146 | #> variable code
147 | #> 1 DOTACION MUNICIPAL HOMBRES Y MUJERES 262
148 | #>
149 | #> $`09. CEMENTERIO`
150 | #> variable code
151 | #> 1 1. INFORMACION GENERAL 516
152 | #> 2 A. INGRESOS CEMENTERIO (M$) 456
153 | #> 3 B. GASTOS CEMENTERIO (M$) 457
154 | #>
155 |