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<?xml-stylesheet type="text/xsl" href="assets/xml/rss.xsl" media="all"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Juan M Barrios Home</title><link>http://jmbarrios.github.io/</link><description>My personal site</description><atom:link href="http://jmbarrios.github.io/rss.xml" rel="self" type="application/rss+xml"></atom:link><language>es</language><copyright>Contents © 2020 &lt;a href="mailto:j.m.barrios@gmail.com"&gt;Juan M Barrios&lt;/a&gt; </copyright><lastBuildDate>Fri, 17 Jan 2020 16:54:12 GMT</lastBuildDate><generator>Nikola (getnikola.com)</generator><docs>http://blogs.law.harvard.edu/tech/rss</docs><item><title>Haciendo un mapa de conteos de datos del SNIB</title><link>http://jmbarrios.github.io/posts/haciendo-un-mapa-de-conteos-de-datos-del-snib/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
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&lt;p&gt;En esta ocasión nuestro objetivo es generar un mapa de densidad de 
ocurrencias usando los datos del &lt;a href="http://www.snib.mx/"&gt;SNIB&lt;/a&gt; pare esto otra vez usaremos nuestros
datos de ejemplares de 
&lt;a href="http://www.snib.mx/ejemplares/mamiferos.201807.csv.zip"&gt;mamíferos&lt;/a&gt; descargados 
desde el &lt;a href="http://geoportal.conabio.gob.mx/"&gt;Geoportal&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Primero generaremos un &lt;em&gt;raster&lt;/em&gt; sobre México que nos servirá como base para 
hacer el análisis. Para esto tenemos que obtener los datos 
geográficos necesarios para hacer dicho &lt;em&gt;raster&lt;/em&gt;.  Un &lt;em&gt;raster&lt;/em&gt; es 
una malla sobre una región de la tierra donde cada cuadrito, &lt;em&gt;pixel&lt;/em&gt;, de la malla se le 
asigna un valor. La resolución del &lt;em&gt;raster&lt;/em&gt; es el &lt;em&gt;área&lt;/em&gt; de cada cuadro, en este
ejemplo trabajaremos con un raster de resolución de 10Km x 10Km.&lt;/p&gt;

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&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;magrittr&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;dplyr&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;readr&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;sf&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ggplot2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;maps&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;raster&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;rasterVis&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;RColorBrewer&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;st_as_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;maps&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;map&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s"&gt;'world'&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                &lt;span class="n"&gt;regions&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;"Mexico"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                &lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                &lt;span class="n"&gt;fill&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;TRUE&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; &lt;span class="nf"&gt;st_crs&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union


Linking to GEOS 3.8.0, GDAL 3.0.2, PROJ 6.2.1

Loading required package: sp


Attaching package: ‘raster’


The following object is masked from ‘package:dplyr’:

    select


The following object is masked from ‘package:magrittr’:

    extract


Loading required package: lattice

Loading required package: latticeExtra


Attaching package: ‘latticeExtra’


The following object is masked from ‘package:ggplot2’:

    layer


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&lt;pre&gt;Coordinate Reference System:
  EPSG: 4326 
  proj4string: "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"&lt;/pre&gt;
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&lt;p&gt;La variable &lt;code&gt;mx_border&lt;/code&gt; es un &lt;em&gt;simplefeature&lt;/em&gt; el cual no tiene proyección 
asociada, en este caso está en coordenadas geográficas, por lo que lo 
proyectaremos usando una proyección confrome cónica de Lambert pero definida
para el caso de México, la &lt;a href="https://epsg.io/6372"&gt;EPSG:6372&lt;/a&gt;.&lt;/p&gt;

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&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
  &lt;span class="nf"&gt;st_transform&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;6372&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
  &lt;span class="nf"&gt;st_crs&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;

&lt;span class="n"&gt;mx_bbox&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
  &lt;span class="nf"&gt;st_bbox&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre&gt;Coordinate Reference System:
  EPSG: 6372 
  proj4string: "+proj=lcc +lat_0=12 +lon_0=-102 +lat_1=17.5 +lat_2=29.5 +x_0=2500000 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"&lt;/pre&gt;
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&lt;p&gt;En particular notemos que en la nueva proyección se tiene que la unidad de 
proyección es en metros.&lt;/p&gt;

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&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;mx_proj&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;st_crs&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;%$%&lt;/span&gt; 
    &lt;span class="n"&gt;proj4string&lt;/span&gt;
&lt;span class="n"&gt;ANALYSIS_RESOLUTION&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="m"&gt;10000&lt;/span&gt; &lt;span class="c1"&gt;# 10,000m = 10Km &lt;/span&gt;

&lt;span class="n"&gt;ncol&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;ceiling&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"xmax"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"xmin"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;ANALYSIS_RESOLUTION&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;nrow&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;ceiling&lt;/span&gt;&lt;span class="p"&gt;((&lt;/span&gt;&lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"ymax"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"ymin"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="n"&gt;ANALYSIS_RESOLUTION&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;mx_raster_bbox&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;raster&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
    &lt;span class="n"&gt;ncol&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ncol&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
    &lt;span class="n"&gt;nrow&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;nrow&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
    &lt;span class="n"&gt;xmn&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"xmin"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
    &lt;span class="n"&gt;xmx&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"xmax"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
    &lt;span class="n"&gt;ymn&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"ymin"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
    &lt;span class="n"&gt;ymx&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;mx_bbox[[&lt;/span&gt;&lt;span class="s"&gt;"ymax"&lt;/span&gt;&lt;span class="n"&gt;]]&lt;/span&gt;
&lt;span class="p"&gt;)&lt;/span&gt; 
&lt;span class="nf"&gt;projection&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mx_raster_bbox&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mx_proj&lt;/span&gt;
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&lt;p&gt;La variable &lt;code&gt;mx_raster_bbox&lt;/code&gt; es un raster, sin valores para cada &lt;em&gt;pixel&lt;/em&gt; pero
con la resolución que queremos para nuestro análisis. Ahora lo que haremos es 
usar un una función la cual asigna un valor si la geometría intersecta un 
&lt;em&gt;pixel&lt;/em&gt;, en este caso los registros del [SNIB], esta función es 
&lt;a href="https://www.rdocumentation.org/packages/raster/versions/2.6-7/topics/rasterize"&gt;&lt;code&gt;raster::rasterize&lt;/code&gt;&lt;/a&gt;,
además si un o más geometrias tocan un pixel usa una función para agrupar los 
valores, en este caso usaremos &lt;code&gt;count&lt;/code&gt;&lt;/p&gt;

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&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;MAMMALS_URI&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="s"&gt;'http://www.snib.mx/ejemplares/mamiferos.201807.csv.zip'&lt;/span&gt;
&lt;span class="n"&gt;mammals_zip&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;tempfile&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;fileext&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;'.zip'&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;curl&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;curl_download&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;MAMMALS_URI&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;mammals_zip&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mammals_zip&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;unzip&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;files&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;c&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s"&gt;'mamiferos.csv'&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;exdir&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;tempdir&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;read_csv&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;progress&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="n"&gt;dplyr&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;select&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;idejemplar&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;longitud&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;latitud&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;st_as_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;coords&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;c&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s"&gt;"longitud"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s"&gt;"latitud"&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
           &lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;4326&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;%&amp;lt;&amp;gt;%&lt;/span&gt; &lt;span class="nf"&gt;st_transform&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;6372&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;ptm&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;proc.time&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;span class="n"&gt;mx_raster&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;rasterize&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;as&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mammals_data&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s"&gt;"Spatial"&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; 
                       &lt;span class="n"&gt;mx_raster_bbox&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                       &lt;span class="n"&gt;field&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;1&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                       &lt;span class="n"&gt;fun&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;"count"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;proc.time&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;-&lt;/span&gt; &lt;span class="n"&gt;ptm&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;/div&gt;

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&lt;div class="output_subarea output_stream output_stderr output_text"&gt;
&lt;pre&gt;Parsed with column specification:
cols(
  .default = col_character(),
  longitud = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  latitud = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  categoriaresidenciaaves = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  formadecrecimiento = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  taxonextinto = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  ultimafechaactualizacion = &lt;span class="ansi-blue-fg"&gt;col_date(format = "")&lt;/span&gt;,
  categoriainfraespecie2 = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  categoriainfraespecie2valida = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24claveestadomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24nombreestadomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24clavemunicipiomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24nombremunicipiomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  altitudmapa = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  fechadeterminacion = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  diadeterminacion = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  mesdeterminacion = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  aniodeterminacion = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  diacolecta = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  mescolecta = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  aniocolecta = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;
  # ... with 2 more columns
)

See spec(...) for full column specifications.

Warning message:
“425069 parsing failures.
 row                 col           expected actual                            file
2362 altitudmapa         a double           (null) '/tmp/Rtmpta7yZW/mamiferos.csv'
2363 altitudmapa         a double           (null) '/tmp/Rtmpta7yZW/mamiferos.csv'
2364 altitudmapa         a double           (null) '/tmp/Rtmpta7yZW/mamiferos.csv'
2365 altitudmapa         a double           (null) '/tmp/Rtmpta7yZW/mamiferos.csv'
2571 mt24claveestadomapa 1/0/T/F/TRUE/FALSE 30     '/tmp/Rtmpta7yZW/mamiferos.csv'
.... ................... .................. ...... ...............................
See problems(...) for more details.
”
&lt;/pre&gt;
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&lt;pre&gt;   user  system elapsed 
  7.029   0.000   7.041 &lt;/pre&gt;
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&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Por útimo graficaremos el resultado.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [5]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;colr&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;colorRampPalette&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;brewer.pal&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="m"&gt;41&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s"&gt;'Spectral'&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="nf"&gt;levelplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mx_raster&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
          &lt;span class="n"&gt;margin&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;                       
          &lt;span class="n"&gt;colorkey&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
            &lt;span class="n"&gt;space&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;'bottom'&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;                   
            &lt;span class="n"&gt;labels&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;at&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;0&lt;/span&gt;&lt;span class="o"&gt;:&lt;/span&gt;&lt;span class="m"&gt;40&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;font&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;4&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;  
          &lt;span class="p"&gt;),&lt;/span&gt;    
          &lt;span class="n"&gt;par.settings&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="nf"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;
            &lt;span class="n"&gt;axis.line&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="nf"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;col&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s"&gt;'transparent'&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; 
          &lt;span class="p"&gt;),&lt;/span&gt;
          &lt;span class="n"&gt;scales&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="nf"&gt;list&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;draw&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;            
          &lt;span class="n"&gt;col.regions&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="n"&gt;colr&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;                   
          &lt;span class="n"&gt;at&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="nf"&gt;seq&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="m"&gt;0&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="m"&gt;40&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;len&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="m"&gt;40&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;           
    &lt;span class="nf"&gt;layer&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;sp.polygons&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;as&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mx_border&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s"&gt;"Spatial"&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;lwd&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;1.5&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="output_subarea output_stream output_stderr output_text"&gt;
&lt;pre&gt;Warning message in brewer.pal(41, "Spectral"):
“n too large, allowed maximum for palette Spectral is 11
Returning the palette you asked for with that many colors
”
&lt;/pre&gt;
&lt;/div&gt;
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&lt;div class="output_png output_subarea "&gt;
&lt;img 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0ORtXNyLq2ndV3j6ria0eYKsxKE8emSXsLvzFI6KXoqR7nd0P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%0AgCP+D5gRhMA7ijgXAAAAAElFTkSuQmCC" width="420" height="420"&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [6]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="nf"&gt;sessionInfo&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;

    &lt;div class="prompt"&gt;&lt;/div&gt;




&lt;div class="output_text output_subarea "&gt;
&lt;pre&gt;R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Arch Linux

Matrix products: default
BLAS:   /usr/lib/libopenblasp-r0.3.7.so
LAPACK: /usr/lib/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2  rasterVis_0.47      latticeExtra_0.6-29
 [4] lattice_0.20-38     raster_3.0-7        sp_1.3-1           
 [7] maps_3.3.0          ggplot2_3.2.1       sf_0.8-0           
[10] readr_1.3.1         dplyr_0.8.3         magrittr_1.5       

loaded via a namespace (and not attached):
 [1] pbdZMQ_0.3-3       zoo_1.8-6          tidyselect_0.2.5   repr_1.0.2        
 [5] purrr_0.3.3        colorspace_1.4-1   vctrs_0.2.1        htmltools_0.4.0   
 [9] viridisLite_0.3.0  base64enc_0.1-3    rlang_0.4.1        e1071_1.7-1       
[13] hexbin_1.28.0      pillar_1.4.2       foreign_0.8-72     glue_1.3.1        
[17] withr_2.1.2        DBI_1.0.0          uuid_0.1-2         jpeg_0.1-8.1      
[21] rgeos_0.5-2        munsell_0.5.0      gtable_0.3.0       codetools_0.2-16  
[25] evaluate_0.13      maptools_0.9-9     curl_3.3           parallel_3.6.2    
[29] class_7.3-15       IRdisplay_0.7.0    Rcpp_1.0.1         KernSmooth_2.23-16
[33] scales_1.0.0       backports_1.1.4    classInt_0.4-2     IRkernel_1.1      
[37] jsonlite_1.6       hms_0.5.2          png_0.1-7          digest_0.6.18     
[41] grid_3.6.2         rgdal_1.4-8        tools_3.6.2        lazyeval_0.2.2    
[45] tibble_2.1.3       crayon_1.3.4       pkgconfig_2.0.2    zeallot_0.1.0     
[49] assertthat_0.2.1   R6_2.4.0           units_0.6-3        compiler_3.6.2    &lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [ ]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;&lt;/div&gt;</description><category>CONABIO</category><category>geo</category><category>R</category><category>SNIB</category><guid>http://jmbarrios.github.io/posts/haciendo-un-mapa-de-conteos-de-datos-del-snib/</guid><pubDate>Mon, 06 Aug 2018 17:31:20 GMT</pubDate></item><item><title>Jugando con los registros del SNIB</title><link>http://jmbarrios.github.io/posts/jugando-con-los-registros-del-snib/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;El Sistema Nacional de Información sobre Biodiversidad de México (SNIB) es una 
base de datos con registro de ocurrencias biológicas que ha recopilado la 
Comisión Nacional para el Uso y Conocimiento de la Biodiversidad (CONABIO) 
desde su creación. Esta colección integra gran parte de las colecciones 
biológicas de México.&lt;/p&gt;
&lt;p&gt;Hay dos maneras principales de accesar a la información contenida en el SNIB,
la primera es descargando los datos desde el &lt;a href="http://geoportal.conabio.gob.mx/"&gt;Geoportal&lt;/a&gt; de CONABIO y la 
segunda desde el la página del &lt;a href="http://www.snib.mx/"&gt;SNIB&lt;/a&gt;. Un buen consejo 
es siempre usar el método de descarga desde el sitio del &lt;a href="http://geoportal.conabio.gob.mx/"&gt;Geoportal&lt;/a&gt;, estos 
los encontramos en Biodiversidad → Ejemplares y ahí escogemos el &lt;em&gt;grupo&lt;/em&gt; 
biológico en el que estemos interesados en este &lt;em&gt;post&lt;/em&gt; usaremos los registros 
de &lt;a href="http://www.snib.mx/ejemplares/mamiferos.201807.csv.zip"&gt;mamíferos&lt;/a&gt; y el 
mapa de &lt;a href="http://www.conabio.gob.mx/informacion/gis/maps/geo/anpmx.zip"&gt;áreas naturales protegidas de 
México&lt;/a&gt; el cual 
también descargamos del &lt;a href="http://geoportal.conabio.gob.mx/"&gt;Geoportal&lt;/a&gt;.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [1]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;magrittr&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [2]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;MAMMALS_URI&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="s"&gt;'http://www.snib.mx/ejemplares/mamiferos.201807.csv.zip'&lt;/span&gt;
&lt;span class="n"&gt;ANPS_MX_URI&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="s"&gt;'http://www.conabio.gob.mx/informacion/gis/maps/geo/anpmx.zip'&lt;/span&gt;

&lt;span class="n"&gt;mammals_zip&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;tempfile&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;fileext&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;'.zip'&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;anps_zip&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;tempfile&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;fileext&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;'.zip'&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;curl&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;curl_download&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;MAMMALS_URI&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;mammals_zip&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="n"&gt;curl&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;curl_download&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ANPS_MX_URI&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;anps_zip&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;En el archivo zip de mamíferos se tienen los siguientes archivos:&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [3]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;mammals_zip&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;unzip&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;list&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;TRUE&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="output_html rendered_html output_subarea "&gt;
&lt;table&gt;
&lt;caption&gt;A data.frame: 16 × 3&lt;/caption&gt;
&lt;thead&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;Name&lt;/th&gt;&lt;th scope="col"&gt;Length&lt;/th&gt;&lt;th scope="col"&gt;Date&lt;/th&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;dbl&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;dttm&amp;gt;&lt;/th&gt;&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
	&lt;tr&gt;&lt;td&gt;mamiferos.csv        &lt;/td&gt;&lt;td&gt;769181127&lt;/td&gt;&lt;td&gt;2018-07-25 14:59:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferos.html       &lt;/td&gt;&lt;td&gt;   412294&lt;/td&gt;&lt;td&gt;2018-07-26 16:46:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferos.png        &lt;/td&gt;&lt;td&gt;   325960&lt;/td&gt;&lt;td&gt;2018-07-26 15:26:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferos.xml        &lt;/td&gt;&lt;td&gt;   221369&lt;/td&gt;&lt;td&gt;2018-07-26 14:11:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosca.csv      &lt;/td&gt;&lt;td&gt;   745501&lt;/td&gt;&lt;td&gt;2018-07-25 16:23:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferoseua.csv     &lt;/td&gt;&lt;td&gt;  6609036&lt;/td&gt;&lt;td&gt;2018-07-25 16:25:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferoslicencia.csv&lt;/td&gt;&lt;td&gt;    85796&lt;/td&gt;&lt;td&gt;2018-07-26 09:26:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm11.csv   &lt;/td&gt;&lt;td&gt; 36854955&lt;/td&gt;&lt;td&gt;2018-07-25 15:24:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm12.csv   &lt;/td&gt;&lt;td&gt; 85395267&lt;/td&gt;&lt;td&gt;2018-07-25 15:29:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm13.csv   &lt;/td&gt;&lt;td&gt;189050486&lt;/td&gt;&lt;td&gt;2018-07-25 15:39:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm14a.csv  &lt;/td&gt;&lt;td&gt;140288600&lt;/td&gt;&lt;td&gt;2018-07-25 15:49:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm14b.csv  &lt;/td&gt;&lt;td&gt;136649435&lt;/td&gt;&lt;td&gt;2018-07-25 16:00:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm15.csv   &lt;/td&gt;&lt;td&gt;142415775&lt;/td&gt;&lt;td&gt;2018-07-25 16:13:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferosutm16.csv   &lt;/td&gt;&lt;td&gt; 31179967&lt;/td&gt;&lt;td&gt;2018-07-25 16:21:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;mamiferos_s.png      &lt;/td&gt;&lt;td&gt;    22280&lt;/td&gt;&lt;td&gt;2018-07-26 15:29:00&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;README.txt           &lt;/td&gt;&lt;td&gt;     2943&lt;/td&gt;&lt;td&gt;2018-07-27 13:03:00&lt;/td&gt;&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;

&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Los archivos &lt;code&gt;.html&lt;/code&gt; y &lt;code&gt;.xml&lt;/code&gt; corresponden a archivos de metadatos. Los 
archivos &lt;code&gt;mamiferosutm*.csv&lt;/code&gt; son lo archivos de registros correspondientes 
a las regiones UTM nombradas. Mientras que &lt;code&gt;mamiferosca.csv&lt;/code&gt; y 
&lt;code&gt;mamiferoseua.csv&lt;/code&gt; corresponden a registros en Centroamérica y Estados 
Unidos de América. El archivo que &lt;code&gt;mamiferoslicencia.csv&lt;/code&gt; contiene la 
licencia de uso de cada datos según el proyecto asociado. Y el archivo
que usaremos es el &lt;code&gt;mamiferos.csv&lt;/code&gt; el cual es el concentrado de todos
los registros, aunque muchas veces es demasiado grande para ser trabajado
en un computadora (depende de la cantidad de memoria RAM de tu equipo).&lt;/p&gt;
&lt;p&gt;En todo momento usaremos las bibliotecas: &lt;code&gt;dplyr&lt;/code&gt;, &lt;code&gt;readr&lt;/code&gt;, &lt;code&gt;sf&lt;/code&gt;, &lt;code&gt;maps&lt;/code&gt;, 
&lt;code&gt;ggplot2&lt;/code&gt; y &lt;code&gt;magrittr&lt;/code&gt;.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [4]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;dplyr&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;readr&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;sf&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ggplot2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;maps&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;maptools&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;pre&gt;
Attaching package: ‘dplyr’


The following objects are masked from ‘package:stats’:

    filter, lag


The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union


Linking to GEOS 3.8.0, GDAL 3.0.2, PROJ 6.2.1

Loading required package: sp

Checking rgeos availability: TRUE

&lt;/pre&gt;
&lt;/div&gt;
&lt;/div&gt;

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&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Para leer el archivo usaremos &lt;code&gt;read_csv&lt;/code&gt;, en lugar de el comando base de
&lt;code&gt;R&lt;/code&gt; ya que es más confiable.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [5]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mammals_zip&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;unzip&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;files&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;c&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s"&gt;'mamiferos.csv'&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;exdir&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;tempdir&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;read_csv&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;

&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;%&amp;lt;&amp;gt;%&lt;/span&gt; &lt;span class="nf"&gt;select&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;idejemplar&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;reinovalido&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;phylumdivisionvalido&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;clasevalida&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;ordenvalido&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;familiavalida&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;generovalido&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;especievalida&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;diacolecta&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;mescolecta&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;aniocolecta&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;probablelocnodecampo&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;longitud&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
                        &lt;span class="n"&gt;latitud&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                        &lt;span class="n"&gt;estadomapa&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
  &lt;span class="nf"&gt;st_as_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;coords&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;c&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s"&gt;"longitud"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="s"&gt;"latitud"&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt;
           &lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;4326&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; &lt;span class="nf"&gt;head&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

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&lt;div class="output_subarea output_stream output_stderr output_text"&gt;
&lt;pre&gt;Parsed with column specification:
cols(
  .default = col_character(),
  longitud = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  latitud = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  categoriaresidenciaaves = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  formadecrecimiento = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  taxonextinto = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  ultimafechaactualizacion = &lt;span class="ansi-blue-fg"&gt;col_date(format = "")&lt;/span&gt;,
  categoriainfraespecie2 = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  categoriainfraespecie2valida = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24claveestadomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24nombreestadomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24clavemunicipiomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  mt24nombremunicipiomapa = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  altitudmapa = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  fechadeterminacion = &lt;span class="ansi-yellow-fg"&gt;col_logical()&lt;/span&gt;,
  diadeterminacion = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  mesdeterminacion = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  aniodeterminacion = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  diacolecta = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  mescolecta = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;,
  aniocolecta = &lt;span class="ansi-green-fg"&gt;col_double()&lt;/span&gt;
  # ... with 2 more columns
)

See spec(...) for full column specifications.

Warning message:
“425069 parsing failures.
 row                 col           expected actual                            file
2362 altitudmapa         a double           (null) '/tmp/Rtmpq0hQKv/mamiferos.csv'
2363 altitudmapa         a double           (null) '/tmp/Rtmpq0hQKv/mamiferos.csv'
2364 altitudmapa         a double           (null) '/tmp/Rtmpq0hQKv/mamiferos.csv'
2365 altitudmapa         a double           (null) '/tmp/Rtmpq0hQKv/mamiferos.csv'
2571 mt24claveestadomapa 1/0/T/F/TRUE/FALSE 30     '/tmp/Rtmpq0hQKv/mamiferos.csv'
.... ................... .................. ...... ...............................
See problems(...) for more details.
”
&lt;/pre&gt;
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&lt;div class="output_html rendered_html output_subarea "&gt;
&lt;table&gt;
&lt;caption&gt;A sf: 6 × 14&lt;/caption&gt;
&lt;thead&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;idejemplar&lt;/th&gt;&lt;th scope="col"&gt;reinovalido&lt;/th&gt;&lt;th scope="col"&gt;phylumdivisionvalido&lt;/th&gt;&lt;th scope="col"&gt;clasevalida&lt;/th&gt;&lt;th scope="col"&gt;ordenvalido&lt;/th&gt;&lt;th scope="col"&gt;familiavalida&lt;/th&gt;&lt;th scope="col"&gt;generovalido&lt;/th&gt;&lt;th scope="col"&gt;especievalida&lt;/th&gt;&lt;th scope="col"&gt;diacolecta&lt;/th&gt;&lt;th scope="col"&gt;mescolecta&lt;/th&gt;&lt;th scope="col"&gt;aniocolecta&lt;/th&gt;&lt;th scope="col"&gt;probablelocnodecampo&lt;/th&gt;&lt;th scope="col"&gt;estadomapa&lt;/th&gt;&lt;th scope="col"&gt;geometry&lt;/th&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;dbl&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;dbl&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;dbl&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;POINT [°]&amp;gt;&lt;/th&gt;&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
	&lt;tr&gt;&lt;td&gt;47289ba599f987513bae537cf5f4bda8&lt;/td&gt;&lt;td&gt;Animalia&lt;/td&gt;&lt;td&gt;Chordata&lt;/td&gt;&lt;td&gt;Mammalia&lt;/td&gt;&lt;td&gt;Chiroptera&lt;/td&gt;&lt;td&gt;Phyllostomidae&lt;/td&gt;&lt;td&gt;Artibeus  &lt;/td&gt;&lt;td&gt;Artibeus lituratus                    &lt;/td&gt;&lt;td&gt;19&lt;/td&gt;&lt;td&gt;10&lt;/td&gt;&lt;td&gt;1999&lt;/td&gt;&lt;td&gt;NA&lt;/td&gt;&lt;td&gt;CHIAPAS&lt;/td&gt;&lt;td&gt;POINT (-93.81914 15.94414)&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;bd94ea2dd0ea46e05376aea743cb55eb&lt;/td&gt;&lt;td&gt;Animalia&lt;/td&gt;&lt;td&gt;Chordata&lt;/td&gt;&lt;td&gt;Mammalia&lt;/td&gt;&lt;td&gt;Chiroptera&lt;/td&gt;&lt;td&gt;Phyllostomidae&lt;/td&gt;&lt;td&gt;Artibeus  &lt;/td&gt;&lt;td&gt;Artibeus lituratus                    &lt;/td&gt;&lt;td&gt;19&lt;/td&gt;&lt;td&gt;10&lt;/td&gt;&lt;td&gt;1999&lt;/td&gt;&lt;td&gt;NA&lt;/td&gt;&lt;td&gt;CHIAPAS&lt;/td&gt;&lt;td&gt;POINT (-93.81914 15.94414)&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;f363a73c42a16767df054de7fa165d6e&lt;/td&gt;&lt;td&gt;Animalia&lt;/td&gt;&lt;td&gt;Chordata&lt;/td&gt;&lt;td&gt;Mammalia&lt;/td&gt;&lt;td&gt;Rodentia  &lt;/td&gt;&lt;td&gt;Heteromyidae  &lt;/td&gt;&lt;td&gt;Heteromys &lt;/td&gt;&lt;td&gt;Heteromys salvini crispus             &lt;/td&gt;&lt;td&gt;20&lt;/td&gt;&lt;td&gt;10&lt;/td&gt;&lt;td&gt;1999&lt;/td&gt;&lt;td&gt;NA&lt;/td&gt;&lt;td&gt;CHIAPAS&lt;/td&gt;&lt;td&gt;POINT (-93.81914 15.94414)&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;3d279e1f803665450a43ff32a7a7701a&lt;/td&gt;&lt;td&gt;Animalia&lt;/td&gt;&lt;td&gt;Chordata&lt;/td&gt;&lt;td&gt;Mammalia&lt;/td&gt;&lt;td&gt;Primates  &lt;/td&gt;&lt;td&gt;Atelidae      &lt;/td&gt;&lt;td&gt;Ateles    &lt;/td&gt;&lt;td&gt;Ateles geoffroyi vellerosus           &lt;/td&gt;&lt;td&gt;19&lt;/td&gt;&lt;td&gt; 5&lt;/td&gt;&lt;td&gt;1989&lt;/td&gt;&lt;td&gt;NA&lt;/td&gt;&lt;td&gt;CHIAPAS&lt;/td&gt;&lt;td&gt;POINT (-91.11667 16.71944)&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;4505a17f94f13b01676da0c877dee92b&lt;/td&gt;&lt;td&gt;NA      &lt;/td&gt;&lt;td&gt;NA      &lt;/td&gt;&lt;td&gt;NA      &lt;/td&gt;&lt;td&gt;NA        &lt;/td&gt;&lt;td&gt;NA            &lt;/td&gt;&lt;td&gt;NA        &lt;/td&gt;&lt;td&gt;NA                                    &lt;/td&gt;&lt;td&gt;22&lt;/td&gt;&lt;td&gt; 2&lt;/td&gt;&lt;td&gt;1999&lt;/td&gt;&lt;td&gt;NA&lt;/td&gt;&lt;td&gt;CHIAPAS&lt;/td&gt;&lt;td&gt;POINT (-92.80556 15.65528)&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;dfe4724c85b3304fcac2d033866447c6&lt;/td&gt;&lt;td&gt;Animalia&lt;/td&gt;&lt;td&gt;Chordata&lt;/td&gt;&lt;td&gt;Mammalia&lt;/td&gt;&lt;td&gt;Rodentia  &lt;/td&gt;&lt;td&gt;Cricetidae    &lt;/td&gt;&lt;td&gt;Peromyscus&lt;/td&gt;&lt;td&gt;Peromyscus guatemalensis guatemalensis&lt;/td&gt;&lt;td&gt;22&lt;/td&gt;&lt;td&gt; 2&lt;/td&gt;&lt;td&gt;1999&lt;/td&gt;&lt;td&gt;NA&lt;/td&gt;&lt;td&gt;CHIAPAS&lt;/td&gt;&lt;td&gt;POINT (-92.80556 15.65528)&lt;/td&gt;&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;

&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Lo que hemos conseguido al hacer esta lectura es tener un &lt;code&gt;data.frame&lt;/code&gt; con una
columna especial que tiene la geometría del registro, en este caso el punto 
asociado a la ocurrencia de un taxón, ahora bien no hemos considerado todas las
columnas de los registros si no solo algunas (para una explicación de que dato
viene en cada columna se puede consultar el 
&lt;a href="http://www.snib.mx/documents/docsnib.html"&gt;&lt;em&gt;Diccionario de datos de Ejemplares Geoportal&lt;/em&gt;&lt;/a&gt;
).&lt;/p&gt;
&lt;p&gt;Ahora podemos resolver preguntas básicas a los datos como, cual es la 
representación de las familias del orden &lt;em&gt;Rodentia&lt;/em&gt; en lo datos.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [6]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;filter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ordenvalido&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s"&gt;"Rodentia"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;ggplot&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; 
        &lt;span class="nf"&gt;geom_bar&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;mapping&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;aes&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;forcats&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;fct_infreq&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;familiavalida&lt;/span&gt;&lt;span class="p"&gt;)))&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
        &lt;span class="nf"&gt;coord_flip&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; 
        &lt;span class="nf"&gt;theme_minimal&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
        &lt;span class="nf"&gt;labs&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;x&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;"Familia"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;y&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;"Conteos"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
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    &lt;div class="prompt"&gt;&lt;/div&gt;




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jc50XnTO1paWyI7deIp0RF9KmQuf209bNKuvT6IcNS1Y513db%0AdmzRrfNqamqOWR8NrMEOEaVgQ6+RekhvTex/PTlxRk6vmpczrVQ6lk6YsfZ89MOGpc8597Ra%0Al6oCdhTSnApxTwbt2KJP5xW5XYqtLDJi3aEbIy+QpMKtvgwTcc4dfOlGty/DOGfevoxjt/gy%0AjPP02J6XTQKkRuvn7qQ7c8/J4L7GvudLnSvtx1cEal1Iq91XoMvW51VBiUJ6ZKs7tezYoi88%0A6erp8nguynskXbxHUuX3x99PzXF/hLQl2LTePY8rKZVNjpbItP0upP25XSI7c+0YpE2zwiJr%0ALDu26MnJzmN353r8OBxIuoCkym9IrfdNW/OTokCFHA6+9EbJjNkNh+7YuK8k67NI5ubGntkF%0Ar2wIviExSC25D1esvN+yY4tGimbuXJe5z+NIQNIFJFW+f7Ohb/8zizecd2YOlqw+dXX5QTm6%0AYtH/axR5Y/EJuVlesvaMyKVi5+4LS0RCm0u2NhdFBhft27Ns5TteBwKSLiCp4kuriQYkVUAS%0AIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUASIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUAS%0AIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUASIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUAS%0AIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUASIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUAS%0AIBkBSRWQBEhGQFIFJAGSEZBUAUmAZAQkVUASIBkBSRWQBEhGQFIFJAGSEZBUAUlSBtIUX8YB%0AkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDIC%0AkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDIC%0AkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDIC%0AkiogCZCMgKQKSOINUo4VbdcXbr4Wlppg/2xlTtwHO0vF7q2z7NFvoBuQdAFJ1dhB2nzerdm8%0A1bbqBiE1bY33WHep2L1AGghIqlIf0v5hV7pjM8Mhxc9dKhaQBgKSqvSBVJV//v4KaVo8NXd9%0AuMs92asJ1s7PeqjBPXkrj57+Vcmlhdn3bnVO+vKO5Wc+3BBdyj21q5yfXfSmA+n0w5NzV9yQ%0AgZV43Egg6QKSqrGD9EKdW5dUZf/w9I32aesvfjDvqf5XpMBD584/vNCF1NPe3l4280ZL1poL%0Ax+58XmrumFdbO7coupRz78XAhvOvZFl2KPjc+XfyXpDYSgaK3ByxjtCUkRdIUp3NvgxzsyXk%0AzzhtHb4M0xG67ss4/j09nV4X7RodJGvg1abKqhXZNd+56WPrchSSe8Mb0wc+Tjg+6ZKUz3I0%0AH8nsjd5zMD8GaVWxc/9Gy75a0SeypnRwJQMj2KFbNOVWCxCNQ0OvkaM7tbOcc4XVgWAwOMGq%0A7n9FclwcHYDUmHVYZPlaZy5kNdVYzj3HByEV7HRuPuWc2rW+vvnxYOngSmKvSN0jdiM0ZeQF%0AklRXsy/DdLeE/Bnn+g1fhukMtfsyjn9PT5fXRYfOnUcHyf1sYcOKgVeR2IcNA5A6Z61zrpS6%0AkK5ZH9dYMhzSfBfSWcuun7bswNn1pYMr8RbvkXTxHkmVDx82uG4O5oedF5fC8BcgRZ58yHkN%0Akm2znY14a2LvFyBFT+1esuzNDzrTtaWDK/G2kUDSBSRVY/1hQ1M/pJ67S84dzX9W7MBbXcMg%0A7cxu6Ozs7G3JKqs/kfe8DEJylop+2LC57mCeZb88pfpKxaTC3thKvAUkXUBSNdbfbCjuhyRX%0AFk/J2+A8Rc9OenUYpPnRZSqkvij7B9vCg5DcpdwTv5/Pm1z4oWV3r8y+5/mzuc8PrsRTQNIF%0AJFV81y7RgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDIC%0AkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDIC%0AkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDIC%0AkiogCZCMgKQKSAIkIyCpApKkDCR/nikgqQKSAMkISKqAJEAyApIqIAmQjICkCkgCJCMgqQKS%0AAMkISKqAJEAyApIqIEnKQJriyw+SgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIk%0AIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIk%0AIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIkIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSAIk%0AIyCpApIAyQhIqoAkQDICkiogCZCMgKQKSOIR0hNFRUXLdraNfu39XVjSP20qUmygG5B0AUnV%0A2EHKenLXzudm5n80+tWL/YMGqS3qn6+zbMUaBEjagKRqDCG95lx0FRaOfvViW3WD80AaCEiq%0A0gSS1DomWreUlL7mDPPhM09sDMnTR52bf7LzrfI3l5UeErlU2rr+kHRtX1p22rm9c1vJxk96%0AHrPmv+We2l3bXPJStQMptoLePcuWH/e6kUDSBSRVYw1JJv6sM2/Brg0TX5XaCWt2F9wb2bjQ%0AOcizTuzKzS3fnLlZqnIXbW3onXP/7ueCb0j3rAW7F0++dMY6GKrMkZbcB3ctvduyYysIPzi/%0A4sWp+wZH6Buxmw6kkZdITj0tfozS19ca8mec9pu+DNMV6vBlnN5mX4Zxnp5er4uGVZBmbm8o%0AuS7yzHLZOkfk0403aoLtciynd5d1QeRYMFRlOa8x+6feFNmRF9kzo08i8za7p3YOpI2z+kSe%0AtOzYCg5Nd/6wfD8Q+/zCDt2iKVNutQSR/w29Ro4GUq7zCvLZyZfzSqUyUHLYOT2J3HVYlq2V%0AXTOdOyOZJ6ss5yV+9SrnyqfWlWUb+on0Q1qwzZk/7r5H6l/Bs7nFxcWFVu3AAJGOEbvuQBp5%0AieTU3uzHKB0dzSF/xmm97ssw10OtvozTcc2fYUbx9Ayd044CUmfgvdbCvOXbSkpF6jfeH3ii%0AS9aXdGZWya557t15h6omOJOS55yLNqu+sHw4pPsqnHnnPVJsBWXFVW43vIzOeyRtvEdSNdbv%0AkfZmd7zknK/JhlKpbRBpzD4gVZkH8iOyK9t5wloDVVVBZ6mN7md7Z63O1Suc6YF9A5BK3Nep%0Ag5YdW8HuAud64wvhEQYdFpB0AUnV2EK68XrmS7L53h6pyyuRsrntUnfHMYnk5rwgsstaH+56%0AcmY4Culi4Ihcf+hJORM8LfVZ+23rQxfSieBJaZpu2bEVtGTuDLcsKPa4kUDSBSRVYwhpYlaW%0AlflyRK5MnzT9oe3BnaE5wamBHznDPWt95ECauzgzOL1OopDkYGb2hMJmkT3BbGulLXNzDzqQ%0AIlsCkycesOzYCuRkTlag4KrHjQSSLiCpGjtIHzpvZz6Knob11X/unJK1SKTpfPQTtx33Oxe7%0AHpUrHztjd1ZHl+6pvxaddtW5074LbR01zrTzYk+4amgFEv6oyfPmAkkXkFSNw5dWw7N2ShTS%0A2AYkXUBS5T+kw/fmXhcgjTogqUpfSE3/HD3yOsf6fxBIuoCkir+PlGhAUgUkAZIRkFQBSYBk%0ABCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSYBkBCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSYBk%0ABCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSYBkBCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSYBk%0ABCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSVIGkj/PFJBUAUmAZAQkVUASIBkBSRWQBEhGQFIF%0AJAGSEZBUAUmAZAQkVUASIBkBSRWQJGUgTfHlB0lAUgUkAZIRkFQBSeJBav2ThgQ3JqkBSReQ%0AVCUD0rsFuW5/nlGTnG1KTkDSBSRVSYB09hvf+LWM3/p3X//6I0napuQEJF1AUpUESAXf+qTn%0Afzwjn3331SRtU3ICki4gqUoCpH/8PyIP3SlS/ttJ2qbkBCRdQFKVBEhZfydS9qci9RmXkrRR%0ASQlIuoCkKgmQfvgr5+SNX26WAxmnk7RRSQlIuoCkKgmQPv3Vr71589t/ver3fr0rSRuVlICk%0AC0iqkvHx94k73pTX/3XGv9qdnE1KUkDSBSRVSfuBbN/ZjoQ3JqkBSReQVPEVoUQDkiogyXBI%0AD/3tRee/WGeSuGEJByRdQFKVIKR7/+S8zPmzWO8nccMSDki6gKSKU7tEA5IqIAmQjICkCkgy%0AHNLQaR2ndmMckFSlCKQH/mJ4HyRxwxIOSLqApIpTu0QDkiogCZCMgKQKSMLPkYyApApIws+R%0AjICkCkjCqZ0RkFQBSYBkBCRVQJIvQup+4He/1d/bIz/s07d2H/tFf2Xp8q4v3vL5jsEbW8oV%0AG+gGJF1AUpUESMszfvOu+6PVjfSg8AuBmQtycj/8l/dU5nzxltOZgzfWWfboN9ANSLqApCoJ%0AkKw/7PXyoD1TzzqvXqvy/uXzZP+LlykHUuxGIA0EJFUpBOkeT4fr9ckHo+NNqZRP3b9KW/ea%0AhCpu/vPuyoFTu7N73+0evMeB5N4Yfm9vtQspUlnx00/cR1/Yd8Dz4QQkXUBSlQRIFd/+3MNj%0A3psw+Lp1appz8VqB1Ewt/OHjE7a6Z3GRFVMWT53bFrun/9SupzB7UX6xZdsLsxfNDZwU2ZBV%0AsiDzF5wb/sKApAtIqpLxqd3cf/P4xk1ul0d4zO7pg7ODkKztIhtnuWbezr4q3XnlJqQ9U69I%0AV4Fln5/8sUjJKucmR+z+nO6B1djXRs6FdItFklLIj0H8GyfN/nd8223exxn6k9eEdP53MgZ6%0AcwRIO38RpDaRY/mumVVrnJuaPjUhLdzoXDsUfY90s3ZeqWyatWPHjhesCwOribSOWIsLaeRF%0AktM1PwZxhgn5M05ziy/DtISafRnnNnx6rseB9OAvLTp1LtrNESCdnNB/yrD54DBIzvR4FFJh%0AuUGsH9LsV5xr1ZYd3np3bmFBqayeW+52ZYRRhsWpnS5O7VQl4dTuf0308piWzAPupDXwupya%0A6sy8bEAqXefM1r4fu6cfUrH7inTMsvdnNzgAS2VbsXP9xqmwt40Eki4gqUoCpL/9354etD2n%0A0jm6f5jbLqetz6TnfgPSoakt0pv/YuyefkgVU69Kz6OWvf5hka55/yQXg2dF1sz2uJFA0gUk%0AVUmA9MY3Nnl5UF9ZYOYjk/JOO2948vNWzzIh2SW5S3NntsfuiX1qN2XJjKctu3pC0eo5JdOO%0Ay5bMRbMnnfW4kUDSBSRVSYBU/N2M7/xxtJ+P/LCGw3tORPde+2ubXvn0NQm5b4wa90Z/ZBR5%0Av+JIz+A9A18RCp+sONNdHnGWOdTcdaBK5MKrB5u9biSQdAFJVRIgPfL9WF5fLHwJSLqApIpv%0AfycakFQBSfhlzEZAUgUk4ZcxGwFJFZCEX8ZsBCRVQBJ+GbMRkFQBSfhlzEZAUgUk4ZcxGwFJ%0AFZCEX8ZsBCRVQBJ+GbMRkFQBSfhlzEZAUgUk4ZcxGwFJFZBkOKSVpUncluQGJF1AUpUgpL/6%0AC/fykYeStj3JC0i6gKQqKZD6L2+zgKQLSKqAlGhAUgUkAZIRkFQBSYBkBCRVQBIgGQFJFZDE%0AgPT7253+IHq5ffvVJG5YwgFJF5BUJQopY3gj/UurvgckXUBSlSCkQ1uH5/HfQPUnIOkCkir+%0A8ZNEA5IqIAmQjICkCkgCJCMgqQKSpAwkf54pIKkCkgDJCEiqgCRAMgKSKiAJkIyApApIAiQj%0AIKkCkgDJCEiqgCRAMgKSKiBJykCaEm2sxwGSKiAJkIyApApIAiQjIKkCkgDJCEiqgCRAMgKS%0AKiAJkIyApApIAiQjIKkCkgDJCEiqgCRAMgKSKiAJkIyApApIAiQjIKkCkgDJCEiqgCRAMgKS%0AKiAJkIyApApIAiQjIKkCkgDJCEiqgCRAMgKSKiAJkIyApApIAiQjIKkCkgDJCEiqgCRAMgKS%0AKiAJkIyApApIAiQjIKkCkgDJCEiqgCRAMgKSKiBJUiH1VNVHp5GqWvOOjpr+aVeVds1AUgUk%0AVeMNqcmaHH3iaq08847KnP5pnWXr1gwkXUBSNf6QMt91p5szvwCprbJ/CqSBgKTqywNp2Sp3%0AOn1ZnrS7Z3ctddJ5Tlqboqd2nfU9UUhdFxvD7lLhhkbPmwskXUBSNf6Q3szudV53Jh/Ik1PT%0AnBteK5Cq/Ncnveae2r0YyMz8sQOpPJgVyP9Y5GjO5OD9XncMkHQBSdX4Q/rsnpMiW1YeHgYp%0A86lu9z3S8eA7kfo7LbvBOhLpKCiTxsxj0r36gdgGR9pHrG0A0shLJd715rEeob/mkD/jtLT5%0AMkxbqNWXcdqv+TPMKJ6ejrGBdHnTapFZJ4ZDsj6NftiwxD3p223ZnRedk7qlpbJ1Xk1NzTHr%0Ao4GH2qFb1A/pVksR+dvQa2RyIdVm9zVM6h4OKWBHIc2pcK6fcU7tTq+alzOtVJ7OK3K7FHts%0AZMS6ByCNvFTihVvHeoT+nHMHX7rR7cswPaFOX8axW3wZxnl6bM/LjhGkyN2nykslBqnCgRSU%0AKKRHtrpTyz4Z3NfY93ypvPCkq6fL47ko75F08R5J1fi/R7osG565/6gL6efBdpHFQ5A2zQqL%0ArLHs9YXO9ZJSOTn5mnOql+vx43Ag6QKSqtsBUnVmZpcLqTnz8UOrJw9Basl9uGLl/ZZ9OPjS%0AGyUzZjdEimbuXJe5z+OagaQLSKrGG1KoqFkiizc7cJaJVK8oWFq5Vi4VO3dcWOLcublka3NR%0ARA6WrD51dflB6duzbOU7XtcMJF1AUjXekMYuIOkCkiogJRqQVAFJgGQEJFVAEiAZAUkVkARI%0ARkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARI%0ARkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARI%0ARkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSRJGSTRM6YAABeESURBVEj+PFNAUgUk%0AAZIRkFQBSYBkBCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSYBkBCRVQJKUgTRldCnHAZIqIAmQ%0AjICkCkgCJCMgqQKSAMkISKqAJEAyApIqIAmQjICkCkgCJCMgqQKSAMkISKqAJEAyApIqIAmQ%0AjICkCkgCJCMgqQKSAMkISKqAJEAyApIqIAmQjICkCkgCJCMgqQKSAMkISKqAJEAyApIqIAmQ%0AjICkCkgCJCMgqQKSAMkISKqAJEAyApIqIAmQjICkCkgCJCMgqQKSAMkISKqAJEAyApIqIAmQ%0AjICkCkiih9S8blbmzDWfeVq2Mqd/WmfZusGApAtIqvyEFJo298CZnz085WMvCzdt7Z8CaSAg%0AqUpDSOEFT/RFJ0tG8yggDQQkVWkIqTZwOTq9XOm83iyemrs+LHJpYfa9W8NSk7dr6pS1zUuy%0A7z0rBeXOMsXPuad2lfOzi950IJ1+eHLuihtDj/IUkHQBSZWPkPbdMzjbPm39xQ/mPSUtWWsu%0AHLvzeakJLGo8bE19u/7hefLyAyKtE2odSBcDG86/kmXZoeBz59/Je2HwUQNFbo5Yx2ghjby6%0AuHU2Kx84ylpC/ozT1uHLMB2h676M49/T0+l10a4EIW140Ll413Jq2jXfmf3Yulw+y2F8JLO3%0AxnL+uL3vWZE3pkmTdVn2z3A/bFhV7Cy10bKvVjinhGtKJfaogfXZoVs0Ski3Wh1RUhp6jdRB%0AesU9eeo4f77SalodCAaDE6zq5Wudm0JWU43lTB/d6TibJnL/Hnn0Jy6kAucGOeWc2rW+vvnx%0AYKnEHjWwvkj3iN0YLaSRVxe3rmblA0dZS8ifca7f8GWYzlC7L+P49/R0eV106NxZB+mMdSE6%0ArbKaNqzov6nUhXTN+rgf0q5+SC8tCAUaXUjzXUhnLbt+2rIDZ9eXSuxR3uI9ki7eI6ny8T1S%0A5OEFvc7ELrKaDuaHndeawvC22c7ob03sNSB9HNhcIIOndi9Z9mb3nHBtqcQe5W04IOkCkio/%0Af45Uf+ec16oOz185sann7pJzR/OflZassvoTec+LAUlmT6iIQroY2Fx3MM+yX55SfaViUmFv%0A7FHeApIuIKny9ZsNrWvnZD/2uqwLyZXFU/I2OM9YfVH2D7aFvwBpa6C5/5sNP583ufBDy+5e%0AmX3P82dznx98lKeApAtIqm7H79ptKk7GWoCkC0iqbj9I4StTDydjPUDSBSRVtx+kXVZhXzLW%0AAyRdQFJ1+0HquXzrZTytB0iqgKTq9oOUrICkC0iqgAQkMyCpAhKQzICkCkhAMgOSKiAByQxI%0AqoAEJDMgqQISkMyApApIQDIDkiogAckMSKqABCQzIKkCEpDMgKQKSEAyA5IqIAHJDEiqgAQk%0AMyCpAhKQzICkCkhAMgOSKiAByQxIqoAEJDMgqQISkMyApApIQDIDkiogAckMSKqABCQzIKlK%0AZ0j+PFNAUgUkAZIRkFQBSYBkBCRVQBIgGQFJFZAESEZAUgUkAZIRkFQBSYBkBCRVQJKUgTTK%0AnyMpf7IEJFVAEiAZAUkVkARIRkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQ%0AVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQ%0AVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQ%0AVAFJgGQEJFVAkiRBqixbvPnY0OAXlph3x643FSk20A1IuoCkatwgbcks2f18btng6LVF5v2x%0A63WWrRsASLqApGq8IN0IHHEuLweP3mpBIA0EJFXpDqneOuVOXqkW6dxWsvET91Tu3FLnlpNr%0A5VJp6/pD7qndtc0lL1U7kFq3lJS+5mxn755ly497HQFIuoCkarwg9dx1z77L0bnuWQt2L57c%0AUJkjp6Y5V18rkKrcRVvd6y25D+5aerdld+Yt2LVh4qsSfnB+xYtT9w2uo2/EbiYOaeQBBupp%0A8bRYwrWG/Bmn/aYvw3SFOnwZp7fZl2Gcp6fX66LhZEKSz1blWNPL6kX2zOiTyLzNwyFZzsuO%0Ac33jrD6RJy27oeS6yDPL5dB05w/L9wNtA2uwQ7coYUi3GoBI0dBrZHI+/o7U7bwv+J4s2xC9%0AZkDqjF5fsM25ftx9j/TZyZfzSuXZ3OLi4kKrNvb4jhG7njikkQcYqL3Z02IJ1xzyZ5zW674M%0Acz3U6ss4Hdf8GWYUT8/QOW0SIJ074V7aBY9KYbkB6YADaUL/9fsqnKnzHqm1MG/5tpJSKSuu%0AcrvhbQTeI+niPZKq8XqPtP/OXndSViSrVzjTA/tcSNnO3BYHUlCikEpWOdODlv2Sc/InG0pl%0Ad4FzvfGF8AirHRaQdAFJ1XhBapta0mT3nZh0UM4ET0t91n4HTq31nnyaOwTpRPCkNE237M33%0A9khdXom0ZO4Mtywo9jgCkHQBSdW4/UD28sOBoJW93ZnbE8y2VtoOHHuxdc/EFUOQIlsCkyce%0AsOwr0ydNf2h7cKeczMkKFFz1OACQdAFJ1Th+166rrql3YOaaSEeNM9d0sq6jTjqrZeB658We%0AcJVIX/3nzjmds0fCHzV53lwg6QKSKr60CiQzIKkCEpDMgKQKSEAyA5IqIAHJDEiqgAQkMyCp%0AAhKQzICkCkhAMgOSKiAByQxIqoAEJDMgqQISkMyApApIQDIDkiogAckMSKqABCQzIKkCEpDM%0AgKQKSEAyA5IqIAHJDEiqgAQkMyCpAhKQzICkCkhAMgOSKiAByQxIqoAEJDMgqQISkMyApApI%0AQDIDkiogAckMSKqABCQzIKlKZ0j+PFNAUgUkAZIRkFQBSYBkBCRVQBIgGQFJFZAESEZAUgUk%0AAZIRkFQBSYBkBCRVQJKUgZTwz5FSsYT3G5BUASnNSni/AUkVkNKshPcbkFQBKc1KeL8BSRWQ%0A0qyE9xuQVAEpzUp4vwFJFZDSrIT3G5BUASnNSni/AUkVkNKshPcbkFQBKc1KeL8BSRWQ0qyE%0A9xuQVAEpzUp4vwFJFZDSrIT3G5BUASnNSni/AUkVkNKshPcbkFQBKc1KeL8BSRWQ0qyE9xuQ%0AVAEpzUp4vwFJFZDSrIT3G5BUASnNSni/AUkVkNKshPcbkFQBKc1KeL8BSRWQ0qyE9xuQVAEp%0AzUp4vwFJFZDSrIT3G5BUjSGk8sboAOW6/5PLu/qnLeWKDXQDki4gqRpDSNZx9/Jjq27wlsja%0AK57HqMzpn9ZZ9ii2bFhA0gUkVb5CsofN3yq7q38KpNGl21nDApIqnyBd2HcgJOFtVlnNwLxc%0A2Rs+flqk+pU320RCFS37Xv1ELux9Myx7LzjLn3jbPbULv7e32oUUqaz46ScSW43HgKQLSKr8%0AgbQhq2RB5of9kPrnperuskdPyaqsxfdNq5WaybOXz87aUFASXCurVjj/93cdcE7tegqzF+UX%0AW7a9MHvR3MDJ2Go8biSQdAFJ1VhCmr/EqdCBVJnzucj+nG572HxVYIfIO3c0SOSpAqmxjklv%0A9vywvJgv70zulapguwNpz9Qr0lVg2ecnfyxSsmrwoQPrt6+N3JcT0i12yq0LJbyGL+U4Ie/j%0ADP0eII+Qlpc7Pefg2TRrx44dL1gX7GHzVZazvnVLneUuWm01VljkkXJH1jTpyTol60rcDxsW%0AbnTuPRR9j3Szdl7p4EMH1h9pGbHmLyekkXeKh5qbE16Fp2FC/ozTcs2nYUKeF20bLaTBU7vV%0Ac11S5VfsYfNVAefOJeudi+vWxRrLmT7qvCt6d5rIijXhaW+7kGa/4txabdnhrXfnFhaUDj7U%0A0+ic2inj1E6VL++RthU7MzdOhe1h81VBZ/rsk87FJavVgHRs2qnJPS6kYvcV6Zhl789uENlc%0AOvhQbxsJJF1AUuULpIvBsyJrZovtnJbF5qOQTtzRKJGn54oBqStz9uroz5Eqpl6Vnkcte/3D%0Azo3z/mnwod4Cki4gqfLnU7stmYtmTzorkbyFlbH5KCRZNXnp3GnnTEiyzPogCqmncMqSGU9b%0AdvWEotVzSqYdjz3UW0DSBSRVPn1F6MKrB5udyaXddbH5K9ujC1XtPeL+HKncmT1UI9K025mp%0AyLP7vyIUPllxprs8Io17DzV3HagaXI2ngKQLSKpuxy+tPrYpGWsBki4gqbr9IJ1dPaExGesB%0Aki4gqbr9IJ1e9UFS1gMkXUBSdftBSlZA0gUkVUBKsxLeb0BSBaQ0K+H9BiRVQEqzEt5vQFIF%0ApDQr4f0GJFVASrMS3m9AUgWkNCvh/QYkVUBKsxLeb0BSBaQ0K+H9BiRVQEqzEt5vQFIFpDQr%0A4f0GJFVASrMS3m9AUgWkNCvh/QYkVUBKsxLeb0BSBaQ0K+H9BiRVQEqzEt5vQFIFpDQr4f0G%0AJFVASrMS3m9AUgWkNCvh/QYkVUBKsxLeb0BSBaQ0K+H9BiRVQEqzEt5vQFKVzpD8eabs1lsv%0Ak4zaPP+uwsQCkiogJRqQVAFJgGQEJFVAEiAZAUkVkARIRkBSBSQBkhGQVAFJgGQEJFVAkpSB%0ANN4/0qEvaZ6PUSARxc/zMQokovh5PkaBRBQ/z8cokIji5/kYBRJR/Dwfo0Aiip/nYxRIRPHz%0AfIwCiSh+no9RIBHFz/MxCiSi+Hk+RoFEFD/PxyiQiOLn+RgFElH8PB+jQCKKn+djFEhE8fN8%0AjAKJKH6ej1EgEcXP8zEKJKL4eT5GgUQUP8/HKJCI4uf5GAUSUfw8H6NAIoqf52MUSETx83yM%0AAokofp6P0cQhtVxoG5zvqPlFS8Ru7apSDgEkGqc8H6OJQjqVbwWsJ64NXKvM+eL9kSPtg7fW%0AWbZuECDROOX5GE0Q0snAtlBfbcFDA/9YclvlFxewrbrBW4FEqZbnYzQxSD0ztriTRqtWOs9J%0Aa1P0JK6nvsO9MdzQGJHIGetgKHprZ31PFFLXxcbw4N1ehwESjU+eKSQG6YzV//7oo+tSlf/6%0ApNfck7gDE7MCZWE5mjM5eH9Lz2PW/LfcW18MZGb+2IFUHswK5H8sA3d7HAZINE55ppAYpP13%0ADs5WZT7V7b4bOh98T85nHmjMPCbdqx+IuKd2zq3Hg+9E6u+07AbrSKSjoExidw88ONI+Ym1A%0AovFp5AOzvSNJkMrvHYJkfRr9sKFsmXPl7VNb59XU1ByzPhqAtGSVc+tuy+686JzULS2V2N0D%0AD7ZDt2i89yd9SbvFcTl0TpUYpLcm9r+mnDgjVQE7Cqloa/SWp/OK3C4NQJpTIe6JoC2nV83L%0AmVY6eHdsRZER6wYSjU8jH5iRobf5iUFqtH7uTroz90hVUKKQlmx2pu3NLzzp8uiKndo94vKq%0AtOyTwX2Nfc+XSuxub8PwHonGKc8UEvz4+6k57o+QtgSbBiG9NMt5ZXpw3cnJzh27c+0BSJtm%0AhUXWWPb6QmepklKJ3e1tFCDROOVZQoKQWu+btuYnRQHnxC0GqfOex15ZOrEhUjRz57rMfRLJ%0A3Nzo3NqS+3DFyvst+3DwpTdKZswevNtbQKJxyrOERL/Z0Lf/mcUbzjszl4qdiwtLnNO6LUvW%0ANjh37Fm28h3npjcWn3BvDW0u2dpcFJGDJatPXV1+cPBuTwGJxinPEPjSKlH8PB+jQCKKn+dj%0AFEhE8fN8jAKJKH6ej1EgEcXP8zEKJKL4eT5GgUQUP8/HKJCI4uf5GAUSUfw8H6NAIoqf52MU%0ASETx83yMAokofp6PUSARxc/zMQokovh5PkaBRBQ/z8cokIji5/kYBRJR/Dwfo0Aiip/nYxRI%0ARPHzfIwCiSh+no9RIBHFz/MxCiSi+Hk+RoFEFD/PxyiQiOLn+RgFElH8PB+jKQKp05dx7FZf%0AhpG2kD/j3OjxZZhen56eiNdfTJdgbSHPv0tyKCANC0iqgCRAMgKSKiAJkIyApApIAiQjIKkC%0AkgDJCEiqgCRAMgKSKiAJkIyApApIAiQjIKkCkgDJCEiqgCQpAqmvrduXcSIdvgwjN9r8Gaer%0A15dh+tq6fBnHv6cnXSER3e4BiSgJAYkoCQGJKAkBiSgJAYkoCQGJKAkBiSgJpQKkhseyF5wf%0A743Qt9tyCopEtk7P3xyOP02N2re5lx1PTZ1zyMP09u+NOufigvsUWTUix+ZOWeZ+vSXeNH4p%0AAOlmzuqadZk+fRlgDFq3qLKy8n2R7VNPvJe3Of40Ndpxv3u58MGzP53w3q2nt33tdx53Lt++%0Ay3mKKm9IdWBPVeF8iTsdoRSAtC/flsis7eO9GeoWvRSdhPMOiryV3RVvOs5b6a39RZYLqcFq%0AEikrvuX0du/ymlzLhbSzqP/6spUi1wIfxp2OUApAWrbGudhw+z8r8Zp1pKtd3IMv5JzyWB/G%0Am473ZnrqaMUSF9K+mc7FiWDkVtPx3FQvXa2ouMOFVPajcPQLsVN/5lw8sD3udIRSANIC96x8%0A933jvRnaInc8FLDm1Mj7Adu5NulYvOn4bqXnKlxIWwrFfWNx/VbT8dxQj2W5kIrvy7Kmvi69%0A1lnnyuL18aYjrScFIM3a41wcyB3vzdB2LXNTa2jF1La3stxrdx6INx2/LRxVUUhli52LT6zG%0AW03Hc0M9FoU0Z2FTR4V1usVyP3lYsTzedKT1pACkh8qdi92zxnszEqo76/DPA+6ZzqS34k3H%0AdwM9F4X0wmPivuK03Go6nhvqsSikaEXP9PS/8pTFm460nhSAVOL+D2x+bLw3I7Hm7K53j6ub%0A1tl40/HeQo9FIe11/1g7GbBvNR3PDfXYEKR1T0i2+8fZvPK40xFKAUivznT+yJ6bsp/aHZvj%0AvFXonHQqnHvYeQM+uSvedLy302NRSB9ZV0Q2LLzlNAVyIYWmn3HmHnk++rlWe6A67nSEUgBS%0A+9QXLpdP9ulvZye/9txFH1QXz7elPP/chZmbJO40NYpCksce/+R45ru3nt7+RV+RFt57tG5D%0AVkhO3/FmU8k8iTsdoRSAJB8VpvQ3G64uy71rTbtI5MXp+Zvs+NPUqB/SjeVT7zvkYXr7F4XU%0As2Fmzg8bnJmjc6c82TbCNH6pAInotg9IREkISERJCEhESQhIREkISERJCEhESQhIaVHfh1X+%0A/PvbFCcgpUEX/uYbGRlfmXRxvLfjyxyQUr/Xv/lrC17+8d1f+91rv/j+Df8+Fb6GneIBKeXr%0A/o/fveBO92bE+asmT2XEEUbJC0gp3/KMl/tnvveb7jf27HPno1/cO1st1YfOhUVOz854pda9%0ApeH9gd+LEltEpOlkKvzlu1QISCnfX/72gIpQg8Om7FsZGd9y/wbX9yc8kJGR8V/OyV85k1yR%0APd/JyPjmgj4Ztsi5P3Pu+tP6cdz29AlIKd9v/M2wK2UZ/3Dw4D9kOEy+/yu/vbPu2a9+f+DU%0Abm+GdfCt+75y7/BF/uA7ZW8/9dW/HrctT6eAlOq1ZOQPXen+zf8ecU7dvvdbPfL9DPevdf7f%0A7wxA+k//033dmvGVT4cWuZKxwLnpyRnjtOHpFZBSve6vTRq6Up3xrDv5UUaNfP/b7tz03+iH%0A9GnG7DedlmS8OrRI7699exuf5yUpIKV8v/e9gZl3Cs+8mhH914j2Z+yX7/+ROzdjANLRjIF+%0AMmyRt/5rxlf+28Ir47Td6RWQUr7g1z/qn7k348LRjB3u3PaMo/L9P3bnYpBqM14cWHzYIiI1%0AP/qHr34nZf8W/+0UkFK+oxl50ennv/47cjmjwJ2dm3H5C5C6f2mOe/XlGe1Di9Q+9ZEz8+OM%0Al8Zls9MsIKV+eRl3tYpc/ENXxD9+813nHO+bATEgfSYy65f3i5z/1p8MW+RUxgMRkZXRlyZK%0AMCClfi1ZGV/5z/8242uPOvMXvvv1v/yLr/+Hi8Mh7cz42w3y+R999Xt//tVfPTtskcjfZ/z+%0AP/5uxoSU+YdXbueAlA4dfCw4+fEz0dm20uzs5e4/2v9I9GPtlRNEep/4+zKR7h/lTnj8irFI%0A78bcv7tnB46SEZCIkhCQiJIQkIiSEJCIkhCQiJIQkIiSEJCIkhCQiJIQkIiSEJCIkhCQiJIQ%0AkIiSEJCIkhCQiJLQ/wfMVD8BfYSg8AAAAABJRU5ErkJggg==" width="420" height="420"&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
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&lt;p&gt;En general podríamos trabajar muchas más preguntas de este estilo pero gracias
a que  agregamos la geometría asociada a los datos también podemos ocuparla 
para hacer cosas más interesantes.&lt;/p&gt;

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&lt;div class="cell border-box-sizing code_cell rendered"&gt;
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&lt;div class="prompt input_prompt"&gt;In [7]:&lt;/div&gt;
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    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;mx_border&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;st_as_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;maps&lt;/span&gt;&lt;span class="o"&gt;::&lt;/span&gt;&lt;span class="nf"&gt;map&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="s"&gt;'world'&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                &lt;span class="n"&gt;regions&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="s"&gt;"Mexico"&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                &lt;span class="n"&gt;plot&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
                                &lt;span class="n"&gt;fill&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;TRUE&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt;

&lt;span class="n"&gt;anps_zip&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;unzip&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;exdir&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="nf"&gt;tempdir&lt;/span&gt;&lt;span class="p"&gt;())&lt;/span&gt;

&lt;span class="n"&gt;mx_national_parks&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;file.path&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;tempdir&lt;/span&gt;&lt;span class="p"&gt;(),&lt;/span&gt; &lt;span class="s"&gt;'anpmx.shp'&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;st_read&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;select&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ID_ANP&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
           &lt;span class="n"&gt;NOMBRE&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
           &lt;span class="n"&gt;CAT_DECRET&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt;
           &lt;span class="n"&gt;REGION&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;filter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;CAT_DECRET&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s"&gt;"PN"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="nf"&gt;ggplot&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
    &lt;span class="nf"&gt;geom_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;mx_border&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
    &lt;span class="nf"&gt;geom_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;mx_national_parks&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; 
            &lt;span class="nf"&gt;aes&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;fill&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ID_ANP&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; 
            &lt;span class="n"&gt;show.legend&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; 
    &lt;span class="nf"&gt;theme_minimal&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;pre&gt;Reading layer `anpmx' from data source `/tmp/Rtmpq0hQKv/anpmx.shp' using driver `ESRI Shapefile'
Simple feature collection with 182 features and 19 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -118.6344 ymin: 11.9686 xmax: -85.39723 ymax: 32.48333
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
&lt;/pre&gt;
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c3FyGE55MJsvKysrKyhodHaWuLDOZTEaj8eDBg7GxsVlZWamp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%0AhCXyYXUAAAAASUVORK5CYII=" width="420" height="420"&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Vamos a restringir nuestra atención al parque nacional 
&lt;em&gt;Cumbres del Ajusco&lt;/em&gt;. Este parque nacional se encuentra en la CDMX
y en particular existe un conejo endemico el &lt;em&gt;Teporingo&lt;/em&gt;. Vamos a explorar
los registro de mamíferos del este parque nacional que existan en los datos
del SNIB.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
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&lt;div class="prompt input_prompt"&gt;In [8]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;ajusco_national_park&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mx_national_parks&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;filter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;NOMBRE&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s"&gt;"Cumbres del Ajusco"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;st_geometry&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;st_transform&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;6369&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;st_transform&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;6369&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="n"&gt;snib_data_ajusco&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;mammals_data&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;filter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;lengths&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;st_within&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;.,&lt;/span&gt; &lt;span class="n"&gt;ajusco_national_park&lt;/span&gt;&lt;span class="p"&gt;))&lt;/span&gt; &lt;span class="o"&gt;&amp;gt;&lt;/span&gt; &lt;span class="m"&gt;0&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;

&lt;span class="nf"&gt;ggplot&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
    &lt;span class="nf"&gt;geom_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;ajusco_national_park&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
    &lt;span class="nf"&gt;geom_sf&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;data&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;snib_data_ajusco&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="nf"&gt;aes&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;colour&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="n"&gt;idejemplar&lt;/span&gt;&lt;span class="p"&gt;),&lt;/span&gt; &lt;span class="n"&gt;show.legend&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="kc"&gt;FALSE&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt;
    &lt;span class="nf"&gt;theme_bw&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

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vqtXqmJiYO0V98803H3nkEa7YtoE4x87X15c5djfS6/U7duzY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%0AAAAAQlDsAAAAhPgfpI3yMFgPSKQAAAAASUVORK5CYII=" width="420" height="420"&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;Además tenemos los siguientes órdenes representados,&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [9]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;snib_data_ajusco&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;st_set_geometry&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="kc"&gt;NULL&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;group_by&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ordenvalido&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;

    &lt;div class="prompt"&gt;&lt;/div&gt;



&lt;div class="output_html rendered_html output_subarea "&gt;
&lt;table&gt;
&lt;caption&gt;A grouped_df: 6 × 2&lt;/caption&gt;
&lt;thead&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;ordenvalido&lt;/th&gt;&lt;th scope="col"&gt;n&lt;/th&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;int&amp;gt;&lt;/th&gt;&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
	&lt;tr&gt;&lt;td&gt;Carnivora   &lt;/td&gt;&lt;td&gt; 1&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Chiroptera  &lt;/td&gt;&lt;td&gt; 2&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Cingulata   &lt;/td&gt;&lt;td&gt; 1&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Lagomorpha  &lt;/td&gt;&lt;td&gt;13&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Rodentia    &lt;/td&gt;&lt;td&gt;97&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Soricomorpha&lt;/td&gt;&lt;td&gt; 3&lt;/td&gt;&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;

&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing text_cell rendered"&gt;&lt;div class="prompt input_prompt"&gt;
&lt;/div&gt;&lt;div class="inner_cell"&gt;
&lt;div class="text_cell_render border-box-sizing rendered_html"&gt;
&lt;p&gt;En particular en los registros de &lt;em&gt;Lagomorpha&lt;/em&gt; se tiene el &lt;em&gt;Romerolagus diazi&lt;/em&gt; o teporingo.&lt;/p&gt;

&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [10]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;snib_data_ajusco&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;st_set_geometry&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="kc"&gt;NULL&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;filter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ordenvalido&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s"&gt;'Lagomorpha'&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
    &lt;span class="nf"&gt;group_by&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;especievalida&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt; 
    &lt;span class="nf"&gt;count&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;

&lt;div class="output_wrapper"&gt;
&lt;div class="output"&gt;


&lt;div class="output_area"&gt;

    &lt;div class="prompt"&gt;&lt;/div&gt;



&lt;div class="output_html rendered_html output_subarea "&gt;
&lt;table&gt;
&lt;caption&gt;A grouped_df: 3 × 2&lt;/caption&gt;
&lt;thead&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;especievalida&lt;/th&gt;&lt;th scope="col"&gt;n&lt;/th&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;int&amp;gt;&lt;/th&gt;&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
	&lt;tr&gt;&lt;td&gt;Romerolagus diazi                   &lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Sylvilagus cunicularius             &lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;td&gt;Sylvilagus cunicularius cunicularius&lt;/td&gt;&lt;td&gt;7&lt;/td&gt;&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;

&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;
&lt;div class="cell border-box-sizing code_cell rendered"&gt;
&lt;div class="input"&gt;
&lt;div class="prompt input_prompt"&gt;In [11]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
    &lt;div class="input_area"&gt;
&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;snib_data_ajusco&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;  
  &lt;span class="nf"&gt;st_transform&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;crs&lt;/span&gt; &lt;span class="o"&gt;=&lt;/span&gt; &lt;span class="m"&gt;4326&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
  &lt;span class="nf"&gt;filter&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;especievalida&lt;/span&gt; &lt;span class="o"&gt;==&lt;/span&gt; &lt;span class="s"&gt;"Romerolagus diazi"&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="o"&gt;%&amp;gt;%&lt;/span&gt;
  &lt;span class="nf"&gt;select&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;idejemplar&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;ordenvalido&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;especievalida&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;&lt;/div&gt;

    &lt;/div&gt;
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&lt;div class="output_html rendered_html output_subarea "&gt;
&lt;table&gt;
&lt;caption&gt;A sf: 2 × 4&lt;/caption&gt;
&lt;thead&gt;
	&lt;tr&gt;&lt;th&gt;&lt;/th&gt;&lt;th scope="col"&gt;idejemplar&lt;/th&gt;&lt;th scope="col"&gt;ordenvalido&lt;/th&gt;&lt;th scope="col"&gt;especievalida&lt;/th&gt;&lt;th scope="col"&gt;geometry&lt;/th&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;th&gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;chr&amp;gt;&lt;/th&gt;&lt;th scope="col"&gt;&amp;lt;POINT [°]&amp;gt;&lt;/th&gt;&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
	&lt;tr&gt;&lt;th scope="row"&gt;1&lt;/th&gt;&lt;td&gt;977c83f6aa37099818e7099bd2dc9f5d&lt;/td&gt;&lt;td&gt;Lagomorpha&lt;/td&gt;&lt;td&gt;Romerolagus diazi&lt;/td&gt;&lt;td&gt;POINT (-99.25833 19.20833)&lt;/td&gt;&lt;/tr&gt;
	&lt;tr&gt;&lt;th scope="row"&gt;2&lt;/th&gt;&lt;td&gt;ee8d6d14edcec9bb64441c44d8a67467&lt;/td&gt;&lt;td&gt;Lagomorpha&lt;/td&gt;&lt;td&gt;Romerolagus diazi&lt;/td&gt;&lt;td&gt;POINT (-99.25691 19.20564)&lt;/td&gt;&lt;/tr&gt;
&lt;/tbody&gt;
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&lt;div class="prompt input_prompt"&gt;In [12]:&lt;/div&gt;
&lt;div class="inner_cell"&gt;
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&lt;div class=" highlight hl-r"&gt;&lt;pre&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="nf"&gt;sessionInfo&lt;/span&gt;&lt;span class="p"&gt;()&lt;/span&gt;
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&lt;pre&gt;R version 3.6.2 (2019-12-12)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Arch Linux

Matrix products: default
BLAS:   /usr/lib/libopenblasp-r0.3.7.so
LAPACK: /usr/lib/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] maptools_0.9-9 sp_1.3-1       maps_3.3.0     ggplot2_3.2.1  sf_0.8-0      
[6] readr_1.3.1    dplyr_0.8.3    magrittr_1.5  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1         pillar_1.4.2       compiler_3.6.2     forcats_0.4.0     
 [5] base64enc_0.1-3    class_7.3-15       tools_3.6.2        zeallot_0.1.0     
 [9] digest_0.6.18      uuid_0.1-2         lattice_0.20-38    jsonlite_1.6      
[13] evaluate_0.13      tibble_2.1.3       gtable_0.3.0       pkgconfig_2.0.2   
[17] rlang_0.4.1        IRdisplay_0.7.0    DBI_1.0.0          rgdal_1.4-8       
[21] curl_3.3           IRkernel_1.1       geojson_0.3.2      geojsonio_0.8.0   
[25] e1071_1.7-1        httr_1.4.1         jqr_1.1.0          withr_2.1.2       
[29] repr_1.0.2         rgeos_0.5-2        vctrs_0.2.1        hms_0.5.2         
[33] classInt_0.4-2     grid_3.6.2         tidyselect_0.2.5   glue_1.3.1        
[37] R6_2.4.0           foreign_0.8-72     pbdZMQ_0.3-3       purrr_0.3.3       
[41] scales_1.0.0       backports_1.1.4    htmltools_0.4.0    units_0.6-3       
[45] assertthat_0.2.1   colorspace_1.4-1   labeling_0.3       V8_2.3            
[49] KernSmooth_2.23-16 lazyeval_0.2.2     munsell_0.5.0      crayon_1.3.4      &lt;/pre&gt;
&lt;/div&gt;

&lt;/div&gt;

&lt;/div&gt;
&lt;/div&gt;

&lt;/div&gt;&lt;/div&gt;</description><category>CONABIO</category><category>R</category><category>SNIB</category><guid>http://jmbarrios.github.io/posts/jugando-con-los-registros-del-snib/</guid><pubDate>Fri, 03 Aug 2018 03:48:59 GMT</pubDate></item><item><title>Generar un movimiento browniano usando puentes brownianos</title><link>http://jmbarrios.github.io/posts/generar-un-movimiento-browniano-usando-puentes-brownianos/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;p&gt;&lt;img alt="Puente Browniano" src="http://jmbarrios.github.io/images/brownianbridge.png"&gt;&lt;/p&gt;
&lt;p&gt;Supongamos que conocemos la posición de un movimiento browniano a los tiempos
\(u\) y \(t\), \(W(u)=x\) y \(W(t)=y\), entonces la distribución del
movimiento Browniano condicionado a este hecho se le conoce como puente
browniano para los tiempos \(u &amp;lt; s &amp;lt; t\). Más aun, la distrbución de la
variable \(W(s)\mid W(u)=x, W(t)=y\) es normal con parámetros&lt;/p&gt;
&lt;p&gt;\[
 \mu=\frac{(t-s)x+(s-u)y}{t-u}
\]
\[
 \sigma^2=\frac{(s-u)(t-s)}{(t-u)}
\]&lt;/p&gt;
&lt;p&gt;Como el objetivo es simular trayectorias del movimiento en un segmento
\([0, T]\) se puede hacer lo siguiente. Primero generar un valor para
\(W(T)\) para después tomar el punto medio, \(T/2\), y ahí generar un nuevo
valor a partir del hecho que en el tiempo 0 y \(T\) ya se conoce la posición
del movimiento Browniano, es decir el nuevo valor sigue la distribución del
puente Browniano. Después seguir este procedimiento en cada una de las dos
partes que se forman, \([0,T/2]\) y \([T/2,T]\), y continuar así tantas
veces como se desee.&lt;/p&gt;&lt;/div&gt;</description><category>movimiento browniano</category><category>puente browniano</category><category>simulacion</category><guid>http://jmbarrios.github.io/posts/generar-un-movimiento-browniano-usando-puentes-brownianos/</guid><pubDate>Fri, 03 Feb 2012 18:11:55 GMT</pubDate></item><item><title>Método de aceptación-rechazo</title><link>http://jmbarrios.github.io/posts/metodo-de-aceptacion-rechazo/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;p&gt;El objetivo que se tiene es simular una variable aleatoria \(X\) con función de densidad \(f(x)\) para esto se hará uso de una variable auxiliar \(Y\) la cual tiene función de densidad \(g(y)\) y cumple que&lt;/p&gt;
&lt;p&gt;\[
  \frac{f(y)}{g(y)}\leq c\quad\mbox{para toda }y.
\]&lt;/p&gt;
&lt;p&gt;&lt;img alt="accept-reject" src="http://jmbarrios.github.io/images/accept-reject.png"&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Algoritmo.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Paso 1.&lt;/strong&gt; Simula \(Y\) con la densidad \(g\) y simular un número aleatorio \(U\).&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Paso 2.&lt;/strong&gt; Si \(U\leq f(Y)/cg(Y)\), haz \(X=Y\). de otra manera regresa al paso 1.&lt;/p&gt;
&lt;p&gt;Una aplicación de este método es generar una variable \(X\) con distribución Beta(\(\alpha\),\(\beta\)) con \(\alpha\geq 1,\beta\geq 1\). Claramente en este caso la densidad de \(X\) está acotada superiormente por una cantidad \(M\).  Por lo que si hacemos \(Y\) una variable uniforme en el intervalo (0,1)&lt;/p&gt;
&lt;p&gt;\[
f_X(y)/f_Y(y)\leq M
\]&lt;/p&gt;
&lt;p&gt;y aceptaremos ese valor si un número aleatorio \(U\) es menor a \(f_X(Y)/M\).&lt;/p&gt;
&lt;p&gt;Para implementar este método en R podemos usar el siguiente código.&lt;/p&gt;
&lt;p&gt;&lt;script src="https://gist.github.com/jmbarrios/4eaff8a4e000af9a3deb.js?file=genBeta.r"&gt;&lt;/script&gt;
        &lt;noscript&gt;&lt;pre&gt;404: Not Found
&lt;/pre&gt;&lt;/noscript&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Ejercicio.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;Es bien sabido que si \(X\) es una variable Gamma(\(n\), \(\lambda\)) esta puede ser expresada como la suma de \(n\) variables exponenciales de parámetro \(\lambda\), además generar variables exponenciales se puede hacer de una manera sencilla ya que \(-log(U)/\lambda\) con uniforme(0,1) sigue esta distribución.&lt;/p&gt;
&lt;p&gt;Ahora bien si \(X\) se distribuye Gamma(\(\alpha\), \(\beta\)) con \(\alpha\notin\mathbb{N}\) no resulta trivial generar. Una opción es generar un variable \(Y\) con distribución Gamma(\(\lfloor \alpha\rfloor\), \(\beta\)) y ver si es posible encontrar una constante \(M\) que cumpla con las condiciones para poder aplicar el método de aceptación-rechazo.&lt;/p&gt;&lt;/div&gt;</description><category>R</category><category>simulacion</category><guid>http://jmbarrios.github.io/posts/metodo-de-aceptacion-rechazo/</guid><pubDate>Mon, 28 Feb 2011 23:59:56 GMT</pubDate></item><item><title>El algoritmo Metropolis-Hastings</title><link>http://jmbarrios.github.io/posts/el-algoritmo-metropolis-hastings/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;p&gt;Desde el punto de vista de simulación estocástica un problema muy común es el de simular un variable aleatoria con una distribución dada \(\pi(x)\) sobre \(\mathcal{S}\), si bien es cierto que hay varios métodos para hacer esto, resultan insuficientes.&lt;/p&gt;
&lt;p&gt;&lt;img alt="Sample run Metropolis-Hastings" src="http://jmbarrios.github.io/images/mcmc.png"&gt;&lt;/p&gt;
&lt;p&gt;En este sentido &lt;a href="http://link.aip.org/link/JCPSA6/v21/i6/p1087/s1"&gt;Nicolas Metropolis (1953)&lt;/a&gt; propuso un algoritmo el cual se basa en la construcción de una cadena de Markov , \({\bf X}=(X_n)\), ergódica la cual tenga a \(\pi(x)\) como distribución estacionaria. Bajo estas condiciones se tiene que&lt;/p&gt;
&lt;p&gt;\[
  \frac{N_x(n)}{n}\longrightarrow\pi(x)s=1\qquad\mbox{casi seguramente},
\]&lt;/p&gt;
&lt;p&gt;donde \(N_x(n)\) es el número de visitas a \(x\) hasta el tiempo \(n\). Por lo cual basta con generar la cadena de Markov  \({\bf X}\) y a partir de cierto momento los valores generados tendrán una distribución muy parecida a \(\pi(x)\).&lt;/p&gt;
&lt;p&gt;Pero, ¿cómo construir estás cadena?, es aquí donde está el gran mérito de N. Metropolis. Su propuesta fue generar una cadena de la siguiente manera.&lt;/p&gt;
&lt;p&gt;Supongamos que podemos generar una cadena de Markov con transición simétrica \({\bf \Theta}=(\theta(x,y))\). Definimos \({\bf X}={X_n, n\geq 0}\) como sigue:&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;\(X_0=x^{(0)}\) para algún \(x^{(0)}\) en \(\mathcal{S}\).&lt;/li&gt;
&lt;li&gt;Para \(n=1,2,\dots\). Si \(X_{n-1}=x_{n-1}\)&lt;/li&gt;
&lt;li&gt;Generar \(y\) a partir de \(\theta(x_{n-1},\cdot)\),&lt;/li&gt;
&lt;li&gt;calcular \(\alpha(x_{n-1},y)=\min{1,\frac{\pi(y)}{\pi(x_{n-1})}}\),&lt;/li&gt;
&lt;li&gt;sea \(X_n\) la variable  que toma el valor \(y\) con probabilidad \(\alpha(x_{n-1},y)\) y \(x_{n-1}\) con probabilidad \(1-\alpha(x_{n-1},y)\).&lt;/li&gt;
&lt;/ol&gt;
&lt;p&gt;Por lo tanto \({\bf X}\) es una cadena de Markov reversible con espacio de estados \(\mathcal{S}\) y función de transición dada por&lt;/p&gt;
&lt;p&gt;\[
  P(x,y)=\begin{cases}
    \theta(x,y)\alpha(x,y), x\neq y,\
    \theta(x,x)+\sum_{y\neq x}\theta(x,y)[1-\alpha(x,y)],  x=y.
  \end{cases}
\]&lt;/p&gt;
&lt;p&gt;Esta cadena tiene como distribución estacionaria a \(\pi(x)\) (Ejercicio) por lo cual basta generar un número &lt;strong&gt;suficiente&lt;/strong&gt; de iteraciones para poder tener números generados por tal distribución.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Ejemplo:&lt;/strong&gt; Queremos generar una números aleatorios con distribución beta (2.7,6.3). La cadena de Markov que podemos generar va a ser \(U_1,U_2,\dots\) variables aleatorias con distribución uniforme en (0,1) y también vemos que el valor \(\alpha\) es fácil de calcular ya que este es&lt;/p&gt;
&lt;p&gt;\[
  \alpha(x_{n-1}, y)=\min\left(1,\frac{y^{1.7}(1-y)^{5.3}}{x_{n-1}^{1.7}(1-x_{n-1})^{5.3}}\right).
\]&lt;/p&gt;
&lt;p&gt;Nótese que \(\mathcal{S}\) es el intervalo [0,1], por lo que la cadena de Markov tiene espacio de estados no numerable. Si bien pareciera este un cambio muy pequeño fue necesario construir mucha teoría para formalizar la convergencia y existencia de este método.&lt;/p&gt;
&lt;p&gt;Esto lo podemos implementar de manera fácil en R y además podemos visualizar la densidad empírica de nuestra simulación.&lt;/p&gt;
&lt;pre class="code literal-block"&gt;&lt;span&gt;&lt;/span&gt;&lt;span class="n"&gt;a&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="m"&gt;2.7&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt;
&lt;span class="n"&gt;b&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="m"&gt;6.3&lt;/span&gt;&lt;span class="p"&gt;;&lt;/span&gt;
&lt;span class="n"&gt;Nsim&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="m"&gt;5000&lt;/span&gt;
&lt;span class="n"&gt;X&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;runif&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="m"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt; &lt;span class="c1"&gt;# inicializar la cadena&lt;/span&gt;
&lt;span class="nf"&gt;for &lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;i&lt;/span&gt; &lt;span class="n"&gt;in&lt;/span&gt; &lt;span class="m"&gt;2&lt;/span&gt;&lt;span class="o"&gt;:&lt;/span&gt;&lt;span class="n"&gt;Nsim&lt;/span&gt;&lt;span class="p"&gt;){&lt;/span&gt;
   &lt;span class="n"&gt;Y&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;runif&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="m"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
   &lt;span class="n"&gt;rho&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="nf"&gt;dbeta&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;Y&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;a&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;b&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;/&lt;/span&gt;&lt;span class="nf"&gt;dbeta&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X[i&lt;/span&gt;&lt;span class="m"&gt;-1&lt;/span&gt;&lt;span class="n"&gt;]&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;a&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;b&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
   &lt;span class="n"&gt;X[i]&lt;/span&gt; &lt;span class="o"&gt;&amp;lt;-&lt;/span&gt; &lt;span class="n"&gt;X[i&lt;/span&gt;&lt;span class="m"&gt;-1&lt;/span&gt;&lt;span class="n"&gt;]&lt;/span&gt; &lt;span class="o"&gt;+&lt;/span&gt; &lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;Y&lt;/span&gt;&lt;span class="o"&gt;-&lt;/span&gt;&lt;span class="n"&gt;X[i&lt;/span&gt;&lt;span class="m"&gt;-1&lt;/span&gt;&lt;span class="n"&gt;]&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;*&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="nf"&gt;runif&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="m"&gt;1&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;&lt;span class="o"&gt;&amp;lt;&lt;/span&gt;&lt;span class="n"&gt;rho&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="p"&gt;}&lt;/span&gt;


&lt;span class="nf"&gt;library&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;ggplot2&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;span class="nf"&gt;qplot&lt;/span&gt;&lt;span class="p"&gt;(&lt;/span&gt;&lt;span class="n"&gt;X&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;geom&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="s"&gt;'density'&lt;/span&gt;&lt;span class="p"&gt;,&lt;/span&gt; &lt;span class="n"&gt;adjust&lt;/span&gt;&lt;span class="o"&gt;=&lt;/span&gt;&lt;span class="m"&gt;3&lt;/span&gt;&lt;span class="p"&gt;)&lt;/span&gt;
&lt;/pre&gt;


&lt;p&gt;&lt;img alt="Densidad empirica para Beta(2.7,6.3)" src="http://jmbarrios.github.io/images/density.png"&gt;&lt;/p&gt;
&lt;p&gt;Algunas referencias:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Casella, G and Robert, C. Introducing Monte Carlo Methods with R (Use R)&lt;/li&gt;
&lt;li&gt;Hoel, P; Port, S and Stone, C. Introduction to Stochastic Processes&lt;/li&gt;
&lt;li&gt;Nummelin, E. General Irreducible Markov Chains and Non-Negative Operators (Cambridge Tracts in Mathematics)&lt;/li&gt;
&lt;/ul&gt;&lt;/div&gt;</description><category>cadenas de Markov</category><category>ggplot2</category><category>mcmc</category><category>metropolis-hastings</category><category>R</category><category>simulacion</category><guid>http://jmbarrios.github.io/posts/el-algoritmo-metropolis-hastings/</guid><pubDate>Sat, 09 Oct 2010 05:29:51 GMT</pubDate></item><item><title>ggplot y funciones de distribución</title><link>http://jmbarrios.github.io/posts/ggplot-y-funciones-de-distribucion/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;p&gt;R además de ser un estupendo software estadístico es capaz de generar gráficos
muy elegantes gracias al paquete ggPlot.&lt;/p&gt;
&lt;p&gt;Consideremos cierta máquina la cual puede fallar algunos días a la semana. La
información obtenida al monitorear su funcionamiento por un año es el siguiente.&lt;/p&gt;
&lt;ul class="simple"&gt;
&lt;li&gt;&lt;p&gt;9 semanas no falló,&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;14 semanas falló un día,&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;13 semanas falló dos días,&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;9 semanas falló tres días,&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;4 semanas falló cuatro días,&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;2 semanas falló cinco días,&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;1 semana no funcionó ninguno de los seis días.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Lo primero que haremos es definir el muestreo que obtuvimos de ver el número
de días a la semana que la máquina se encontraba descompuesta y la proporción
que representa del total de semanas observadas.&lt;/p&gt;
&lt;p&gt;&lt;script src="https://gist.github.com/jmbarrios/3512564.js"&gt;&lt;/script&gt;
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&lt;/pre&gt;&lt;/noscript&gt;&lt;/p&gt;
&lt;p&gt;Habiendo definido la densidad correspondiente al número de fallas. Veamos
como graficar la correpondiente densidad y su distribución.&lt;/p&gt;
&lt;p&gt;&lt;script src="https://gist.github.com/jmbarrios/3512575.js"&gt;&lt;/script&gt;
        &lt;noscript&gt;&lt;pre&gt;404: Not Found
&lt;/pre&gt;&lt;/noscript&gt;&lt;/p&gt;
&lt;img alt="/images/bargraph.png" src="http://jmbarrios.github.io/images/bargraph.png"&gt;
&lt;p&gt;Ahora veamos el caso similar pero para una variable aleatoria con densidad
continua. Para lo cual usaremos la función integrate para calcular las
correspondientes integrales para calcular la distribución.&lt;/p&gt;
&lt;p&gt;&lt;script src="https://gist.github.com/jmbarrios/3512608.js"&gt;&lt;/script&gt;
        &lt;noscript&gt;&lt;pre&gt;404: Not Found
&lt;/pre&gt;&lt;/noscript&gt;&lt;/p&gt;&lt;/div&gt;</description><category>ggplot2</category><category>R</category><guid>http://jmbarrios.github.io/posts/ggplot-y-funciones-de-distribucion/</guid><pubDate>Tue, 28 Sep 2010 04:03:31 GMT</pubDate></item><item><title>Método Monte Carlo</title><link>http://jmbarrios.github.io/posts/metodo-monte-carlo/</link><dc:creator>Juan M Barrios</dc:creator><description>&lt;div&gt;&lt;p&gt;El Método Monte Carlo es un método numérico para simular sistemas físicos o
matemáticos la virtud de esté método radica en un eficacia mayor a los usuales
debido que hace uso de métodos aleatorios.&lt;/p&gt;
&lt;p&gt;&lt;img alt="Rayleigh-Taylor Stability" src="http://jmbarrios.github.io/images/Rayleigh-Taylor_instability.jpg"&gt;&lt;/p&gt;
&lt;p&gt;Uno de los principales usos de este método es la integración consiste en tratar
de obtener el valor
\[
  \mu=\int f(x) dV
\]
para lo cual se generan variables aleatorias \(X_1,X_2,\dots\) independientes
con densidad \(V\), por lo que la ecuación anterior es igual a
\(\mathbb{E}f(X_i)=\mu\), y por lo tanto, por la ley de los Grandes Números,
se sigue que
\[
  \frac{\sum_{i=1}^n f(X_i)-n\mu}{n}=\frac{\sum_{i=1}^n f(X_i)}{n}-\mu\underset{n\rightarrow\infty}{\longrightarrow} 0\qquad \mbox{c.s.}.
\]&lt;/p&gt;
&lt;p&gt;Entonces si quiero obtener el valor \(\mu\) basta con que promedie un número
\(n\) suficientemente grande de variables; de aquí se desprende la primera
pregunta y la más importante para entregar resultados: ¿qué tan grande debe
ser $latex n$ para obtener un resultado bueno?.&lt;/p&gt;
&lt;p&gt;Un primer acercamiento a esta pregunta es calcular la varianza de
\(\tilde{I}&lt;em i="1"&gt;n=\frac{\sum&lt;/em&gt;^n f(X_i)}{n}\) de donde
\[
  \mathbb{V}\mbox{ar} \tilde{I}=\frac{\mathbb{V}\mbox{ar} f(X_i)}{n}=\frac{\sigma^2}{n},
\]
esto implica que lo único importante es hacer \(n\) suficientemente grande
para que \(\frac{\sigma^2}{n}&amp;lt;\epsilon\), pero esto no siempre es suficiente
para tener una buena aproximación ya que la varianza de una variable no siempre
nos dice cosas sobre como es la variable aleatoria (vease casos en los que es
inútil la 
&lt;a href="http://en.wikipedia.org/wiki/Chebyshev%27s_inequality"&gt;desigualdad de Chebyshev&lt;/a&gt;),
es aquí donde el otro gran teorema de la Probabilidad entra a resolver esta duda.&lt;/p&gt;
&lt;p&gt;El Teorema del Límite Central nos dice que
\[
 \frac{\sum_{i=1}^n f(X_i)-n\mu}{\sqrt{n}\sigma}\underset{n\rightarrow\infty}{\longrightarrow} N(0,1)\qquad \mbox{en distribución},
\]
esto es
\[
  \mathbb{P}\left(\frac{X_1+X_2+\dots+X_n-n\mu}{\sqrt{n}\sigma}\leq x\right)\underset{n\rightarrow\infty}{\longrightarrow}\Phi(x)
\]
donde \(\Phi(x)\) es la distribución normal. Pero qué tiene que ver esto con
el problema de decir cual es la \(n\) que debemos poner para obtener una buena 
estimación.&lt;/p&gt;
&lt;p&gt;Notemos que \(|\tilde{I}_n - \mu|\) es nuestro error en la aproximación por lo
cual usando el Teorema del Límite Central se tiene que
\[
  \mathbb{P} \left(|\tilde{I}_n-\mu|&amp;lt;\frac{x\sigma}{\sqrt{n}}\right)\approx\mathbb{P}(|Z_n|&amp;lt;x)=2\Phi(x)-1.
\]&lt;/p&gt;
&lt;p&gt;Aplicando esto se tiene por ejemplo que si quiero que con un 95% de eficacia el
error sea menor que 1/1000 tenemos
\[
  2\Phi(x)-1=0.95\qquad \Longrightarrow\qquad \Phi(x)=1.95/2=0.975
\]
de donde, usando un tabla para \(\Phi(x)\), se tiene que \(x=1.96\) y ahora
\(n\) se tiene que elegir tal que \(1.96\,\sigma/\sqrt{n}\leq 1/1000\), y es
así como se resuelve la pregunta tan molesta de que tan grande debe ser \(n\).
Como se puede ver también en este método además del error de la aproximación
se tiene un error de confiabilidad.&lt;/p&gt;
&lt;p&gt;Queda un problema en el aire el cual ya no discutiré pero debe de tomarse en
cuenta este es: ¿cómo calcular \(\sigma\)?, a veces será más fácil que calcular
la esperanza pero la gran mayoría de las veces será igual o más complicado que
calcular esta.&lt;/p&gt;&lt;/div&gt;</description><category>monte carlo</category><category>simulacion</category><guid>http://jmbarrios.github.io/posts/metodo-monte-carlo/</guid><pubDate>Wed, 07 Apr 2010 22:24:37 GMT</pubDate></item></channel></rss>