cuaderno

library(ggplot2)
library(sf)

## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1
## v purrr   0.3.4

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(dplyr)

cultivos<- read.table(file = "Evaluaciones_Agropecuarias_Municipales_EVA.csv",header = T, sep = ";")

eva_guainia <-  filter(cultivos, DEPARTAMENTO == "GUAINIA")

names(eva_guainia)<- c("ID","Departamentos","CA_Municipio","Municipio","Grupo_de_cultivo","subgrupo_de_cultivo","Cultivo","Desagregacia regional y/o sistema productivo","Ano","Periodo","Area_cultivadas_ha","Area cosechada","Produccion_T","Rendimiento","Estado_fisico poduccion","Nombre_cientifico","ciclo_de_cultivo")
head(eva_guainia)

##   ID Departamentos CA_Municipio Municipio Grupo_de_cultivo subgrupo_de_cultivo
## 1 94       GUAINIA        94001   INIRIDA         FRUTALES            AGUACATE
## 2 94       GUAINIA        94001   INIRIDA         FRUTALES            AGUACATE
## 3 94       GUAINIA        94001   INIRIDA         FRUTALES            AGUACATE
## 4 94       GUAINIA        94001   INIRIDA       HORTALIZAS             AHUYAMA
## 5 94       GUAINIA        94001   INIRIDA       HORTALIZAS             AHUYAMA
## 6 94       GUAINIA        94001   INIRIDA       HORTALIZAS             AHUYAMA
##    Cultivo Desagregacia regional y/o sistema productivo  Ano Periodo
## 1 AGUACATE                                     AGUACATE 2014    2014
## 2 AGUACATE                                     AGUACATE 2015    2015
## 3 AGUACATE                                     AGUACATE 2016    2016
## 4  AHUYAMA                                      AHUYAMA 2014   2014A
## 5  AHUYAMA                                      AHUYAMA 2014   2014B
## 6  AHUYAMA                                      AHUYAMA 2015   2015A
##   Area_cultivadas_ha Area cosechada Produccion_T Rendimiento
## 1                  1              0            0            
## 2                  1              1            1         1,5
## 3                  1              1            1         1,5
## 4                  3              2           30        13,2
## 5                  2              1           12          12
## 6                  3              3           42        13,2
##   Estado_fisico poduccion      Nombre_cientifico ciclo_de_cultivo
## 1            FRUTO FRESCO PERSEA AMERICANA MILL.       PERMANENTE
## 2            FRUTO FRESCO PERSEA AMERICANA MILL.       PERMANENTE
## 3            FRUTO FRESCO PERSEA AMERICANA MILL.       PERMANENTE
## 4        HORTALIZA FRESCA      CUCURBITA MOSHATA      TRANSITORIO
## 5        HORTALIZA FRESCA      CUCURBITA MOSHATA      TRANSITORIO
## 6        HORTALIZA FRESCA      CUCURBITA MOSHATA      TRANSITORIO

tail(eva_guainia)

##     ID Departamentos CA_Municipio     Municipio      Grupo_de_cultivo
## 157 94       GUAINIA        94343 BARRANCO MINA TUBERCULOS Y PLATANOS
## 158 94       GUAINIA        94886      CACAHUAL TUBERCULOS Y PLATANOS
## 159 94       GUAINIA        94883    SAN FELIPE TUBERCULOS Y PLATANOS
## 160 94       GUAINIA        94001       INIRIDA TUBERCULOS Y PLATANOS
## 161 94       GUAINIA        94888      MORICHAL TUBERCULOS Y PLATANOS
## 162 94       GUAINIA        94888      MORICHAL TUBERCULOS Y PLATANOS
##     subgrupo_de_cultivo Cultivo Desagregacia regional y/o sistema productivo
## 157                YUCA    YUCA                              YUCA INDUSTRIAL
## 158                YUCA    YUCA                              YUCA INDUSTRIAL
## 159                YUCA    YUCA                              YUCA INDUSTRIAL
## 160                YUCA    YUCA                              YUCA INDUSTRIAL
## 161                YUCA    YUCA                              YUCA INDUSTRIAL
## 162                YUCA    YUCA                              YUCA INDUSTRIAL
##      Ano Periodo Area_cultivadas_ha Area cosechada Produccion_T Rendimiento
## 157 2016    2016                658            658         6583          10
## 158 2016    2016                226            220           22          10
## 159 2016    2016                 69             69          685          10
## 160 2016    2016                 38             38          764          20
## 161 2017    2017                120            120           12          10
## 162 2018    2018                100            100            1          10
##     Estado_fisico poduccion Nombre_cientifico ciclo_de_cultivo
## 157        TUBERCULO FRESCO MANIHOT ESCULENTA            ANUAL
## 158        TUBERCULO FRESCO MANIHOT ESCULENTA            ANUAL
## 159        TUBERCULO FRESCO MANIHOT ESCULENTA            ANUAL
## 160        TUBERCULO FRESCO MANIHOT ESCULENTA            ANUAL
## 161        TUBERCULO FRESCO MANIHOT ESCULENTA            ANUAL
## 162        TUBERCULO FRESCO MANIHOT ESCULENTA            ANUAL

muni_guainia<- sf::read_sf ("94_GUAINIA/ADMINISTRATIVO/MGN_MPIO_POLITICO.shp")

muni_guainia

## Simple feature collection with 9 features and 9 fields
## geometry type:  POLYGON
## dimension:      XY
## bbox:           xmin: -70.94249 ymin: 1.165633 xmax: -66.84722 ymax: 4.045026
## geographic CRS: WGS 84
## # A tibble: 9 x 10
##   DPTO_CCDGO MPIO_CCDGO MPIO_CNMBR  MPIO_CRSLC   MPIO_NAREA MPIO_NANO DPTO_CNMBR
##   <chr>      <chr>      <chr>       <chr>             <dbl>     <int> <chr>     
## 1 94         94001      INÍRIDA     Decreto 159~     15970.      2017 GUAINÍA   
## 2 94         94343      BARRANCO M~ Resolución ~      9468.      2017 GUAINÍA   
## 3 94         94663      MAPIRIPANA  ACUERDO COM~      4928.      2017 GUAINÍA   
## 4 94         94883      SAN FELIPE  Resolución ~      2926.      2017 GUAINÍA   
## 5 94         94884      PUERTO COL~ Resolución ~     15701.      2017 GUAINÍA   
## 6 94         94885      LA GUADALU~ Resolución ~      1179.      2017 GUAINÍA   
## 7 94         94886      CACAHUAL    Resolución ~      2335.      2017 GUAINÍA   
## 8 94         94887      PANÁ-PANÁ ~ Resolución ~     10227.      2017 GUAINÍA   
## 9 94         94888      MORICHAL (~ 1988              8555.      2017 GUAINÍA   
## # ... with 3 more variables: Shape_Leng <dbl>, Shape_Area <dbl>,
## #   geometry <POLYGON [°]>

class(muni_guainia$MPIO_CCDGO)

## [1] "character"

head(muni_guainia$MPIO_CCDGO)

## [1] "94001" "94343" "94663" "94883" "94884" "94885"

class(eva_guainia$CA_Municipio)

## [1] "integer"

muni_guainia$COD_MUN <- as.double(muni_guainia$MPIO_CCDGO)

class(muni_guainia$COD_MUN)

## [1] "numeric"

head(muni_guainia$COD_MUN)

## [1] 94001 94343 94663 94883 94884 94885

eva_guainia$COD_MUN<-as.double(eva_guainia$CA_Municipio)

class(eva_guainia$COD_MUN)

## [1] "numeric"

head(eva_guainia$COD_MUN)

## [1] 94001 94001 94001 94001 94001 94001

###platano el platano es uno de los productos que hacen parte de la categoria tuberculos y platano se identificara la produccion en toneladas en el aÃ±o 2016 y 2010, para hacer una comparacion con la Yuca

platano_gaui <- eva_guainia %>% filter(Cultivo =="PLATANO")%>%
  dplyr::select(Municipio , COD_MUN , Ano , Periodo , Produccion_T, Rendimiento )
platano_gaui

##        Municipio COD_MUN  Ano Periodo Produccion_T Rendimiento
## 1        INIRIDA   94001 2007    2007          975         7,5
## 2        INIRIDA   94001 2008    2008         1125         7,5
## 3        INIRIDA   94001 2009    2009         1688        9,38
## 4        INIRIDA   94001  201    2010         1936        8,68
## 5        INIRIDA   94001 2011    2011          143         6,5
## 6        INIRIDA   94001 2012    2012         1945         6,8
## 7        INIRIDA   94001 2013    2013         2176         6,8
## 8        INIRIDA   94001 2014    2014           32          10
## 9  BARRANCO MINA   94343 2014    2014          104           8
## 10       INIRIDA   94001 2015    2015           32          10
## 11 BARRANCO MINA   94343 2015    2015          240           8
## 12 BARRANCO MINA   94343 2016    2016          823           7
## 13       INIRIDA   94001 2016    2016          847           7
## 14    SAN FELIPE   94883 2016    2016           28           7
## 15 BARRANCO MINA   94343 2017    2017         2044           7
## 16       INIRIDA   94001 2017    2017          102           5
## 17      MORICHAL   94888 2017    2017           39           7
## 18    SAN FELIPE   94883 2017    2017           35           7
## 19 BARRANCO MINA   94343 2018    2018         2009           7
## 20       INIRIDA   94001 2018    2018          102           5
## 21      MORICHAL   94888 2018    2018           25           7
## 22    SAN FELIPE   94883 2018    2018           21           7

unique(platano_gaui$Ano)

##  [1] 2007 2008 2009  201 2011 2012 2013 2014 2015 2016 2017 2018

###convertyir de formato largo a ancho se pasara de formato para poder incertarlo con las otras tablas

platano_gaui$Produccion_T<- as.numeric(platano_gaui$Produccion_T)

platano_gaui%>% replace(is.na(.),0)-> platano_gaui2

platano_gaui %>% 
  group_by( Municipio ,COD_MUN, Ano) %>% 
  summarise(Produccion_T=sum(Produccion_T))->platano_gaui2

## `summarise()` has grouped output by 'Municipio', 'COD_MUN'. You can override using the `.groups` argument.

head(platano_gaui2)

## # A tibble: 6 x 4
## # Groups:   Municipio, COD_MUN [2]
##   Municipio     COD_MUN   Ano Produccion_T
##   <chr>           <dbl> <int>        <dbl>
## 1 BARRANCO MINA   94343  2014          104
## 2 BARRANCO MINA   94343  2015          240
## 3 BARRANCO MINA   94343  2016          823
## 4 BARRANCO MINA   94343  2017         2044
## 5 BARRANCO MINA   94343  2018         2009
## 6 INIRIDA         94001   201         1936

se reunen los datos de las dos temporadas del aÃ±o A y B e una sola tabla, este permite manejar mejor los datos de toneladas

platano_gaui2 %>% 
  group_by(COD_MUN)%>%
  gather("Produccion_T", key = variable, value = numero) %>%
  unite(combi,variable,Ano)%>%
  pivot_wider(names_from = combi, values_from = numero, values_fill = 0)->
  platano_gaui3
platano_gaui3

## # A tibble: 4 x 14
## # Groups:   COD_MUN [4]
##   Municipio     COD_MUN Produccion_T_2014 Produccion_T_2015 Produccion_T_2016
##   <chr>           <dbl>             <dbl>             <dbl>             <dbl>
## 1 BARRANCO MINA   94343               104               240               823
## 2 INIRIDA         94001                32                32               847
## 3 MORICHAL        94888                 0                 0                 0
## 4 SAN FELIPE      94883                 0                 0                28
## # ... with 9 more variables: Produccion_T_2017 <dbl>, Produccion_T_2018 <dbl>,
## #   Produccion_T_201 <dbl>, Produccion_T_2007 <dbl>, Produccion_T_2008 <dbl>,
## #   Produccion_T_2009 <dbl>, Produccion_T_2011 <dbl>, Produccion_T_2012 <dbl>,
## #   Produccion_T_2013 <dbl>

mun_platano_gaui<- left_join( muni_guainia , platano_gaui3 ,  by ="COD_MUN")

summary(mun_platano_gaui)

##   DPTO_CCDGO         MPIO_CCDGO         MPIO_CNMBR         MPIO_CRSLC       
##  Length:9           Length:9           Length:9           Length:9          
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    MPIO_NAREA      MPIO_NANO     DPTO_CNMBR          Shape_Leng   
##  Min.   : 1179   Min.   :2017   Length:9           Min.   :1.751  
##  1st Qu.: 2926   1st Qu.:2017   Class :character   1st Qu.:4.030  
##  Median : 8555   Median :2017   Mode  :character   Median :6.826  
##  Mean   : 7921   Mean   :2017                      Mean   :5.903  
##  3rd Qu.:10227   3rd Qu.:2017                      3rd Qu.:7.805  
##  Max.   :15970   Max.   :2017                      Max.   :9.257  
##                                                                   
##    Shape_Area               geometry    COD_MUN       Municipio        
##  Min.   :0.09434   POLYGON      :9   Min.   :94001   Length:9          
##  1st Qu.:0.23461   epsg:4326    :0   1st Qu.:94663   Class :character  
##  Median :0.69179   +proj=long...:0   Median :94884   Mode  :character  
##  Mean   :0.63866                     Mean   :94702                     
##  3rd Qu.:0.82504                     3rd Qu.:94886                     
##  Max.   :1.28699                     Max.   :94888                     
##                                                                        
##  Produccion_T_2014 Produccion_T_2015 Produccion_T_2016 Produccion_T_2017
##  Min.   :  0       Min.   :  0       Min.   :  0.0     Min.   :  35.0   
##  1st Qu.:  0       1st Qu.:  0       1st Qu.: 21.0     1st Qu.:  38.0   
##  Median : 16       Median : 16       Median :425.5     Median :  70.5   
##  Mean   : 34       Mean   : 68       Mean   :424.5     Mean   : 555.0   
##  3rd Qu.: 50       3rd Qu.: 84       3rd Qu.:829.0     3rd Qu.: 587.5   
##  Max.   :104       Max.   :240       Max.   :847.0     Max.   :2044.0   
##  NA's   :5         NA's   :5         NA's   :5         NA's   :5        
##  Produccion_T_2018 Produccion_T_201 Produccion_T_2007 Produccion_T_2008
##  Min.   :  21.0    Min.   :   0     Min.   :  0.0     Min.   :   0.0   
##  1st Qu.:  24.0    1st Qu.:   0     1st Qu.:  0.0     1st Qu.:   0.0   
##  Median :  63.5    Median :   0     Median :  0.0     Median :   0.0   
##  Mean   : 539.2    Mean   : 484     Mean   :243.8     Mean   : 281.2   
##  3rd Qu.: 578.8    3rd Qu.: 484     3rd Qu.:243.8     3rd Qu.: 281.2   
##  Max.   :2009.0    Max.   :1936     Max.   :975.0     Max.   :1125.0   
##  NA's   :5         NA's   :5        NA's   :5         NA's   :5        
##  Produccion_T_2009 Produccion_T_2011 Produccion_T_2012 Produccion_T_2013
##  Min.   :   0      Min.   :  0.00    Min.   :   0.0    Min.   :   0     
##  1st Qu.:   0      1st Qu.:  0.00    1st Qu.:   0.0    1st Qu.:   0     
##  Median :   0      Median :  0.00    Median :   0.0    Median :   0     
##  Mean   : 422      Mean   : 35.75    Mean   : 486.2    Mean   : 544     
##  3rd Qu.: 422      3rd Qu.: 35.75    3rd Qu.: 486.2    3rd Qu.: 544     
##  Max.   :1688      Max.   :143.00    Max.   :1945.0    Max.   :2176     
##  NA's   :5         NA's   :5         NA's   :5         NA's   :5

mapa de la produccion en toneladas de platano en el aÃ±o 2016

library(leaflet)
bins<- c(0,106,212,318,424,530,636,742,848)
pal <- colorBin("YlOrRd", domain = mun_platano_gaui$Produccion_T_2016, bins = bins)

map<-leaflet(data = mun_platano_gaui)%>%
  addTiles()%>%
  addPolygons(label = ~Produccion_T_2016,
              popup = ~MPIO_CNMBR,
               fillColor = ~ pal(Produccion_T_2016),
              color="#444444",
              weight = 1, 
              smoothFactor = 0.5,
              opacity = 1.0,
              fillOpacity = 0.5,
              highlightOptions = highlightOptions(color = "red",weight =2,bringToFront = TRUE )
              )%>%
  addProviderTiles(providers$OpenStreetMap)%>%
  addLegend("bottomright", pal= pal, values = ~Produccion_T_2016,
            title = "Produccion en Toneladas de platano 2016 [Ton](DANE,2018)",opacity = 1)
map

mapa de la produccion de Platano en el aÃ±o 2010

library(leaflet)
bins<- c(0,243,486,729,972,1215,1458,1701,1944)
pal <- colorBin("YlOrRd", domain = mun_platano_gaui$Produccion_T_201, bins = bins)

map<-leaflet(data = mun_platano_gaui)%>%
  addTiles()%>%
  addPolygons(label = ~Produccion_T_201,
              popup = ~MPIO_CNMBR,
               fillColor = ~ pal(Produccion_T_201),
              color="#444444",
              weight = 1, 
              smoothFactor = 0.5,
              opacity = 1.0,
              fillOpacity = 0.5,
              highlightOptions = highlightOptions(color = "red",weight =2,bringToFront = TRUE )
              )%>%
  addProviderTiles(providers$OpenStreetMap)%>%
  addLegend("bottomright", pal= pal, values = ~Produccion_T_201,
            title = "Produccion en Toneladas de platano 2010 [Ton](DANE,2018)",opacity = 1)
map

###Area de siembra de la Platano en tes parte identificaremos las hectarias cultivadas en estos dos aÃ±os, es importante resaltar que en berificaremos si la productividad es buena.

platano_gaui_hasem <- eva_guainia %>% filter(Cultivo =="PLATANO")%>%
  dplyr::select(Municipio , COD_MUN , Ano , Periodo , Produccion_T, Rendimiento,Area_cultivadas_ha)
platano_gaui_hasem

##        Municipio COD_MUN  Ano Periodo Produccion_T Rendimiento
## 1        INIRIDA   94001 2007    2007          975         7,5
## 2        INIRIDA   94001 2008    2008         1125         7,5
## 3        INIRIDA   94001 2009    2009         1688        9,38
## 4        INIRIDA   94001  201    2010         1936        8,68
## 5        INIRIDA   94001 2011    2011          143         6,5
## 6        INIRIDA   94001 2012    2012         1945         6,8
## 7        INIRIDA   94001 2013    2013         2176         6,8
## 8        INIRIDA   94001 2014    2014           32          10
## 9  BARRANCO MINA   94343 2014    2014          104           8
## 10       INIRIDA   94001 2015    2015           32          10
## 11 BARRANCO MINA   94343 2015    2015          240           8
## 12 BARRANCO MINA   94343 2016    2016          823           7
## 13       INIRIDA   94001 2016    2016          847           7
## 14    SAN FELIPE   94883 2016    2016           28           7
## 15 BARRANCO MINA   94343 2017    2017         2044           7
## 16       INIRIDA   94001 2017    2017          102           5
## 17      MORICHAL   94888 2017    2017           39           7
## 18    SAN FELIPE   94883 2017    2017           35           7
## 19 BARRANCO MINA   94343 2018    2018         2009           7
## 20       INIRIDA   94001 2018    2018          102           5
## 21      MORICHAL   94888 2018    2018           25           7
## 22    SAN FELIPE   94883 2018    2018           21           7
##    Area_cultivadas_ha
## 1                 250
## 2                 390
## 3                 368
## 4                 815
## 5                 479
## 6                 519
## 7                 115
## 8                 320
## 9                 130
## 10                345
## 11                130
## 12                294
## 13                121
## 14                  4
## 15                294
## 16                244
## 17                  6
## 18                  5
## 19                294
## 20                239
## 21                  8
## 22                  7

unique(platano_gaui_hasem $Ano)

##  [1] 2007 2008 2009  201 2011 2012 2013 2014 2015 2016 2017 2018

platano_gaui_hasem$Area_cultivadas_ha<- as.numeric(platano_gaui_hasem$Area_cultivadas_ha)

convercion de formato largo a ancho

platano_gaui_hasem %>% replace ( is.na (.) ,0 ) -> platano_gaui_hasem2

platano_gaui_hasem %>% 
  group_by( Municipio,COD_MUN,Ano) %>% 
  summarise(Area_cultivadas_ha=sum(Area_cultivadas_ha))->platano_gaui_hasem2

## `summarise()` has grouped output by 'Municipio', 'COD_MUN'. You can override using the `.groups` argument.

head(platano_gaui_hasem2)

## # A tibble: 6 x 4
## # Groups:   Municipio, COD_MUN [2]
##   Municipio     COD_MUN   Ano Area_cultivadas_ha
##   <chr>           <dbl> <int>              <dbl>
## 1 BARRANCO MINA   94343  2014                130
## 2 BARRANCO MINA   94343  2015                130
## 3 BARRANCO MINA   94343  2016                294
## 4 BARRANCO MINA   94343  2017                294
## 5 BARRANCO MINA   94343  2018                294
## 6 INIRIDA         94001   201                815

platano_gaui_hasem2 %>% 
  group_by(COD_MUN)%>%
  gather("Area_cultivadas_ha", key = variable, value = numero) %>%
  unite(combi,variable,Ano)%>%
  pivot_wider(names_from = combi, values_from = numero, values_fill = 0)->
 platano_gaui_hasem3
platano_gaui_hasem3

## # A tibble: 4 x 14
## # Groups:   COD_MUN [4]
##   Municipio   COD_MUN Area_cultivadas_ha~ Area_cultivadas_ha~ Area_cultivadas_h~
##   <chr>         <dbl>               <dbl>               <dbl>              <dbl>
## 1 BARRANCO M~   94343                 130                 130                294
## 2 INIRIDA       94001                 320                 345                121
## 3 MORICHAL      94888                   0                   0                  0
## 4 SAN FELIPE    94883                   0                   0                  4
## # ... with 9 more variables: Area_cultivadas_ha_2017 <dbl>,
## #   Area_cultivadas_ha_2018 <dbl>, Area_cultivadas_ha_201 <dbl>,
## #   Area_cultivadas_ha_2007 <dbl>, Area_cultivadas_ha_2008 <dbl>,
## #   Area_cultivadas_ha_2009 <dbl>, Area_cultivadas_ha_2011 <dbl>,
## #   Area_cultivadas_ha_2012 <dbl>, Area_cultivadas_ha_2013 <dbl>

mun_platano_gaui_hasem<- left_join( muni_guainia ,platano_gaui_hasem3 ,  by ="COD_MUN")

summary(mun_platano_gaui_hasem)

##   DPTO_CCDGO         MPIO_CCDGO         MPIO_CNMBR         MPIO_CRSLC       
##  Length:9           Length:9           Length:9           Length:9          
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##    MPIO_NAREA      MPIO_NANO     DPTO_CNMBR          Shape_Leng   
##  Min.   : 1179   Min.   :2017   Length:9           Min.   :1.751  
##  1st Qu.: 2926   1st Qu.:2017   Class :character   1st Qu.:4.030  
##  Median : 8555   Median :2017   Mode  :character   Median :6.826  
##  Mean   : 7921   Mean   :2017                      Mean   :5.903  
##  3rd Qu.:10227   3rd Qu.:2017                      3rd Qu.:7.805  
##  Max.   :15970   Max.   :2017                      Max.   :9.257  
##                                                                   
##    Shape_Area               geometry    COD_MUN       Municipio        
##  Min.   :0.09434   POLYGON      :9   Min.   :94001   Length:9          
##  1st Qu.:0.23461   epsg:4326    :0   1st Qu.:94663   Class :character  
##  Median :0.69179   +proj=long...:0   Median :94884   Mode  :character  
##  Mean   :0.63866                     Mean   :94702                     
##  3rd Qu.:0.82504                     3rd Qu.:94886                     
##  Max.   :1.28699                     Max.   :94888                     
##                                                                        
##  Area_cultivadas_ha_2014 Area_cultivadas_ha_2015 Area_cultivadas_ha_2016
##  Min.   :  0.0           Min.   :  0.0           Min.   :  0.0          
##  1st Qu.:  0.0           1st Qu.:  0.0           1st Qu.:  3.0          
##  Median : 65.0           Median : 65.0           Median : 62.5          
##  Mean   :112.5           Mean   :118.8           Mean   :104.8          
##  3rd Qu.:177.5           3rd Qu.:183.8           3rd Qu.:164.2          
##  Max.   :320.0           Max.   :345.0           Max.   :294.0          
##  NA's   :5               NA's   :5               NA's   :5              
##  Area_cultivadas_ha_2017 Area_cultivadas_ha_2018 Area_cultivadas_ha_201
##  Min.   :  5.00          Min.   :  7.00          Min.   :  0.0         
##  1st Qu.:  5.75          1st Qu.:  7.75          1st Qu.:  0.0         
##  Median :125.00          Median :123.50          Median :  0.0         
##  Mean   :137.25          Mean   :137.00          Mean   :203.8         
##  3rd Qu.:256.50          3rd Qu.:252.75          3rd Qu.:203.8         
##  Max.   :294.00          Max.   :294.00          Max.   :815.0         
##  NA's   :5               NA's   :5               NA's   :5             
##  Area_cultivadas_ha_2007 Area_cultivadas_ha_2008 Area_cultivadas_ha_2009
##  Min.   :  0.0           Min.   :  0.0           Min.   :  0            
##  1st Qu.:  0.0           1st Qu.:  0.0           1st Qu.:  0            
##  Median :  0.0           Median :  0.0           Median :  0            
##  Mean   : 62.5           Mean   : 97.5           Mean   : 92            
##  3rd Qu.: 62.5           3rd Qu.: 97.5           3rd Qu.: 92            
##  Max.   :250.0           Max.   :390.0           Max.   :368            
##  NA's   :5               NA's   :5               NA's   :5              
##  Area_cultivadas_ha_2011 Area_cultivadas_ha_2012 Area_cultivadas_ha_2013
##  Min.   :  0.0           Min.   :  0.0           Min.   :  0.00         
##  1st Qu.:  0.0           1st Qu.:  0.0           1st Qu.:  0.00         
##  Median :  0.0           Median :  0.0           Median :  0.00         
##  Mean   :119.8           Mean   :129.8           Mean   : 28.75         
##  3rd Qu.:119.8           3rd Qu.:129.8           3rd Qu.: 28.75         
##  Max.   :479.0           Max.   :519.0           Max.   :115.00         
##  NA's   :5               NA's   :5               NA's   :5

mapa del area cosechada en el aÃ±o 2016 del platano

library(leaflet)
bins<- c(0,37,74,111,148,185,222,259,296)
pal <- colorBin("YlOrRd", domain = mun_platano_gaui_hasem$Area_cultivadas_ha_2016, bins = bins)

map<-leaflet(data = mun_platano_gaui_hasem)%>%
  addTiles()%>%
  addPolygons(label = ~Area_cultivadas_ha_2016,
              popup = ~MPIO_CNMBR,
               fillColor = ~ pal(Area_cultivadas_ha_2016),
              color="#444444",
              weight = 1, 
              smoothFactor = 0.5,
              opacity = 1.0,
              fillOpacity = 0.5,
              highlightOptions = highlightOptions(color = "red",weight =2,bringToFront = TRUE )
              )%>%
  addProviderTiles(providers$OpenStreetMap)%>%
  addLegend("bottomright", pal= pal, values = ~Area_cultivadas_ha_2016,
            title = "Area cultivadas de Platano 2016  [Ha](DANE,2018)",opacity = 1)
map

mapa del are cosechada del platano en el aÃ±o 2010

library(leaflet)
bins<- c(0,102,204,306,408,510,612,714,816)
pal <- colorBin("YlOrRd", domain = mun_platano_gaui_hasem$Area_cultivadas_ha_201, bins = bins)

map<-leaflet(data = mun_platano_gaui_hasem)%>%
  addTiles()%>%
  addPolygons(label = ~Area_cultivadas_ha_201,
              popup = ~MPIO_CNMBR,
               fillColor = ~ pal(Area_cultivadas_ha_201),
              color="#444444",
              weight = 1, 
              smoothFactor = 0.5,
              opacity = 1.0,
              fillOpacity = 0.5,
              highlightOptions = highlightOptions(color = "red",weight =2,bringToFront = TRUE )
              )%>%
  addProviderTiles(providers$OpenStreetMap)%>%
  addLegend("bottomright", pal= pal, values = ~Area_cultivadas_ha_201,
            title = "Area cultivadas de Platano 2010  [Ha](DANE,2018)",opacity = 1)
map

sessionInfo()

## R version 4.0.4 (2021-02-15)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Colombia.1252  LC_CTYPE=Spanish_Colombia.1252   
## [3] LC_MONETARY=Spanish_Colombia.1252 LC_NUMERIC=C                     
## [5] LC_TIME=Spanish_Colombia.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] leaflet_2.0.4.1 forcats_0.5.1   stringr_1.4.0   dplyr_1.0.5    
##  [5] purrr_0.3.4     readr_1.4.0     tidyr_1.1.3     tibble_3.1.0   
##  [9] tidyverse_1.3.0 sf_0.9-7        ggplot2_3.3.3  
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.6              lubridate_1.7.10        leaflet.providers_1.9.0
##  [4] class_7.3-18            assertthat_0.2.1        digest_0.6.27          
##  [7] utf8_1.1.4              R6_2.5.0                cellranger_1.1.0       
## [10] backports_1.2.1         reprex_1.0.0            evaluate_0.14          
## [13] e1071_1.7-5             httr_1.4.2              pillar_1.5.1           
## [16] rlang_0.4.10            readxl_1.3.1            rstudioapi_0.13        
## [19] rmarkdown_2.7           htmlwidgets_1.5.3       munsell_0.5.0          
## [22] proxy_0.4-25            broom_0.7.5             compiler_4.0.4         
## [25] modelr_0.1.8            xfun_0.22               pkgconfig_2.0.3        
## [28] htmltools_0.5.1.1       tidyselect_1.1.0        fansi_0.4.2            
## [31] crayon_1.4.1            dbplyr_2.1.0            withr_2.4.1            
## [34] grid_4.0.4              jsonlite_1.7.2          gtable_0.3.0           
## [37] lifecycle_1.0.0         DBI_1.1.1               magrittr_2.0.1         
## [40] units_0.7-0             scales_1.1.1            KernSmooth_2.23-18     
## [43] cli_2.3.1               stringi_1.5.3           farver_2.1.0           
## [46] fs_1.5.0                xml2_1.3.2              ellipsis_0.3.1         
## [49] generics_0.1.0          vctrs_0.3.6             RColorBrewer_1.1-2     
## [52] tools_4.0.4             glue_1.4.2              hms_1.0.0              
## [55] crosstalk_1.1.1         yaml_2.2.1              colorspace_2.0-0       
## [58] classInt_0.4-3          rvest_1.0.0             knitr_1.31             
## [61] haven_2.3.1

cuaderno_4.2

Juan Guillermo Narvaez Diaz

17/4/2021

convercion de formato largo a ancho