CUADERNO4

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.3     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1

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

library(dplyr)
library(ggplot2)
library(sf)

## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1

library(mapview)
library(leaflet.providers)
library(leaflet)
library(RColorBrewer)
library(leafem)

library(readr)
eva_santander <- read_csv("Evaluaciones_Agropecuarias_Municipales_EVA.csv")

## Rows: 14672 Columns: 17

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (11): CODDPTO, DPTO, MUN, GRUPO, SUBGRUPO, CULTIVO, SISTEMAPROD, PERIODO...
## dbl  (6): CODMUN, ANO, HASIEMBRA, HACOSECHA, PRODUCCION, RENDIMIENTO

## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(eva_santander)

## Warning: One or more parsing issues, see `problems()` for details

mun_santander = st_read("C:/Users/JUPA/Documents/geomatica/68_SANTANDER/ADMINISTRATIVO/MGN_MPIO_POLITICO.shp")

## Reading layer `MGN_MPIO_POLITICO' from data source 
##   `C:\Users\JUPA\Documents\geomatica\68_SANTANDER\ADMINISTRATIVO\MGN_MPIO_POLITICO.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 87 features and 9 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.52895 ymin: 5.707536 xmax: -72.47706 ymax: 8.14501
## Geodetic CRS:  WGS 84

mun_santander

## Simple feature collection with 87 features and 9 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.52895 ymin: 5.707536 xmax: -72.47706 ymax: 8.14501
## Geodetic CRS:  WGS 84
## First 10 features:
##    DPTO_CCDGO MPIO_CCDGO      MPIO_CNMBR                           MPIO_CRSLC
## 1          68      68001     BUCARAMANGA                                 1623
## 2          68      68013          AGUADA                                 1775
## 3          68      68020         ALBANIA                 Ordenanza 33 de 1919
## 4          68      68051         ARATOCA                                 1750
## 5          68      68077         BARBOSA Ordenanza 30 del 25 de Abril de 1936
## 6          68      68079       BARICHARA                                 1799
## 7          68      68081 BARRANCABERMEJA Ordenanza 13 del 17 de Abril de 1922
## 8          68      68092         BETULIA                                 1874
## 9          68      68101         BOLIVAR                                 1844
## 10         68      68121         CABRERA                                 1869
##    MPIO_NAREA MPIO_NANO DPTO_CNMBR Shape_Leng  Shape_Area
## 1   152.90497      2017  SANTANDER  0.6922752 0.012513526
## 2    75.23125      2017  SANTANDER  0.4758098 0.006146093
## 3   166.21697      2017  SANTANDER  0.8761299 0.013570466
## 4   169.79155      2017  SANTANDER  0.6746922 0.013882031
## 5    46.66489      2017  SANTANDER  0.2703415 0.003810882
## 6   137.27581      2017  SANTANDER  0.5610888 0.011223345
## 7  1326.83512      2017  SANTANDER  2.7351901 0.108590745
## 8   431.24871      2017  SANTANDER  1.2718180 0.035286963
## 9  1010.11035      2017  SANTANDER  4.3603864 0.082514928
## 10   65.57431      2017  SANTANDER  0.3515706 0.005360404
##                          geometry
## 1  POLYGON ((-73.08418 7.23063...
## 2  POLYGON ((-73.56261 6.24032...
## 3  POLYGON ((-73.73616 5.87092...
## 4  POLYGON ((-72.98158 6.76065...
## 5  POLYGON ((-73.58988 5.99809...
## 6  POLYGON ((-73.22126 6.73288...
## 7  POLYGON ((-73.6939 7.254447...
## 8  POLYGON ((-73.53993 7.15392...
## 9  POLYGON ((-74.50132 6.27574...
## 10 POLYGON ((-73.25696 6.6213,...

mun_santander$KM2 <- st_area(st_transform(mun_santander, 3116))/1E6
mun_santander$KM2 <- as.numeric(mun_santander$KM2)
mun_santander$KM2 <- round(mun_santander$KM2,3)
min(mun_santander$KM2)

## [1] 19.694

max(mun_santander$KM2)

## [1] 3174.289

mapview(mun_santander, zcol="MPIO_NAREA", col.regions=brewer.pal(9, "YlGn")) %>%
  addStaticLabels(., label= mun_santander$MPIO_CNMBR, textsize= "7px", 
                   style= list("color"="black"))

## Warning: Found less unique colors (9) than unique zcol values (87)! 
## Interpolating color vector to match number of zcol values.

class(mun_santander$MPIO_CCDGO)

## [1] "character"

class(eva_santander$CODMUN)

## [1] "numeric"

mun_santander$CODMUN <-  as.double(mun_santander$MPIO_CCDGO)
class(mun_santander$CODMUN)

## [1] "numeric"

palma_santander <- eva_santander %>%  filter(CULTIVO== "PALMA DE ACEITE")  %>%  dplyr::select(MUN, CODMUN, ANO, PERIODO, PRODUCCION, RENDIMIENTO) 
palma_santander

## # A tibble: 100 x 6
##    MUN                    CODMUN   ANO PERIODO PRODUCCION RENDIMIENTO
##    <chr>                   <dbl> <dbl> <chr>        <dbl>       <dbl>
##  1 PUERTO WILCHES          68575  2007 2007        111827        3.6 
##  2 SABANA DE TORRES        68655  2007 2007         19562        3.3 
##  3 SAN VICENTE DE CHUCURI  68689  2007 2007          5925        3.12
##  4 RIONEGRO                68615  2007 2007          6010        3.78
##  5 BARRANCABERMEJA         68081  2007 2007          5240        3.5 
##  6 BETULIA                 68092  2007 2007             0       NA   
##  7 SIMACOTA                68745  2007 2007            75        3.41
##  8 PUERTO WILCHES          68575  2008 2008        121644        3.6 
##  9 SABANA DE TORRES        68655  2008 2008         25516        3.74
## 10 SAN VICENTE DE CHUCURI  68689  2008 2008         11025        3.5 
## # ... with 90 more rows

cacao_santander <- eva_santander %>%  filter(CULTIVO== "CACAO")  %>%  dplyr::select(MUN, CODMUN, ANO, PERIODO, PRODUCCION, RENDIMIENTO) 
cacao_santander

## # A tibble: 476 x 6
##    MUN                    CODMUN   ANO PERIODO PRODUCCION RENDIMIENTO
##    <chr>                   <dbl> <dbl> <chr>        <dbl>       <dbl>
##  1 SAN VICENTE DE CHUCURI  68689  2007 2007          7200        0.6 
##  2 EL CARMEN DE CHUCURI    68235  2007 2007          4500        0.6 
##  3 LANDAZURI               68385  2007 2007          2993        0.51
##  4 RIONEGRO                68615  2007 2007          2231        0.55
##  5 CIMITARRA               68190  2007 2007           807        0.57
##  6 LEBRIJA                 68406  2007 2007          1480        0.8 
##  7 EL PLAYON               68255  2007 2007           660        0.51
##  8 LA PAZ                  68397  2007 2007           490        0.7 
##  9 BETULIA                 68092  2007 2007           242        0.7 
## 10 SANTA HELENA DEL OPON   68720  2007 2007           198        0.6 
## # ... with 466 more rows

unique(cacao_santander$ANO)

##  [1] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

unique(cacao_santander$PERIODO)

##  [1] "2007" "2008" "2009" "2010" "2011" "2012" "2013" "2014" "2015" "2016"
## [11] "2017" "2018"

unique(palma_santander$ANO)

##  [1] 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

unique(palma_santander$PERIODO)

##  [1] "2007" "2008" "2009" "2010" "2011" "2012" "2013" "2014" "2015" "2016"
## [11] "2017" "2018"

cacao_santander %>% 
  gather("ANO", "PERIODO", "PRODUCCION", "RENDIMIENTO" , key = variable, value = number)

## # A tibble: 1,904 x 4
##    MUN                    CODMUN variable number
##    <chr>                   <dbl> <chr>    <chr> 
##  1 SAN VICENTE DE CHUCURI  68689 ANO      2007  
##  2 EL CARMEN DE CHUCURI    68235 ANO      2007  
##  3 LANDAZURI               68385 ANO      2007  
##  4 RIONEGRO                68615 ANO      2007  
##  5 CIMITARRA               68190 ANO      2007  
##  6 LEBRIJA                 68406 ANO      2007  
##  7 EL PLAYON               68255 ANO      2007  
##  8 LA PAZ                  68397 ANO      2007  
##  9 BETULIA                 68092 ANO      2007  
## 10 SANTA HELENA DEL OPON   68720 ANO      2007  
## # ... with 1,894 more rows

summary(cacao_santander)

##      MUN                CODMUN           ANO         PERIODO         
##  Length:476         Min.   :68001   Min.   :2007   Length:476        
##  Class :character   1st Qu.:68245   1st Qu.:2010   Class :character  
##  Mode  :character   Median :68377   Median :2013   Mode  :character  
##                     Mean   :68415   Mean   :2013                     
##                     3rd Qu.:68584   3rd Qu.:2016                     
##                     Max.   :68895   Max.   :2018                     
##                                                                      
##    PRODUCCION      RENDIMIENTO   
##  Min.   :   0.0   Min.   :0.200  
##  1st Qu.:  25.0   1st Qu.:0.500  
##  Median :  85.0   Median :0.580  
##  Mean   : 542.3   Mean   :0.611  
##  3rd Qu.: 251.0   3rd Qu.:0.700  
##  Max.   :7490.0   Max.   :1.160  
##                   NA's   :13

summary(palma_santander)

##      MUN                CODMUN           ANO         PERIODO         
##  Length:100         Min.   :68081   Min.   :2007   Length:100        
##  Class :character   1st Qu.:68190   1st Qu.:2010   Class :character  
##  Mode  :character   Median :68575   Median :2013   Mode  :character  
##                     Mean   :68472   Mean   :2013                     
##                     3rd Qu.:68655   3rd Qu.:2016                     
##                     Max.   :68745   Max.   :2018                     
##                                                                      
##    PRODUCCION      RENDIMIENTO   
##  Min.   :     0   Min.   :1.800  
##  1st Qu.:   148   1st Qu.:2.688  
##  Median :  8371   Median :3.280  
##  Mean   : 22839   Mean   :3.116  
##  3rd Qu.: 18669   3rd Qu.:3.500  
##  Max.   :123050   Max.   :4.100  
##                   NA's   :8

cacao_santander %>% replace(is.na(.), 0) -> cacao_santander2
palma_santander %>% replace(is.na(.), 0) -> palma_santander2

cacao_santander %>% group_by(MUN, CODMUN, ANO) %>%
   summarize(PRODUCCION=sum(PRODUCCION)) -> cacao_santander2

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

palma_santander %>% group_by(MUN, CODMUN, ANO) %>%
   summarize(PRODUCCION=sum(PRODUCCION)) -> palma_santander2

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

cacao_santander2

## # A tibble: 476 x 4
## # Groups:   MUN, CODMUN [47]
##    MUN             CODMUN   ANO PRODUCCION
##    <chr>            <dbl> <dbl>      <dbl>
##  1 BARRANCABERMEJA  68081  2007         35
##  2 BARRANCABERMEJA  68081  2008         42
##  3 BARRANCABERMEJA  68081  2009        110
##  4 BARRANCABERMEJA  68081  2010         57
##  5 BARRANCABERMEJA  68081  2011         61
##  6 BARRANCABERMEJA  68081  2012         85
##  7 BARRANCABERMEJA  68081  2013         87
##  8 BARRANCABERMEJA  68081  2014         70
##  9 BARRANCABERMEJA  68081  2015         70
## 10 BARRANCABERMEJA  68081  2016         77
## # ... with 466 more rows

palma_santander2

## # A tibble: 100 x 4
## # Groups:   MUN, CODMUN [10]
##    MUN             CODMUN   ANO PRODUCCION
##    <chr>            <dbl> <dbl>      <dbl>
##  1 BARRANCABERMEJA  68081  2007       5240
##  2 BARRANCABERMEJA  68081  2008       5576
##  3 BARRANCABERMEJA  68081  2009       5687
##  4 BARRANCABERMEJA  68081  2010       6120
##  5 BARRANCABERMEJA  68081  2011       6363
##  6 BARRANCABERMEJA  68081  2012      15058
##  7 BARRANCABERMEJA  68081  2013      15744
##  8 BARRANCABERMEJA  68081  2014      15080
##  9 BARRANCABERMEJA  68081  2015      16900
## 10 BARRANCABERMEJA  68081  2016      13560
## # ... with 90 more rows

cacao_santander2 %>% 
  group_by(CODMUN) %>% 
  gather("PRODUCCION", key = variable, value = number)   %>% 
  unite(combi, variable, ANO) %>%
  pivot_wider(names_from = combi, values_from = number, values_fill = 0) ->                                                                    cacao_santander3                             

palma_santander2 %>% 
  group_by(CODMUN) %>% 
  gather("PRODUCCION", key = variable, value = number)   %>% 
  unite(combi, variable, ANO) %>%
  pivot_wider(names_from = combi, values_from = number, values_fill = 0) ->                                                                    palma_santander3                             

cacao_santander3

## # A tibble: 47 x 14
## # Groups:   CODMUN [47]
##    MUN             CODMUN PRODUCCION_2007 PRODUCCION_2008 PRODUCCION_2009 PRODUCCION_2010
##    <chr>            <dbl>           <dbl>           <dbl>           <dbl>           <dbl>
##  1 BARRANCABERMEJA  68081              35              42             110              57
##  2 BETULIA          68092             242             364             466             569
##  3 BOLIVAR          68101              45              80             102             114
##  4 BUCARAMANGA      68001              18               6               7              10
##  5 CEPITA           68160               0               0               0               0
##  6 CHARTA           68169               0               0               0               1
##  7 CHIMA            68176               7               0              10              18
##  8 CHIPATA          68179               0               0               0               0
##  9 CIMITARRA        68190             807            1208            1426            2025
## 10 CONTRATACION     68211              18              16              20              23
## # ... with 37 more rows, and 8 more variables: PRODUCCION_2011 <dbl>,
## #   PRODUCCION_2012 <dbl>, PRODUCCION_2013 <dbl>, PRODUCCION_2014 <dbl>,
## #   PRODUCCION_2015 <dbl>, PRODUCCION_2016 <dbl>, PRODUCCION_2017 <dbl>,
## #   PRODUCCION_2018 <dbl>

palma_santander3

## # A tibble: 10 x 14
## # Groups:   CODMUN [10]
##    MUN    CODMUN PRODUCCION_2007 PRODUCCION_2008 PRODUCCION_2009 PRODUCCION_2010
##    <chr>   <dbl>           <dbl>           <dbl>           <dbl>           <dbl>
##  1 BARRA~  68081            5240            5576            5687            6120
##  2 BETUL~  68092               0               0             148             148
##  3 CIMIT~  68190               0               0               0               0
##  4 EL CA~  68235               0               0               0               0
##  5 PUERT~  68573               0               0               0              50
##  6 PUERT~  68575          111827          121644          123050          123050
##  7 RIONE~  68615            6010            6460            6653            9020
##  8 SABAN~  68655           19562           25516           28684           39161
##  9 SAN V~  68689            5925           11025           11900           12250
## 10 SIMAC~  68745              75             103             117               0
## # ... with 8 more variables: PRODUCCION_2011 <dbl>, PRODUCCION_2012 <dbl>,
## #   PRODUCCION_2013 <dbl>, PRODUCCION_2014 <dbl>, PRODUCCION_2015 <dbl>,
## #   PRODUCCION_2016 <dbl>, PRODUCCION_2017 <dbl>, PRODUCCION_2018 <dbl>

mun_cacao_santander = left_join(mun_santander, cacao_santander3, by="CODMUN")
summary(mun_cacao_santander)

##   DPTO_CCDGO         MPIO_CCDGO         MPIO_CNMBR         MPIO_CRSLC       
##  Length:87          Length:87          Length:87          Length:87         
##  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.   :  19.69   Min.   :2017   Length:87          Min.   :0.2700  
##  1st Qu.:  91.01   1st Qu.:2017   Class :character   1st Qu.:0.4765  
##  Median : 201.73   Median :2017   Mode  :character   Median :0.7392  
##  Mean   : 351.28   Mean   :2017                      Mean   :1.0298  
##  3rd Qu.: 426.17   3rd Qu.:2017                      3rd Qu.:1.2240  
##  Max.   :3174.28   Max.   :2017                      Max.   :4.3604  
##                                                                      
##    Shape_Area           KM2              CODMUN          MUN           
##  Min.   :0.00161   Min.   :  19.69   Min.   :68001   Length:87         
##  1st Qu.:0.00744   1st Qu.:  91.01   1st Qu.:68210   Class :character  
##  Median :0.01649   Median : 201.73   Median :68370   Mode  :character  
##  Mean   :0.02873   Mean   : 351.28   Mean   :68409                     
##  3rd Qu.:0.03486   3rd Qu.: 426.17   3rd Qu.:68595                     
##  Max.   :0.25946   Max.   :3174.29   Max.   :68895                     
##                                                                        
##  PRODUCCION_2007  PRODUCCION_2008  PRODUCCION_2009  PRODUCCION_2010 
##  Min.   :   0.0   Min.   :   0.0   Min.   :   0.0   Min.   :   0.0  
##  1st Qu.:   0.0   1st Qu.:   0.0   1st Qu.:   0.0   1st Qu.:   2.0  
##  Median :  18.0   Median :  16.0   Median :  28.0   Median :  35.0  
##  Mean   : 463.7   Mean   : 454.6   Mean   : 406.4   Mean   : 441.2  
##  3rd Qu.: 100.0   3rd Qu.: 100.0   3rd Qu.: 107.5   3rd Qu.: 124.5  
##  Max.   :7200.0   Max.   :7490.0   Max.   :5640.0   Max.   :5350.0  
##  NA's   :40       NA's   :40       NA's   :40       NA's   :40      
##  PRODUCCION_2011  PRODUCCION_2012  PRODUCCION_2013  PRODUCCION_2014 
##  Min.   :   0.0   Min.   :   0.0   Min.   :   0.0   Min.   :   0.0  
##  1st Qu.:   6.5   1st Qu.:  11.0   1st Qu.:  12.0   1st Qu.:  17.0  
##  Median :  42.0   Median :  49.0   Median :  69.0   Median :  57.0  
##  Mean   : 455.4   Mean   : 431.1   Mean   : 458.4   Mean   : 400.6  
##  3rd Qu.: 124.0   3rd Qu.: 184.0   3rd Qu.: 243.5   3rd Qu.: 199.5  
##  Max.   :5014.0   Max.   :4993.0   Max.   :5145.0   Max.   :5000.0  
##  NA's   :40       NA's   :40       NA's   :40       NA's   :40      
##  PRODUCCION_2015  PRODUCCION_2016  PRODUCCION_2017  PRODUCCION_2018 
##  Min.   :   0.0   Min.   :   0.0   Min.   :   0.0   Min.   :   0.0  
##  1st Qu.:  16.5   1st Qu.:  23.5   1st Qu.:  27.0   1st Qu.:  30.0  
##  Median :  95.0   Median :  95.0   Median :  77.0   Median :  94.0  
##  Mean   : 465.1   Mean   : 498.6   Mean   : 487.0   Mean   : 529.6  
##  3rd Qu.: 226.5   3rd Qu.: 240.0   3rd Qu.: 232.5   3rd Qu.: 334.0  
##  Max.   :5400.0   Max.   :6625.0   Max.   :6540.0   Max.   :6540.0  
##  NA's   :40       NA's   :40       NA's   :40       NA's   :40      
##           geometry 
##  POLYGON      :87  
##  epsg:4326    : 0  
##  +proj=long...: 0  
##                    
##                    
##                    
## 

m_p_santander = left_join(mun_santander, palma_santander3, by="CODMUN")
summary(m_p_santander)

##   DPTO_CCDGO         MPIO_CCDGO         MPIO_CNMBR         MPIO_CRSLC       
##  Length:87          Length:87          Length:87          Length:87         
##  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.   :  19.69   Min.   :2017   Length:87          Min.   :0.2700  
##  1st Qu.:  91.01   1st Qu.:2017   Class :character   1st Qu.:0.4765  
##  Median : 201.73   Median :2017   Mode  :character   Median :0.7392  
##  Mean   : 351.28   Mean   :2017                      Mean   :1.0298  
##  3rd Qu.: 426.17   3rd Qu.:2017                      3rd Qu.:1.2240  
##  Max.   :3174.28   Max.   :2017                      Max.   :4.3604  
##                                                                      
##    Shape_Area           KM2              CODMUN          MUN           
##  Min.   :0.00161   Min.   :  19.69   Min.   :68001   Length:87         
##  1st Qu.:0.00744   1st Qu.:  91.01   1st Qu.:68210   Class :character  
##  Median :0.01649   Median : 201.73   Median :68370   Mode  :character  
##  Mean   :0.02873   Mean   : 351.28   Mean   :68409                     
##  3rd Qu.:0.03486   3rd Qu.: 426.17   3rd Qu.:68595                     
##  Max.   :0.25946   Max.   :3174.29   Max.   :68895                     
##                                                                        
##  PRODUCCION_2007  PRODUCCION_2008  PRODUCCION_2009     PRODUCCION_2010   
##  Min.   :     0   Min.   :     0   Min.   :     0.00   Min.   :     0.0  
##  1st Qu.:     0   1st Qu.:     0   1st Qu.:    29.25   1st Qu.:    12.5  
##  Median :  2658   Median :  2840   Median :  2917.50   Median :  3134.0  
##  Mean   : 14864   Mean   : 17032   Mean   : 17623.90   Mean   : 18979.9  
##  3rd Qu.:  5989   3rd Qu.:  9884   3rd Qu.: 10588.25   3rd Qu.: 11442.5  
##  Max.   :111827   Max.   :121644   Max.   :123050.00   Max.   :123050.0  
##  NA's   :77       NA's   :77       NA's   :77          NA's   :77        
##  PRODUCCION_2011  PRODUCCION_2012     PRODUCCION_2013 PRODUCCION_2014  
##  Min.   :     0   Min.   :     0.00   Min.   :    0   Min.   :    0.0  
##  1st Qu.:    12   1st Qu.:    12.75   1st Qu.:   37   1st Qu.:  333.8  
##  Median :  3256   Median :  6724.00   Median : 8112   Median : 3406.0  
##  Mean   : 19057   Mean   : 20812.40   Mean   :15975   Mean   :18181.3  
##  3rd Qu.: 12291   3rd Qu.: 17804.50   3rd Qu.:15674   3rd Qu.:15007.5  
##  Max.   :119340   Max.   :119340.00   Max.   :71167   Max.   :72775.0  
##  NA's   :77       NA's   :77          NA's   :77      NA's   :77       
##  PRODUCCION_2015   PRODUCCION_2016   PRODUCCION_2017   PRODUCCION_2018   
##  Min.   :  107.0   Min.   :  102.0   Min.   :    0.0   Min.   :     0.0  
##  1st Qu.:  573.8   1st Qu.:  426.8   1st Qu.:  377.5   1st Qu.:   691.2  
##  Median :12311.0   Median : 9677.5   Median : 9390.0   Median : 10555.0  
##  Mean   :22332.2   Mean   :19391.3   Mean   :20869.5   Mean   : 23274.7  
##  3rd Qu.:18444.0   3rd Qu.:14629.5   3rd Qu.:15172.0   3rd Qu.: 14274.5  
##  Max.   :95468.0   Max.   :87234.0   Max.   :91000.0   Max.   :109307.0  
##  NA's   :77        NA's   :77        NA's   :77        NA's   :77        
##           geometry 
##  POLYGON      :87  
##  epsg:4326    : 0  
##  +proj=long...: 0  
##                    
##                    
##                    
## 

head(mun_cacao_santander[,1:10])

## Simple feature collection with 6 features and 10 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.96194 ymin: 5.707536 xmax: -72.93536 ymax: 7.266616
## Geodetic CRS:  WGS 84
##   DPTO_CCDGO MPIO_CCDGO  MPIO_CNMBR                           MPIO_CRSLC
## 1         68      68001 BUCARAMANGA                                 1623
## 2         68      68013      AGUADA                                 1775
## 3         68      68020     ALBANIA                 Ordenanza 33 de 1919
## 4         68      68051     ARATOCA                                 1750
## 5         68      68077     BARBOSA Ordenanza 30 del 25 de Abril de 1936
## 6         68      68079   BARICHARA                                 1799
##   MPIO_NAREA MPIO_NANO DPTO_CNMBR Shape_Leng  Shape_Area     KM2
## 1  152.90497      2017  SANTANDER  0.6922752 0.012513526 152.905
## 2   75.23125      2017  SANTANDER  0.4758098 0.006146093  75.231
## 3  166.21697      2017  SANTANDER  0.8761299 0.013570466 166.217
## 4  169.79155      2017  SANTANDER  0.6746922 0.013882031 169.792
## 5   46.66489      2017  SANTANDER  0.2703415 0.003810882  46.665
## 6  137.27581      2017  SANTANDER  0.5610888 0.011223345 137.276
##                         geometry
## 1 POLYGON ((-73.08418 7.23063...
## 2 POLYGON ((-73.56261 6.24032...
## 3 POLYGON ((-73.73616 5.87092...
## 4 POLYGON ((-72.98158 6.76065...
## 5 POLYGON ((-73.58988 5.99809...
## 6 POLYGON ((-73.22126 6.73288...

head(m_p_santander[,1:10])

## Simple feature collection with 6 features and 10 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -73.96194 ymin: 5.707536 xmax: -72.93536 ymax: 7.266616
## Geodetic CRS:  WGS 84
##   DPTO_CCDGO MPIO_CCDGO  MPIO_CNMBR                           MPIO_CRSLC
## 1         68      68001 BUCARAMANGA                                 1623
## 2         68      68013      AGUADA                                 1775
## 3         68      68020     ALBANIA                 Ordenanza 33 de 1919
## 4         68      68051     ARATOCA                                 1750
## 5         68      68077     BARBOSA Ordenanza 30 del 25 de Abril de 1936
## 6         68      68079   BARICHARA                                 1799
##   MPIO_NAREA MPIO_NANO DPTO_CNMBR Shape_Leng  Shape_Area     KM2
## 1  152.90497      2017  SANTANDER  0.6922752 0.012513526 152.905
## 2   75.23125      2017  SANTANDER  0.4758098 0.006146093  75.231
## 3  166.21697      2017  SANTANDER  0.8761299 0.013570466 166.217
## 4  169.79155      2017  SANTANDER  0.6746922 0.013882031 169.792
## 5   46.66489      2017  SANTANDER  0.2703415 0.003810882  46.665
## 6  137.27581      2017  SANTANDER  0.5610888 0.011223345 137.276
##                         geometry
## 1 POLYGON ((-73.08418 7.23063...
## 2 POLYGON ((-73.56261 6.24032...
## 3 POLYGON ((-73.73616 5.87092...
## 4 POLYGON ((-72.98158 6.76065...
## 5 POLYGON ((-73.58988 5.99809...
## 6 POLYGON ((-73.22126 6.73288...

mapview(mun_cacao_santander, zcol= "PRODUCCION_2017", col.regions=brewer.pal(9, "YlGn")) %>%
  addStaticLabels(., label= mun_cacao_santander$MPIO_CNMBR, textsize= "7px", 
                   style= list("color"="black"))

## Warning: Found less unique colors (9) than unique zcol values (39)! 
## Interpolating color vector to match number of zcol values.

mapview(m_p_santander, zcol= "PRODUCCION_2017", col.regions=brewer.pal(9, "YlGn")) %>%
  addStaticLabels(., label= m_p_santander$MPIO_CNMBR, textsize= "7px", 
                   style= list("color"="black"))

summary(eva_santander)

##    CODDPTO              DPTO               CODMUN          MUN           
##  Length:14672       Length:14672       Min.   :68001   Length:14672      
##  Class :character   Class :character   1st Qu.:68209   Class :character  
##  Mode  :character   Mode  :character   Median :68397   Mode  :character  
##                                        Mean   :68414                     
##                                        3rd Qu.:68615                     
##                                        Max.   :68895                     
##                                        NA's   :27                        
##     GRUPO             SUBGRUPO           CULTIVO          SISTEMAPROD       
##  Length:14672       Length:14672       Length:14672       Length:14672      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##       ANO         PERIODO            HASIEMBRA         HACOSECHA      
##  Min.   :2006   Length:14672       Min.   :    0.0   Min.   :    0.0  
##  1st Qu.:2010   Class :character   1st Qu.:   10.0   1st Qu.:    8.0  
##  Median :2013   Mode  :character   Median :   31.0   Median :   26.0  
##  Mean   :2013                      Mean   :  259.8   Mean   :  210.9  
##  3rd Qu.:2016                      3rd Qu.:  130.0   3rd Qu.:  100.0  
##  Max.   :2018                      Max.   :41544.0   Max.   :35100.0  
##  NA's   :27                        NA's   :27        NA's   :27       
##    PRODUCCION      RENDIMIENTO         ESTADO          NCIENTIFICO       
##  Min.   :     0   Min.   :  0.070   Length:14672       Length:14672      
##  1st Qu.:    32   1st Qu.:  1.500   Class :character   Class :character  
##  Median :   138   Median :  6.000   Mode  :character   Mode  :character  
##  Mean   :  1288   Mean   :  9.323                                        
##  3rd Qu.:   533   3rd Qu.: 12.225                                        
##  Max.   :233500   Max.   :200.000                                        
##  NA's   :27       NA's   :248                                            
##     CICLO          
##  Length:14672      
##  Class :character  
##  Mode  :character  
##                    
##                    
##                    
## 

cacao_santander <- eva_santander %>%  filter(CULTIVO== "CACAO")  %>%  dplyr::select(MUN, CODMUN, ANO, PERIODO, HACOSECHA) 

cacao_santander %>% group_by(MUN, CODMUN, ANO) %>%
   summarize(HACOSECHA=sum(HACOSECHA)) -> cacao_santander2

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

cacao_santander2 %>% 
  group_by(CODMUN) %>% 
  gather("HACOSECHA", key = variable, value = number)   %>% 
  unite(combi, variable, ANO) %>%
  pivot_wider(names_from = combi, values_from = number, values_fill = 0) ->                                                                    cacao_santander3

mun_cacao_santander = left_join(mun_santander, cacao_santander3, by="CODMUN")
mun_cacao_santander

## Simple feature collection with 87 features and 24 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.52895 ymin: 5.707536 xmax: -72.47706 ymax: 8.14501
## Geodetic CRS:  WGS 84
## First 10 features:
##    DPTO_CCDGO MPIO_CCDGO      MPIO_CNMBR                           MPIO_CRSLC
## 1          68      68001     BUCARAMANGA                                 1623
## 2          68      68013          AGUADA                                 1775
## 3          68      68020         ALBANIA                 Ordenanza 33 de 1919
## 4          68      68051         ARATOCA                                 1750
## 5          68      68077         BARBOSA Ordenanza 30 del 25 de Abril de 1936
## 6          68      68079       BARICHARA                                 1799
## 7          68      68081 BARRANCABERMEJA Ordenanza 13 del 17 de Abril de 1922
## 8          68      68092         BETULIA                                 1874
## 9          68      68101         BOLIVAR                                 1844
## 10         68      68121         CABRERA                                 1869
##    MPIO_NAREA MPIO_NANO DPTO_CNMBR Shape_Leng  Shape_Area      KM2 CODMUN
## 1   152.90497      2017  SANTANDER  0.6922752 0.012513526  152.905  68001
## 2    75.23125      2017  SANTANDER  0.4758098 0.006146093   75.231  68013
## 3   166.21697      2017  SANTANDER  0.8761299 0.013570466  166.217  68020
## 4   169.79155      2017  SANTANDER  0.6746922 0.013882031  169.792  68051
## 5    46.66489      2017  SANTANDER  0.2703415 0.003810882   46.665  68077
## 6   137.27581      2017  SANTANDER  0.5610888 0.011223345  137.276  68079
## 7  1326.83512      2017  SANTANDER  2.7351901 0.108590745 1326.837  68081
## 8   431.24871      2017  SANTANDER  1.2718180 0.035286963  431.249  68092
## 9  1010.11035      2017  SANTANDER  4.3603864 0.082514928 1010.110  68101
## 10   65.57431      2017  SANTANDER  0.3515706 0.005360404   65.574  68121
##                MUN HACOSECHA_2007 HACOSECHA_2008 HACOSECHA_2009 HACOSECHA_2010
## 1      BUCARAMANGA             30             12             13             10
## 2             <NA>             NA             NA             NA             NA
## 3             <NA>             NA             NA             NA             NA
## 4             <NA>             NA             NA             NA             NA
## 5             <NA>             NA             NA             NA             NA
## 6             <NA>             NA             NA             NA             NA
## 7  BARRANCABERMEJA             58             65            110             95
## 8          BETULIA            346            520            520            603
## 9          BOLIVAR            135            160            204            227
## 10            <NA>             NA             NA             NA             NA
##    HACOSECHA_2011 HACOSECHA_2012 HACOSECHA_2013 HACOSECHA_2014 HACOSECHA_2015
## 1               9             10             10             46            300
## 2              NA             NA             NA             NA             NA
## 3              NA             NA             NA             NA             NA
## 4              NA             NA             NA             NA             NA
## 5              NA             NA             NA             NA             NA
## 6              NA             NA             NA             NA             NA
## 7             102            106            175            140            140
## 8             652            450            450            440            450
## 9             220            250            281            330            318
## 10             NA             NA             NA             NA             NA
##    HACOSECHA_2016 HACOSECHA_2017 HACOSECHA_2018                       geometry
## 1             300            313            331 POLYGON ((-73.08418 7.23063...
## 2              NA             NA             NA POLYGON ((-73.56261 6.24032...
## 3              NA             NA             NA POLYGON ((-73.73616 5.87092...
## 4              NA             NA             NA POLYGON ((-72.98158 6.76065...
## 5              NA             NA             NA POLYGON ((-73.58988 5.99809...
## 6              NA             NA             NA POLYGON ((-73.22126 6.73288...
## 7             155            126            147 POLYGON ((-73.6939 7.254447...
## 8             450            470            470 POLYGON ((-73.53993 7.15392...
## 9             230            450            460 POLYGON ((-74.50132 6.27574...
## 10             NA             NA             NA POLYGON ((-73.25696 6.6213,...

sessionInfo()

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
## 
## 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] leafem_0.1.6            RColorBrewer_1.1-2      leaflet_2.0.4.1        
##  [4] leaflet.providers_1.9.0 mapview_2.10.0          sf_1.0-3               
##  [7] forcats_0.5.1           stringr_1.4.0           dplyr_1.0.7            
## [10] purrr_0.3.4             readr_2.0.1             tidyr_1.1.4            
## [13] tibble_3.1.3            ggplot2_3.3.5           tidyverse_1.3.1        
## 
## loaded via a namespace (and not attached):
##  [1] fs_1.5.0           satellite_1.0.3    lubridate_1.8.0    bit64_4.0.5       
##  [5] webshot_0.5.2      httr_1.4.2         tools_4.1.1        backports_1.2.1   
##  [9] bslib_0.2.5.1      utf8_1.2.2         R6_2.5.0           KernSmooth_2.23-20
## [13] DBI_1.1.1          colorspace_2.0-2   raster_3.4-13      withr_2.4.2       
## [17] sp_1.4-5           tidyselect_1.1.1   bit_4.0.4          compiler_4.1.1    
## [21] cli_3.0.1          rvest_1.0.1        xml2_1.3.2         sass_0.4.0        
## [25] scales_1.1.1       classInt_0.4-3     proxy_0.4-26       systemfonts_1.0.2 
## [29] digest_0.6.27      rmarkdown_2.10     svglite_2.0.0      base64enc_0.1-3   
## [33] pkgconfig_2.0.3    htmltools_0.5.1.1  dbplyr_2.1.1       htmlwidgets_1.5.3 
## [37] rlang_0.4.11       readxl_1.3.1       rstudioapi_0.13    farver_2.1.0      
## [41] jquerylib_0.1.4    generics_0.1.0     jsonlite_1.7.2     crosstalk_1.1.1   
## [45] vroom_1.5.4        magrittr_2.0.1     s2_1.0.7           Rcpp_1.0.7        
## [49] munsell_0.5.0      fansi_0.5.0        lifecycle_1.0.0    stringi_1.7.3     
## [53] yaml_2.2.1         grid_4.1.1         parallel_4.1.1     crayon_1.4.1      
## [57] lattice_0.20-44    haven_2.4.3        hms_1.1.0          leafpop_0.1.0     
## [61] knitr_1.33         pillar_1.6.2       uuid_0.1-4         markdown_1.1      
## [65] codetools_0.2-18   stats4_4.1.1       wk_0.5.0           reprex_2.0.1      
## [69] glue_1.4.2         evaluate_0.14      modelr_0.1.8       png_0.1-7         
## [73] vctrs_0.3.8        tzdb_0.1.2         cellranger_1.1.0   gtable_0.3.0      
## [77] assertthat_0.2.1   xfun_0.25          mime_0.11          broom_0.7.9       
## [81] e1071_1.7-8        class_7.3-19       units_0.7-2        ellipsis_0.3.2    
## [85] brew_1.0-6