Notebook2-Geomática Básica

list.files("C:/Users/Lenovo/Documents/CuadernosR", pattern=c('csv'))

[1] "co.csv"                                        
[2] "Evaluaciones_Agropecuarias_Municipales_EVA.csv"
[3] "narino_platanos_2020.csv"                      
[4] "narino_tuberculosyplatanos_2020.csv"           

list.files('C:/Users/Lenovo/Documents/CuadernosR/')

 [1] "co.csv"                                                 
 [2] "drive-download-20220409T154620Z-001.zip"                
 [3] "drive-download-20220409T162205Z-001.zip"                
 [4] "Evaluaciones_Agropecuarias_Municipales_EVA.csv"         
 [5] "MunicipiosNarino.cpg"                                   
 [6] "MunicipiosNarino.dbf"                                   
 [7] "MunicipiosNarino.prj"                                   
 [8] "MunicipiosNarino.qpj"                                   
 [9] "MunicipiosNarino.shp"                                   
[10] "MunicipiosNarino.shp.BE500P01SG07AGR.13708.5600.sr.lock"
[11] "MunicipiosNarino.shx"                                   
[12] "narino_platanos_2020.csv"                               
[13] "narino_tuberculosyplatanos_2020.csv"                    

(platanos = read.csv("C:/Users/Lenovo/Documents/CuadernosR/narino_platanos_2020.csv"))

munnar <- st_read("C:/Users/Lenovo/Documents/CuadernosR/MunicipiosNarino.shp")

Reading layer `MunicipiosNarino' from data source 
  `C:\Users\Lenovo\Documents\CuadernosR\MunicipiosNarino.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 66 features and 11 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -79.01021 ymin: 0.3613481 xmax: -76.83368 ymax: 2.683898
Geodetic CRS:  WGS 84

(mun.tmp = st_read('C:/Users/Lenovo/Documents/CuadernosR/MunicipiosNarino.shp'))

Reading layer `MunicipiosNarino' from data source 
  `C:\Users\Lenovo\Documents\CuadernosR\MunicipiosNarino.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 66 features and 11 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -79.01021 ymin: 0.3613481 xmax: -76.83368 ymax: 2.683898
Geodetic CRS:  WGS 84
Simple feature collection with 66 features and 11 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -79.01021 ymin: 0.3613481 xmax: -76.83368 ymax: 2.683898
Geodetic CRS:  WGS 84
First 10 features:
   DPTO_CCDGO MPIO_CCDGO MPIO_CNMBR
1          52        083      BELÉN
2          52        110    BUESACO
3          52        203      COLÓN
4          52        480     NARIÑO
5          52        506     OSPINA
6          52        720    SAPUYES
7          52        786  TAMINANGO
8          52        788     TANGUA
9          52        240  CHACHAGÜÍ
10         52        254   EL PEÑOL
                                                    MPIO_CRSLC
1                            Ordenanza 53 Noviembre 29 de 1985
2                                                         1899
3                                         Ordenanza 37 de 1921
4  Ordenanza 027 de Noviembre. 29 de 1999. Decreto 0312 del 24
5                                         Ordenanza 50 de 1865
6                                                         1849
7                                                         1834
8                                        Ordenanza 103 de 1874
9                            Ordenanza 20 Noviembre 24 de 1992
10                        Ordenanza 036 de Diciembre 7 de 1998
   MPIO_NAREA MPIO_CCNCT MPIO_NANO DPTO_CNMBR  SHAPE_AREA
1    41.84541      52083      2020     NARIÑO 0.003391678
2   635.96083      52110      2020     NARIÑO 0.051533090
3    61.75053      52203      2020     NARIÑO 0.005005108
4    25.31281      52480      2020     NARIÑO 0.002050175
5    64.84321      52506      2020     NARIÑO 0.005249269
6   115.54851      52720      2020     NARIÑO 0.009351438
7   234.65783      52786      2020     NARIÑO 0.019009395
8   217.95977      52788      2020     NARIÑO 0.017652117
9   146.27176      52240      2020     NARIÑO 0.011849554
10  119.85744      52254      2020     NARIÑO 0.009707107
   SHAPE_LEN ORIG_FID                       geometry
1  0.3732840        0 POLYGON ((-77.07227 1.63422...
2  1.2292312        1 POLYGON ((-77.23516 1.45240...
3  0.4592866        2 POLYGON ((-77.04473 1.67173...
4  0.2642048        3 POLYGON ((-77.34282 1.31465...
5  0.3371496        4 POLYGON ((-77.55776 1.07006...
6  0.6599792        5 POLYGON ((-77.71499 1.0915,...
7  0.6601636        6 POLYGON ((-77.32644 1.67981...
8  0.7808421        7 POLYGON ((-77.36152 1.19568...
9  0.7373282        8 POLYGON ((-77.30295 1.51777...
10 0.5550189        9 POLYGON ((-77.39239 1.60127...

mun.tmp %>% select(MPIO_CCNCT, MPIO_CNMBR, MPIO_NAREA) -> municipios
municipios

Simple feature collection with 66 features and 3 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -79.01021 ymin: 0.3613481 xmax: -76.83368 ymax: 2.683898
Geodetic CRS:  WGS 84
First 10 features:
   MPIO_CCNCT MPIO_CNMBR MPIO_NAREA
1       52083      BELÉN   41.84541
2       52110    BUESACO  635.96083
3       52203      COLÓN   61.75053
4       52480     NARIÑO   25.31281
5       52506     OSPINA   64.84321
6       52720    SAPUYES  115.54851
7       52786  TAMINANGO  234.65783
8       52788     TANGUA  217.95977
9       52240  CHACHAGÜÍ  146.27176
10      52254   EL PEÑOL  119.85744
                         geometry
1  POLYGON ((-77.07227 1.63422...
2  POLYGON ((-77.23516 1.45240...
3  POLYGON ((-77.04473 1.67173...
4  POLYGON ((-77.34282 1.31465...
5  POLYGON ((-77.55776 1.07006...
6  POLYGON ((-77.71499 1.0915,...
7  POLYGON ((-77.32644 1.67981...
8  POLYGON ((-77.36152 1.19568...
9  POLYGON ((-77.30295 1.51777...
10 POLYGON ((-77.39239 1.60127...

(cities = read_csv("C:/Users/Lenovo/Documents/CuadernosR/co.csv"))

Rows: 1102 Columns: 9
-- Column specification -----------------------------------------
Delimiter: ","
chr (5): city, country, iso2, admin_name, capital
dbl (4): lat, lng, population, population_proper

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.

sf.cities <-  st_as_sf(x = cities, 
                        coords = c("lng", "lat"))
sf.cities

Simple feature collection with 1102 features and 7 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -81.7006 ymin: -4.215 xmax: -67.4858 ymax: 13.3817
CRS:           NA

st_crs(sf.cities) <- 4326

sf.cities.joined <- st_join(sf.cities, municipios, join = st_within)
sf.cities.joined

Simple feature collection with 1102 features and 10 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -81.7006 ymin: -4.215 xmax: -67.4858 ymax: 13.3817
Geodetic CRS:  WGS 84

narino.cities = dplyr::filter(sf.cities.joined, admin_name=='Nariño')

narino.cities

Simple feature collection with 64 features and 10 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -78.7647 ymin: 0.8081 xmax: -76.9706 ymax: 2.5078
Geodetic CRS:  WGS 84

class(platanos$Cod_Mun)

[1] "integer"

class(municipios$MPIO_CCNCT)

[1] "character"

platanos$Cod_Mun = as.character(platanos$Cod_Mun)

munic_platanos = left_join(municipios, platanos, by = c("MPIO_CCNCT" = "Cod_Mun"))

munic_platanos

Simple feature collection with 66 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -79.01021 ymin: 0.3613481 xmax: -76.83368 ymax: 2.683898
Geodetic CRS:  WGS 84
First 10 features:
   MPIO_CCNCT MPIO_CNMBR MPIO_NAREA  X     Municipio    Grupo
1       52083      BELÉN   41.84541 NA          <NA>     <NA>
2       52110    BUESACO  635.96083 NA          <NA>     <NA>
3       52203      COLÓN   61.75053  5 Colón (Génova Plátanos
4       52480     NARIÑO   25.31281 NA          <NA>     <NA>
5       52506     OSPINA   64.84321 NA          <NA>     <NA>
6       52720    SAPUYES  115.54851 NA          <NA>     <NA>
7       52786  TAMINANGO  234.65783 11     Taminango Plátanos
8       52788     TANGUA  217.95977 NA          <NA>     <NA>
9       52240  CHACHAGÜÍ  146.27176 23     Chachaguí Plátanos
10      52254   EL PEÑOL  119.85744 NA          <NA>     <NA>
   max_prod                       geometry
1        NA POLYGON ((-77.07227 1.63422...
2        NA POLYGON ((-77.23516 1.45240...
3      6000 POLYGON ((-77.04473 1.67173...
4        NA POLYGON ((-77.34282 1.31465...
5        NA POLYGON ((-77.55776 1.07006...
6        NA POLYGON ((-77.71499 1.0915,...
7      2655 POLYGON ((-77.32644 1.67981...
8        NA POLYGON ((-77.36152 1.19568...
9       450 POLYGON ((-77.30295 1.51777...
10       NA POLYGON ((-77.39239 1.60127...

breaks <- classIntervals(munic_platanos$max_prod, n = 6, style = 'fisher')

Warning in classIntervals(munic_platanos$max_prod, n = 6, style = "fisher") :
  var has missing values, omitted in finding classes

lab_vec <- vector(length = length(breaks$brks)-1)
rounded_breaks <- round(breaks$brks,2)
lab_vec[1] <- paste0('[', rounded_breaks[1],' - ', rounded_breaks[2],']')
for(i in 2:(length(breaks$brks) - 1)){
  lab_vec[i] <- paste0('(',rounded_breaks[i], ' - ', rounded_breaks[i+1], ']')
}

munic_platanos <-  munic_platanos %>%
  mutate(faktor_class = factor(cut(max_prod, breaks$brks, include.lowest = T), labels = lab_vec))
munic_platanos$Produccion = munic_platanos$faktor_class

munic_platanos$mid <- sf::st_centroid(munic_platanos$geometry)

LONG = st_coordinates(munic_platanos$mid)[,1]

LAT = st_coordinates(munic_platanos$mid)[,2]

ggplot(data = munic_platanos) +
   geom_sf(aes(fill = Produccion)) +
   geom_label_repel(aes(x = LONG, y = LAT, label = MPIO_CNMBR), 
                    label.padding =     unit(0.05,"lines"),  
                    label.r = unit(0.025, "lines"),
                    label.size = 0.05)

Warning: ggrepel: 48 unlabeled data points (too many overlaps). Consider increasing max.overlaps

(tuberculosyplatanos = read.csv("C:/Users/Lenovo/Documents/CuadernosR/narino_tuberculosyplatanos_2020.csv"))

tuberculosyplatanos$Cod_Mun = as.character(tuberculosyplatanos$Cod_Mun)

munic_tuberculosyplatanos = left_join(municipios, tuberculosyplatanos, by = c("MPIO_CCNCT" = "Cod_Mun"))

munic_tuberculosyplatanos

Simple feature collection with 66 features and 7 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -79.01021 ymin: 0.3613481 xmax: -76.83368 ymax: 2.683898
Geodetic CRS:  WGS 84
First 10 features:
   MPIO_CCNCT MPIO_CNMBR MPIO_NAREA  X     Municipio
1       52083      BELÉN   41.84541 58         Belén
2       52110    BUESACO  635.96083 29       Buesaco
3       52203      COLÓN   61.75053 44 Colón (Génova
4       52480     NARIÑO   25.31281 39        Nariño
5       52506     OSPINA   64.84321  5        Ospina
6       52720    SAPUYES  115.54851  7       Sapuyes
7       52786  TAMINANGO  234.65783 42     Taminango
8       52788     TANGUA  217.95977 14        Tangua
9       52240  CHACHAGÜÍ  146.27176 55     Chachaguí
10      52254   EL PEÑOL  119.85744 37      El Peñol
                   Grupo max_prod                       geometry
1  Tubérculos Y Plátanos     33.0 POLYGON ((-77.07227 1.63422...
2  Tubérculos Y Plátanos    544.0 POLYGON ((-77.23516 1.45240...
3  Tubérculos Y Plátanos    160.0 POLYGON ((-77.04473 1.67173...
4  Tubérculos Y Plátanos    208.0 POLYGON ((-77.34282 1.31465...
5  Tubérculos Y Plátanos  41137.5 POLYGON ((-77.55776 1.07006...
6  Tubérculos Y Plátanos  16270.0 POLYGON ((-77.71499 1.0915,...
7  Tubérculos Y Plátanos    180.0 POLYGON ((-77.32644 1.67981...
8  Tubérculos Y Plátanos   7200.0 POLYGON ((-77.36152 1.19568...
9  Tubérculos Y Plátanos     40.0 POLYGON ((-77.30295 1.51777...
10 Tubérculos Y Plátanos    218.0 POLYGON ((-77.39239 1.60127...

facet = "max_prod"
tuberculosyplatanos_map =  
  tm_shape(munic_tuberculosyplatanos) + tm_polygons(facet) + tm_text(text = "MPIO_CNMBR", size = 0.7, fontfamily = "sans") +
  tm_shape(narino.cities) + tm_symbols(shape = 2, col = "red", size = 0.20) +
  tm_credits("Data source: BaseSIPRA2020", fontface = "bold") +
  tm_layout(main.title = "Produccion de tuberculos y platanos en 2020",
            main.title.fontface = "bold.italic", 
            legend.title.fontfamily = "monospace") +
  tm_scale_bar(position = c("left", "bottom"))

tmap_mode("view")

tmap mode set to interactive viewing

tuberculosyplatanos_map 

Credits not supported in view mode.
Symbol shapes other than circles or icons are not supported in view mode.

#Lizarazo, I., 2022. Getting started with thematic maps. Available at https://rpubs.com/ials2un/thematic_maps_v2.