library(sf)
## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(dplyr)
##
## 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
library(readr)
list.files("C:/Users/EPI_m/OneDrive/Escritorio/Geomatica/Rstudio", pattern=c('csv'))
## [1] "Evaluaciones_Agropecuarias_Municipales_EVA.csv"
## [2] "Vichada_Cereales_2020.csv"
## [3] "vichada_platanos_2020.csv"
## [4] "vichada_Tuberculos_2020.csv"
list.files('C:/Users/EPI_m/OneDrive/Escritorio/Geomatica/Rstudio')
## [1] "20210624_BaseSIPRA2020.xlsx"
## [2] "Evaluaciones_Agropecuarias_Municipales_EVA.csv"
## [3] "Mi-primer-cuaderno2.html"
## [4] "Mi-primer-cuaderno2.tex"
## [5] "Mi-segundo-cuaderno.html"
## [6] "Mi-segundo-cuaderno.Rmd"
## [7] "Mi-tercer.cuaderno.html"
## [8] "Mi-tercer.cuaderno.rmd"
## [9] "Mi primer cuaderno2.nb.html"
## [10] "Mi primer cuaderno2.Rmd"
## [11] "rsconnect"
## [12] "Vichada_Cereales_2020.csv"
## [13] "vichada_platanos_2020.csv"
## [14] "vichada_Tuberculos_2020.csv"
(Platanos = read_csv("C:/Users/EPI_m/OneDrive/Escritorio/Geomatica/Rstudio/vichada_platanos_2020.csv",show_col_types = FALSE))
## # A tibble: 4 x 4
## Cod_Mun Municipio Grupo max_prod
## <dbl> <chr> <chr> <dbl>
## 1 99773 Cumaribo Plátanos 10250
## 2 99524 La Primavera Plátanos 657
## 3 99624 Santa Rosalía Plátanos 320
## 4 99001 Puerto Carreño Plátanos 180
(mun.tmp = st_read('C:/Users/EPI_m/OneDrive/Escritorio/Geomatica/Qgis/Datos/Municipios_Vichada.shp'))
## Reading layer `Municipios_Vichada' from data source
## `C:\Users\EPI_m\OneDrive\Escritorio\Geomatica\Qgis\Datos\Municipios_Vichada.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 4 features and 11 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS: WGS 84
## Simple feature collection with 4 features and 11 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS: WGS 84
## DPTO_CCDGO MPIO_CCDGO MPIO_CNMBR
## 1 99 624 SANTA ROSALÍA
## 2 99 001 PUERTO CARREÑO
## 3 99 524 LA PRIMAVERA
## 4 99 773 CUMARIBO
## MPIO_CRSLC MPIO_NAREA
## 1 Modificado por Ordenanza No. 21 de 30 de Noviembre de 2019 3691.869
## 2 Decreto 1594 de Ago 5 de 1974 12204.951
## 3 Modificado por Ordenanza No. 21 de 30 de Noviembre de 2019 18569.338
## 4 Ordenanza 66 de Noviembre 22 de 1996 65597.212
## MPIO_CCNCT MPIO_NANO DPTO_CNMBR SHAPE_AREA SHAPE_LEN ORIG_FID
## 1 99624 2020 VICHADA 0.2999597 3.805847 51
## 2 99001 2020 VICHADA 0.9859169 5.474851 52
## 3 99524 2020 VICHADA 1.5062235 8.080061 53
## 4 99773 2020 VICHADA 5.3085803 18.794383 54
## geometry
## 1 POLYGON ((-70.65603 5.37316...
## 2 POLYGON ((-67.80972 6.32431...
## 3 POLYGON ((-69.0331 6.218449...
## 4 POLYGON ((-68.47074 5.55046...
mun.tmp %>% select(MPIO_CCDGO, MPIO_CCNCT, MPIO_NAREA,MPIO_CNMBR) -> municipios
municipios
## Simple feature collection with 4 features and 4 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS: WGS 84
## MPIO_CCDGO MPIO_CCNCT MPIO_NAREA MPIO_CNMBR
## 1 624 99624 3691.869 SANTA ROSALÍA
## 2 001 99001 12204.951 PUERTO CARREÑO
## 3 524 99524 18569.338 LA PRIMAVERA
## 4 773 99773 65597.212 CUMARIBO
## geometry
## 1 POLYGON ((-70.65603 5.37316...
## 2 POLYGON ((-67.80972 6.32431...
## 3 POLYGON ((-69.0331 6.218449...
## 4 POLYGON ((-68.47074 5.55046...
(cities = read_csv("C:/Users/EPI_m/OneDrive/Escritorio/Geomatica/Qgis/Datos/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.
## # A tibble: 1,102 x 9
## city lat lng country iso2 admin_name capital population
## <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl>
## 1 Bogotá 4.61 -74.1 Colombia CO Bogotá primary 9464000
## 2 Medellín 6.24 -75.6 Colombia CO Antioquia admin 2529403
## 3 Cali 3.44 -76.5 Colombia CO Valle del Cauca admin 2471474
## 4 Barranquilla 11.0 -74.8 Colombia CO Atlántico admin 1274250
## 5 Cartagena 10.4 -75.5 Colombia CO Bolívar admin 1036412
## 6 Soacha 4.58 -74.2 Colombia CO Cundinamarca minor 995268
## 7 Palermo 2.89 -75.4 Colombia CO Huila minor 800000
## 8 Cúcuta 7.91 -72.5 Colombia CO Norte de Santander admin 750000
## 9 Soledad 10.9 -74.8 Colombia CO Atlántico minor 698852
## 10 Pereira 4.81 -75.7 Colombia CO Risaralda admin 590554
## # ... with 1,092 more rows, and 1 more variable: population_proper <dbl>
## Rows: 1102 Columns: 9
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
## # A tibble: 1,102 x 8
## city country iso2 admin_name capital population population_prop~
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Bogotá Colombia CO Bogotá primary 9464000 7963000
## 2 Medellín Colombia CO Antioquia admin 2529403 2529403
## 3 Cali Colombia CO Valle del Ca~ admin 2471474 2471474
## 4 Barranquilla Colombia CO Atlántico admin 1274250 1274250
## 5 Cartagena Colombia CO Bolívar admin 1036412 1036412
## 6 Soacha Colombia CO Cundinamarca minor 995268 995268
## 7 Palermo Colombia CO Huila minor 800000 800000
## 8 Cúcuta Colombia CO Norte de San~ admin 750000 750000
## 9 Soledad Colombia CO Atlántico minor 698852 342556
## 10 Pereira Colombia CO Risaralda admin 590554 590554
## # ... with 1,092 more rows, and 1 more variable: geometry <POINT>
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 11 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -81.7006 ymin: -4.215 xmax: -67.4858 ymax: 13.3817
## Geodetic CRS: WGS 84
## # A tibble: 1,102 x 12
## city country iso2 admin_name capital population population_prop~
## * <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Bogotá Colombia CO Bogotá primary 9464000 7963000
## 2 Medellín Colombia CO Antioquia admin 2529403 2529403
## 3 Cali Colombia CO Valle del Ca~ admin 2471474 2471474
## 4 Barranquilla Colombia CO Atlántico admin 1274250 1274250
## 5 Cartagena Colombia CO Bolívar admin 1036412 1036412
## 6 Soacha Colombia CO Cundinamarca minor 995268 995268
## 7 Palermo Colombia CO Huila minor 800000 800000
## 8 Cúcuta Colombia CO Norte de San~ admin 750000 750000
## 9 Soledad Colombia CO Atlántico minor 698852 342556
## 10 Pereira Colombia CO Risaralda admin 590554 590554
## # ... with 1,092 more rows, and 5 more variables: geometry <POINT [°]>,
## # MPIO_CCDGO <chr>, MPIO_CCNCT <chr>, MPIO_NAREA <dbl>, MPIO_CNMBR <chr>
Vichada.cities = dplyr::filter(sf.cities.joined, admin_name=='Vichada')
Vichada.cities
## Simple feature collection with 4 features and 11 fields
## Geometry type: POINT
## Dimension: XY
## Bounding box: xmin: -70.8756 ymin: 4.4461 xmax: -67.4858 ymax: 6.1889
## Geodetic CRS: WGS 84
## # A tibble: 4 x 12
## city country iso2 admin_name capital population population_proper
## * <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Cumaribo Colombia CO Vichada minor 43138 43138
## 2 La Primavera Colombia CO Vichada minor 17626 17626
## 3 Puerto Carreño Colombia CO Vichada admin 16763 16763
## 4 Santa Rosalía Colombia CO Vichada minor 4255 4255
## # ... with 5 more variables: geometry <POINT [°]>, MPIO_CCDGO <chr>,
## # MPIO_CCNCT <chr>, MPIO_NAREA <dbl>, MPIO_CNMBR <chr>
library(tmap)
library(ggplot2)
library(ggrepel)
library(classInt)
class(Platanos$Cod_Mun)
## [1] "numeric"
class(municipios$MPIO_CCNCT)
## [1] "character"
Platanos$Cod_Mun = as.character(Platanos$Cod_Mun)
class(Platanos$Cod_Mun)
## [1] "character"
munic_platanos = left_join(municipios,Platanos, by = c("MPIO_CCNCT" = "Cod_Mun"))
munic_platanos
## Simple feature collection with 4 features and 7 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS: WGS 84
## MPIO_CCDGO MPIO_CCNCT MPIO_NAREA MPIO_CNMBR Municipio Grupo
## 1 624 99624 3691.869 SANTA ROSALÍA Santa Rosalía Plátanos
## 2 001 99001 12204.951 PUERTO CARREÑO Puerto Carreño Plátanos
## 3 524 99524 18569.338 LA PRIMAVERA La Primavera Plátanos
## 4 773 99773 65597.212 CUMARIBO Cumaribo Plátanos
## max_prod geometry
## 1 320 POLYGON ((-70.65603 5.37316...
## 2 180 POLYGON ((-67.80972 6.32431...
## 3 657 POLYGON ((-69.0331 6.218449...
## 4 10250 POLYGON ((-68.47074 5.55046...
breaks <- classIntervals(munic_platanos$max_prod, n = 3, style = 'fisher')
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)
(tuberculos = read_csv("C:/Users/EPI_m/OneDrive/Escritorio/Geomatica/Rstudio/vichada_Tuberculos_2020.csv",show_col_types = FALSE))
## # A tibble: 4 x 4
## Cod_Mun Municipio Grupo max_prod
## <dbl> <chr> <chr> <dbl>
## 1 99773 Cumaribo Tubérculos Y Plátanos 4000
## 2 99524 La Primavera Tubérculos Y Plátanos 1470
## 3 99624 Santa Rosalía Tubérculos Y Plátanos 432
## 4 99001 Puerto Carreño Tubérculos Y Plátanos 300
tuberculos$Cod_Mun = as.character(tuberculos$Cod_Mun)
#municipios$MPIO_CCDGO = as.character(municipios$MPIO_CCNCT)
munic_tuberculos = left_join(municipios, tuberculos, by = c("MPIO_CCNCT" = "Cod_Mun"))
munic_tuberculos
## Simple feature collection with 4 features and 7 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -71.07793 ymin: 2.737109 xmax: -67.4098 ymax: 6.324317
## Geodetic CRS: WGS 84
## MPIO_CCDGO MPIO_CCNCT MPIO_NAREA MPIO_CNMBR Municipio
## 1 624 99624 3691.869 SANTA ROSALÍA Santa Rosalía
## 2 001 99001 12204.951 PUERTO CARREÑO Puerto Carreño
## 3 524 99524 18569.338 LA PRIMAVERA La Primavera
## 4 773 99773 65597.212 CUMARIBO Cumaribo
## Grupo max_prod geometry
## 1 Tubérculos Y Plátanos 432 POLYGON ((-70.65603 5.37316...
## 2 Tubérculos Y Plátanos 300 POLYGON ((-67.80972 6.32431...
## 3 Tubérculos Y Plátanos 1470 POLYGON ((-69.0331 6.218449...
## 4 Tubérculos Y Plátanos 4000 POLYGON ((-68.47074 5.55046...
facet = "max_prod"
tuberculos_map =
tm_shape(munic_tuberculos) + tm_polygons(facet) + tm_text(text = "Municipio", size = 0.7, fontfamily = "sans") +
tm_shape(Vichada.cities) + tm_symbols(shape = 2, col = "red", size = 0.20) +
tm_credits("Data source: UPRA (2020)", fontface = "bold") +
tm_layout(main.title = "Produccion de oleaginosas 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
tuberculos_map
## Credits not supported in view mode.
## Symbol shapes other than circles or icons are not supported in view mode.