Mapa temático de los dos cultivos predominantes en el departamento de Boyacá
Jose Gerardo Bermudez Diaz
29/03/22
1. Introducción
“En este cuaderno se va a elaborar un mapa temático cuya función sera mostrar la participación de los dos grupos de cultivos mas importantes del departamento de Boyacá”
2. Configuración
library(sf)## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUElibrary(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, unionlibrary(readr)3. Leer fichas relacionadas con municipios, cultivos y ciudades.
list.files("C:/Users/GERARDO/Desktop/GB2022/datos", pattern=c('csv'))## [1] "co.csv"
## [2] "Evaluaciones_Agropecuarias_Municipales_EVA.csv"
## [3] "J.ber_Hort_2020.csv"
## [4] "J.ber_Tuber_2020.csv"list.files('C:/Users/GERARDO/Desktop/GB2022/datos/15_BOYACA/Administrativo')## [1] "MGN_DPTO_POLITICO.cpg"
## [2] "MGN_DPTO_POLITICO.dbf"
## [3] "MGN_DPTO_POLITICO.prj"
## [4] "MGN_DPTO_POLITICO.sbn"
## [5] "MGN_DPTO_POLITICO.sbx"
## [6] "MGN_DPTO_POLITICO.shp"
## [7] "MGN_DPTO_POLITICO.shp.DG_EST54.1344.12652.sr.lock"
## [8] "MGN_DPTO_POLITICO.shp.xml"
## [9] "MGN_DPTO_POLITICO.shx"
## [10] "MGN_MPIO_POLITICO.cpg"
## [11] "MGN_MPIO_POLITICO.dbf"
## [12] "MGN_MPIO_POLITICO.prj"
## [13] "MGN_MPIO_POLITICO.sbn"
## [14] "MGN_MPIO_POLITICO.sbx"
## [15] "MGN_MPIO_POLITICO.shp"
## [16] "MGN_MPIO_POLITICO.shp.DG_EST54.1344.12652.sr.lock"
## [17] "MGN_MPIO_POLITICO.shp.xml"
## [18] "MGN_MPIO_POLITICO.shx"(Tuberculos = read_csv("C:/Users/GERARDO/Desktop/GB2022/datos/J.ber_Tuber_2020.csv",show_col_types = FALSE))(mun.tmp = st_read('C:/Users/GERARDO/Desktop/GB2022/datos/15_BOYACA/Administrativo/MGN_MPIO_POLITICO.shp'))## Reading layer `MGN_MPIO_POLITICO' from data source
## `C:\Users\GERARDO\Desktop\GB2022\Datos\15_BOYACA\ADMINISTRATIVO\MGN_MPIO_POLITICO.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 123 features and 9 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -74.66496 ymin: 4.655196 xmax: -71.94885 ymax: 7.055557
## Geodetic CRS: WGS 84mun.tmp %>% select(MPIO_CCDGO, MPIO_CNMBR, MPIO_NAREA) -> municipios
municipios(cities = read_csv("C:/Users/GERARDO/Desktop/GB2022/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.sf.cities <- st_as_sf(x = cities,
coords = c("lng", "lat"))
sf.citiesst_crs(sf.cities) <- 43264. Subconjunto de datos relevantes para nuestro departamento
sf.cities.joined <- st_join(sf.cities, municipios, join = st_within)
sf.cities.joinedbyc.cities = dplyr::filter(sf.cities.joined, admin_name=='Boyacá')
byc.cities5. Hacer un mapa para el grupo de cultivos mas importante
library(tmap)library(ggplot2)
library(ggrepel)
library(classInt)class(Tuberculos$Cod_Mun)## [1] "numeric"class(municipios$MPIO_CCDGO)## [1] "character"Tuberculos$Cod_Mun = as.character(Tuberculos$Cod_Mun)munic_Tuberculos = left_join(municipios, Tuberculos, by = c("MPIO_CCDGO" = "Cod_Mun"))
munic_Tuberculosbreaks <- classIntervals(munic_Tuberculos$max_prod, n = 6, style = 'fisher')## Warning in classIntervals(munic_Tuberculos$max_prod, n = 6, style = "fisher"):
## var has missing values, omitted in finding classes#label breaks
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$brks) - 1)){
lab_vec[i] <- paste0('(',rounded_breaks[i], ' - ', rounded_breaks[i+1], ']')
}munic_Tuberculos <- munic_Tuberculos %>%
mutate(faktor_class = factor(cut(max_prod, breaks$brks, include.lowest = T), labels = lab_vec))
# change attribute name
munic_Tuberculos$Produccion = munic_Tuberculos$faktor_classmunic_Tuberculos$mid <- sf::st_centroid(munic_Tuberculos$geometry)LONG = st_coordinates(munic_Tuberculos$mid)[,1]LAT = st_coordinates(munic_Tuberculos$mid)[,2]ggplot(data = munic_Tuberculos) +
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: 111 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
6. Hacer un nuevo mapa para el segundo grupo de cultivos mas grande
(Hortalizas = read_csv("C:/Users/GERARDO/Desktop/GB2022/datos/J.ber_Hort_2020.csv",show_col_types = FALSE))Hortalizas$Cod_Mun = as.character(Hortalizas$Cod_Mun)munic_Hortalizas = left_join(municipios, Hortalizas, by = c("MPIO_CCDGO" = "Cod_Mun"))
munic_Hortalizasfacet = "max_prod"
Hortalizas_map =
tm_shape(munic_Hortalizas) + tm_polygons(facet) + tm_text(text = "MPIO_CNMBR", size = 0.7, fontfamily = "sans") +
tm_shape(byc.cities) + tm_symbols(shape = 2, col = "red", size = 0.20) +
tm_credits("Data source: UPRA (2020)", fontface = "bold") +
tm_layout(main.title = "Produccion de hortalizas 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 viewingHortalizas_map## Credits not supported in view mode.## Symbol shapes other than circles or icons are not supported in view mode.7. Referencias
“Lizarazo, I. Reading and processing municipal agricultural statistics for 2020. Available at: https://rpubs.com/ials2un/readingEVAv1.”
sessionInfo()## R version 4.1.3 (2022-03-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19043)
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## Matrix products: default
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## 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
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] classInt_0.4-3 ggrepel_0.9.1 ggplot2_3.3.5 tmap_3.3-3 readr_2.1.2
## [6] dplyr_1.0.8 sf_1.0-7
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## [13] glue_1.6.2 digest_0.6.29 RColorBrewer_1.1-2
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## [19] XML_3.99-0.9 pkgconfig_2.0.3 raster_3.5-15
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## [37] magrittr_2.0.2 crayon_1.5.1 evaluate_0.15
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## [46] stringr_1.4.0 munsell_0.5.0 compiler_4.1.3
## [49] jquerylib_0.1.4 e1071_1.7-9 rlang_1.0.2
## [52] units_0.8-0 grid_4.1.3 tmaptools_3.1-1
## [55] dichromat_2.0-0 rstudioapi_0.13 htmlwidgets_1.5.4
## [58] crosstalk_1.2.0 leafem_0.1.6 base64enc_0.1-3
## [61] rmarkdown_2.13 wk_0.6.0 gtable_0.3.0
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## [67] markdown_1.1 R6_2.5.1 knitr_1.38
## [70] fastmap_1.1.0 bit_4.0.4 utf8_1.2.2
## [73] KernSmooth_2.23-20 stringi_1.7.6 parallel_4.1.3
## [76] Rcpp_1.0.8.3 vctrs_0.3.8 png_0.1-7
## [79] leaflet_2.1.1 tidyselect_1.1.2 xfun_0.30