Last updated: 2021-05-19
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Knit directory: booksn_ppm/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 78f0623 | Antonio J Perez-Luque | 2021-05-19 | Build site. |
| html | 30b0f35 | Antonio J Perez-Luque | 2021-05-18 | Build site. |
| Rmd | 66b69f4 | Antonio J Perez-Luque | 2021-05-18 | add data of species |
| html | 5ba1412 | Antonio J Perez-Luque | 2021-05-17 | Build site. |
| Rmd | 5dfa4b7 | Antonio J Perez-Luque | 2021-05-17 | update repo |
| html | 09743fc | Antonio J Perez-Luque | 2021-05-17 | Build site. |
| Rmd | f6f2948 | Antonio J Perez-Luque | 2021-05-17 | select data for SN |
La idea es seleccionar un conjunto de parcelas en torno a Sierra Nevada de la forma menos subjetiva posible.
library("tidyverse")
library("here")
library("finch")
library("sf")
library("flextable")
library("DT")sn <- st_read("data/data_raw/geoinfo/sn_enp.shp") Reading layer `sn_enp' from data source `/Users/ajpelu/Google Drive/MS/books/2021_SN/booksn_ppm/data/data_raw/geoinfo/sn_enp.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 10 fields
geometry type: POLYGON
dimension: XY
bbox: xmin: 442911.5 ymin: 4085480 xmax: 536730.7 ymax: 4123200
projected CRS: ED50 / UTM zone 30Nload(here::here("data/data_raw/mapa_rodales.RData"))
rodales <- st_as_sf(mapa.rodales) %>%
st_transform(crs = st_crs(sn)) %>%
st_make_valid()
rodal2019 <- st_read(here::here("data/data_raw/geoinfo/coberturas_procesionaria/COB270616_ETRS89.shp")) %>%
st_transform(crs = st_crs(sn)) %>%
st_make_valid()Reading layer `COB270616_ETRS89' from data source `/Users/ajpelu/Google Drive/MS/books/2021_SN/booksn_ppm/data/data_raw/geoinfo/coberturas_procesionaria/COB270616_ETRS89.shp' using driver `ESRI Shapefile'
Simple feature collection with 4550 features and 11 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 103172.9 ymin: 3991740 xmax: 599551.6 ymax: 4276615
projected CRS: ETRS89 / UTM zone 30NGeneramos varios buffers (10, 15, 20, 25 km) del límite de SN
Calculamos la cantidad de parcelas que caen en cada buffer, clasificadas por los niveles de elevación que hemos definido previamente.
ojo parece que había un problema con la seleccion que hacía de las parcelas (la capa que me paso Luis no se hacia bien la interseccion). Por ello hemos realizado esta operación con dos conjuntos de datos espaciales (es indiferente porque lo que queremos es ver que buffer elegir)
bufferiza <- function(parcelas, enp, d){
buffer <- st_buffer(enp, units::set_units(d, "km"))
namebuffer <- paste0('buffer_',d)
df <- st_intersection(parcelas, buffer) %>%
st_drop_geometry() %>%
# dplyr::select(code = `N.rodal`) %>%
mutate({{namebuffer}} := 1)
return(df)
}
i25 <- bufferiza(rodales, sn, 25) %>% dplyr::select(code = `N.rodal`, buffer_25)
i25b <- bufferiza(rodal2019, sn, 25) %>% dplyr::select(code = `N_CODIGO`, buffer_25)
i20 <- bufferiza(rodales, sn, 20) %>% dplyr::select(code = `N.rodal`, buffer_20)
i20b <- bufferiza(rodal2019, sn, 20) %>% dplyr::select(code = `N_CODIGO`, buffer_20)
i15 <- bufferiza(rodales, sn, 15) %>% dplyr::select(code = `N.rodal`, buffer_15)
i15b <- bufferiza(rodal2019, sn, 15) %>% dplyr::select(code = `N_CODIGO`, buffer_15)
i10 <- bufferiza(rodales, sn, 10) %>% dplyr::select(code = `N.rodal`, buffer_10)
i10b <- bufferiza(rodal2019, sn, 10) %>% dplyr::select(code = `N_CODIGO`, buffer_10)
df <- i25 %>%
full_join(i20) %>%
full_join(i15) %>%
full_join(i10)
dfb <- i25b %>%
full_join(i20b) %>%
full_join(i15b) %>%
full_join(i10b) coplas2019 <- read_csv(here::here("data/coplas2019.csv"))
df_elev <- df %>%
left_join(coplas2019) %>%
dplyr::select(code, buffer_25:buffer_10, sp, elevF, sp_abrev, especie)
df_elevb <- dfb %>%
left_join(coplas2019) %>%
dplyr::select(code, buffer_25:buffer_10, sp, elevF, sp_abrev, especie)
n_parcelas <- df_elev %>%
group_by(elevF) %>%
summarise(across(starts_with('buf'), sum, na.rm = TRUE)) %>%
filter(!is.na(elevF))
n_parcelasb <- df_elevb %>%
group_by(elevF) %>%
summarise(across(starts_with('buf'), sum, na.rm = TRUE)) %>%
filter(!is.na(elevF)) DT::datatable(n_parcelasb)exclude <- c("AL088001", "AL088002", "AL088003", "AL088004", "AL088005",
"AL088006", "AL078001", "AL078002", "AL078003", "AL074001",
"AL074002", "AL013001", "AL013002")
coplas.sn <- coplas2019 %>%
left_join(i20b) %>%
filter(!(code %in% exclude)) %>%
mutate(buffer_20 = case_when(
code %in% c("GR140011", "GR140013") ~ 1,
TRUE ~ buffer_20)) %>%
filter(buffer_20 == 1) %>%
dplyr::select(-buffer_20)
write_csv(coplas.sn, here::here("data/coplas2019sn.csv"))
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] DiagrammeR_1.0.6.1 sp_1.4-5 DT_0.17 flextable_0.6.3
[5] sf_0.9-7 finch_0.4.0 here_1.0.1 forcats_0.5.1
[9] stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.4.0
[13] tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3 tidyverse_1.3.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] fs_1.5.0 lubridate_1.7.10 RColorBrewer_1.1-2 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.0.2 backports_1.2.1 bslib_0.2.4
[9] R6_2.5.0 KernSmooth_2.23-18 rgeos_0.5-5 DBI_1.1.1
[13] lazyeval_0.2.2 colorspace_2.0-0 withr_2.4.1 tidyselect_1.1.0
[17] curl_4.3 compiler_4.0.2 git2r_0.28.0 cli_2.3.0
[21] rvest_0.3.6 jsonld_2.2 xml2_1.3.2 officer_0.3.16
[25] sass_0.3.1 scales_1.1.1 classInt_0.4-3 rappdirs_0.3.3
[29] systemfonts_1.0.0 digest_0.6.27 rmarkdown_2.6.6 base64enc_0.1-3
[33] pkgconfig_2.0.3 htmltools_0.5.1.1 dbplyr_2.1.0 htmlwidgets_1.5.3
[37] rlang_0.4.10 readxl_1.3.1 rstudioapi_0.13 visNetwork_2.0.9
[41] jquerylib_0.1.3 generics_0.1.0 emld_0.5.1 jsonlite_1.7.2
[45] crosstalk_1.1.1 zip_2.1.1 magrittr_2.0.1 Rcpp_1.0.6
[49] munsell_0.5.0 gdtools_0.2.3 lifecycle_1.0.0 stringi_1.5.3
[53] whisker_0.4 yaml_2.2.1 jqr_1.2.0 grid_4.0.2
[57] EML_2.0.4 promises_1.2.0.1 crayon_1.4.1 lattice_0.20-41
[61] haven_2.3.1 hms_1.0.0 knitr_1.31 pillar_1.4.7
[65] uuid_0.1-4 reprex_1.0.0 glue_1.4.2 evaluate_0.14
[69] V8_3.4.0 hoardr_0.5.2 data.table_1.13.6 modelr_0.1.8
[73] vctrs_0.3.6 httpuv_1.5.5 cellranger_1.1.0 gtable_0.3.0
[77] assertthat_0.2.1 xfun_0.20 broom_0.7.4 e1071_1.7-4
[81] later_1.1.0.1 class_7.3-18 units_0.6-7 ellipsis_0.3.1