Last updated: 2021-05-19

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Knit directory: booksn_ppm/

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library("tidyverse")
library("here")
library("finch")
library("sf")
library("sp")
library("readxl")
library("DT")

Datos de parcelas

  • Leemos el archivo que publicamos en GBIF

  • Utilizamos un shape con las parcelas, al cual le calculamos en su momento la elevación mínima, máxima y promedio de la parcela (este shape no se publicó; se llama rodales_stats.shp)

  • Realizamos una clasificación de las parcelas en altitud (elevF):

    • elev_mean <= 600: 0verylow;
    • elev_mean >600 & elev_mean <=1200: 1low
    • elev_mean >1200 & elev_mean <=1700: 2medium
    • elev_mean >1700: 3high
  • Generamos un dataset con los siguientes campos:

    • código de parcela,
    • area (ha)
    • perímetro
    • provincia
    • elevacion (min, max, mean, elevF)
    • lat, long
# https://ipt.gbif.es/archive.do?r=coplas&v=2.4
f <- finch::dwca_read("https://ipt.gbif.es/archive.do?r=coplas&v=2.4")

# Read the data files
eventRaw <- read_delim(f$data[1], delim = "\t") # event.txt
occRaw <- read_delim(f$data[2], delim = "\t") # occurrence.txt
mofRaw <- read_delim(f$data[3], delim = "\t") # extendedmeasurementorfact.txt

rodales <- st_read(here::here("data/data_raw/geoinfo/rodales_stats.shp")) 
Reading layer `rodales_stats' from data source `/Users/ajpelu/Google Drive/MS/books/2021_SN/booksn_ppm/data/data_raw/geoinfo/rodales_stats.shp' using driver `ESRI Shapefile'
Simple feature collection with 4389 features and 7 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -7.436747 ymin: 36.04137 xmax: -1.875561 ymax: 38.61815
geographic CRS: WGS 84
rodal_elev <- rodales %>% st_drop_geometry() %>% 
  rename_all(tolower) %>% 
  dplyr::select(code = codigo, 
                perim = perimetro, 
                elev_mean = mean) %>% 
  mutate(across(where(is.numeric), round, 2))
  

parcelas <- eventRaw %>% 
  mutate(code = stringr::str_remove(eventID, "(\\-).*"),
         area_ha = sampleSizeValue/10000) %>% 
  dplyr::select(code,
                area_ha,
                prov = stateProvince, 
                elev_min = minimumElevationInMeters,
                elev_max = maximumElevationInMeters,
                lat = decimalLatitude,
                long = decimalLongitude) %>% unique() %>% 
  inner_join(rodal_elev) %>% 
  mutate(
    elevF = case_when(
      elev_mean <= 600 ~ "0verylow",
      elev_mean >600 & elev_mean <=1200 ~ "1low",
      elev_mean >1200 & elev_mean <=1700 ~ "2medium",
      elev_mean >1700 ~ "3high"
    )
  )

Datos de infestación

  • Leemos los datos de infestación (COPLAS) de las parcelas desde 1992 hasta 2019
# Read all sheets from excel 
path <- here::here("data/data_raw/Grados_Infestacion_1992_2019.xlsx")
raw_infesta <- path %>% 
  excel_sheets() %>% 
  set_names() %>% 
  map_df(read_excel, path = path) 

names(raw_infesta) <- c("code","infestacion","year")

Corregimos algunos errores en la nomenclatura de las parcelas. Parece que hay un problema con que algunas parcelas están llamadas de diferente forma con “-” y sin “-”

raw_infesta <- raw_infesta %>% 
  mutate(code = stringr::str_remove_all(code, "-"))

¿Cuantos plots?

length(unique(raw_infesta$code))
[1] 4828

Parece que hay duplicados. Vamos a buscarlos: - Creamos un campo codeyear (code+year) - Buscamos duplicados, esto es para una misma fecha + parcela varios valores de infestacion

duplicados <- raw_infesta %>% 
  unite("codeyear", c(code,year), sep="-", remove=FALSE) %>% 
  group_by(codeyear) %>% 
  count() %>% 
  filter(n>1) %>% 
  separate(codeyear, c("code", "year"), sep="-", remove=FALSE) 

parcelas_duplicadas <- duplicados %>% 
  dplyr::select(code) %>% unique()

Vamos a usar los datos que ya teníamos publicados en GBIF, que parece que tenian solucionado el filtrado de datos y le vamos a añadir nuevos datos (desde 2015 a la actualidad)

infesta2015 <- mofRaw %>% 
  mutate(
    code = stringr::str_remove(id, "(\\-).*")) %>% 
  dplyr::select(
  code,
  infestacion = measurementValue,
  year = measurementDeterminedDate) %>%
  pivot_wider(names_from = year, 
              values_from = infestacion)

De los datos nuevos filtramos > 2015

raw_infesta2019 <- raw_infesta %>% filter(year > 2015) 

duplicados2019 <- raw_infesta2019 %>% 
  filter(year > 2015) %>% 
  unite("codeyear", c(code,year), sep="-", remove=FALSE) %>% 
  group_by(codeyear) %>% 
  count() %>% 
  filter(n>1) %>% 
  separate(codeyear, c("code", "year"), sep="-", remove=FALSE) 

parcelas_duplicadas2019 <- duplicados2019 %>% 
  dplyr::select(code) %>% unique()

infesta2019 <- raw_infesta2019 %>% 
  filter(year > 2015) %>%
  pivot_wider(names_from = year, 
              values_from = infestacion, 
              values_fn = {min})
coplas <- infesta2015 %>% 
  left_join(infesta2019) 

Datos de Especies de pinos

Hemos llevado a cabo la asignación de los pinos por dos vías:

  • (1). Cruzar cobertura de coplas con cobertura de pinos (issue)

Hemos usado, datos de formaciones de: https://laboratoriorediam.cica.es/geonetwork/srv/spa/catalog.search#/metadata/a25e289b-4d4d-49fc-af06-b64adf29e81b

ph <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Phalepensis.shp")) %>% 
  mutate(pinus = "halepensis")

rodales_t <- st_make_valid(rodales) %>% st_transform(crs = st_crs(ph)) 

parcelas_centroid <- parcelas %>% dplyr::select(code, lat, long) %>% 
  st_as_sf(coords = c("long", "lat"), crs = 4326) %>% 
  st_transform(crs = st_crs(ph)) 
# st_write(parcelas_centroid, here::here(here::here("data_raw/geoinfo/parcelas_centroid.shp")), 
#          append = FALSE)

pn <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Pnigra.shp")) %>% 
  mutate(pinus = "nigra")
pp <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Ppinaster.shp")) %>% 
  mutate(pinus = "pinaster")
ps <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Psylvestris.shp")) %>% 
  mutate(pinus = "sylvestris")
ppinea <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Ppinea.shp")) %>% 
  mutate(pinus = "pinea")
pr <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Pradiata.shp")) %>% 
  mutate(pinus = "radiata")
pc <- st_read(here::here("data/data_raw/geoinfo/dist_pinus/Pcanariensis.shp")) %>% 
  mutate(pinus = "canariensis")


iph <- st_intersection(st_make_valid(ph), rodales_t) %>% st_drop_geometry() %>% 
  dplyr::select(Codigo, pinus) %>% unique() 

ipp <- st_intersection(st_make_valid(pp), rodales_t) %>% st_drop_geometry() %>% 
    dplyr::select(Codigo, pinus) %>% unique() 
ips <- st_intersection(st_make_valid(ps), rodales_t) %>% st_drop_geometry() %>% 
    dplyr::select(Codigo, pinus) %>% unique() 
ipn <- st_intersection(st_make_valid(pn), rodales_t) %>% st_drop_geometry() %>% 
    dplyr::select(Codigo, pinus) %>% unique() 
ippinea <- st_intersection(st_make_valid(ppinea), rodales_t) %>% st_drop_geometry() %>% 
    dplyr::select(Codigo, pinus) %>% unique() 
ipc <- st_intersection(st_make_valid(pc), rodales_t) %>% st_drop_geometry() %>% 
    dplyr::select(Codigo, pinus) %>% unique() 
ipr <- st_intersection(st_make_valid(pr), rodales_t) %>% st_drop_geometry() %>% 
    dplyr::select(Codigo, pinus) %>% unique() 


u <- bind_rows(iph, ipp, ips, ipn, ipc, ipr, ippinea)

u <- u %>% mutate(v = 1) %>% 
  pivot_wider(names_from = pinus, values_from = v)


i <- iph %>% dplyr::select(Codigo, pinus) %>% unique()
    1. Usar datos procedentes de la base de datos antigua (proporcionada por L. Cayuela)
load(here::here("data/data_raw/mapa_rodales.RData"))

parcela_sp <- mapa.rodales@data %>% dplyr::select(code = N.rodal, sp = Especie)

Le añadimos los pinos a la parcela

coplas_sp <- coplas %>% inner_join(parcela_sp) %>% 
  inner_join(parcelas) %>% 
  dplyr::relocate(code, prov, area_ha, sp, elev_mean, elev_min, elev_max, elevF, perim, lat, long, `1993`:`2015`, `2016`, `2017`, `2018`, `2019`)

coplas_sp <- coplas_sp %>% 
  mutate(sp_abrev = recode(sp,"PINUS HALEPENSIS" = "phale",
                     "PINUS SYLVESTRIS" = "psylv",
                     "PINUS NIGRA SSP AUSTR." = "pnig_aus",
                     "PINUS NIGRA SSP SALZM." = "pnig_sal",
                     "PINUS PINASTER" = "ppinas",
                     "PINUS PINEA" = "ppinea",
                     "PINUS UNCINATA" = "punci",
                     "PINUS CANARIENSIS" = "pcana"),
         especie = case_when(
  sp_abrev %in% c("pnig_aus", "pnig_sal") ~ "P. nigra",
  sp_abrev == "phale" ~ "P. halepensis",
  sp_abrev == "psylv" ~ "P. sylvestris",
  sp_abrev == "ppinas" ~ "P. pinaster", 
  sp_abrev == "ppinea" ~ "P. pinea")) 


write_csv(coplas_sp, here::here("data/coplas2019.csv"))
DT::datatable(coplas_sp)

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] DT_0.17         readxl_1.3.1    sp_1.4-5        sf_0.9-7       
 [5] finch_0.4.0     here_1.0.1      forcats_0.5.1   stringr_1.4.0  
 [9] dplyr_1.0.4     purrr_0.3.4     readr_1.4.0     tidyr_1.1.2    
[13] tibble_3.0.6    ggplot2_3.3.3   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] httr_1.4.2         sass_0.3.1         jsonlite_1.7.2     modelr_0.1.8      
 [5] bslib_0.2.4        assertthat_0.2.1   cellranger_1.1.0   yaml_2.2.1        
 [9] lattice_0.20-41    pillar_1.4.7       backports_1.2.1    glue_1.4.2        
[13] uuid_0.1-4         digest_0.6.27      promises_1.2.0.1   rvest_0.3.6       
[17] EML_2.0.4          colorspace_2.0-0   htmltools_0.5.1.1  httpuv_1.5.5      
[21] pkgconfig_2.0.3    broom_0.7.4        haven_2.3.1        scales_1.1.1      
[25] whisker_0.4        later_1.1.0.1      emld_0.5.1         git2r_0.28.0      
[29] generics_0.1.0     ellipsis_0.3.1     withr_2.4.1        lazyeval_0.2.2    
[33] cli_2.3.0          magrittr_2.0.1     crayon_1.4.1       evaluate_0.14     
[37] fs_1.5.0           xml2_1.3.2         class_7.3-18       tools_4.0.2       
[41] data.table_1.13.6  hms_1.0.0          lifecycle_1.0.0    V8_3.4.0          
[45] munsell_0.5.0      reprex_1.0.0       compiler_4.0.2     jquerylib_0.1.3   
[49] e1071_1.7-4        jqr_1.2.0          rlang_0.4.10       classInt_0.4-3    
[53] units_0.6-7        grid_4.0.2         jsonld_2.2         rstudioapi_0.13   
[57] htmlwidgets_1.5.3  rappdirs_0.3.3     crosstalk_1.1.1    rmarkdown_2.6.6   
[61] gtable_0.3.0       DBI_1.1.1          curl_4.3           R6_2.5.0          
[65] lubridate_1.7.10   knitr_1.31         rprojroot_2.0.2    KernSmooth_2.23-18
[69] hoardr_0.5.2       stringi_1.5.3      Rcpp_1.0.6         vctrs_0.3.6       
[73] dbplyr_2.1.0       tidyselect_1.1.0   xfun_0.20