Last updated: 2021-05-19
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Knit directory: booksn_ppm/
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| File | Version | Author | Date | Message |
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| Rmd | d5ebdb5 | Antonio J Perez-Luque | 2021-05-19 | update sp_abrev |
| html | b01a7a5 | Antonio J Perez-Luque | 2021-05-18 | Build site. |
| Rmd | e491fe0 | Antonio J Perez-Luque | 2021-05-18 | include sp rename at prepare Data |
| html | c73db62 | Antonio J Perez-Luque | 2021-05-17 | Build site. |
| Rmd | fd92f89 | Antonio J Perez-Luque | 2021-05-17 | add preparacion de datos |
library("tidyverse")
library("here")
library("finch")
library("sf")
library("sp")
library("readxl")
library("DT")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):
Generamos un dataset con los siguientes campos:
# 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$data[3], delim = "\t") # extendedmeasurementorfact.txt
rodales <- st_read(here::here("data/data_raw/geoinfo/rodales_stats.shp")) Reading layer `rodales_stats' from data source `/Users/ajpelu/Google Drive/MS/books/2021_SN/booksn_ppm/data/data_raw/geoinfo/rodales_stats.shp' using driver `ESRI Shapefile'
Simple feature collection with 4389 features and 7 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: -7.436747 ymin: 36.04137 xmax: -1.875561 ymax: 38.61815
geographic CRS: WGS 84rodal_elev <- rodales %>% st_drop_geometry() %>%
rename_all(tolower) %>%
dplyr::select(code = codigo,
perim = perimetro,
elev_mean = mean) %>%
mutate(across(where(is.numeric), round, 2))
parcelas <- eventRaw %>%
mutate(code = stringr::str_remove(eventID, "(\\-).*"),
area_ha = sampleSizeValue/10000) %>%
dplyr::select(code,
area_ha,
prov = stateProvince,
elev_min = minimumElevationInMeters,
elev_max = maximumElevationInMeters,
lat = decimalLatitude,
long = decimalLongitude) %>% unique() %>%
inner_join(rodal_elev) %>%
mutate(
elevF = case_when(
elev_mean <= 600 ~ "0verylow",
elev_mean >600 & elev_mean <=1200 ~ "1low",
elev_mean >1200 & elev_mean <=1700 ~ "2medium",
elev_mean >1700 ~ "3high"
)
)# Read all sheets from excel
path <- here::here("data/data_raw/Grados_Infestacion_1992_2019.xlsx")
raw_infesta <- path %>%
excel_sheets() %>%
set_names() %>%
map_df(read_excel, path = path)
names(raw_infesta) <- c("code","infestacion","year")Corregimos algunos errores en la nomenclatura de las parcelas. Parece que hay un problema con que algunas parcelas están llamadas de diferente forma con “-” y sin “-”
raw_infesta <- raw_infesta %>%
mutate(code = stringr::str_remove_all(code, "-"))¿Cuantos plots?
length(unique(raw_infesta$code))[1] 4828Parece 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) Hemos llevado a cabo la asignación de los pinos por dos vías:
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()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. halpensis",
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