## ---- message=FALSE, eval=FALSE------------------------------------------
# library("rgbif")
# library("rgdal")
# library("sp")
# library("raster")
# library("dplyr")
# library("sf")
# library("knitr")
## ---- eval=FALSE---------------------------------------------------------
# ### uuid of Sinfonevada
# sinfo_uuid <- 'db6cd9d7-7be5-4cd0-8b3c-fb6dd7446472'
#
# ### See metadata
# sinfo_meta <- datasets(uuid = sinfo_uuid)
#
# # Get table of ocurrences
# sf <- occ_data(datasetKey=sinfo_meta$data$key, limit = 8000)
## ---- eval=FALSE---------------------------------------------------------
# # Get only the fields of interest
# df <- sf$data %>% dplyr::select(decimalLatitude, decimalLongitude, scientificName)
#
# # How many species by plot
# richness_loc <- df %>%
# group_by(decimalLatitude, decimalLongitude) %>%
# count() %>% tibble::rownames_to_column(var='id_plot') %>% data.frame() %>%
# rename(rich = n)
## ---- eval=FALSE---------------------------------------------------------
# # Prepare Ecosystems data
# wd <- getwd()
#
# temporalwd <- setwd(tempdir())
# unzip('../inst/extdata/ecosistemas_sn.zip', exdir = temporalwd)
#
# eco <- readOGR(dsn=temporalwd, layer = 'ecosistemas', encoding="UTF-8", verbose = TRUE)
#
# # Transform projection
# eco_t <- spTransform(eco, CRS("+init=epsg:4326"))
## ---- eval=FALSE---------------------------------------------------------
# # Create an spatial point dataframe for the plots
# richness_sp <- SpatialPointsDataFrame(richness_loc[,c("decimalLongitude", "decimalLatitude")],
# richness_loc)
# projection(richness_sp) <- CRS("+init=epsg:4326")
## ---- eval=FALSE---------------------------------------------------------
# # See this example
# # https://gis.stackexchange.com/questions/226035/join-spatial-point-data-with-multiple-polygon-data-using-r
#
# # Convert to sf-objects
# richness_sp.sf <- st_as_sf(richness_sp)
# eco_t.sf <- st_as_sf(eco_t)
#
# # Keep all "meuse.sf", sort by row.names(meuse.sf). Default overlay is "intersects".
# aux <- st_join(richness_sp.sf, eco_t.sf[,c('COD_ECOSIS', 'ECOSISTE_1')])
#
# # Convert back to Spatial*
# richness_sp_eco <- as(aux, "Spatial")
## ---- eval=FALSE---------------------------------------------------------
# aux <- aux %>% mutate(newECO = recode_factor(COD_ECOSIS,
# `8`="Pine plantations",
# `2`="High-mountain meadows",
# `3`="High-mountain shrubland",
# `5`="Mid-mountain shrubland",
# `1`="Pastures",
# `6`="Aquatic systems",
# `NA`="NA",
# .default = 'Natural Forests'))
#
# aux <- aux %>% mutate(ECOSISTE_1 = recode_factor(ECOSISTE_1,
# `Matorral de alta monta\361a (enebrales, sabinales, piornales)` =
# "Matorral de alta montana (enebrales, sabinales, piornales)",
# `Matorrales de media monta\361a (retamares, tomillares, etc.)`=
# "Matorrales de media montana (retamares, tomillares, etc.)",
# `Pastos de media monta\361aMaSis` = "Pastos de media montana",
# `Pinares aut\363ctonos de P. sylvestris` = "Pinares autoctonos de P. sylvestris",
# `Pinares aut\363ctonos sobre dolom\355as` = "Pinares autoctonos sobre dolomias",
# `Repoblaciones de con\355feras`= "Repoblaciones de coniferas",
# `Sistemas acu\341ticos` = "Sistemas acuaticos"))
## ---- eval=FALSE---------------------------------------------------------
# richSinfo <- aux %>%
# group_by(ECOSISTE_1, COD_ECOSIS) %>%
# summarise(mean = mean(rich),
# sd = sd(rich),
# se = sd/sqrt(length(rich)),
# n = length(rich),
# min = min(rich),
# max = max(rich),
# median = median(rich)) %>%
# as.data.frame() %>%
# dplyr::select(-geometry)
## ---- eval=FALSE---------------------------------------------------------
# richSinfo_agg <- aux %>%
# group_by(newECO) %>%
# summarise(mean = mean(rich),
# sd = sd(rich),
# se = sd/sqrt(length(rich)),
# n = length(rich),
# min = min(rich),
# max = max(rich),
# median = median(rich)) %>%
# as.data.frame() %>%
# dplyr::select(-geometry)
#
## ---- eval=FALSE---------------------------------------------------------
# richPot <- data.frame(cbind(
# eco = c('Plantations', 'Quercus ilex forests', 'Natural deciduous forests'),
# n = c(442, 45, 26),
# potRich = c(13.09, 14.92, 17.55),
# lowerInterval = c(12.82, 13.72, 15.62),
# upperInterval = c(13.34, 16.11, 19.66)))
#
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