In special-uor/special.epd: SPECIAL Research Group's Version of the European Pollen Database (EPD)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
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special.epd: SPECIAL Research Group's Version of the European Pollen Database (EPD)

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The goal of special.epd is to provide access to the SPECIAL Research group's version of the European Pollen Database (EPD).

Installation

You can(not) install the released version of special.epd from CRAN with:

install.packages("special.epd")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("special-uor/special.epd", "dev")

Example

Load tables to working environment

data("entity", package = "special.epd")
data("date_info", package = "special.epd")
data("sample", package = "special.epd")
data("age_model", package = "special.epd")
data("pollen_count", package = "special.epd")

Create a snapshot of entities

The function special.epd::snapshot takes few different parameters and based on the first one, x, it returns a variety of snapshots.

This function returns a list with 5 components:

• entity: data frame (tibble object) with the metadata associated to the entities.
• date_info: data frame (tibble object) with the dating information. This one can be linked to the entity table using the column called ID_ENTITY.
• sample: data frame (tibble object) with the sampling information. This one can be linked to the entity table using the column called ID_ENTITY.
• age_model: : data frame (tibble object) with the "new" age models (created with ageR). This one can be linked to the sample table using the column called ID_SAMPLE.
• pollen_count: list of data frames (tibble objects) containing the pollen counts for 3 levels of "amalgamation":
  • clean
  • intermediate
  • amalgamated

All these data frames can be linked to the sample table using the column called ID_SAMPLE.

:warning: NOTE: the output is returned "invisibly", so you should assign the output of the function to a variable.

output <- special.epd::snapshot(...)
output$entity
output$date_info
output$sample
output$pollen_count$clean
output$pollen_count$intermediate
output$pollen_count$intermediate

Using the entity_name

special.epd::snapshot("MBA3")

Using the site_name

special.epd::snapshot("Abant Golu", use_site_name = TRUE)

Using the ID_ENTITY

special.epd::snapshot(2)

Using the ID_SITE

special.epd::snapshot(3, use_id_site = TRUE)

Extracting multiple records at once

special.epd::snapshot(1:10)

Extracting all the records at once

This will run very slow, so if only few entities are required, it would be better to indicate which, based on the previous examples.

out <- special.epd::snapshot()

Export data as individual CSV files

The function special.epd::write_csvs takes to parameters:

• .data: a list of class snapshot, this one can be generated using the function special.epd::snapshot (see previous section).
• prefix: a prefix name to be included in each individual files, this prefix can include a relative or absolute path to a directory in the local machine.

Without a path

`%>%` <- special.epd::`%>%`
special.epd::snapshot("MBA3") %>%
  special.epd::write_csvs(prefix = "MBA3")

Output

paths <- list.files(pattern = "MBA3", full.names = TRUE)
tree <- data.tree::as.Node(data.frame(pathString = paths))
data.tree::SetGraphStyle(tree, rankdir = "TB")
data.tree::SetNodeStyle(tree,
                        style = "filled,rounded",
                        shape = "box")
print(tree)

Including a path

`%>%` <- special.epd::`%>%`
special.epd::snapshot("MBA3") %>%
  special.epd::write_csvs(prefix = "/special.uor/epd/MBA3")

Output

paths <- list.files(pattern = "MBA3", full.names = TRUE) %>%
  stringr::str_replace_all("./", "/special.uor/epd/")
tree <- data.tree::as.Node(data.frame(pathString = paths))
data.tree::SetGraphStyle(tree, rankdir = "TB")
data.tree::SetNodeStyle(tree,
                        style = "filled,rounded",
                        shape = "box")
print(tree)

paths <- list.files(pattern = "MBA3", full.names = TRUE)
unlink(paths)

Spatial distribution of the entities

`%>%` <- special.epd::`%>%`
special.epd::entity %>%
  smpds::plot_climate(var = "elevation", units = "m ASL", 
                      ylim = c(25, 85),
                      xlim = c(-30, 170))

Extract Potential Natural Vegetation (PNV)

Using the package smpds [https://github.com/special-uor/smpds] we can extract the PNV for each entity and create a plot:

`%>%` <- special.epd::`%>%`
special.epd_pnv <- special.epd::entity %>%
  smpds::extract_biome()

# For a quicker execution
special.epd_pnv <- special.epd::entity %>%
  smpds::parallel_extract_biome(cpus = 4)

# Plot the PNV
special.epd_pnv %>%
  smpds::plot_biome(ylim = c(25, 85),
                    xlim = c(-30, 170))

`%>%` <- special.epd::`%>%`
special.epd_pnv <- special.epd::entity %>%
  smpds::parallel_extract_biome(cpus = 12)

# Plot the PNV
special.epd_pnv %>%
  smpds::plot_biome(ylim = c(25, 85),
                    xlim = c(-30, 170))

Summary of the database

`%>%` <- special.epd::`%>%`
special_epd_summary <- special.epd::db_summary()
tibble::tibble(
    `# Entities` = nrow(special_epd_summary),
    `with Dates` = sum(special_epd_summary$has_DATES, na.rm = TRUE),
    `with Age models (using IntCal20)` = sum(special_epd_summary$has_AM, na.rm = TRUE),
    `with Pollen counts` = sum(special_epd_summary$has_COUNTS, na.rm = TRUE)
  ) %>%
  knitr::kable()

special-uor/special.epd documentation built on April 10, 2022, 11:06 a.m.