In special-uor/smpds: The SPECIAL Modern Pollen Data Set for Climate Reconstructions

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SMPDS: The SPECIAL Modern Pollen Data for Climate Reconstructions

r badger::badge_devel("special-uor/smpds", "yellow") r badger::badge_github_actions("special-uor/smpds") r badger::badge_cran_release("smpds", "black") r badger::badge_doi("10.5281/zenodo.6598832", "blue") r badger::badge_codecov(token = 'UOX3PKOPVT')

The goal of smpds is to provide access to the SPECIAL Modern Pollen Data Set for Climate Reconstructions (SMPDS).

Installation

You can(not) install the released version of SMPDS from CRAN with:

install.packages("smpds")

And the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("special-uor/smpds")

Example

Load tables to the working environment

data("climate", package = "smpds")
data("entity", package = "smpds")
data("pollen_count", package = "smpds")
data("taxon_name", package = "smpds")

Create a snapshot of entities

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

This function returns a list with 3 components:

• entity: data frame (tibble object) with the metadata associated to the entities.
• climate: data frame (tibble object) with the climate and vegetation reconstructions. This one can be linked to the entity 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 entity 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 <- smpds::snapshot(...)
output$entity
output$climate
output$pollen_count$clean
output$pollen_count$intermediate
output$pollen_count$intermediate

Using the entity_name

smpds::snapshot("juodonys_core")

Using the site_name

smpds::snapshot("Petresiunai", use_site_name = TRUE)

Using the ID_ENTITY

smpds::snapshot(2)

Using the ID_SITE

smpds::snapshot(3, use_id_site = TRUE)

Extracting multiple records at once

smpds::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 <- smpds::snapshot()

Export data as individual CSV files

The function smpds::write_csvs takes to parameters:

• .data: a list of class snapshot, this one can be generated using the function smpds::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

`%>%` <- smpds::`%>%`
smpds::snapshot("juodonys_core") %>%
  smpds::write_csvs(prefix = "juodonys_core")

Output

paths <- list.files(pattern = "juodonys_core", 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

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

Output

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

Spatial distribution of the entities

Potential Natural Vegetation (PNV)

smpds::SMPDSv2 %>%
  smpds::plot_biome()

Growing Degree Days aboce 0 deg Celsius (gdd0)

smpds::SMPDSv2 %>%
  smpds::plot_gdd()

Mean Temperature of the COldest month (mtco)

smpds::SMPDSv2 %>%
  smpds::plot_mtco()

Moisture Index (mi)

smpds::SMPDSv2 %>%
  smpds::plot_mi()

Code of Conduct

Please note that the SMPDS project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Publications

This package is a companion to the following dataset:

Villegas-Diaz, R., Harrison, S. P., 2022. The SPECIAL Modern Pollen Data Set for Climate Reconstructions, version 2 (SMPDSv2). University of Reading. Dataset. https://doi.org/10.17864/1947.000389

special-uor/smpds documentation built on July 9, 2024, 5:39 p.m.