SMPDS: The SPECIAL Modern Pollen Data for Climate Reconstructions

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.

⚠️ 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")
#> # A tibble: 1 × 6
#>   ID_SITE ID_ENTITY ID_SAMPLE site_name entity_name   pollen_counts$clean
#>     <int>     <int>     <int> <chr>     <chr>                       <int>
#> 1    3890      7901         1 Juodonys  juodonys_core                   0
#> # ℹ 2 more variables: pollen_counts$intermediate <int>, $amalgamated <int>

Using the site_name

smpds::snapshot("Petresiunai", use_site_name = TRUE)
#> # A tibble: 1 × 6
#>   ID_SITE ID_ENTITY ID_SAMPLE site_name   entity_name     pollen_counts$clean
#>     <int>     <int>     <int> <chr>       <chr>                         <int>
#> 1    6690     14229         2 Petresiunai petresiunai_121                   0
#> # ℹ 2 more variables: pollen_counts$intermediate <int>, $amalgamated <int>

Using the ID_ENTITY

smpds::snapshot(2)
#> # A tibble: 1 × 6
#>   ID_SITE ID_ENTITY ID_SAMPLE site_name entity_name pollen_counts$clean
#>     <int>     <int>     <int> <chr>     <chr>                     <int>
#> 1       1         2      9710 05-Mo     05-Mo-10                      0
#> # ℹ 2 more variables: pollen_counts$intermediate <int>, $amalgamated <int>

Using the ID_SITE

smpds::snapshot(3, use_id_site = TRUE)
#> # A tibble: 1 × 6
#>   ID_SITE ID_ENTITY ID_SAMPLE site_name  entity_name pollen_counts$clean
#>     <int>     <int>     <int> <chr>      <chr>                     <int>
#> 1       3        37     15871 11 [HFL11] HFL11                         0
#> # ℹ 2 more variables: pollen_counts$intermediate <int>, $amalgamated <int>

Extracting multiple records at once

smpds::snapshot(1:10)
#> # A tibble: 10 × 6
#>    ID_SITE ID_ENTITY ID_SAMPLE site_name entity_name pollen_counts$clean
#>      <int>     <int>     <int> <chr>     <chr>                     <int>
#>  1       1         1      9709 05-Mo     05-Mo                         0
#>  2       1         2      9710 05-Mo     05-Mo-10                      0
#>  3       1         3      9711 05-Mo     05-Mo-11                      0
#>  4       1         4      9712 05-Mo     05-Mo-12                      0
#>  5       1         5      9713 05-Mo     05-Mo-13                      0
#>  6       1         6      9714 05-Mo     05-Mo-14                      0
#>  7       1         7      9715 05-Mo     05-Mo-15                      0
#>  8       1         8      9716 05-Mo     05-Mo-16                      0
#>  9       1         9      9717 05-Mo     05-Mo-17                      0
#> 10       1        10      9718 05-Mo     05-Mo-18                      0
#> # ℹ 2 more variables: pollen_counts$intermediate <int>, $amalgamated <int>

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")
#> # A tibble: 1 × 6
#>   ID_SITE ID_ENTITY ID_SAMPLE site_name entity_name   pollen_counts$clean
#>     <int>     <int>     <int> <chr>     <chr>                       <int>
#> 1    3890      7901         1 Juodonys  juodonys_core                   0
#> # ℹ 2 more variables: pollen_counts$intermediate <int>, $amalgamated <int>

Output

#>                                          levelName
#> 1 .                                               
#> 2  ¦--juodonys_core_metadata.csv                  
#> 3  ¦--juodonys_core_pollen_counts_amalgamated.csv 
#> 4  ¦--juodonys_core_pollen_counts_clean.csv       
#> 5  °--juodonys_core_pollen_counts_intermediate.csv

Including a path

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

Output

#>                                              levelName
#> 1 special.uor                                         
#> 2  °--epd                                             
#> 3      ¦--juodonys_core_metadata.csv                  
#> 4      ¦--juodonys_core_pollen_counts_amalgamated.csv 
#> 5      ¦--juodonys_core_pollen_counts_clean.csv       
#> 6      °--juodonys_core_pollen_counts_intermediate.csv

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