special.epd: SPECIAL Research Group’s Version of the European Pollen Database (EPD)

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.

⚠️ 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")
#> # A tibble: 1 × 8
#>   ID_SITE ID_ENTITY site_name    entity_name dates samples age_model
#>     <int>     <int> <chr>        <chr>       <int>   <int>     <int>
#> 1       1         1 Aalkistensee MBA3            7      57        57
#> # … with 1 more variable: pollen_counts <tibble[,3]>

Using the site_name

special.epd::snapshot("Abant Golu", use_site_name = TRUE)
#> # A tibble: 1 × 8
#>   ID_SITE ID_ENTITY site_name  entity_name dates samples age_model
#>     <int>     <int> <chr>      <chr>       <int>   <int>     <int>
#> 1       4         4 Abant Golu ABANT           5      65        65
#> # … with 1 more variable: pollen_counts <tibble[,3]>

Using the ID_ENTITY

special.epd::snapshot(2)
#> # A tibble: 1 × 8
#>   ID_SITE ID_ENTITY site_name entity_name dates samples age_model
#>     <int>     <int> <chr>     <chr>       <int>   <int>     <int>
#> 1       2         2 Aammiq    AMMIQ           4      96        96
#> # … with 1 more variable: pollen_counts <tibble[,3]>

Using the ID_SITE

special.epd::snapshot(3, use_id_site = TRUE)
#> # A tibble: 1 × 8
#>   ID_SITE ID_ENTITY site_name        entity_name dates samples age_model
#>     <int>     <int> <chr>            <chr>       <int>   <int>     <int>
#> 1       3         3 Aansser peat bog ANS             4      64        64
#> # … with 1 more variable: pollen_counts <tibble[,3]>

Extracting multiple records at once

special.epd::snapshot(1:10)
#> # A tibble: 10 × 8
#>    ID_SITE ID_ENTITY site_name          entity_name dates samples age_model
#>      <int>     <int> <chr>              <chr>       <int>   <int>     <int>
#>  1       1         1 Aalkistensee       MBA3            7      57        57
#>  2       2         2 Aammiq             AMMIQ           4      96        96
#>  3       3         3 Aansser peat bog   ANS             4      64        64
#>  4       4         4 Abant Golu         ABANT           5      65        65
#>  5       5         5 Abborrtjarnen      ABBO            7     107       107
#>  6       6         6 Abernethy Forest   AF1974          7      49        49
#>  7       7         7 Abiare             ABIARE          1      34         0
#>  8       8         8 Above Cadubh       ACH3            1       7         0
#>  9       9         9 Above Loch an Eang AFF5            4      10        10
#> 10      10        10 Achit-Nur          ACHIT8          4      20        20
#> # … with 1 more variable: pollen_counts <tibble[,3]>

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")
#> # A tibble: 1 × 8
#>   ID_SITE ID_ENTITY site_name    entity_name dates samples age_model
#>     <int>     <int> <chr>        <chr>       <int>   <int>     <int>
#> 1       1         1 Aalkistensee MBA3            7      57        57
#> # … with 1 more variable: pollen_counts <tibble[,3]>

Output

#>                                 levelName
#> 1 .                                      
#> 2  ¦--MBA3_age_model.csv                 
#> 3  ¦--MBA3_dates.csv                     
#> 4  ¦--MBA3_metadata.csv                  
#> 5  ¦--MBA3_pollen_counts_amalgamated.csv 
#> 6  ¦--MBA3_pollen_counts_clean.csv       
#> 7  ¦--MBA3_pollen_counts_intermediate.csv
#> 8  °--MBA3_samples.csv

Including a path

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

Output

#>                                     levelName
#> 1 special.uor                                
#> 2  °--epd                                    
#> 3      ¦--MBA3_age_model.csv                 
#> 4      ¦--MBA3_dates.csv                     
#> 5      ¦--MBA3_metadata.csv                  
#> 6      ¦--MBA3_pollen_counts_amalgamated.csv 
#> 7      ¦--MBA3_pollen_counts_clean.csv       
#> 8      ¦--MBA3_pollen_counts_intermediate.csv
#> 9      °--MBA3_samples.csv

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))

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()

# Entities	with Dates	with Age models (using IntCal20)	with Pollen counts
1667	1667	1476	1667