Setting up token and connecting to SQLite database

In order to start working with pestr package, you should register yourself (free of charge) to EPPO Data Services. Then run create_eppo_token and assign results to a variable which will be used by functions that connect to REST API.

eppo_token <- create_eppo_token('<<your_EPPO_token>>')

Next, you can run eppo_database_download function that will download (by default to your working directory, which can be override with filepath argument) archive with the SQLite file. If you are on Linux operating system, file will be extracted into your working directory (or other directory provided in filepath argument). On Windows you will be asked to extract the database file manually.

eppo_database_download()

Last step of setup is to connect to database file, which can be easily done with eppo_database_connect function.

eppo_SQLite <- eppo_database_connect()

With this three short steps you are ready to go.

Extracting tables with scientific, common and synonym names

Currently searching for pest names supports scientific names, synonyms and common names. By default search will work with partial names – e.g. when you query for Cydia packardi you get information related to this species, while when you query for Cydia you get information on whole genus. Likewise, when you search for Droso you will get information on all species that contain Droso in their names. Moreover you can pass whole vector of terms in one shot, e.g. c('Xylella', 'Cydia packardi', 'Droso').

# Create vector of names that you are looking for
pests_query <- c('Cydia', "Triticum aestivum", "abcdef", "cadang")

Than you should start with querying for names and assigning your results to a variable. This variable will contain eppocodes that are used by other functions to extract data from EPPO REST API. eppo_names_tables takes two arguments: first is a vector of names to query the database, second is variable with connection to SQLite database.

pest_names <- eppo_names_tables(pests_query, eppo_SQLite)
names(pest_names)
#names that exist in database
head(pest_example[[1]], 5)
#names that were not found
head(pest_example[[2]], 5)
#preferred names for eppocodes from first table
head(pest_example[[3]], 5)
#all names that are associated to eppocodes from first data frame
head(pest_example[[4]], 5)

#> [1] "exist_in_DB"          "not_in_DB"            "pref_names"          
#> [4] "all_associated_names"

As a result you will get list containing 3 data.frames and vector:

• exist_in_DB – data.frame with names that are present in EPPO Data Services;
• not_in_DB – vector with names that are not present in database;
• pref_names – data.frame with preferred names and eppocodes,
• all_associated_names – data.frame with all associated names to eppocodes from third data.frame.

eppo_tabletools_ functions to extract categorization, hosts, taxonomy, distribution and pests

This functions works separately from each other, thus there is no need to use all of them. There is no need to use them in any particular order. Functions for categorization, hosts taxonomy and pests takes two arguments:

• names_table – variable containing result of eppo_names_tables;
• token – variable created with create_eppo_token

OR three arguments:

• token – same as above;
• raw_eppocodes – character vector of eppocodes (e.q. `c(“XYLEFA”, “ABIAL”));
• use_raw_codes – logical set to TRUE.

pests_cat <- eppo_tabletools_cat(pest_names, eppo_token)
#long format table
head(pests_cat[[1]], 5)
#comapct table with information for each eppocode condensed into one cell
head(pests_cat[[2]],5)

pests_cat <- eppo_tabletools_cat(token = eppo_token,
                                 raw_eppocodes = c("LASPPA", "TRZAX", "CCCVD0"),
                                 use_raw_codes = TRUE)
pest_cat[[2]]

pests_hosts <- eppo_tabletools_hosts(pest_names, eppo_token)

head(pests_hosts[[1]], 5)
head(pests_hosts[[2]], 5)

virs_eppocodes <- pest_names$all_associated_names %>%
  dplyr::filter(grepl("viroid", fullname) | grepl("virus", fullname)) %>%
  .[,5] %>% #eppocodes are in 5th column
  unique()

virs_taxonomy <- eppo_tabletools_taxo(token = eppo_token,
                                      raw_eppocodes = virs_eppocodes,
                                      use_raw_codes = TRUE)
virs_taxonomy$long_table #you can also access list elements by their names
virs_taxonomy$compact_table

abies_pests <- eppo_tabletools_pests(token = eppo_token,
                                     raw_eppocodes = "ABIAL",
                                     use_raw_codes = TRUE)
head(abies_pests[[1]], 5)
head(abies_pests[[2]], 5)

pest_distri <- eppo_tabletools_distri(pest_names)
head(pestr_distri[[1]], 5)
head(pestr_distri[[2]], 5)

Whole condensed table in one shot (currently does not include eppo_tabletools_pests):

Last, but not least, package offers a simple wrapper over above mentioned functions. If you want to make one table with all the informations: names, categorization, hosts, distribution and taxonomy – condensed to one cell per pest, please use eppo_table_full function that takes arguments:

• names vector – a character vector of pests/hosts names;
• sqlConnection – a variable for SQLite connection (result of eppo_database_connect);
• token – an variable storing EPPO token (eppo_token).

eppo_fulltable <- eppo_table_full(c("Meloidogyne ethiopica", "Crataegus mexicana"),
                                  eppo_SQLite,
                                  eppo_token)

eppo_fulltable