Assume that you are assessing pest threats to Abies alba – European silver fir. It would be wise to go to EPPO Global Database, type Abies alba in search field and check all the information you need, copy paste them into spreadsheet and make some analyses. On the other hand, you can just use
pestr package and do everything easily from
R directly saving time on whole copy-pasting procedures. To make this example easy lets assume that your goal is to check number of taxons and number of particular types (major, minor, etc.) of pests. Therefore steps you would need to make in
R are presented below.
I assume that you already have eppocodes.sqlite database in your working directory. If not you should download it manually from EPPO Data Services or using
eppo_database_download function. I also assume that you have basic knowledge of using
dplyr package and piping
First you need valid eppocode of Abies alba. To do this you will need to use
eppo_names_tables function and save your results to a variable like in the code example below:
<- eppo_names_tables("Abies alba")abies_alba
This variable will be our input for next step.
We can now look for all the existing pests of Abies alba that exists in EPPO Data Services. To do this we need to use
eppo_tabeltools_pests function. There are two options how you can access the information.
eppo_names_tablesoutput as an argument:
You will need result of
eppo_names_tables function as first argument and the second one is token – which allows you to connect to EPPO API. You get it by registering to EPPO Data Services which is free of charge. You need to paste your token to
create_eppo_token function and assign the results to a variable (here we use
eppo_token) that will be used among all
pestr functions that connect to EPPO API.
eppo_tabletools_pests will take 3 arguments with this approach:
Below code shows this in action:
### Firsr create eppo_token variable <- eppo_create_token("<place your eppo token here>") eppo_token ### For token argument, please use eppo_token <- eppo_tabletools_pests(token = eppo_token, abies_alba_pests raw_eppocodes = "ABIAL", use_raw_codes = TRUE)
Looking at structure of
abies_alba_pests variable we see that it is a list containing 2
str(abies_alba_pests) #> List of 2 #> $ long_table :'data.frame': 51 obs. of 5 variables: #> ..$ eppocode : chr [1:51] "ABIAL" "ABIAL" "ABIAL" "ABIAL" ... #> ..$ pests_eppocode: chr [1:51] "MELMME" "MELMMD" "ACLRGL" "ACLRVA" ... #> ..$ idclass : int [1:51] 10 10 9 9 9 9 9 9 9 9 ... #> ..$ labelclass : chr [1:51] "Experimental" "Experimental" "Host" "Host" ... #> ..$ fullname : chr [1:51] "Melampsora medusae (as Abies)" "Melampsora medusae f. sp. deltoidis (as Abies)" "Acleris gloverana (as Abies)" "Acleris variana (as Abies)" ... #> $ compact_table: tibble [1 × 2] (S3: grouped_df/tbl_df/tbl/data.frame) #> ..$ eppocode: chr "ABIAL" #> ..$ pests : chr "Experimental: Melampsora medusae (as Abies), Melampsora medusae f. sp. deltoidis (as Abies); Host: Acleris glov"| __truncated__ #> ..- attr(*, "groups")= tibble [1 × 2] (S3: tbl_df/tbl/data.frame) #> .. ..$ eppocode: chr "ABIAL" #> .. ..$ .rows : list<int> [1:1] #> .. .. ..$ : int 1 #> .. .. ..@ ptype: int(0) #> .. ..- attr(*, ".drop")= logi TRUE
The long_table element contains what we actually need for our analyses: pest_eppocode and labelclass columns. Lets start with latter.
Pest types are stored in labelclass column. We will use few very basic
dplyr package commands to check numbers we are interested in.
### First select colums labelclass from long_table element, ### and use table to check frequencies $long_table %>% abies_alba_pests::select(labelclass) %>% dplyrtable() #> . #> Experimental Host Major host #> 2 46 3 ### Than we can create very simple barplot to visualize number of ### species in particular type of pest $long_table %>% abies_alba_pests::select(labelclass) %>% dplyrtable() %>% barplot(xlab = "Type of pest", ylab = "Number of species", col = "#AF0011", ylim = c(0, 30))
Obtaining number of pests taxons is not very complicated. Since we can use
eppocodes directly, we can pass whole column to
eppo_tabletools_taxo function, which will retrieve data on pests taxonomy.
### Extract eppocodes of pests <- abies_alba_pests$long_table %>% pests_eppocodes ::select(pests_eppocode) %>% dplyrunlist()
Now, we can pass
pest_eppocodes variable as
<- eppo_tabletools_taxo(token = eppo_token, pests_taxonomy raw_eppocodes = pests_eppocodes, use_raw_codes = TRUE)
This time we can take a shortcut and use second element of list
compact_table. We can now check the numbers using
table function and plot results with simple
$compact_table %>% pests_taxonomy::select(taxonomy) %>% dplyrtable() #> . #> Arthropoda Chromista Fungi Nematoda Plantae #> 34 2 10 1 4 $compact_table %>% pests_taxonomy::select(taxonomy) %>% dplyrtable() %>% barplot(xlab = "Classification of pest", ylab = "Number of species", col = "#AF0011", ylim = c(0, 30))
Now, knowing basics of how functions interacts, you are ready to play with your own workflows and analyses.