Pathway information in different types

[damianooldoni]

While trying to update indicators and helping @timadriaens for making some graphs, I found the following:

description %>% distinct(type)
distinct: removed 12,232 rows (>99%), 12 rows remaining
# A tibble: 12 x 1
   type                               
   <chr>                              
 1 pathway                            
 2 degree of establishment            
 3 native range                       
 4 Introduced species vector dispersal
 5 Introduced species impact          
 6 Introduced species abundance       
 7 introduction pathway               
 8 Introduced species remark          
 9 Introduced species management      
10 pathway of introduction            
11 Introduced species population      
12 Introduced species population trend

There are 3(!) types for encoding pathway information, where type pathway is the most used (and the correct one):

type_pathways<- c(
  "pathway",
  "introduction pathway",
  "pathway of introduction"
)
description %>% 
  filter(type %in% type_pathways) %>% 
  group_by(type) %>% 
  count() %>%
  arrange(desc(n))
# A tibble: 3 x 2
# Groups:   type [3]
  type                        n
  <chr>                   <int>
1 pathway                  3283
2 introduction pathway      163
3 pathway of introduction    61

Description data with type introduction pathway

All these data come from the Checklist of alien herpetofauna of Belgium (https://doi.org/10.15468/pnxu4c):

description %>%
  filter(type %in% "introduction pathway") %>%
  group_by(type) %>%
  distinct(source) %>%
  mutate(from_herpetofauna = str_detect(.data$source,
                                        pattern  = "herpetofauna",
                                        negate = FALSE)) %>%
  group_by(from_herpetofauna) %>%
  count()
from_herpetofauna     n
  <lgl>             <int>
1 TRUE                 89

Description data with type pathway of introduction

All these data come from Checklist of alien species in the Scheldt estuary in Flanders, Belgium (https://doi.org/10.15468/8zq9s4):

description %>%
  filter(type %in% "pathway of introduction") %>%
  group_by(type) %>%
  distinct(source) %>% 
  mutate(from_scheldt_estuary = str_detect(.data$source,
                                        pattern  = "Scheldt estuary",
                                        negate = FALSE)) %>%
  group_by(from_scheldt_estuary) %>%
  count()
from_scheldt_estuary     n
  <lgl>                <int>
1 TRUE                    54

I think this are issues to solve at checklist level. @peterdesmet: what do you think?

[peterdesmet]

Yes, cf. #69 this is something best solved at the checklist level.