In dbca-wa/etlTurtleNesting: Data ETL of Turtle Nesting Census Data from ODK Central to WAStD

```{=html}

```r
knitr::opts_chunk$set(echo = TRUE)
library(etlTurtleNesting)
library(drake)
library(wastdr)
library(pointblank)
library(magrittr)
library(reactable)
library(gt)
readd(odkc_ex)
readd(user_mapping)
loadd(odkc_ex)
loadd(user_mapping)

a <- user_mapping %>%
    annotate_user_mapping() %>%
    pointblank::create_agent() %>%
    pointblank::col_vals_lt("dist", 0.01) %>%
    interrogate()

actions_table <- a %>%
  pointblank::get_data_extracts() %>%
  magrittr::extract2(1) %>%
  dplyr::rowwise() %>% 
  dplyr::mutate(
    active_at = get_user_area(odkc_ex, odkc_username),
    dist = round(dist,3)
    ) %>% 
  dplyr::select(-email, -phone) %>% 
  dplyr::arrange(active_at, -dist) %>% 
  gt::gt() %>%
  gt::fmt_markdown(columns = gt::everything()) %>%
  gt::cols_label(
    odkc_username = gt::html("<h5>Cleaned</h5>\n<small>ODK Collect username</small>"),
    active_at = gt::html("<small>Active at</small>"),
    wastd_matched = gt::html("<h5>We matched</h5>\n<small>WAStD User</small>"),
    search_wastd = gt::html("<h5>Search</h5>\n<small>likely candidates</small>"),
    dist = gt::html("<h5>Dissimilarity</h5>\n<small>smaller is better</small>")
  ) %>%
  gt::tab_spanner(
    label = "WASTD User match",
    columns = c(odkc_username, active_at, wastd_matched, search_wastd, dist)
  ) %>%
  gt::tab_spanner(
    label = "WASTD User profile (chosen as best match)",
    columns = c(role, pk, username, name, nickname, aliases)
  ) %>% 
  gt::tab_style(
    style = list(
      gt::cell_text(weight = "bold", color="red")
      ),
    locations = gt::cells_body(
      columns = dist,
      rows = dist > 0.1
    )
  ) %>% 
  gt::tab_style(
    style = list(
      gt::cell_text(color="orange")
      ),
    locations = gt::cells_body(
      columns = dist,
      rows = dist < 0.1
    )
  )

User matching results {.tabset}

Please review {.active}

Work through the issues directly in the table below.

Close matches (typos) have a dissimilarity distance of less than 0.1. For these there likely exists a WAStD user profile already, and their mis-spelled name can be added as an alias.

Likely mismatches have a distance of more than 0.1 and are highlighted red. For these we are likely missing a WAStD user profile.

Your actions:

• Users who put team lists in username - please re-train ASAP:

  • Username is tablet holder.
  • SVS team is other names.
  • Full names as given to us pre season.
  • No first names, no nicknames, no team lists, no abbreviations.

• If a "We matched WAStD User" is definitely not the person from the "ODK Collect username":

  • Find the correct user in WAStD.
  • Use the individual links in "Search likely candidates" to search WAStD for users matching part of the ODK username.
  • Really not a WAStD user yet? Create new WAStD user.
  • Update the real WAStD user's "aliases" field with the ODK username. Important: drop commas from ODK username, but separate distinct aliases with a comma. This is crucial for our user matching algorithm.

• If a "We matched WAStD User" is the person from the "ODK Collect username" but has a bad match:

  • This happens e.g. when the ODK Username is a part (given name), an alias (abbreviation), or a team list.

  • Open the "We matched WAStD user" profile link and update the alias with the ODK username.

  • Next time we run the user matching, the match should come up with a lower dissimilarity measure (or even drop off the validation shortlist).

actions_table

Data and Methods

• This QA report ran on r Sys.time().
• The ODK Central data was downloaded on r odkc_ex$downloaded_on.
• The WAStD Users were downloaded just before the workbook ran.

During the data import from ODK Central to WAStD, wastdr maps each distinct ODK Collect "username" as written by data collectors to actual WAStD user profiles.

The matching is done by fuzzyjoin::stringdist_left_join using the Jaro-Winker distance between the ODKC username and the WAStD name and aliases. Aliases are split up by comma. The Jaro-Winker distance was chosen as it returns the highest number of correct matches.

Details about available distance measures can be found here.

You can download a spreadsheet of mismatches from the CSV button:

a

Note: updating WAStD will not refresh this report automatically - we have to re-run the data import.

When we re-run the user matching with fresh data, users with updated aliases should match up.

The QA validation will pick up all username matches with a dissimilarity of 0.01 or higher. Perfect matches will have a dissimilarity below 0.01.

Adding new data collectors to WAStD

Coordinators of data capture programs supply us with a spreadsheet in this exact format:

• Column "name": The full name, including given and surname, e.g. "Florian Mayer"

  • Use correct capitalisation. Do not write "florian mayer" or "FLORIAN MAYER". The case does not matter for the user matching, but it matters for displaying in reports.

• Column "email": A valid email, e.g. [e-mail].

  • Omit any HTML markup, e.g. "mailto:", or anything involving <pointy brackets>.

• Column "phone": A valid phone number with the Australian prefix +61.

  • Valid example: "[phone]".
  • Whitespace will be discarded, so "[phone]" works too.
  • Format the column as "text". Excel has a nasty habit of auto-formatting columns and dropping the leading "+". This is very tedious to fix. E.g. "61414123456" is invalid.

We add the following columns:

• "username", generated from the snake-cased "name", e.g. florian_mayer.
• "role", which we set to a predefined text such as "Cable Beach Broome Volunteer 2020-21". As user registrations come in batches from distinct locations, we get a cleaner result if we set the role centrally following a naming convention. Less work for the coordinator!

dbca-wa/etlTurtleNesting documentation built on Nov. 18, 2022, 8:03 a.m.