Updated Global vars, updated vignette worfklow

and tests

.Rbuildignore

@@ -8,7 +8,7 @@
 ^\.github$
 ^codecov\.yml$
 ^inst/data$
-^ignore/$
+^ignore$
 
 
 

DESCRIPTION

@@ -32,14 +32,16 @@ Imports:
     shiny,
     shinybusy,
     stringr,
-    shinythemes
+    shinythemes,
+    tidyr
 Remotes: 
     traitecoevo/APCalign
 Suggests: 
-    job,
     bsplus,
-    testthat (>= 3.0.0),
+    here,
+    job,
     knitr,
-    rmarkdown
+    rmarkdown,
+    testthat (>= 3.0.0)
 Config/testthat/edition: 3
 VignetteBuilder: knitr

R/gbif_download.R

@@ -11,10 +11,7 @@
 #'
 #' @return None. The function saves the processed data to the specified output directory.
 #' @export
-#'
-#' @examples
-#' download_gbif_obs("Puma concolor")
-#' download_gbif_obs("Puma concolor", min_year = 2000, country_code = "US", save_raw_data = TRUE)
+
 download_gbif_obs <- function(taxon,
                               min_year = 1923,
                               max_year = as.numeric(format(Sys.Date(), "%Y")),
@@ -37,6 +34,7 @@ download_gbif_obs <- function(taxon,
 #'
 #' @param taxon character, genus/family/kingdom
 #' @param min_year numeric, year cut off for query, only records where year >= min_year will be included 
+#' @param max_year numeric, year cut off for query, only records where year <= max_year will be included 
 #' @param country_code character, code for country
 #' @export
 

R/infinitylists-package.R

@@ -60,7 +60,14 @@ utils::globalVariables(
     "write.csv",
     "Link", 
     "Repository",
-    "Establishment means",
-    "repository"
+    "Establishment Means",
+    "repository",
+    "eventDate_as_date",
+    "eventDate_ymd",
+    "establishmentMeans",
+    "hasGeospatialIssue",
+    "link",
+    "country", 
+    "count"
   )
 )

tests/testthat/_snaps/galah_download.md

@@ -14,5 +14,5 @@
       6 Hemico~ Hemi~ Cordu~ 1924-10-10 00:00:00 -28.7  152. Collection     QM        
       7 Synthe~ Synt~ Synth~ 1924-01-01 00:00:00 -41.9  145. Collection     QM        
       # i 4 more variables: `Recorded by` <chr>, `Record Id` <chr>, Link <chr>,
-      #   `Establishment means` <chr>
+      #   `Establishment Means` <chr>
 

vignettes/diy.Rmd

@@ -7,30 +7,20 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
-knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>",
-  eval = FALSE
-)
 
-library(infinitylists)
-library(galah)
-library(arrow)
-library(tidyverse)
-```
 
 One major benefit of infinitylists using a Living Atlas node is that is gives users the ability to create their own version of infinitylists for whichever Living Atlas you would like to use. Unfortunately, there are some slight inconsistencies in data coverage and naming between Living Atlas data providers. This makes creating your own infinitylists not an entirely straightforward process but I hope this article will be able to give you some guidance.
 
 Here, I will walk through the process on how to adapt the source code of infinitylists so you can create your own version of infinitylists for any country and taxa of your choice. 
 
-If you have any questions about this process, please do not hesitate and reach out but submitting an issue at the [infinitylists repository](XX).
+If you have any questions about this process, please do not hesitate and reach out but submitting an issue at the [infinitylists repository](https://github.com/traitecoevo/infinitylists).
 
 ### Load dependencies
 
 We are going to need a few packages to create your own infinitylists. Go ahead and install these if you don't have these in your version of R. Otherwise, load them and we can get started 
 
-```{r setup}
+
+``` r
 # install.packages("devtools")
 devtools::install_github("traitecoevo/infinitylists")
 library(infinitylists)
@@ -47,7 +37,8 @@ We will be using [{galah}](https://galah.ala.org.au/R/) to download occurrence r
 
 Here I've saved the credentials in my R environment so its not shared publicly. I can call on these environment variables using `Sys.getenv()`. You can also do so with `usethis::edit_r_environ`.
 
-```{r}
+
+``` r
 # Set atlas
 galah_config(
   atlas = "Global",
@@ -64,7 +55,8 @@ Once we have all that set up, we can request data from GBIF Global. Here I am do
 
 Note that depending on how many records are requested, the download will take some time.
 
-```{r}
+
+``` r
 download_gbif_obs("Podarcis",
                   min_year = 2000,
                   max_year = 2024,
@@ -75,7 +67,8 @@ download_gbif_obs("Podarcis",
 
 You can check roughly how big your download is but using the `query()` function with `galah::atlas_counts()`. Note this will not be the find number of records that goes into infinitylist as we do further exclusions and data cleaning behind the scenes. 
 
-```{r}
+
+``` r
 query_gbif_global("Podarcis",
                   min_year = 2000,
                   max_year = 2024,
@@ -89,7 +82,8 @@ You can investigate the full download by specifying `save_raw_data = TRUE` in `d
 
 Once the download is complete, you are all set! Launch infinitylist and you will find your download under the dropdown menu "taxa"
 
-```{r}
+
+``` r
 infinitylistApp()
 ```
 
@@ -104,7 +98,8 @@ If you specified `save_raw_data = TRUE` in `download_gbif_obs()`, this code will
 - `"GBIF-preprocessed-"` is the raw download **before** our data cleaning. 
 - `"Living-Atlas-"` is the final **cleaned** download of the data you view in the app. 
 
-```{r}
+
+``` r
 # Locate file path of downloads
 system.file(package = "infinitylists") |> 
   file.path("data") |> 
@@ -113,16 +108,31 @@ system.file(package = "infinitylists") |>
 
 Copy the file path and pasted it in the `read_parquet()` function to open the download in R. 
 
-```{r}
+
+``` r
 gbif_podarcis <- arrow::read_parquet("infinitylists/inst/data/Living-Atlas-Podarcis-2024-09-13.parquet")
 ```
 
-```{r include=FALSE, eval=TRUE}
-gbif_podarcis <- arrow::read_parquet(here::here("inst/data/Living-Atlas-Podarcis-2024-09-13.parquet"))
-```
 
-```{r, eval=TRUE}
+
+
+``` r
 gbif_podarcis |> print(n = 10)
+#> # A tibble: 6,155 × 11
+#>    Species            Genus Family `Collection Date`     Lat  Long `Voucher Type` Repository
+#>  * <chr>              <chr> <chr>  <dttm>              <dbl> <dbl> <chr>          <chr>     
+#>  1 Podarcis tiliguer… Poda… Lacer… 2020-10-18 09:20:00  41.6  8.81 Photograph     https://w…
+#>  2 Podarcis tiliguer… Poda… Lacer… 2024-07-01 14:41:00  42.3  8.87 Photograph     https://w…
+#>  3 Podarcis tiliguer… Poda… Lacer… 2022-05-30 00:00:00  41.8  9.23 Photograph     https://w…
+#>  4 Podarcis siculus   Poda… Lacer… 2024-05-17 10:37:00  42.8  9.48 Photograph     https://w…
+#>  5 Podarcis muralis   Poda… Lacer… 2023-07-28 17:05:00  43.5 -1.52 Photograph     https://w…
+#>  6 Podarcis liolepis  Poda… Lacer… 2020-04-08 00:00:00  44.0  3.52 Photograph     https://w…
+#>  7 Podarcis liolepis  Poda… Lacer… 2014-05-09 08:44:00  43.6  3.02 Photograph     https://w…
+#>  8 Podarcis muralis   Poda… Lacer… 2023-05-14 19:55:00  47.6  1.34 Photograph     https://w…
+#>  9 Podarcis muralis   Poda… Lacer… 2023-10-14 12:39:00  44.6  6.17 Photograph     https://w…
+#> 10 Podarcis muralis   Poda… Lacer… 2020-06-18 04:07:00  43.9 -1.38 Photograph     https://w…
+#> # ℹ 6,145 more rows
+#> # ℹ 3 more variables: `Recorded by` <chr>, `Establishment Means` <lgl>, Link <chr>
 ```
 
 

vignettes/diy.Rmd.orig

@@ -0,0 +1,130 @@
+---
+title: "Create your own infinitylists"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{DIY infinitylists}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = FALSE
+)
+
+library(infinitylists)
+library(galah)
+library(arrow)
+library(dplyr)
+
+# knitr::knit("vignettes/diy.Rmd.orig", output = "vignettes/diy.Rmd")
+```
+
+One major benefit of infinitylists using a Living Atlas node is that is gives users the ability to create their own version of infinitylists for whichever Living Atlas you would like to use. Unfortunately, there are some slight inconsistencies in data coverage and naming between Living Atlas data providers. This makes creating your own infinitylists not an entirely straightforward process but I hope this article will be able to give you some guidance.
+
+Here, I will walk through the process on how to adapt the source code of infinitylists so you can create your own version of infinitylists for any country and taxa of your choice. 
+
+If you have any questions about this process, please do not hesitate and reach out but submitting an issue at the [infinitylists repository](XX).
+
+### Load dependencies
+
+We are going to need a few packages to create your own infinitylists. Go ahead and install these if you don't have these in your version of R. Otherwise, load them and we can get started 
+
+```{r setup}
+# install.packages("devtools")
+devtools::install_github("traitecoevo/infinitylists")
+library(infinitylists)
+library(galah)
+library(arrow)
+library(tidyverse)
+```
+
+You will need to register for a [GBIF account](https://www.gbif.org/). Click on the "Login" button on the top left corner and click on the "Register" tab. Note down your login credentials for safe-keeping once you have verified your account and created a password.
+
+### Configure galah
+
+We will be using [{galah}](https://galah.ala.org.au/R/) to download occurrence records used our infinitylist. To do so, we need to configure the settings so the package knows to point to the Global GBIF API. 
+
+Here I've saved the credentials in my R environment so its not shared publicly. I can call on these environment variables using `Sys.getenv()`. You can also do so with `usethis::edit_r_environ`.
+
+```{r}
+# Set atlas
+galah_config(
+  atlas = "Global",
+  username = Sys.getenv("GBIF_USERNAME"),
+  password = Sys.getenv("GBIF_PWD"),
+  email = Sys.getenv("GBIF_EMAIL")
+)
+
+```
+
+### Submit data request
+
+Once we have all that set up, we can request data from GBIF Global. Here I am downloading records for the skink genus 'Podarcis', from years 2000 to 2004. Under `country_code`, I've specified `"FR"` for records found in France. Here is a [list of codes](https://en.wikipedia.org/wiki/ISO_3166-2) for each country. The `download_gbif_obs` function will download the records and say it internally inside the infinitylist R package so you can use it immediately.
+
+Note that depending on how many records are requested, the download will take some time.
+
+```{r}
+download_gbif_obs("Podarcis",
+                  min_year = 2000,
+                  max_year = 2024,
+                  country_code = "FR")
+```
+
+### Pre-download check
+
+You can check roughly how big your download is but using the `query()` function with `galah::atlas_counts()`. Note this will not be the find number of records that goes into infinitylist as we do further exclusions and data cleaning behind the scenes. 
+
+```{r}
+query_gbif_global("Podarcis",
+                  min_year = 2000,
+                  max_year = 2024,
+                  country_code = "FR") |> 
+  galah::atlas_counts()
+```
+
+You can investigate the full download by specifying `save_raw_data = TRUE` in `download_gbif_obs()`
+
+### Launch infinitylist and explore!
+
+Once the download is complete, you are all set! Launch infinitylist and you will find your download under the dropdown menu "taxa"
+
+```{r}
+infinitylistApp()
+```
+
+### Open downloaded data
+
+The following code identifies the file path of where your GBIF Global records are downloaded if you want to open data in R or export it for other uses. This is usually handy if you want to orientate the map to where your download is from using `"Choose a lat/long"`.
+
+In the next code chunk, replace `"Podarcis"` in the `pattern` argument with the name of the taxa you have downloaded data for in the previous step. This code will provide the full file paths of objects that match the `pattern` argument.
+
+If you specified `save_raw_data = TRUE` in `download_gbif_obs()`, this code will you two file paths. The file with the prefix: 
+
+- `"GBIF-preprocessed-"` is the raw download **before** our data cleaning. 
+- `"Living-Atlas-"` is the final **cleaned** download of the data you view in the app. 
+
+```{r}
+# Locate file path of downloads
+system.file(package = "infinitylists") |> 
+  file.path("data") |> 
+  list.files(pattern = "Podarcis", full.names = TRUE) # Match for Podarcis
+```
+
+Copy the file path and pasted it in the `read_parquet()` function to open the download in R. 
+
+```{r}
+gbif_podarcis <- arrow::read_parquet("infinitylists/inst/data/Living-Atlas-Podarcis-2024-09-13.parquet")
+```
+
+```{r include=FALSE, eval=TRUE}
+gbif_podarcis <- arrow::read_parquet(here::here("inst/data/Living-Atlas-Podarcis-2024-09-13.parquet"))
+```
+
+```{r, eval=TRUE}
+gbif_podarcis |> print(n = 10)
+```
+
+