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title: Introduction to gatoRs
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---

gatoRs (Geographic and Taxonomic Occurrence R-Based Scrubbing) provides users with tools to streamline the downloading and processing biodiversity data.

## Data Downloading 

### Identifying Synonyms
Historically, many names may have been used to refer to your taxa of interest. For example, specimen representing <i>Galax urceolata</i> (Diapensiaceae) can be found under the scientific  name <i>Galax aphylla</i>, despite the latter being invalidated over 50 years ago ([see more here](https://en.wikipedia.org/wiki/Galax)). Since synonyms are common, we designed gatoRs retrieve biodiversity records based on a list of names, however the user must supply the synonym list.   

There are many databases available to compile synonym lists for plant species including:

  - [World Flora Online - WFO Plant List](https://wfoplantlist.org/)
  - [TROPICOS](https://www.tropicos.org/)
  - [World Checklist of Vascular Plants](https://powo.science.kew.org/)
  - [USDA PLANTS Database](https://plants.usda.gov/home)
  - [International Plant Names Index](https://www.ipni.org/)
  - [World Plants](https://www.worldplants.de/world-plants-complete-list/complete-plant-list)
  
 Many R packages have been developed to access these databases including: 
 
  - [taxize](https://docs.ropensci.org/taxize/)
  - [RTNRS](https://github.com/EnquistLab/RTNRS)
  - [WorldFlora](https://cran.r-project.org/package=WorldFlora)
  - [Taxonstand](https://cran.r-project.org/package=Taxonstand)
  - [U.Taxonstand](https://github.com/ecoinfor/U.Taxonstand)
     

### Download with gatoRs
With `gators_download()` you can obtain biodiversity records for your species of interest from both GBIF and iDigBio. This function is innovative in how it searches iDigBio. Unlike `spocc::occ()`, we do not query the iDigBio API using the scientific name field, as this will only return exact matches. Instead, we designed a "pseudo-fuzzy match" to search all fields for partial matches to the supplied scientific names. Additionally, the columns returned have been handpicked to aid in processing records for investigations of species distributions (see more `gators_download()`). 

After you identify synonyms, create a list of all possible names for your species of interest with the first name in the list as the accepted name (ex. `c("Galax urceolata", "Galax aphylla")`). Note, the first name in your list will be used to identify the GBIF species code when `gbif_match = "code"`. 

Example:
```
library(gatoRs)
galaxdf <- gators_download(synonyms.list = c("Galax urceolata", "Galax aphylla"), 
                write.file = TRUE,
                filename = "base_folder/my_file.csv", # Location to save file - must end in .csv
                gbif.match = "fuzzy",
                idigbio.filter = TRUE)
```

___   


## Data Processing    
We downloaded 7742 observations for <i>Galax urceolata</i> in the example above. Of these observations, only those with locality information will be helpful when investigating this species distribution. 

### Identify Records Missing Locality Information
Locality information can be redacted or skewed due to protect threatened taxa, often locality information will be provided upon request or can be identified through georeferencing. We created functions to aid in this process.   

#### Redacted Records
Locality information can be redacted or skewed due to protect threatened taxa; often locality information will be provided to aid research upon request. 

To find data that needs to be manually received by an institution via a permit (or removed from the data set), use `needed_records()`. After receiving the data from herbaria, manually merge the obtained records with your original data set.

Example: 
```
redacted_info <- needed_records(galaxdf)
```

#### Records to Georeference
Some records may be missing latitude and longitude values, however locality information can be used to assign coordinates to the record through georeferencing.

To find data lacking coordinates but containing locality information, use `need_to_georeference()`. You should georeference these records and then manually merge the obtain records with your original data set.

Example: 
```
to_georeference <- need_to_georeference(galaxdf)
```

#### Merging Retained Records   
After receiving the data from herbaria or through georeferencing, you will want to
merge the obtained records with your original data set. 

Note, the following steps will only work if columns are equivalent between the two data sets you hope to merge. If the columns are not equivalent, we recommend bcd::bcd_standardize_datasets() 

Example: 
```
# Avoid duplicates by removing the records you are about to merge
dfsub <- remove_missing(galaxdf)
# Use gator_merge to merge the main data set with retrieved records
dfnew <- gators_merge(dfsub, retrieved_records)
# Version control - save a copy prior to any additional processing!!
write.csv(dfnew, "data/merged_data_Galax_urceolata_YYYYMMDD.csv") ## Version control!
# Set gatordf equal to the newly merged data set
gatordf <- dfnew
```

### Occurrence Data Cleaning  
Here we walk through each cleaning function, however we also created a simple one-step option `full_clean()`, see below.   

#### Resolve Taxon Names
To find data containing scientific names corresponding to your desired species, use `taxa_clean()`. Use your downloaded data from the first step as input, as well as a synonyms list, the accepted name, and the filter option (exact, fuzzy, or interactive).

Example:
```
galaxdf <- taxa_clean(df = galaxdf,  
                      synonyms.list = c("Galax urceolata", "Galax aphylla"), 
                      taxa.filter = "fuzzy", 
                      accepted.name = "Galax urceolata") # creates a new column with accepted name for easy comparison
```


#### Clean Locality  
##### Basic Locality Clean 
Here we remove any records with missing coordinates, impossible coordinates, coordinates at (0,0), and any that are flagged as skewed. The skewed records can be identified with the `remove_skewed()` function and row value for the 'InformationWitheld' column. We also provide the option to round the provided latitude and longitude values to a specified number of decimal places.   

```
galaxdf <- basic_locality_clean(df = galaxdf,  
                      remove.zero = TRUE, # Records at (0,0) are removed
                      precision = TRUE, # latitude and longitude are rounded 
                      digits = 2, # round to 2 decimal places
                      remove.skewed = TRUE)
```

##### Find and Remove Flagged Points
To find records that may have problematic coordinates, use `process_flagged()`. This function can either automate the process of finding and removing problematic points (`interactive = FALSE`) or allow for manual inspection. The latter will let you manually remove points deemed improper by viewing the points on a graph.

This function utilizes `CoordinateCleaner::clean_coordinates()`. 

Example: 
```
galaxdf <- process_flagged(galaxdf, interactive = TRUE)
```

```{r echo = FALSE, fig.align='center'}
knitr::include_graphics("img/map1.png", dpi = 200)
```
**Figure 1**: Interactive map that appears when `process_flagged()` is run.    



```{r echo = FALSE, fig.align='center'}
knitr::include_graphics("img/map2.png", dpi = 200)
```
**Figure 2**: Interactive map that appears after we remove points interactively with `process_flagged()`. Points 1, 3, 5, 6, and 7 were removed.



#### Remove Duplicate Records
Here we identify and remove both (1) specimen duplicates and (2) aggregator duplicates based on each specimens coordinates, occurrenceID, and eventDate. To leverage all date information available, set `remove.unparseable = FALSE` to manually populate 
the year, month, and day columns. Here, we also confirm all ID (UUID and key) are unique to remove any  within-aggregator duplicates that may accumulate due to processing errors.


Example:
```
galaxdf <- remove_duplicates(galaxdf, remove.unparseable = TRUE)
```

#### Remove Particular Record Bases
Sometimes, certain bases of records may want to be removed from the data set. To do this, we provide `basis_clean()`. 
This function can be used interactively by simply supplying a df, but no basis.list value.If a list of basis is provided, the function will select only records where the basisOfRecord value fuzzy match values in the list provided.

Example:
```
galaxdf <- basis_clean(galaxdf, 
                       basis.list = c("HUMAN_OBSERVATION", "PRESERVED_SPECIMEN", 
                                      "MATERIAL_SAMPLE", "LIVING_SPECIMEN",  
                                      "PreservedSpecimen", "Preserved Specimen"))
```

#### Spatial Correction 
The last processing step is spatial correction. Collection efforts can lead to the clustering of points and filtering can help reduce this clustering. Here we provide functions to reduce the effects of sampling bias using randomization approach and retain only one point per pixel. 


##### One Point Per Pixel   
Maxent will only retain one point per pixel. To make the ecological niche analysis comparable, we will retain only one point per pixel.

Example:
```
galaxdf <- one_point_per_pixel(galaxdf, 
                       resolution = 0.5, # for 30 arc sec raster layers
                       precision = TRUE, 
                       digits = 2) 
```

##### Spatial thining 
We thin points by utilizing `spThin::thin()`. This step reduces the effects of sampling bias using a randomization approach.

**Step 1**: What should your minimum distance be? 
    
Here we first calculate minimum nearest neighbor distance in km:

Example:
```
library(fields)
nnDm <- rdist.earth(as.matrix(data.frame(lon = df$longitude, lat = df$latitude)), miles = FALSE, R = NULL)
nnDmin <- do.call(rbind, lapply(1:5, function(i) sort(nnDm[,i])[2]))
min(nnDmin)
```

Here the current minimum distance is 2.22 km. Based on literature, we find a 2 meters (or 0.002 km) distance was enough to collect unique genets, so we do not need to thin our points. 

**Step 2**: Thin occurrence records using spThin through gatoRs.     
    
When you do need to thin your records, here is a great function to do so!

Example:
```
df <- thin_points(df, 
                    distance = 0.002, # in km 
                    reps = 100)
```


### Full Clean
Finally, instead of step-by-step cleaning, we created a single function to streamline this process. 


Example:
```
## Read raw csv
rawdf <- read.csv("base_folder/my_file.csv")
## Set your full clean preferences equal to the above
df_quick_clean <- full_clean(rawdf,
                            synonyms.list =  c("Galax urceolata", "Galax aphylla"), 
                            remove.NA.occ.id = FALSE,
                            remove.NA.date = FALSE,
                            accepted.name =  "Galax urceolata",
                            remove.zero = TRUE,
                            precision = TRUE,
                            digits = 2,
                            remove.skewed = TRUE,
                            basis.list = c("HUMAN_OBSERVATION", "PRESERVED_SPECIMEN", 
                                            "MATERIAL_SAMPLE",  "LIVING_SPECIMEN", 
                                            "PreservedSpecimen", "Preserved Specimen"),
                            remove.flagged = TRUE,
                            thin.points = TRUE,
                            distance = 0.002,
                            reps = 100,
                            one.point.per.pixel = TRUE)
```

## Downstream Data Proccessing

### Prepared data for MAXENT
The `data_chomp()` function subsets the data set to include only the columns needed for Maxent: the user-provided accepted name, latitude, and longitude.  
Example:
```
maxent_ready <- data_chomp(df, 
                           accepted.name = "Galax urceolata" )
write.csv(maxent_ready, "data/formaxent_Galax_urceolata_YYYYMMDD.csv", 
          row.names = FALSE)
```

### Prepared data for publication
To aid in data preparation for publication and to comply with GBIF’s data use agreement, our `citation_bellow()` function will return the citation information for these records as a list (this function name is based on gators bellowing). Additionally, our `remove_redacted()` will remove records where the aggregator value is not equal to iDigBio or GBIF. The aggregator column can be used to indicate where redacted records were retrieved from and thus used to filter out non-sharable records. 

Example:
```
## Retrieve GBIF citations
GBIF_citations <- citation_bellow(df) ## Warning, this is very slow
write.csv(GBIF_citations, "data/GBIF_citations_Galax_urceolata_YYYYMMDD.csv", 
          row.names = FALSE)

# Remove redacted records
### If redacted records were retrieved, make sure to remove them before publication! Remove redacted records!
df_pub_ready <- remove_redacted(df)
write.csv(df_pub_ready, "data/publication_ready_Galax_urceolata_YYYYMMDD.csv", 
          row.names = FALSE)
```
