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The following script contains basic steps for cleaning and filtering
-biodiversity records after the downloading process. It is organised by
-module as it is shown in OCCUR.
-
Important: These example filters GBIF records of three species of
-mosses from Spain and Portugal and keep only those that are placed in
-Portugal
-
-
-
-
Load packages
-
-
-
-
library(rgbif)
-library(bdc)
-library(tidyverse) # data manipulation
-library(dplyr)
-library(GADMTools) # administrative units and GIS
-library(CoordinateCleaner) # data cleaning functions
-library(countrycode) # country names standardization
-library(biogeo) # Environment and data
-
-
-
-
-
-
Prepare the environment
-
-
-
-
rm(list = ls(all.names = TRUE))
-
-# wd <- '' # Write working directory path
-# setwd(wd)
-
-
-
-
-
-
Get data
-
-
Only run section a if you want to download your own dataset (needs
-user account in GBIF)
-
-
-
a) Download the dataset from GBIF
-
-
-
-
spList <- c('Homalothecium aureum', 'Syntrichia ruralis', 'Tortella squarrosa')
-gbif_taxKeys <- name_backbone_checklist(spList) %>%
- filter(!matchType == 'NONE') %>%
- pull(usageKey)
-
-# occ_download(pred_in('taxonKey', gbif_taxKeys), # download 3 species names from our list
-# pred('hasCoordinate', TRUE), # this example only download records with associated coordinates
-# format = 'SIMPLE_CSV', user = 'xxx', pwd = 'xxx',email = 'xxx')
-# include user information
-
-# Do not run:
-# occ_download_wait('xxxxxxx-xxxxxxx')
-# occData <- occ_download_get('xxxxxxx-xxxxxxx') %>%
-# occ_download_import()
-
-
-
-
-
-
b) Load dataset: Start here using the data from GitHub
-
-
-
Read occurrences csv and check dimensions
-
-
-
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data <- read.csv("recordsGBIFOCCUR.csv", encoding ='UTF-8', sep="\t")
-
-
-
-
-
-
Reduced version of the GBIF dataset
-
Filter fields
-
-
-
-
colnames(data)
-data <- data %>% select(c("gbifID", "family", "genus", "species", "infraspecificEpithet", "taxonRank", "scientificName", "countryCode", "locality", "stateProvince", "decimalLatitude", "decimalLongitude", "coordinatePrecision", "day", "month", "year", "occurrenceStatus", "basisOfRecord", "recordedBy", "issue"))
-colnames(data)
-
-
-
-
-
-
-
-
MODULE 1: Basis Of Record
-
What type of occurrences do we have?
-
-
-
-
unique(data$basisOfRecord)
-
-
-
-
Remove records without appropriate basis of record:
-
-
-
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data <- data %>% filter(basisOfRecord == 'PRESERVED_SPECIMEN') # in this example we keep only preserved specimens
-
-
-
-
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-
-
MODULE 2: Taxonomy
-
-
1. Is the record identified at a proper taxonomic rank?
-
Let’s see what do we have in our dataset:
-
-
-
-
unique(data$taxonRank)
-
-
-
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Filter those records with appropriate taxonomic rank
-
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data <- data %>% filter(taxonRank == 'SUBSPECIES' | taxonRank == 'SPECIES' )
-
-
-
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-
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-
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MODULE 3: Geography
-
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1. Check coordinates values
-
Discard records with latitude/longitude values equals to zero, exact
-same value or NULL
-
-
-
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data <- data %>%
- filter(decimalLatitude != decimalLongitude) %>%
- filter(between(decimalLatitude, -90, 90)) %>%
- filter(between(decimalLongitude, -180, 180)) %>%
- filter(decimalLatitude != 0) %>%
- filter(decimalLongitude != 0)
-
-
-
-
-
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2. Check coordinates precision
-
Number of decimal digits of coordinates as a measure of precision
-
Function to count number of decimals:
-
-
-
-
#Filter coordinates with at least 1 decimal place
-data <- bdc_coordinates_precision(data = data,
- lat ='decimalLatitude',
- lon = 'decimalLongitude', ndec = 1)
-data <- data %>% filter(.rou == 'TRUE')
-data$.rou <- NULL
-
-
-
-
-
-
3. Check records that don’t meet location criteria
-
Check if coordinates are placed in the assigned country, in the
-following example we check whether records are located in Portugal
-
-
Point in polygon by country analysis
-
Import a shapefile of the study area with Adm. Units borders at
-country level
-
Download world / country shapefile from https://gadm.org/download_world.html
-
-
-
-
countries <- read_sf('gadm41_PRT.gpkg') # Set the correct projection
-st_crs(countries) <- "+proj=longlat +datum=WGS84 +no_defs"
-
-
-
-
-
-
-
Point in polygon: join each point to a polygon based on
-position
-
-
-
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data <- st_join(datapoints, countries) # Exclude those that didn't fall in the country polygon or in 'SEA'
-data <- data %>% filter(COUNTRY == "Portugal")
-# Translate 'countryCode' information (ISO 3166-1-alpha-2 = "iso2c") into country names of GADM 'countryCode'
-data$countryName <- countrycode(data$countryCode,
- origin = "iso2c",
- destination = "country.name")
-# Check the names obtained
-unique(data$countryName)
-# Match country names and label as 'FALSE' errors of location
-data <- data %>%
- mutate(countryCheck = case_when(COUNTRY != data$countryName ~ FALSE,
- TRUE ~ TRUE))
-# Subset and extract records located in country assigned by collector ('correct')
-data <- data %>% filter(countryCheck == 'TRUE')
-data <- as.data.frame(data)
-
-
-
-
-
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Delete Centroids:
-
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# Label coordinates placed in centroids of the country or capital
-cap <- cc_cap (data, lon = "decimalLongitude",
- lat = "decimalLatitude", value = "flagged")
-data <- cbind(data, cap)
-cen <- cc_cen (data, lon = "decimalLongitude",
- lat = "decimalLatitude", value = "flagged")
-data <- cbind(data, cen)
-# Label coordinates placed in gbif headquarters
-gbif <- cc_gbif(data, lon = "decimalLongitude",
- lat = "decimalLatitude", value = "flagged")
-data <- cbind(data, gbif)
-# Label coordinates from museums, gardens, institutions, zoo's…
-inst <- cc_inst(data, lon = "decimalLongitude",
- lat = "decimalLatitude", value = "flagged")
-data <- cbind(data, inst)
-#Filter and exclude centroids:
-data <- data %>% filter(cap == 'TRUE') %>%
- filter(cen == 'TRUE') %>%
- filter(gbif == 'TRUE') %>%
- filter(inst == 'TRUE')
-
-
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MODULE 4: Time
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b. Set temporal range of time:
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data <- data %>% filter(year >= 1980 & year <= 2020)
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MODULE 5: Duplicates
-
Here we have multiple combination to detect duplicate records .
-
Choose the appropriate combination of fields based on your study:
-
-
SpeciesName + Lat Lon + Date + recordedBy
-
-
-
-
dataDuplic1 <- cc_dupl (data, lon = "decimalLongitude",
- lat = "decimalLatitude",
- species = "species",
- additions = c('recordedBy', 'year'))
-
-
-
-
-
-
SpeciesName + Lat Lon + recordedBy
-
-
-
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dataDuplic2 <- cc_dupl (data, lon = "decimalLongitude",
- lat = "decimalLatitude",
- species = "species",
- additions = 'recordedBy')
-
-
-
-
-
-
SpeciesName + Lat Lon + Year
-
-
-
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dataDuplic3 <- cc_dupl (data, lon = "decimalLongitude",
- lat = "decimalLatitude",
- species = "species",
- additions = 'year')
-
-
-
-
-
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SpeciesName + Lat Lon
-
-
-
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dataDuplic4 <- cc_dupl (data, lon = "decimalLongitude",
- lat = "decimalLatitude",
- species = "species")
-
-
-
-
-
-
SpeciesName + Lat Lon with buffer or rounded coordinates
-
-
-
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data$lon_round <- round(data$decimalLongitude, 2)
-data$lat_round <- round(data$decimalLatitude, 2)
-dataDuplic5 <- cc_dupl (data, lon = "lon_round",
- lat = "lat_round", species = "species")
-
-
-
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-
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SpeciesName + cell
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-
-
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datbiogeo_b <- keepmainfields(data,
- ID='gbifID',
- Species='species',
- x='decimalLongitude', y='decimalLatitude')
-dataDuplic6 <- duplicatesexclude(datbiogeo_b, 10) # spatial resolution in minutes
-dataDuplic6 <- merge(data, dataDuplic6, by.x='gbifID', by.y='ID', all.x=TRUE)
-dataDuplic6 <- dataDuplic6 %>% filter(Exclude == 0)
-
-
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SAVE THE CLEANED DATASET
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Here SpeciesName + Lat Lon with rounded coordinates
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write.table(dataDuplic5, 'final_data.csv', sep="\t", row.names = FALSE)
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---
title: "ExampleCase_RCode"
author: "CRF"
date: "2023-11-24"
output:
  html_document:
    df_print: paged
  html_notebook: default
---

The following script contains basic steps for cleaning and filtering biodiversity records after the downloading process. It is organised by module as it is shown in OCCUR.

Important: These example filters GBIF records of three species of mosses from Spain and Portugal and keep only those that are placed in Portugal

```{r klippy, include=FALSE}
klippy::klippy(position = c('top', 'right'), color = 'darkred')
```

### Load packages

```{r message=FALSE, warning=FALSE, class.source='klippy'}
library(rgbif) 
library(bdc) 
library(tidyverse) # data manipulation 
library(dplyr) 
library(GADMTools) # administrative units and GIS 
library(CoordinateCleaner) # data cleaning functions 
library(countrycode) # country names standardization 
library(biogeo) # Environment and data 
```

### Prepare the environment

```{r class.source='klippy'}
rm(list = ls(all.names = TRUE)) 

# wd <- '' # Write working directory path 
# setwd(wd)
```

### Get data

#### Only run section a if you want to download your own dataset (needs user account in GBIF)

#### a) Download the dataset from GBIF

```{r, class.source='klippy'}
spList <- c('Homalothecium aureum', 'Syntrichia ruralis', 'Tortella squarrosa')
gbif_taxKeys <- name_backbone_checklist(spList) %>% 
  filter(!matchType == 'NONE') %>% 
  pull(usageKey)

# occ_download(pred_in('taxonKey', gbif_taxKeys), # download 3 species names from our list 
# pred('hasCoordinate', TRUE), # this example only download records with associated coordinates 
# format = 'SIMPLE_CSV', user = 'xxx', pwd = 'xxx',email = 'xxx') 
# include user information

# Do not run: 
# occ_download_wait('xxxxxxx-xxxxxxx') 
# occData <- occ_download_get('xxxxxxx-xxxxxxx') %>% 
#             occ_download_import()
```

#### b) Load dataset: Start here using the data from GitHub

#### Read occurrences csv and check dimensions

```{r, class.source='klippy'}
data <- read.csv("recordsGBIFOCCUR.csv", encoding ='UTF-8', sep="\t")
```

#### Reduced version of the GBIF dataset

Filter fields

```{r, class.source='klippy'}
colnames(data) 
data <- data %>% select(c("gbifID", "family", "genus", "species", "infraspecificEpithet", "taxonRank", "scientificName", "countryCode", "locality", "stateProvince", "decimalLatitude", "decimalLongitude", "coordinatePrecision", "day", "month", "year", "occurrenceStatus", "basisOfRecord", "recordedBy", "issue")) 
colnames(data)
```

------------------------------------------------------------------------

## MODULE 1: Basis Of Record

What type of occurrences do we have?

```{r, class.source='klippy'}
unique(data$basisOfRecord) 
```

Remove records without appropriate basis of record:

```{r, class.source='klippy'}
data <- data %>% filter(basisOfRecord == 'PRESERVED_SPECIMEN') # in this example we keep only preserved specimens
```

------------------------------------------------------------------------

## MODULE 2: Taxonomy

### 1. Is the record identified at a proper taxonomic rank?

Let's see what do we have in our dataset:

```{r, class.source='klippy'}
unique(data$taxonRank)
```

Filter those records with appropriate taxonomic rank

```{r, class.source='klippy'}
data <- data %>% filter(taxonRank == 'SUBSPECIES' | taxonRank == 'SPECIES' )
```

### 2. Select only records that include authorship information in their original scientific names proposed

Extract authors name included in scientifName information

```{r, class.source='klippy'}
data$authorshipName <- word(data$scientificName, 3, -1)
```

Delete records with no name included

```{r, class.source='klippy'}
data <- data %>% filter(!is.na(authorshipName))
```

------------------------------------------------------------------------

## MODULE 3: Geography

### 1. Check coordinates values

Discard records with latitude/longitude values equals to zero, exact same value or NULL

```{r, class.source='klippy'}
data <- data %>% 
          filter(decimalLatitude != decimalLongitude) %>% 
          filter(between(decimalLatitude, -90, 90)) %>% 
          filter(between(decimalLongitude, -180, 180)) %>% 
          filter(decimalLatitude != 0) %>% 
          filter(decimalLongitude != 0)
```

### 2. Check coordinates precision

Number of decimal digits of coordinates as a measure of precision

Function to count number of decimals:

```{r, class.source='klippy'}
#Filter coordinates with at least 1 decimal place
data <- bdc_coordinates_precision(data = data, 
                                  lat ='decimalLatitude', 
                                  lon = 'decimalLongitude', ndec = 1) 
data <- data %>% filter(.rou == 'TRUE') 
data$.rou <- NULL 
```

### 3. Check records that don't meet location criteria

Check if coordinates are placed in the assigned country, in the following example we check whether records are located in Portugal

#### Point in polygon by country analysis

Import a shapefile of the study area with Adm. Units borders at country level

Download world / country shapefile from <https://gadm.org/download_world.html>

```{r message=FALSE, warning=FALSE, class.source='klippy'}
countries <- read_sf('gadm41_PRT.gpkg') # Set the correct projection 
st_crs(countries) <- "+proj=longlat +datum=WGS84 +no_defs"
```

#### Transform occurrences into spatial points and project:

```{r, class.source='klippy'}
data$x <- data$decimalLongitude 
data$y <- data$decimalLatitude

datapoints <- st_as_sf(x = data,coords = c("x", "y"), 
                       crs = "+proj=longlat +datum=WGS84 +no_defs")
```

#### Point in polygon: join each point to a polygon based on position

```{r eval=FALSE, class.source='klippy'}
data <- st_join(datapoints, countries) # Exclude those that didn't fall in the country polygon or in 'SEA' 
data <- data %>% filter(COUNTRY == "Portugal") 
# Translate 'countryCode' information (ISO 3166-1-alpha-2 = "iso2c") into country names of GADM 'countryCode' 
data$countryName <- countrycode(data$countryCode, 
                                origin = "iso2c", 
                                destination = "country.name")
# Check the names obtained
unique(data$countryName)
# Match country names and label as 'FALSE' errors of location
data <- data %>% 
  mutate(countryCheck = case_when(COUNTRY != data$countryName ~ FALSE, 
                                                          TRUE ~ TRUE)) 
# Subset and extract records located in country assigned by collector ('correct') 
data <- data %>% filter(countryCheck == 'TRUE') 
data <- as.data.frame(data)
```

#### Delete Centroids:

```{r eval=FALSE, class.source='klippy'}
# Label coordinates placed in centroids of the country or capital 
cap <- cc_cap (data, lon = "decimalLongitude", 
                     lat = "decimalLatitude", value = "flagged") 
data <- cbind(data, cap) 
cen <- cc_cen (data, lon = "decimalLongitude", 
                     lat = "decimalLatitude", value = "flagged") 
data <- cbind(data, cen)  
# Label coordinates placed in gbif headquarters
gbif <- cc_gbif(data, lon = "decimalLongitude", 
                    lat = "decimalLatitude", value = "flagged") 
data <- cbind(data, gbif)
# Label coordinates from museums, gardens, institutions, zoo's…
inst <- cc_inst(data, lon = "decimalLongitude",
                      lat = "decimalLatitude", value = "flagged")
data <- cbind(data, inst)
#Filter and exclude centroids:
data <- data %>% filter(cap == 'TRUE') %>% 
                  filter(cen == 'TRUE') %>% 
                  filter(gbif == 'TRUE') %>% 
                  filter(inst == 'TRUE')
                  
```

------------------------------------------------------------------------

## MODULE 4: Time

#### a. Choose level of temporal information:

```{r, class.source='klippy'}
# Only records with complete date of collection
data <- data %>% 
        filter(!is.na(year) | !is.na(month) | !is.na(day))  
# Create a field that combines day/month/year information
data$date <- paste(data$day, data$month, data$year, sep = '/')
```

#### b. Set temporal range of time:

```{r, class.source='klippy'}
data <- data %>% filter(year >= 1980 & year <= 2020)
```

------------------------------------------------------------------------

## MODULE 5: Duplicates

Here we have multiple combination to detect duplicate records .

Choose the appropriate combination of fields based on your study:

#### SpeciesName + Lat Lon + Date + recordedBy

```{r, class.source='klippy'}
dataDuplic1 <- cc_dupl (data, lon = "decimalLongitude", 
                        lat = "decimalLatitude", 
                        species = "species", 
                        additions = c('recordedBy', 'year'))
```

#### SpeciesName + Lat Lon + recordedBy

```{r, class.source='klippy'}
dataDuplic2 <- cc_dupl (data, lon = "decimalLongitude", 
                        lat = "decimalLatitude", 
                        species = "species", 
                        additions = 'recordedBy')
```

#### SpeciesName + Lat Lon + Year

```{r, class.source='klippy'}
dataDuplic3 <- cc_dupl (data, lon = "decimalLongitude", 
                        lat = "decimalLatitude", 
                        species = "species", 
                        additions = 'year')
```

#### SpeciesName + Lat Lon

```{r, class.source='klippy'}
dataDuplic4 <- cc_dupl (data, lon = "decimalLongitude", 
                        lat = "decimalLatitude", 
                        species = "species")
```

#### SpeciesName + Lat Lon with buffer or rounded coordinates

```{r, class.source='klippy'}
data$lon_round <- round(data$decimalLongitude, 2) 
data$lat_round <- round(data$decimalLatitude, 2) 
dataDuplic5 <- cc_dupl (data, lon = "lon_round", 
                        lat = "lat_round", species = "species")
```

#### SpeciesName + cell

```{r, class.source='klippy'}
datbiogeo_b <- keepmainfields(data, 
                              ID='gbifID',
                              Species='species',
                              x='decimalLongitude', y='decimalLatitude') 
dataDuplic6 <- duplicatesexclude(datbiogeo_b, 10) # spatial resolution in minutes
dataDuplic6 <- merge(data, dataDuplic6, by.x='gbifID', by.y='ID', all.x=TRUE) 
dataDuplic6 <- dataDuplic6 %>% filter(Exclude == 0)
```

------------------------------------------------------------------------

#### SAVE THE CLEANED DATASET

#### Here SpeciesName + Lat Lon with rounded coordinates

```{r, class.source='klippy'}
write.table(dataDuplic5, 'final_data.csv', sep="\t", row.names = FALSE)
```

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