CoordinateCleaner

https://docs.ropensci.org/CoordinateCleaner/
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CoordinateCleaner provides automated flagging of common spatial and temporal errors in biological...
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### Install CoordinateCleaner

Source: https://docs.ropensci.org/CoordinateCleaner/index.html

Installation instructions for stable and developmental versions.

```R
install.packages("CoordinateCleaner")
library(CoordinateCleaner)
```

```R
devtools::install_github("ropensci/CoordinateCleaner")
library(CoordinateCleaner)
```

--------------------------------

### Install CoordinateCleaner from GitHub

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_GBIF_data_with_CoordinateCleaner.html

Installs the development version of the package using devtools.

```R
install.packages("devtools")
library(devtools)
install_github("ropensci/CoordinateCleaner")
```

--------------------------------

### Example usage of write_pyrate

Source: https://docs.ropensci.org/CoordinateCleaner/reference/write_pyrate.html

Demonstrates how to prepare a data frame and status vector before calling write_pyrate.

```R
minages <- runif(250, 0, 65)
exmpl <- data.frame(accepted_name = sample(letters, size = 250, replace = TRUE),
                    lng = runif(250, min = 42, max = 51),
                    lat = runif(250, min = -26, max = -11),
                    min_ma = minages,
                    max_ma = minages + runif(250, 0.1, 65))

#a vector with the status for each record, 
#make sure species are only classified as either extinct or extant, 
#otherwise the function will drop an error

status <- sample(c("extinct", "extant"), size = nrow(exmpl), replace = TRUE)

#or from a list of species
status <- sample(c("extinct", "extant"), size = length(letters), replace = TRUE)
names(status) <- letters
status <- status[exmpl$accepted_name]

if (FALSE) { # \dontrun{
write_pyrate(x = exmpl,fname = "test", status = status)
} # }
```

--------------------------------

### cf_equal example usage

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cf_equal.html

Example demonstrating how to flag records with equal minimum and maximum ages in a data frame.

```R
minages <- runif(n = 10, min = 0.1, max = 25)
x <- data.frame(species = letters[1:10], 
                min_ma = minages, 
                max_ma = minages + runif(n = 10, min = 0, max = 10))
x <- rbind(x, data.frame(species = "z", 
                min_ma = 5, 
                max_ma = 5))
                
cf_equal(x, value = "flagged")
#> Testing age validity
#> Flagged 1 records.
#>  [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE
```

--------------------------------

### Example usage of cc_iucn

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_iucn.html

Demonstrates setting up dummy species data and range polygons to test the cc_iucn function.

```R
library(terra)
#> terra 1.9.34

x <- data.frame(species = c("A", "B"),
decimalLongitude = runif(100, -170, 170),
decimalLatitude = runif(100, -80,80))

range_species_A <- cbind(c(-45,-45,-60,-60,-45), c(-10,-25,-25,-10,-10))
rangeA <- terra::vect(range_species_A, "polygons")
range_species_B <- cbind(c(15,15,32,32,15), c(10,-10,-10,10,10))
rangeB <- terra::vect(range_species_B, "polygons")
range <- terra::vect(list(rangeA, rangeB))
range$binomial <- c("A", "B")

cc_iucn(x = x, range = range, buffer = 0)
#> Testing natural ranges
#> Warning: no projection information for reference found, 
#>               assuming '+proj=longlat +datum=WGS84 +no_defs'
#> Removed 100 records.
#> [1] species          decimalLongitude decimalLatitude 
#> <0 rows> (or 0-length row.names)
```

--------------------------------

### cc_gbif Examples

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_gbif.html

Demonstrates how to use cc_gbif to clean a data frame or return a logical vector of flagged records.

```R
x <- data.frame(species = "A", 
                decimalLongitude = c(12.58, 12.58), 
                decimalLatitude = c(55.67, 30.00))
                
cc_gbif(x)
#> Testing GBIF headquarters, flagging records around Copenhagen
#> Removed 1 records.
#>   species decimalLongitude decimalLatitude
#> 2       A            12.58              30
cc_gbif(x, value = "flagged")
#> Testing GBIF headquarters, flagging records around Copenhagen
#> Flagged 1 records.
#> [1] FALSE  TRUE
```

--------------------------------

### Example usage of cc_zero

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_zero.html

Demonstrates cleaning a data frame and returning a logical vector of flagged records.

```R
x <- data.frame(species = "A", 
                decimalLongitude = c(0,34.84, 0, 33.98), 
                decimalLatitude = c(23.08, 0, 0, 15.98))
                
cc_zero(x)
#> Testing zero coordinates
#> Warning: [vect] guessed crs
#> Removed 3 records.
#>   species decimalLongitude decimalLatitude
#> 4       A            33.98           15.98
cc_zero(x, value = "flagged")
#> Testing zero coordinates
#> Warning: [vect] guessed crs
#> Flagged 3 records.
#> [1] FALSE FALSE FALSE  TRUE
```

--------------------------------

### Flag records near biodiversity institutions

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_inst.html

Example demonstrating how to flag records near institutions using a data frame and specific buffer settings.

```R
x <- data.frame(species = letters[1:10],
                decimalLongitude = c(runif(99, -180, 180), 37.577800),
                decimalLatitude = c(runif(99, -90,90), 55.710800))

#large buffer for demonstration, using geod = FALSE for shorter runtime
cc_inst(x, value = "flagged", buffer = 10, geod = FALSE)
#> Testing biodiversity institutions
#> Flagged 1 records.
#>   [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [49]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [61]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [73]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [85]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
#>  [97]  TRUE  TRUE  TRUE FALSE

if (FALSE) { # \dontrun{
#' cc_inst(x, value = "flagged", buffer = 50000) #geod = T
} # }
```

--------------------------------

### Example of plotting spatialvalid results

Source: https://docs.ropensci.org/CoordinateCleaner/reference/plot.spatialvalid.html

Demonstrates creating a spatialvalid object using clean_coordinates and visualizing the results with the plot method.

```R
exmpl <- data.frame(species = sample(letters, size = 250, replace = TRUE),
                    decimalLongitude = runif(250, min = 42, max = 51),
                    decimalLatitude = runif(250, min = -26, max = -11))

test <- clean_coordinates(exmpl, species = "species", 
                          tests = c("sea", "gbif", "zeros"),
                          verbose = FALSE)
#> Warning: [vect] guessed crs

summary(test)
#>     .val     .zer     .gbf .summary 
#>        0        0        0        0 
plot(test)
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the CoordinateCleaner package.
#>   Please report the issue at
#>   <https://github.com/ropensci/CoordinateCleaner/issues>.
```

--------------------------------

### Detect and Clean Duplicate Records

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_dupl.html

Examples demonstrating how to flag duplicate records as a logical vector or return a cleaned data frame with additional columns for duplicate identification.

```R
x <- data.frame(species = letters[1:10], 
                decimalLongitude = sample(x = 0:10, size = 100, replace = TRUE), 
                decimalLatitude = sample(x = 0:10, size = 100, replace = TRUE),
                collector = "Bonpl",
                collector.number = c(1001, 354),
                collection = rep(c("K", "WAG","FR", "P", "S"), 20))

cc_dupl(x, value = "flagged")
```

```R
cc_dupl(x, additions = c("collector", "collector.number"))
```

--------------------------------

### Identify temporal range outliers

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cf_range.html

Example demonstrating the use of cf_range to flag temporal outliers in a generated dataset.

```R
minages <- runif(n = 11, min = 0.1, max = 25)
x <- data.frame(species = c(letters[1:10], "z"),
                lng = c(runif(n = 9, min = 4, max = 16), 75, 7),
                lat = c(runif(n = 11, min = -5, max = 5)),
                min_ma = minages, 
                max_ma = minages + c(runif(n = 10, min = 0, max = 5), 25))

cf_range(x, value = "flagged", taxon = "")
```

--------------------------------

### Identify temporal outliers in fossil data

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cf_age.html

Example usage of cf_age to flag temporal outliers in a sample dataset.

```R
minages <- c(runif(n = 11, min = 10, max = 25), 62.5)
x <- data.frame(species = c(letters[1:10], rep("z", 2)),
                min_ma = minages,
                max_ma = c(minages[1:11] + runif(n = 11, min = 0, max = 5), 65))

cf_age(x, value = "flagged", taxon = "")
```

--------------------------------

### Run cc_cap on sample data

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_cap.html

Example usage of cc_cap to filter a data frame of coordinates, returning either the cleaned data or a logical flag vector.

```R
if (FALSE) { # \dontrun{
x <- data.frame(species = letters[1:10],
                decimalLongitude = c(runif(99, -180, 180), -47.882778),
                decimalLatitude = c(runif(99, -90, 90), -15.793889))

cc_cap(x)
cc_cap(x, value = "flagged")
} # }
```

--------------------------------

### Validate Coordinates with cc_val

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_val.html

This example demonstrates how to use cc_val to clean a data frame containing invalid coordinate entries.

```R
x <- data.frame(species = letters[1:10], 
                decimalLongitude = c(runif(106, -180, 180), NA, "13W33'", "67,09", 305), 
                decimalLatitude = runif(110, -90,90))
                
cc_val(x)
```

--------------------------------

### Detect geographic outliers with cc_outl

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_outl.html

This example demonstrates how to use cc_outl on a data frame containing species names and coordinates. The function returns the input data frame with flagged records.

```R
x <- data.frame(species = letters[1:10],
                decimalLongitude = runif(100, -180, 180),
                decimalLatitude = runif(100, -90,90))

cc_outl(x)
#> Testing geographic outliers
#> Removed 0 records.
#>     species decimalLongitude decimalLatitude
#> 1         a       141.907373      41.7730578
#> 2         b      -100.689707     -16.3607806
#> 3         c       -75.025282      23.5544050
#> 4         d       112.643550     -78.2199030
#> 5         e      -164.303161     -63.2354127
#> 6         f         1.897799     -39.6536823
#> 7         g       -93.003883      63.1182257
#> 8         h        32.091271     -86.1226595
#> 9         i       169.610605     -44.4906216
#> 10        j       -96.220439      28.2080702
#> 11        a       132.270486      52.0468937
#> 12        b      -140.125773      37.5922166
#> 13        c       108.537041     -78.3732614
#> 14        d        56.852804       2.0360419
#> 15        e        54.739547     -53.8328970
#> 16        f       -98.523848      56.5408067
#> 17        g       127.432667      27.6804137
#> 18        h       -23.181466     -26.4146432
#> 19        i       -75.858575      17.3870813
#> 20        j        49.530481      40.9867872
#> 21        a       -84.765795      89.2023371
#> 22        b       -40.943384      -0.3500511
#> 23        c        42.909829     -48.5469415
#> 24        d        15.004110     -35.7742237
#> 25        e        -5.460840      39.6840018
#> 26        f        95.146171     -42.9470247
#> 27        g       165.969697     -70.6146573
#> 28        h       -82.589568     -42.7639118
#> 29        i        83.016080     -56.3928870
#> 30        j      -131.700403       7.6395023
#> 31        a      -162.576638     -78.2066746
#> 32        b        73.298545      68.4693246
#> 33        c      -127.003034     -39.7417433
#> 34        d        96.895920      31.0866609
#> 35        e      -116.513960     -79.1764427
#> 36        f      -140.126760      87.9273990
#> 37        g       157.803267     -39.1904224
#> 38        h       124.729610       9.7402260
#> 39        i        25.611689      70.2151208
#> 40        j        64.473626     -85.8956301
#> 41        a      -147.843992      31.9839217
#> 42        b       102.734685     -29.3292366
#> 43        c       -98.295319     -64.5847030
#> 44        d       -18.655753     -26.5384822
#> 45        e      -121.958051     -17.7399258
#> 46        f      -116.599792      70.0045834
#> 47        g      -108.637876     -33.7294462
#> 48        h       -51.259927     -78.9021525
#> 49        i      -114.720047      32.3075275
#> 50        j        22.012150     -21.9307376
#> 51        a        57.269883      15.9066627
#> 52        b        57.691227      -1.4438637
#> 53        c      -179.135716     -38.5601501
#> 54        d       177.640572     -88.4844729
#> 55        e        45.896123       9.1393735
#> 56        f      -174.733079      56.3815634
#> 57        g      -106.135842      25.8434254
#> 58        h        58.707599     -77.5688395
#> 59        i       -13.052149      41.1689289
#> 60        j       -50.274663      30.9800157
#> 61        a        68.171901      57.5641784
#> 62        b       -86.810116      74.6318804
#> 63        c       126.433812      34.1947225
#> 64        d        -7.198311      -1.4439955
#> 65        e        13.415900      26.4471571
#> 66        f        58.190363      -7.8273078
#> 67        g       -71.692376     -67.4196081
#> 68        h       -85.139454      18.7856953
#> 69        i       -70.183393      58.8288125
#> 70        j       134.984976      21.4880852
#> 71        a        78.637398      61.3896679
#> 72        b       -33.508910     -13.0956106
#> 73        c      -173.030590      48.4716067
#> 74        d      -161.935071      38.1853424
#> 75        e        72.810372     -78.5939025
#> 76        f      -156.106703     -13.3190575
#> 77        g      -166.992347     -74.1100242
#> 78        h      -172.744670     -52.5029725
#> 79        i       -45.798805     -48.9130428
#> 80        j       -98.760455     -80.9204141
#> 81        a        -2.909484      11.1278254
#> 82        b       -19.207744      34.9643060
#> 83        c        68.473764     -48.9703222
#> 84        d       123.386393      82.8564489
#> 85        e       -44.699880      -1.2893056
#> 86        f       174.560288      40.1775634
#> 87        g       162.873479      -1.5546451
#> 88        h       176.363463      62.1639146
#> 89        i       -20.473792     -85.4802606
#> 90        j      -124.645013       8.7120437
#> 91        a       160.101836      81.9818670
#> 92        b         7.007909     -64.7096640
```

--------------------------------

### Identify Artificial Hotspot Records

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_aohi.html

Example usage of cc_aohi to filter a data frame of species occurrences.

```R
x <- data.frame(species = letters[1:10], 
                decimalLongitude = c(runif(99, -180, 180), -47.92), 
                decimalLatitude = c(runif(99, -90,90), -15.78))
cc_aohi(x)
```

--------------------------------

### Prepare and export PBDB data for PyRate

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Formats taxon names, generates mock status data, joins the datasets, and writes the final output for PyRate.

```R
# replace  blanks in taxon names
cl$taxon_name <- gsub("[[:blank:]]{1,}","_", cl$taxon_name)

#simulated current status, soley for demonstration purposes, replace with your own data
mock_status <- data.frame(taxon_name = unique(cl$taxon_name),
                          status = sample(c("extinct", "extant"), 
                                          size = length(unique(cl$taxon_name)), 
                                          replace = TRUE))

#add current status to fossils
cl2 <- inner_join(cl, mock_status, by = "taxon_name")

#Write PyRate input to disk
write_pyrate(cl, fname = "paleobioDB_angiosperms", status = cl2$status,
            taxon = "taxon_name", min_age = "late_age", max_age = "early_age")
```

--------------------------------

### Flagging records with default gazetteer

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Using_custom_gazetteers.html

Demonstrates downloading GBIF data and using the default cc_sea gazetteer, which may be too restrictive for coastal species.

```R
library(CoordinateCleaner)
library(dplyr)
library(ggplot2)
library(rgbif)
library(viridis)
library(terra)

#download data from GBIF
dat <- rgbif::occ_search(scientificName = "Avicennia", limit = 1000,
         hasCoordinate = T)

dat <- dat$data

dat <-  dat %>% 
  dplyr::select(species = name, decimalLongitude = decimalLongitude, 
         decimalLatitude = decimalLatitude, countryCode)

# run with default gazetteer
outl <- cc_sea(dat, value = "flagged")
## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\az64mycy\AppData\Local\Temp\Rtmp4SRhHV", layer: "ne_110m_land"
## with 127 features
## It has 3 fields

plo <- data.frame(dat, outlier =  as.factor(!outl))

#plot results
ggplot() +
  borders(fill = "grey60") +
  geom_point(data = plo, 
             aes(x = decimalLongitude, y = decimalLatitude, col = outlier)) +
  scale_color_viridis(discrete = T, name = "Flagged outlier") +
  coord_fixed() +
  theme_bw() +
  theme(legend.position = "bottom")
```

--------------------------------

### Load and prepare PBDB test dataset

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Download fossil occurrence data from the Paleobiology Database and ensure coordinate and age columns are numeric.

```R
#load data
dat <- paleobioDB::pbdb_occurrences(base_name = "Magnoliopsida", 
                                    vocab = "pbdb", limit = 5000,
                        show = c("coords", "phylo", "attr", "loc", "time", "rem"))
dat <- dat %>% mutate(lng = as.numeric(lng), 
               lat = as.numeric(lat), 
               early_age = as.numeric(early_age),
               late_age = as.numeric(late_age))
rownames(dat) <- NULL
```

--------------------------------

### write_pyrate(x, status, fname, ...)

Source: https://docs.ropensci.org/CoordinateCleaner/reference/write_pyrate.html

Creates the input necessary to run PyRate, based on a data.frame with fossil ages and a vector of the extinction status for each sample.

```APIDOC
## write_pyrate(x, status, fname, taxon = "accepted_name", min_age = "min_ma", max_age = "max_ma", trait = NULL, path = getwd(), replicates = 1, cutoff = NULL, random = TRUE)

### Description
Creates the input necessary to run PyRate, based on a data.frame with fossil ages (as derived e.g. from clean_fossils) and a vector of the extinction status for each sample. Creates files in the working directory.

### Parameters
- **x** (data.frame) - Required - Containing fossil records with taxon names, ages, and geographic coordinates.
- **status** (vector) - Required - A vector of character strings of length nrow(x) indicating for each record “extinct” or “extant”.
- **fname** (character) - Required - The prefix to use for the output files.
- **taxon** (character) - Optional - The column with the taxon name. Default = “accepted_name”.
- **min_age** (character) - Optional - The column with the minimum age. Default = “min_ma”.
- **max_age** (character) - Optional - The column with the maximum age. Default = “max_ma”.
- **trait** (numeric vector) - Optional - Indicating trait values for each record. Default = NULL.
- **path** (character) - Optional - The absolute path to write the output files. Default is the working directory.
- **replicates** (numeric) - Optional - The number of replicates for the randomized age generation. Default = 1.
- **cutoff** (numeric) - Optional - Specify a threshold to exclude fossil occurrences with a high temporal uncertainty.
- **random** (logical) - Optional - Specify whether to take a random age (between MinT and MaxT) for each occurrence or the midpoint age. Default = TRUE.

### Response
- **Output** (files) - PyRate input files generated in the specified path.
```

--------------------------------

### Simulate and Test Rasterized Data

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cd_round.html

Demonstrates how to simulate a dataset with a biased grid and run the cd_round test on it.

```R
#simulate bias grid, one degree resolution, 10% error on a 1000 records dataset
#simulate biased fraction of the data, grid resolution = 1 degree
#simulate non-biased fraction of the data
  bi <- sample(3 + 0:5, size = 100, replace = TRUE)
  mu <- runif(3, 0, 15)
  sig <- runif(3, 0.1, 5)
  cl <- rnorm(n = 900, mean = mu, sd = sig)
  lon <- c(cl, bi)
  
  bi <- sample(9:13, size = 100, replace = TRUE)
  mu <- runif(3, 0, 15)
  sig <- runif(3, 0.1, 5)
  cl <- rnorm(n = 900, mean = mu, sd = sig)
  lat <- c(cl, bi)
  
  #add biased data
  
  inp <- data.frame(decimalLongitude = lon,
                    decimalLatitude = lat,
                    dataset = "test")
            
          
  #run test
  if (FALSE) { # \dontrun{
  cd_round(inp, value = "dataset")
  } # }
```

--------------------------------

### Flagging non-terrestrial records with cc_coun

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_coun.html

Example usage of cc_coun to flag records that do not match the provided country code.

```R
if (FALSE) { # \dontrun{
x <- data.frame(species = letters[1:10],
                decimalLongitude = runif(100, -20, 30),
                decimalLatitude = runif(100, 35,60),
                countrycode = "RUS")

cc_coun(x, value = "flagged")#non-terrestrial records are flagged as wrong.
} # }
```

--------------------------------

### Loading and visualizing a custom buffered gazetteer

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Using_custom_gazetteers.html

Loads the built-in buffered land reference for use as a custom gazetteer.

```R
# The buffered custom gazetteer
data("buffland")
buffland <- terra::vect(buffland)
plot(buffland)
```

--------------------------------

### Flag records near country centroids

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_cen.html

Example usage of cc_cen to test a data frame of species coordinates for proximity to country centroids.

```R
x <- data.frame(species = letters[1:10], 
                decimalLongitude = c(runif(99, -180, 180), -47.92), 
                decimalLatitude = c(runif(99, -90,90), -15.78))
cc_cen(x, geod = FALSE)
#> Testing country centroids
#> Removed 0 records.
#>     species decimalLongitude decimalLatitude
#> 1         a       -41.986559      -67.231328
#> 2         b       179.874885       78.719439
#> 3         c       -54.252343      -51.051559
#> 4         d       161.034576       29.896562
#> 5         e      -102.204009      -53.297878
#> 6         f      -168.446625        8.746459
#> 7         g      -127.686296       58.950255
#> 8         h       127.578200      -67.039623
#> 9         i      -103.266248      -42.986499
#> 10        j      -104.288134      -38.084935
#> 11        a      -165.772552      -87.341577
#> 12        b       160.118929       63.670271
#> 13        c       -91.825925      -15.471391
#> 14        d       101.204125       45.182492
#> 15        e       -76.234619       39.422512
#> 16        f       135.128848        2.763954
#> 17        g       -73.529966       85.470526
#> 18        h       174.069147      -25.060039
#> 19        i        32.341520      -59.328809
#> 20        j        93.297017       50.965321
#> 21        a       120.987110      -84.433699
#> 22        b        94.615009       52.095665
#> 23        c       -29.782827      -60.011338
#> 24        d      -130.293057      -84.831738
#> 25        e      -150.895814       51.471210
#> 26        f        56.153745       58.555415
#> 27        g        36.721389       83.743275
#> 28        h        56.518499      -21.844588
#> 29        i       -61.445821      -58.610461
#> 30        j       172.610719       18.675072
#> 31        a        77.467008       54.983472
#> 32        b       134.146910      -83.397706
#> 33        c       173.982149       41.923920
#> 34        d      -101.317323      -51.250922
#> 35        e        59.230823      -87.119434
#> 36        f       -39.756946      -66.851536
#> 37        g      -163.417090       33.491359
#> 38        h        42.089241       25.549428
#> 39        i        35.450998      -31.079066
#> 40        j       -33.532693      -20.253243
#> 41        a       128.998134       37.350071
#> 42        b         6.365227       35.577977
#> 43        c       172.545628       76.618218
#> 44        d      -173.874352       -7.376810
#> 45        e        62.441217       17.271756
#> 46        f       -46.342842      -60.298022
#> 47        g       150.483829       12.043761
#> 48        h        64.072114       71.653927
#> 49        i        59.454886       17.005011
#> 50        j        92.174792       59.704185
#> 51        a        15.421376       16.813506
#> 52        b       -93.856285       50.214714
#> 53        c         3.201686      -18.401104
#> 54        d       -29.784826       62.978899
#> 55        e        81.701587       43.532205
#> 56        f        49.566799      -32.797761
#> 57        g       -37.292416      -69.897560
#> 58        h       165.413739      -71.802834
#> 59        i       -72.483109       54.017595
#> 60        j      -161.927580      -21.609586
#> 61        a        27.427471      -80.513825
#> 62        b      -101.553909       87.571566
#> 63        c      -134.691743       18.751314
#> 64        d       157.734967      -63.219110
#> 65        e       108.459046        6.976831
#> 66        f        92.899303      -67.357939
#> 67        g        11.723459       83.372716
#> 68        h        16.849719      -81.551697
#> 69        i      -145.466459      -60.946347
#> 70        j       -40.194089       78.567737
#> 71        a      -117.953320       84.722645
#> 72        b        68.661305       37.849329
#> 73        c        63.075061       69.316367
#> 74        d       160.666145       85.837557
#> 75        e      -109.360972      -83.718064
#> 76        f       168.709501      -11.023692
#> 77        g       -40.645340       26.004557
#> 78        h        54.123804       89.069833
#> 79        i       113.254633      -33.360728
#> 80        j      -154.452682       64.058754
#> 81        a         9.658917        7.260783
#> 82        b        94.850937       67.215123
#> 83        c       -23.260809        2.805412
#> 84        d        18.890041       66.502674
#> 85        e      -106.548967       64.174067
#> 86        f      -168.830634      -27.777027
```

--------------------------------

### Load required libraries

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_GBIF_data_with_CoordinateCleaner.html

Loads the necessary R packages for data manipulation, visualization, and cleaning.

```R
library(countrycode)
library(CoordinateCleaner)
library(dplyr)
library(ggplot2)
library(rgbif)
library(sf)
```

--------------------------------

### Clean coordinates with basic and advanced tests

Source: https://docs.ropensci.org/CoordinateCleaner/reference/clean_coordinates.html

Demonstrates basic coordinate validation and how to execute a comprehensive suite of tests including capitals, centroids, and outliers.

```R
exmpl <- data.frame(species = sample(letters, size = 250, replace = TRUE),
                    decimalLongitude = runif(250, min = 42, max = 51),
                    decimalLatitude = runif(250, min = -26, max = -11))

test <- clean_coordinates(x = exmpl, 
                          tests = c("equal"))
#> Testing coordinate validity
#> Flagged 0 records.
#> Testing equal lat/lon
#> Flagged 0 records.
#> Flagged 0 of 250 records, EQ = 0.
                                    
if (FALSE) { # \dontrun{
#run more tests
test <- clean_coordinates(x = exmpl, 
                          tests = c("capitals", 
                          "centroids","equal", 
                          "gbif", "institutions", 
                          "outliers", "seas", 
                          "zeros"))
} # }
                                 
                                    
summary(test)
#>     .val     .equ .summary 
#>        0        0        0 
```

--------------------------------

### Load and inspect the institutions dataset

Source: https://docs.ropensci.org/CoordinateCleaner/reference/institutions.html

Load the institutions data frame and display its structure to understand the available variables and data types.

```R
data(institutions)
str(institutions)
```

--------------------------------

### Execute cc_urb on sample data

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_urb.html

Demonstrates running the function on a data frame to return either cleaned records or a logical vector of flags.

```R
if (FALSE) { # \dontrun{
x <- data.frame(species = letters[1:10],
                decimalLongitude = runif(100, -180, 180),
                decimalLatitude = runif(100, -90,90))

cc_urb(x)
cc_urb(x, value = "flagged")
} # }
```

--------------------------------

### Summarize and visualize cleaning flags

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_GBIF_data_with_CoordinateCleaner.html

Display a summary of flagged records and visualize their spatial distribution.

```R
summary(flags)
plot(flags, lon = "decimalLongitude", lat = "decimalLatitude")
```

--------------------------------

### Load buffland dataset in R

Source: https://docs.ropensci.org/CoordinateCleaner/reference/buffland.html

Loads the global coastline buffer dataset for use with the cc_sea function.

```R
data("buffland")
```

--------------------------------

### Obtain and prepare GBIF data

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_GBIF_data_with_CoordinateCleaner.html

Downloads occurrence data for Panthera leo from GBIF and filters for relevant columns and valid coordinates.

```R
#obtain data from GBIF via rgbif
dat <- occ_search(scientificName = "Panthera leo", 
                  limit = 5000, 
                  hasCoordinate = TRUE)

dat <- dat$data

# names(dat) # a lot of columns

# select columns of interest
dat <- dat %>%
  dplyr::select(species, decimalLongitude, 
                decimalLatitude, countryCode, individualCount,
                gbifID, family, taxonRank, coordinateUncertaintyInMeters,
                year, basisOfRecord, institutionCode, datasetName)

# remove records without coordinates
dat <- dat %>%
  filter(!is.na(decimalLongitude)) %>%
  filter(!is.na(decimalLatitude))
```

--------------------------------

### Visualize fossil records on a map

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Create a world map plot to visualize the spatial distribution of the fossil occurrences.

```R
#plot data to get an overview
wm <- borders("world", colour = "gray50", fill = "gray50")
dat %>% 
  ggplot(aes(x = lng, y = lat)) +
  coord_fixed() +
  wm +
  geom_point(
             colour = "darkred",
             size = 0.5) +
  theme_bw()
```

--------------------------------

### clean_dataset(x, lon, lat, ds, tests, value, verbose, ...)

Source: https://docs.ropensci.org/CoordinateCleaner/reference/clean_dataset.html

Tests for problems associated with coordinate conversions and rounding, based on dataset properties.

```APIDOC
## clean_dataset

### Description
Tests for problems associated with coordinate conversions and rounding, based on dataset properties. Includes tests to identify contributing datasets with potential errors with converting ddmm to dd.dd, and periodicity in the data decimals indicating rounding or a raster basis.

### Usage
clean_dataset(x, lon = "decimalLongitude", lat = "decimalLatitude", ds = "dataset", tests = c("ddmm", "periodicity"), value = "dataset", verbose = TRUE, ...)

### Arguments
- **x** (data.frame) - Required - Containing geographical coordinates and species names.
- **lon** (character) - Optional - The column with the longitude coordinates. Default = "decimalLongitude".
- **lat** (character) - Optional - The column with the latitude coordinates. Default = "decimalLatitude".
- **ds** (character) - Optional - The column with the dataset of each record. Default = "dataset".
- **tests** (vector) - Optional - A vector of character strings indicating which tests to run. Default = c("ddmm", "periodicity").
- **value** (character) - Optional - Defining the output value. Default = "dataset".
- **verbose** (logical) - Optional - If TRUE reports the name of the test and the number of records flagged.
- **...** (dots) - Optional - Additional arguments to be passed to cd_ddmm and cd_round to customize test sensitivity.
```

--------------------------------

### Load AHOI dataset in R

Source: https://docs.ropensci.org/CoordinateCleaner/reference/aohi.html

Loads the AHOI data frame into the R environment for use.

```R
data("aohi")
```

--------------------------------

### Load and inspect countryref data

Source: https://docs.ropensci.org/CoordinateCleaner/reference/countryref.html

Load the countryref dataset into the R environment and display the first few rows to understand its structure.

```R
data(countryref)
head(countryref)
```

--------------------------------

### Inspect minimum ages

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Displays the tail of the table containing minimum age values for the cleaned dataset.

```R
#minimum ages
tail(table(cl$late_age))
```

--------------------------------

### Visualize Early Age Distribution

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Displays a histogram of the early_age column from the dataset.

```R
ggplot(cl) +
  geom_histogram(aes(x = early_age))
```

--------------------------------

### Usage of write_pyrate

Source: https://docs.ropensci.org/CoordinateCleaner/reference/write_pyrate.html

Defines the function signature and parameters for generating PyRate input files.

```R
write_pyrate(
  x,
  status,
  fname,
  taxon = "accepted_name",
  min_age = "min_ma",
  max_age = "max_ma",
  trait = NULL,
  path = getwd(),
  replicates = 1,
  cutoff = NULL,
  random = TRUE
)
```

--------------------------------

### Clean fossil data using clean_fossils wrapper

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Uses the clean_fossils wrapper function to perform automated quality tests on fossil data and separate valid from flagged records.

```R
#run automated testing
flags <- clean_fossils(x = dat, 
                       lat = "lat",
                       lon = "lng",
                       taxon = "taxon_name",
                       min_age = "late_age", max_age = "early_age", 
                       value = "spatialvalid")

head(flags)
cl <- dat[flags$.summary,] #the cleaned records
fl_rec <- dat[!flags$.summary,] # the flagged records for verification
```

--------------------------------

### Validate country identity with ISO3 conversion

Source: https://docs.ropensci.org/CoordinateCleaner/articles/Cleaning_PBDB_fossils_with_CoordinateCleaner.html

Converts country codes to ISO3 format and tests if coordinates match the assigned country, handling potential mismatches like historical political boundaries.

```R
#adapt country code to ISO3, for country test
cs_ma <- "GBR"
names(cs_ma) <- "UK"
cl$cc_iso3 <- countrycode(cl$cc, origin = "iso2c", destination = "iso3c", custom_match = cs_ma)

cl <- cc_coun(cl, lat = "lat", lon = "lng", iso3 = "cc_iso3")
```

--------------------------------

### Identify geographic outliers using quantile method

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cc_outl.html

Uses the quantile method to flag geographic outliers in the dataset x.

```R
cc_outl(x, method = "quantile", value = "flagged")
```

--------------------------------

### Identify spatio-temporal outliers with cf_outl

Source: https://docs.ropensci.org/CoordinateCleaner/reference/cf_outl.html

Demonstrates the usage of cf_outl on a sample data frame containing species, coordinates, and age ranges.

```R
minages <- c(runif(n = 11, min = 10, max = 25), 62.5)
x <- data.frame(species = c(letters[1:10], rep("z", 2)),
                lng = c(runif(n = 10, min = 4, max = 16), 75, 7),
                lat = c(runif(n = 12, min = -5, max = 5)),
                min_ma = minages, 
                max_ma = c(minages[1:11] + runif(n = 11, min = 0, max = 5), 65))

cf_outl(x, value = "flagged", taxon = "")
```
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