Select, reshape, and filter data • forcis

The package forcis provides a lot of functions to filter, reshape, and select FORCIS data. This vignette shows how to use these functions.

Setup

First, let’s import the required packages.

library(forcis)

Before proceeding, let’s download the latest version of the FORCIS database.

# Create a data/ folder ----
dir.create("data")

# Download latest version of the database ----
download_forcis_db(path = "data", version = NULL)

The vignette will use the plankton nets data of the FORCIS database. Let’s import the latest release of the data.

# Import net data ----
net_data <- read_plankton_nets_data(path = "data")

NB: In this vignette, we use a subset of the plankton nets data, not the whole dataset.

`select_taxonomy()`

The FORCIS database provides three different taxonomies: LT (lumped taxonomy), VT (validated taxonomy) and OT (original taxonomy). See the associated data paper for further information.

After importing the data and before going any further, the next step involves choosing the taxonomic level for the analyses. Let’s use the function select_taxonomy() to select the VT taxonomy (validated taxonomy):

# Select taxonomy ----
net_data <- select_taxonomy(net_data, taxonomy = "VT")

`select_columns()`

Because FORCIS data contain more than 100 columns, the function select_columns() can be used to lighten the data.frame to easily handle it and to speed up some computations.

By default, only required columns listed in get_required_columns() and species columns will be kept.

# Select taxonomy ----
net_data <- select_columns(net_data)

But you can also use the argument cols to keep additional columns.

`filter_by_month()`

The filter_by_month() function filters observations based on the month of sampling. It requires two arguments: the data and a numeric vector with values between 1 and 12.

# Filter data by sampling month ----
net_july_aug <- filter_by_month(net_data, months = 7:8)

# Number of original records ----
nrow(net_data)
#> [1] 2451

# Number of filtered records ----
nrow(net_july_aug)
#> [1] 516

`filter_by_year()`

The filter_by_year() function filters observations based on the year of sampling. It requires two arguments: the data and a numeric vector with the years of interest.

# Filter data by sampling year ----
net_90_20 <- filter_by_year(net_data, years = 1990:2020)

# Number of original records ----
nrow(net_data)
#> [1] 2451

# Number of filtered records ----
nrow(net_90_20)
#> [1] 2283

`filter_by_bbox()`

The function filter_by_bbox() can be used to filter FORCIS data by a spatial bounding box (argument bbox).

Let’s filter the plankton net data by a spatial rectangle located in the Indian ocean.

# Filter by spatial bounding box ----
net_data_bbox <- filter_by_bbox(net_data, bbox = c(45, -61, 82, -24))

# Number of original records ----
nrow(net_data)
#> [1] 2451

# Number of filtered records ----
nrow(net_data_bbox)
#> [1] 320

Note that the argument bbox can be either an object of class bbox (package sf) or a vector of four numeric values defining a square bounding box. If a vector of numeric values is provided, coordinates must be defined in the system WGS 84 (epsg=4326).

`filter_by_ocean()`

The function filter_by_ocean() can be used to filter FORCIS data by one or several oceans (argument ocean).

Let’s filter the plankton net data located in the Indian ocean.

# Filter by ocean name ----
net_data_indian <- filter_by_ocean(net_data, ocean = "Indian Ocean")

# Number of original records ----
nrow(net_data)
#> [1] 2451

# Number of filtered records ----
nrow(net_data_indian)
#> [1] 1640

Use the function get_ocean_names() to retrieve the name of World oceans according to the IHO Sea Areas dataset version 3 (used in this package).

# Get ocean names ----
get_ocean_names()
#> [1] "Arctic Ocean"         "Indian Ocean"         "Mediterranean Sea"   
#> [4] "North Atlantic Ocean" "North Pacific Ocean"  "South Atlantic Ocean"
#> [7] "South Pacific Ocean"  "Southern Ocean"

`filter_by_polygon()`

The function filter_by_polygon() can be used to filter FORCIS data a spatial polygon (argument polygon).

Let’s filter the plankton net data by a spatial polygon defining boundaries of the Indian ocean.

# Import spatial polygon ----
file_name <- system.file(file.path("extdata", "IHO_Indian_ocean_polygon.gpkg"), 
                         package = "forcis")
indian_ocean <- sf::st_read(file_name, quiet = TRUE)

# Filter by polygon ----
net_data_poly <- filter_by_polygon(net_data, polygon = indian_ocean)

# Number of original records ----
nrow(net_data)
#> [1] 2451

# Number of filtered records ----
nrow(net_data_poly)
#> [1] 1640

`filter_by_species()`

The filter_by_species() function allows users to filter FORCIS data for one or more species.

It takes a data.frame and a vector of species names (argument species).

Let’s subset plankton net data to only keep observations for G. glutinata and C. nitida.

# Filter by species ----
glutinata_nitida <- filter_by_species(net_data, 
                                      species = c("g_glutinata_VT", "c_nitida_VT"))

# Dimensions of original data ----
dim(net_data)
#> [1] 2451   77

# Dimensions of filtered data ----
dim(glutinata_nitida)
#> [1] 2451   23

`reshape_data()`

The reshape_data() function converts FORCIS data into a long format.

# Convert to long format ----
net_data_long <- reshape_data(net_data)

# Dimensions of original data ----
dim(net_data)
#> [1] 2451   77

# Dimensions of reshaped data ----
dim(net_data_long)
#> [1] 137256     23

Two columns have been created: taxa (taxon names) and counts (taxon counts).

# Column names ----
colnames(net_data_long)
#>  [1] "data_type"                                
#>  [2] "cruise_id"                                
#>  [3] "profile_id"                               
#>  [4] "sample_id"                                
#>  [5] "sample_min_depth"                         
#>  [6] "sample_max_depth"                         
#>  [7] "profile_depth_min"                        
#>  [8] "profile_depth_max"                        
#>  [9] "profile_date_time"                        
#> [10] "cast_net_op_m2"                           
#> [11] "subsample_id"                             
#> [12] "sample_segment_length"                    
#> [13] "subsample_count_type"                     
#> [14] "subsample_size_fraction_min"              
#> [15] "subsample_size_fraction_max"              
#> [16] "site_lat_start_decimal"                   
#> [17] "site_lon_start_decimal"                   
#> [18] "sample_volume_filtered"                   
#> [19] "subsample_all_shells_present_were_counted"
#> [20] "total_of_forams_counted_ind"              
#> [21] "sampling_device_type"                     
#> [22] "taxa"                                     
#> [23] "counts"