Merge branch 'main' into remove-base-declarations

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: trias
 Title: Process Data for the Project Tracking Invasive Alien Species
     (TrIAS)
-Version: 2.3.0
+Version: 2.3.1
 Authors@R: c(
     person("Damiano", "Oldoni", email = "damiano.oldoni@inbo.be",
            role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3445-7562")),
@@ -68,4 +68,4 @@ Encoding: UTF-8
 LazyData: true
 LazyDataCompression: bzip2
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2

R/apply_decision_rules.R

@@ -47,20 +47,17 @@
 #' - (`dr_1` is `TRUE`  and `dr_3` is `FALSE`) or (`dr_1`, `dr_2` and `dr_3` are
 #' `FALSE`): `em = 1` (unclear)
 #' @examples
-#' \dontrun{
 #' df <- dplyr::tibble(
 #'   taxonID = c(rep(1008955, 10), rep(2493598, 3)),
 #'   y = c(seq(2009, 2018), seq(2016, 2018)),
 #'   obs = c(1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 3, 0)
 #' )
 #' apply_decision_rules(df,
-#'   eval_year = c(2016, 2017, 2018),
+#'   eval_year = 2016,
 #'   y_var = "obs",
 #'   taxonKey = "taxonID",
-#'   year = y
+#'   year = "y"
 #' )
-#' }
-#'
 apply_decision_rules <- function(df,
                                  y_var = "ncells",
                                  eval_year,
@@ -173,7 +170,7 @@ apply_decision_rules <- function(df,
   # Get all taxa in df
   taxon_keys <-
     df %>%
-    distinct(!!rlang::sym(taxonKey)) %>%
+    dplyr::distinct(!!rlang::sym(taxonKey)) %>%
     dplyr::pull()
 
   # Find taxa whose timeseries don't contain eval_year, remove them and throw a
@@ -214,7 +211,7 @@ apply_decision_rules <- function(df,
     dplyr::filter(!!rlang::sym(y_var) > 0) %>%
     dplyr::add_tally(wt = NULL) %>%
     dplyr::mutate(dr_1 = n == 1) %>%
-    distinct(!!rlang::sym(taxonKey), dr_1)
+    dplyr::distinct(!!rlang::sym(taxonKey), dr_1)
 
   # Rule 2: last value (at eval_year) above median value?
   dr_2 <-
@@ -300,7 +297,7 @@ apply_decision_rules <- function(df,
 
   em_dr <-
     em_dr %>%
-    bind_rows(taxa_without_em)
+    dplyr::bind_rows(taxa_without_em)
 
   return(em_dr)
 }

R/apply_gam.R

@@ -33,7 +33,7 @@
 #' @param x_label character. x-axis label of output plot. Default: `"year"`.
 #' @param y_label character. y-axis label of output plot. Default: `"number of
 #'   observations"`.
-#' @param saveplot logical. If `TRUE` the plots are authomatically saved.
+#' @param saveplot logical. If `TRUE` the plots are automatically saved.
 #'   Default: `FALSE`.
 #' @param dir_name character. Path of directory where saving plots. If path
 #'   doesn't exists, directory will be created. Example: "./output/graphs/". If
@@ -92,7 +92,7 @@
 #'
 #'   \item `first_derivative`: df. Data.frame with details of first derivatives.
 #'   It contains the following columns:
-#'   - `smooth`: smoooth identifier. Example: `s(year)`.
+#'   - `smooth`: smooth identifier. Example: `s(year)`.
 #'   - `derivative`: numeric. Value of first derivative.
 #'   - `se`: numeric. Standard error of `derivative`.
 #'   - `crit`: numeric. Critical value required such that

R/climate_match.R

@@ -37,7 +37,7 @@
 #' zones in the target nation or region
 #' - `spatial` a sf object containing the observations used 
 #' in the analysis
-#' - `current_map` a leaflet object displaying the degree of wordlwide 
+#' - `current_map` a leaflet object displaying the degree of worldwide 
 #' climate match with the climate from 1980 till 2016
 #' - `future_maps` a list of leaflet objects for each future climate 
 #' scenario, displaying the degree of climate match

R/data-legends.R

@@ -6,10 +6,10 @@
 #'   
 #'   Each data.frame contains two columns: 
 #'   - `GRIDCODE`: (numeric) grid value corresponding to a climate zone 
-#'   - `Classification`: (character) Koppen-{G}eiger climate classification value
-#'   - `Description`: (character) verbose description of the Koppen-{G}eiger
+#'   - `Classification`: (character) Koppen-Geiger climate classification value
+#'   - `Description`: (character) verbose description of the Koppen-Geiger
 #'   climate zone, e.g. "Tropical rainforest climate"
-#'   - `Group`: (character) group the Koppen-{G}eiger climate zone belongs to,
+#'   - `Group`: (character) group the Koppen-Geiger climate zone belongs to,
 #'   e.g. "Tropical"
 #'   - `Precipitation Type`: (character) Type of precipitations associated to
 #'   the climate zone, e.g. "Rainforest"

R/gbif_has_distribution.R

@@ -7,7 +7,7 @@
 #' @param taxon_key (single numeric or character) a single taxon key.
 #' @param ... one or more GBIF distribution properties and related values.
 #' Up to now it supports the following properties:
-#' country (and its synoynym: countryCode), status (and its synonym:
+#' country (and its synonym: countryCode), status (and its synonym:
 #' occurrenceStatus) and establishmentMeans.
 #' @return a logical, TRUE or FALSE.
 #' @export

R/gbif_verify_keys.R

@@ -1,39 +1,37 @@
 #' Check keys against GBIF Backbone Taxonomy
 #'
 #' This function performs three checks:
-#' \itemize{
-#'   \item{\code{keys} are valid GBIF taxon keys. That means that adding a key
+#' - `keys` are valid GBIF taxon keys. That means that adding a key
 #'   at the end of the URL https://www.gbif.org/species/ returns a GBIF page
-#'   related to a taxa.}
-#'   \item{\code{keys} are taxon keys of the GBIF Backbone Taxonomy checklist.
+#'   related to a taxa.
+#' - `keys` are taxon keys of the GBIF Backbone Taxonomy checklist.
 #'   That means that adding a key at the end of the URL
 #'   https://www.gbif.org/species/ returns a GBIF page related to a taxa of the
-#'   GBIF Backbone.)}
-#'   \item{\code{keys} are synonyms of other taxa (taxonomicStauts neither
-#'   \code{ACCEPTED} nor \code{DOUBTFUL}).}
-#'   }.
+#'   GBIF Backbone.)
+#' - `keys` are synonyms of other taxa (taxonomicStatus neither
+#'   `ACCEPTED` nor `DOUBTFUL`).
 #' @param keys (character or numeric) a vector, a list, or a data.frame
 #'   containing the keys to verify.
 #' @param col_keys (character) name of column containing keys in case
-#'   \code{keys} is a data.frame.
+#'   `keys` is a data.frame.
 #'
 #' @return a data.frame with the following columns:
-#'   \itemize{
-#'     \item{\code{key}}{:(numeric) keys as input keys.}
-#'     \item{\code{is_taxonKey}} {: (logical) is the key a valid GBIF taxon
-#'     key?}
-#'   \item{\code{is_from_gbif_backbone}} {:(logical) is the key a valid taxon
-#'   key from GBIF Backbone Taxonomy checklist?}
-#'   \item{\code{is_synonym}} {: (logical) is the key related to a synonym (not
-#'   \code{ACCEPTED} or \code{DOUBTFUL})?}
-#'   }
-#'   If a key didn't pass the first check (\code{is_taxonKey = FALSE}) then
-#'   \code{NA} for other two columns. If a key didn't pass the second check
-#'   (\code{is_from_gbif_backbone = FALSE}) then \code{is_synonym} = \code{NA}.
+#' - `key`: (numeric) keys as input keys.
+#' - `is_taxonKey`: (logical) is the key a valid GBIF taxon key?
+#' - `is_from_gbif_backbone`: (logical) is the key a valid taxon key from 
+#' GBIF Backbone Taxonomy checklist?
+#' - `is_synonym`: (logical) is the key related to a synonym (not
+#'   `ACCEPTED` or `DOUBTFUL`)?
+#'   
+#' If a key didn't pass the first check (`is_taxonKey` = `FALSE`) then
+#' `NA` for other two columns. If a key didn't pass the second check
+#' (`is_from_gbif_backbone` = `FALSE`) then `is_synonym` = `NA`.
+#' 
 #' @export
 #' @importFrom dplyr .data %>%
 #' @importFrom rlang !!
 #' @examples
+#' \dontrun{
 #' # input is a vector
 #' keys1 <- c(
 #'   "12323785387253", # invalid GBIF taxonKey
@@ -50,7 +48,6 @@
 #' )
 #' # input is a named list
 #' keys3 <- keys1
-#' library(purrr)
 #' names(keys3) <- purrr::map_chr(
 #'   c(1:length(keys3)),
 #'   ~ paste(sample(c(0:9, letters, LETTERS), 3),
@@ -64,6 +61,7 @@
 #' gbif_verify_keys(keys2, col_keys = "keys")
 #' gbif_verify_keys(keys3)
 #' gbif_verify_keys(keys4)
+#' }
 gbif_verify_keys <- function(keys, col_keys = "key") {
   assertthat::assert_that(is.data.frame(keys) | is.vector(keys),
     msg = "keys should be a vector, a named list or a data.frame."

R/indicator_native_range_year.R

@@ -5,7 +5,7 @@
 #' [countYearProvince](https://github.com/inbo/reporting-rshiny-grofwildjacht/blob/exoten/reporting-grofwild/R/countYearProvince.R)
 #' plot from reporting - rshiny - grofwildjacht
 #' @param df input data.frame.
-#' @param years (numeric) vector years we are interested to. If \code{NULL}
+#' @param years (numeric) vector years we are interested to. If `NULL`
 #'   (default) all years from minimum and maximum of years of first observation
 #'   are taken into account.
 #' @param type character, native_range level of interest should be one of
@@ -16,22 +16,26 @@
 #'   species".
 #' @param relative (logical) if TRUE (default), each bar is standardised before
 #'   stacking.
-#' @param taxon_key_col character. Name of the column of \code{df} containing
-#'   taxon IDs. Default: \code{"key"}.
+#' @param taxon_key_col character. Name of the column of `df` containing
+#'   taxon IDs. Default: `"key"`.
 #' @param first_observed (character) Name of the column in `data`
 #'   containing temporal information about introduction of the alien species.
 #'   Expressed as years.
-#' @return list with: \itemize{ \item{'static_plot': }{ggplot object, for a
+#' @return list with:
+#' - `static_plot`: ggplot object, for a
 #'   given species the observed number per year and per native range is plotted
-#'   in a stacked bar chart} \item{'interactive_plot': }{plotly object, for a
+#'   in a stacked bar chart.
+#' - `interactive_plot`: plotly object, for a
 #'   given species the observed number per year and per native range is plotted
-#'   in a stacked bar chart} \item{'data': }{data displayed in the plot, as
-#'   data.frame with: \itemize{ \item{'year': }{year at which the species were
-#'   introduced} \item{'native_range': }{native range of the introduced species}
-#'   \item{'n': }{number of species introduced from the native range for a given
-#'   year} \item{'total': }{total number of species, from all around the world,
-#'   introduced during a given year} \item{'perc': }{percentage of species
-#'   introduced from the native range for a given year. (n/total)*100} } } }
+#'   in a stacked bar chart.
+#' - `data`: data displayed in the plot, as a data.frame with: 
+#'   - `year`: year at which the species were introduced.
+#'   - `native_range`: native range of the introduced species.
+#'   - `n`: number of species introduced from the native range for a given year.
+#'   - `total`: total number of species, from all around the world, introduced.
+#'   during a given year.
+#'   - `perc`: percentage of species introduced from the native range for a 
+#'   given year, `n`/`total`*100.
 #' @export
 #' @importFrom dplyr %>% .data
 #' @examples

R/pathways_cbd.R

@@ -1,4 +1,4 @@
-#' Pathways of introdcution as defined by CBD
+#' Pathways of introduction as defined by CBD
 #'
 #' Function to get all CBD pathays of introdution at level 1 (`pathway_level1`)
 #' and level 2 (`pathway_level2`). Added pathway `unknown` at level 1 and level

R/update_download_list.R

@@ -2,7 +2,7 @@
 #'
 #' This function opens a (tab-separated) text file containing all occurrence
 #' downloads from GBIF and updates the status of all downloads with status
-#' `RUNNING` or `PREPARING`. If the specifid download is not present it will be add.
+#' `RUNNING` or `PREPARING`. If the specified download is not present it will be add.
 #'
 #' If a download key is passed which is not present in the file it will be added
 #' as a new line.

R/verify_taxa.R

@@ -666,12 +666,12 @@ verify_taxa <- function(taxa,
   assertthat::assert_that(
     taxa %>%
       dplyr::filter(is.na(.data$bb_key)) %>%
-      dplyr::filter_at(dplyr::vars(starts_with("bb_")),
+      dplyr::filter_at(dplyr::vars(dplyr::starts_with("bb_")),
                        dplyr::all_vars(is.na(.))) %>%
       nrow() ==
       taxa %>%
       dplyr::filter(is.na(.data$bb_key)) %>%
-      dplyr::filter_at(dplyr::vars(starts_with("bb_")),
+      dplyr::filter_at(dplyr::vars(dplyr::starts_with("bb_")),
                        dplyr::any_vars(is.na(.))) %>%
       nrow(),
     msg = "Columns with GBIF Backbone info should be empty for unmatched taxa."
@@ -1212,7 +1212,7 @@ verify_taxa <- function(taxa,
         .data$bb_scientificName,
         .data$n
       ) %>%
-      dplyr::arrange(desc(.data$n)) %>%
+      dplyr::arrange(dplyr::desc(.data$n)) %>%
       dplyr::ungroup()
   } else {
     duplicates <- dplyr::tibble(
@@ -1237,7 +1237,7 @@ verify_taxa <- function(taxa,
   # Order verification by outdated and dateAdded
   verification <-
     verification %>%
-    dplyr::arrange(.data$outdated, desc(.data$dateAdded))
+    dplyr::arrange(.data$outdated, dplyr::desc(.data$dateAdded))
 
   # Add not outdated taxa from verification to not_to_verify_taxa
   taxa <-

R/visualize_pathways_level1.R

@@ -262,18 +262,22 @@ visualize_pathways_level1 <- function(df,
   # rename to default column name
   df <-
     df %>%
-    dplyr::rename_at(vars(tidyselect::all_of(kingdom_names)), ~"group") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(taxon_names)), ~"taxonKey") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(first_observed)), ~"first_observed") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(pathway_level1_names)), ~"pathway_level1")
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(kingdom_names)),
+                     ~"group") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(taxon_names)),
+                     ~"taxonKey") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(first_observed)),
+                     ~"first_observed") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(pathway_level1_names)),
+                     ~"pathway_level1")
   # handle asymmetric category system (Chordata, Not Chordta are not kingdoms)
   if (!is.null(category)) {
     if (!category %in% c("Chordata", "Not Chordata")) {
       df <- df %>% dplyr::filter(.data$group == category)
     } else {
       df <-
         df %>%
-        dplyr::rename_at(vars(phylum_names), ~"phylum_group")
+        dplyr::rename_at(dplyr::vars(phylum_names), ~"phylum_group")
       if (category == "Chordata") {
         df <- df %>% dplyr::filter(.data$phylum_group == category)
       } else {

R/visualize_pathways_level2.R

@@ -327,11 +327,16 @@ visualize_pathways_level2 <- function(df,
   # rename to default column name
   df <-
     df %>%
-    dplyr::rename_at(vars(tidyselect::all_of(kingdom_names)), ~"group") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(taxon_names)), ~"taxonKey") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(first_observed)), ~"first_observed") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(pathway_level1_names)), ~"pathway_level1") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(pathway_level2_names)), ~"pathway_level2")
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(kingdom_names)),
+                     ~"group") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(taxon_names)),
+                     ~"taxonKey") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(first_observed)),
+                     ~"first_observed") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(pathway_level1_names)),
+                     ~"pathway_level1") %>%
+    dplyr::rename_at(dplyr::vars(tidyselect::all_of(pathway_level2_names)),
+                     ~"pathway_level2")
   # Select data with the chosen pathway level 1
   df <-
     df %>%
@@ -343,7 +348,7 @@ visualize_pathways_level2 <- function(df,
     } else {
       df <-
         df %>%
-        dplyr::rename_at(vars(phylum_names), ~"phylum_group")
+        dplyr::rename_at(dplyr::vars(phylum_names), ~"phylum_group")
       if (category == "Chordata") {
         df <- df %>% dplyr::filter(.data$phylum_group == category)
       } else {

R/visualize_pathways_year_level1.R

@@ -285,10 +285,12 @@ visualize_pathways_year_level1 <- function(
   # rename to default column name
   df <-
     df %>%
-    dplyr::rename_at(vars(tidyselect::all_of(kingdom_names)), ~"group") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(taxon_names)), ~"taxonKey") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(first_observed)), ~"first_observed") %>%
-    dplyr::rename_at(vars(tidyselect::all_of(pathway_level1_names)), ~"pathway_level1")
+    dplyr::rename_at(dplyr::vars(dplyr::all_of(kingdom_names)), ~"group") %>%
+    dplyr::rename_at(dplyr::vars(dplyr::all_of(taxon_names)), ~"taxonKey") %>%
+    dplyr::rename_at(dplyr::vars(dplyr::all_of(first_observed)),
+                     ~"first_observed") %>%
+    dplyr::rename_at(dplyr::vars(dplyr::all_of(pathway_level1_names)),
+                     ~"pathway_level1")
 
   # handle asymmetric category system (Chordata, Not Chordata are not kingdoms)
   if (!is.null(category)) {
@@ -297,7 +299,7 @@ visualize_pathways_year_level1 <- function(
     } else {
       df <-
         df %>%
-        dplyr::rename_at(vars(phylum_names), ~"phylum_group")
+        dplyr::rename_at(dplyr::vars(phylum_names), ~"phylum_group")
       if (category == "Chordata") {
         df <- df %>% dplyr::filter(.data$phylum_group == category)
       } else {