Style code (GHA)

R/class-glatos_detections.r

@@ -1,152 +1,160 @@
-#' Construct, check, and validate a glatos_detections object
-#'
-#' @description Creates, checks, or validates a glatos_detections object.
-#'
-#' @param ... Named vectors, minimally one for each required column of the
-#'   specified class:
-#'
-#'   \describe{
-#'   \item{`animal_id`}{must be character, indifies unique individual animal.}
-#'   \item{`detection_timestamp_utc`}{must be POSIXct, timestamps(in UTC) of 
-#'   detection.}
-#'   \item{`deploy_lat`}{must be numeric, latitude, in decimal degrees, WGS84, 
-#'   southern hemisphere is negative.}
-#'   \item{`deploy_long`}{must be numeric, longitude, in decimal degrees, 
-#'    WGS84, western hemisphere is negative.}
-#'   }
-#'
-#' @param x A data.frame or object that inherits from data.frame (e.g.,
-#'   data.table, tibble) and contains all required columns (see `...`).
-#'
-#' @param validate logical, indicates if column names and classes should be
-#'   checked against requirements.
-#'
-#' @examples
-#'
-#' #  glatos_detections
-#' x = data.frame(animal_id = c("153", "153", "153", "153"),
-#'                detection_timestamp_utc = as.POSIXct(c("2012-04-29 01:48:37",
-#'                                                       "2012-04-29 01:52:55",
-#'                                                       "2012-04-29 01:55:12",
-#'                                                       "2012-04-29 01:56:42"),
-#'                                                     tz = "UTC"),
-#'                deploy_lat =c(43.39165, 43.39165, 43.39165, 43.39165),
-#'                deploy_long = c(-83.99264, -83.99264, -83.99264, -83.99264))
-#'
-#' gd_df1 <- glatos_detections(animal_id = x$animal_id,
-#'                             detection_timestamp_utc = 
-#'                               x$detection_timestamp_utc,
-#'                             deploy_lat = x$deploy_lat,
-#'                             deploy_long = x$deploy_long)
-#'
-#'
-#' # as_glatos_detections
-#' gd_df2 <- as_glatos_detections(x)
-#'
-#'
-#' # sf input
-#'
-#' library(sf)
-#'
-#' # use remove = FALSE to keep required columns
-#' x_sf <- sf::st_as_sf(x,
-#'                      coords = c("deploy_long", "deploy_lat"),
-#'                      remove = FALSE)
-#'
-#' gd_sf <- as_glatos_detections(x_sf)
-#'
-#'
-#' # tibble input
-#' library(tibble)
-#'
-#' x_tbl <- as_tibble(x)
-#'
-#' gd_tbl <- as_glatos_detections(x_tbl)
-#'
-#'
-#' # data.frame input; missing column name
-#' library(dplyr) #for rename
-#' x2 <- rename(x,
-#'              fish_id = animal_id,
-#'              det_date_time = detection_timestamp_utc)
-#'
-#' gd2 <- as_glatos_detections(x2)
-#'
-#'
-#' # data.frame input; wrong column class
-#' x3 <- mutate(x, 
-#'              animal_id = as.integer(animal_id),
-#'              detection_timestamp_utc = as.character(detection_timestamp_utc))
-#'
-#' gr3 <- as_glatos_detections(x3)
-#'
-#'
-#' # Validation and checking
-#'
-#' validate_glatos_detections(x)
-#'
-#' is_glatos_detections(x) #FALSE
-#'
-#' is_glatos_detections(gd_df1) #TRUE
-
-
-#' @section Construction: `glatos_detections()` creates a `glatos_detections`
-#'   object from individual vectors (one for each column) and optionally checks
-#'   for required column names and classes using `validate_glatos_detections()`.
-#' @export
-glatos_detections <- function(..., validate = TRUE)  {
-
-  inargs <- list(...)
-
-  x <- as.data.frame(inargs)
-
-  x <- as_glatos_detections(x, validate = validate)
-
-  return (x)
-}
-
-#' @section Coercion: `as_glatos_detections()` coerces a data.frame, or object that
-#'   inherits from data.frame, to `glatos_detections` and optionally checks for
-#'   required column names and classes using `validate_glatos_detections()`.
-#' @rdname glatos_detections
-#' @export
-as_glatos_detections <- function(x, validate = TRUE)  {
-
-  # Input must inherit from data frame
-  if(!inherits(x, "data.frame")) stop("Input x must inherit from data.frame.")
-
-  #add new class as first but keep existing (e.g., data.frame)
-  class(x) <- c("glatos_detections", class(x))
-
-  if(validate) validate_glatos_detections(x)
-
-  return (x)
-}
-
-#' @section Validation:
-#' `is_glatos_detections()` checks class attribute for `"glatos_detections"`
-#' @rdname glatos_detections
-#' @export
-is_glatos_detections <- function(x) inherits(x, "glatos_detections")
-
-
-#' @section Validation:
-#' `validate_glatos_detections()` checks for required column names and classes
-#' @rdname glatos_detections
-#' @export
-validate_glatos_detections <- function(x)  {
-
-  req_cols <- list(animal_id = "character",
-                   detection_timestamp_utc = "POSIXct",
-                   deploy_lat = "numeric",
-                   deploy_long = "numeric")
-
-  glatos_check_col_names(x, req_cols)
-
-  # Check column classes
-
-  glatos_check_col_classes(x, req_cols)
-
-  return(TRUE)
-}
-
+#' Construct, check, and validate a glatos_detections object
+#'
+#' @description Creates, checks, or validates a glatos_detections object.
+#'
+#' @param ... Named vectors, minimally one for each required column of the
+#'   specified class:
+#'
+#'   \describe{
+#'   \item{`animal_id`}{must be character, indifies unique individual animal.}
+#'   \item{`detection_timestamp_utc`}{must be POSIXct, timestamps(in UTC) of
+#'   detection.}
+#'   \item{`deploy_lat`}{must be numeric, latitude, in decimal degrees, WGS84,
+#'   southern hemisphere is negative.}
+#'   \item{`deploy_long`}{must be numeric, longitude, in decimal degrees,
+#'    WGS84, western hemisphere is negative.}
+#'   }
+#'
+#' @param x A data.frame or object that inherits from data.frame (e.g.,
+#'   data.table, tibble) and contains all required columns (see `...`).
+#'
+#' @param validate logical, indicates if column names and classes should be
+#'   checked against requirements.
+#'
+#' @examples
+#'
+#' #  glatos_detections
+#' x <- data.frame(
+#'   animal_id = c("153", "153", "153", "153"),
+#'   detection_timestamp_utc = as.POSIXct(
+#'     c(
+#'       "2012-04-29 01:48:37",
+#'       "2012-04-29 01:52:55",
+#'       "2012-04-29 01:55:12",
+#'       "2012-04-29 01:56:42"
+#'     ),
+#'     tz = "UTC"
+#'   ),
+#'   deploy_lat = c(43.39165, 43.39165, 43.39165, 43.39165),
+#'   deploy_long = c(-83.99264, -83.99264, -83.99264, -83.99264)
+#' )
+#'
+#' gd_df1 <- glatos_detections(
+#'   animal_id = x$animal_id,
+#'   detection_timestamp_utc =
+#'     x$detection_timestamp_utc,
+#'   deploy_lat = x$deploy_lat,
+#'   deploy_long = x$deploy_long
+#' )
+#'
+#'
+#' # as_glatos_detections
+#' gd_df2 <- as_glatos_detections(x)
+#'
+#'
+#' # sf input
+#'
+#' library(sf)
+#'
+#' # use remove = FALSE to keep required columns
+#' x_sf <- sf::st_as_sf(x,
+#'   coords = c("deploy_long", "deploy_lat"),
+#'   remove = FALSE
+#' )
+#'
+#' gd_sf <- as_glatos_detections(x_sf)
+#'
+#'
+#' # tibble input
+#' library(tibble)
+#'
+#' x_tbl <- as_tibble(x)
+#'
+#' gd_tbl <- as_glatos_detections(x_tbl)
+#'
+#'
+#' # data.frame input; missing column name
+#' library(dplyr) # for rename
+#' x2 <- rename(x,
+#'   fish_id = animal_id,
+#'   det_date_time = detection_timestamp_utc
+#' )
+#'
+#' gd2 <- as_glatos_detections(x2)
+#'
+#'
+#' # data.frame input; wrong column class
+#' x3 <- mutate(x,
+#'   animal_id = as.integer(animal_id),
+#'   detection_timestamp_utc = as.character(detection_timestamp_utc)
+#' )
+#'
+#' gr3 <- as_glatos_detections(x3)
+#'
+#'
+#' # Validation and checking
+#'
+#' validate_glatos_detections(x)
+#'
+#' is_glatos_detections(x) # FALSE
+#'
+#' is_glatos_detections(gd_df1) # TRUE
+
+#' @section Construction: `glatos_detections()` creates a `glatos_detections`
+#'   object from individual vectors (one for each column) and optionally checks
+#'   for required column names and classes using `validate_glatos_detections()`.
+#' @export
+glatos_detections <- function(..., validate = TRUE) {
+  inargs <- list(...)
+
+  x <- as.data.frame(inargs)
+
+  x <- as_glatos_detections(x, validate = validate)
+
+  return(x)
+}
+
+#' @section Coercion: `as_glatos_detections()` coerces a data.frame, or object that
+#'   inherits from data.frame, to `glatos_detections` and optionally checks for
+#'   required column names and classes using `validate_glatos_detections()`.
+#' @rdname glatos_detections
+#' @export
+as_glatos_detections <- function(x, validate = TRUE) {
+  # Input must inherit from data frame
+  if (!inherits(x, "data.frame")) stop("Input x must inherit from data.frame.")
+
+  # add new class as first but keep existing (e.g., data.frame)
+  class(x) <- c("glatos_detections", class(x))
+
+  if (validate) validate_glatos_detections(x)
+
+  return(x)
+}
+
+#' @section Validation:
+#' `is_glatos_detections()` checks class attribute for `"glatos_detections"`
+#' @rdname glatos_detections
+#' @export
+is_glatos_detections <- function(x) inherits(x, "glatos_detections")
+
+
+#' @section Validation:
+#' `validate_glatos_detections()` checks for required column names and classes
+#' @rdname glatos_detections
+#' @export
+validate_glatos_detections <- function(x) {
+  req_cols <- list(
+    animal_id = "character",
+    detection_timestamp_utc = "POSIXct",
+    deploy_lat = "numeric",
+    deploy_long = "numeric"
+  )
+
+  glatos_check_col_names(x, req_cols)
+
+  # Check column classes
+
+  glatos_check_col_classes(x, req_cols)
+
+  return(TRUE)
+}