add an improve functions

DESCRIPTION

@@ -10,11 +10,12 @@ Imports:
   ggplot2,
   leaflet,
   magrittr,
+  methods,
   overlap,
+  sf,
   shiny, 
   shinyFiles, 
   shinyTree,
-  tibble,
   tidyr,
   rlang
 Authors@R: c(
@@ -28,7 +29,7 @@ Description: Provided tool to edit, extract, visualize and analyze
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Roxygen: list(markdown = TRUE)
 Suggests: testthat (>= 3.0.0)
 Config/testthat/edition: 3

NAMESPACE

@@ -39,6 +39,9 @@ importFrom(exifr, read_exif)
 # base
 importFrom("stats", "setNames")
 importFrom("utils", "write.csv")
+importFrom("methods", "hasArg")
+importFrom("stats", "cov")
+importFrom("utils", "read.table")
 
 # dplyr
 importFrom(dplyr, as_tibble)
@@ -158,3 +161,9 @@ importFrom(overlap, overlapPlot)
 importFrom(overlap, overlapTrue)
 importFrom(overlap, resample)
 importFrom(overlap, sunTime)
+importFrom(overlap, sunTime)
+
+# sf
+importFrom(sf, st_as_sf)
+importFrom(sf, st_transform)
+importFrom(sf, st_coordinates)

R/mm_bootstrap.R

@@ -50,8 +50,8 @@
 #' mm_boot_CI(est, boots)
 #'
 #' # alternatively:
-#' species_A_gen <- resample(species_A, 1000)
-#' species_B_gen <- resample(species_B, 1000)
+#' species_A_gen <- mm_resample(species_A, 1000)
+#' species_B_gen <- mm_resample(species_B, 1000)
 #' boots <- mm_boot_estimates(species_A_gen, species_B_gen, type="Dhat4", cores=1)
 #' mean(boots)
 #'

R/mm_check_location.R

@@ -1,23 +1,29 @@
 #' Interactive Camera Trap Location Adjustment
 #'
-#' This function launches a shiny application that allows users to visualize and manually adjust the geographic
-#' coordinates of camera trap locations. Users can drag points on an interactive map to update the positions
+#' This function launches a shiny application that allows users to visualize
+#' and manually adjust the geographic coordinates of camera trap locations.
+#' Users can drag points on an interactive map to update the positions
 #' of camera traps, and the updated dataset is saved to the global environment.
 #'
 #' @param data A data frame containing the camera trap data to be processed.
-#' @param longitude A string representing the column name for longitude in the dataset.
-#' @param latitude A string representing the column name for latitude in the dataset.
-#' @param location_name A string representing the column name for the location name or unique identifier
-#'   for each camera trap point.
-#' @param coord_system A string specifying the coordinate system of the input data.
-#'   Choices are `"geographic"` for longitude and latitude, or `"projected"` for projected coordinates.
-#' @param crs An integer representing the coordinate reference system (CRS) in EPSG format.
-#'   Required when `coord_system = "projected"`.
-#' @param new_data_name A string specifying the name of the new dataset with updated coordinates to be created
-#'   in the global environment.
+#' @param longitude A string representing the column name for longitude in the
+#' dataset.
+#' @param latitude A string representing the column name for latitude in
+#' the dataset.
+#' @param location_name A string representing the column name for the
+#' location name or unique identifier for each camera trap point.
+#' @param coord_system A string specifying the coordinate system of
+#' the input data. Choices are `"geographic"` for longitude and latitude,
+#' or `"projected"` for projected coordinates.
+#' @param crs An integer representing the coordinate reference system (CRS)
+#' in EPSG format. Required when `coord_system = "projected"`.
+#' @param new_data_name A string specifying the name of the new dataset with
+#' updated coordinates to be created in the global environment.
 #'
-#' @return A shiny application is launched to display the map and allow manual coordinate adjustments.
-#'   The modified dataset is saved to the global environment under the name provided in `new_data_name`.
+#' @return A shiny application is launched to display the map and allow
+#' manual coordinate adjustments.
+#'   The modified dataset is saved to the global environment under the name
+#'   provided in `new_data_name`.
 #'
 #' @examples
 #' \dontrun{
@@ -38,7 +44,8 @@
 #'     coord_system = "geographic",
 #'     new_data_name = "updated_camera_traps"
 #'   )
-#'   # After adjustments, the updated dataset will be available in the global environment as `updated_camera_traps`.
+#'   # After adjustments, the updated dataset will be available in the global
+#'   environment as `updated_camera_traps`.
 #' }
 #'
 #' @import shiny
@@ -61,24 +68,28 @@ mm_check_location <- function(data,
   lat_ <- paste0(dplyr::ensym(latitude))
 
   data <- data %>%
-    dplyr::mutate(obs = paste0(longitude, latitude)) %>%
-    dplyr::distinct(obs, .keep_all = TRUE) %>%
-    dplyr::select(-obs) %>%
-    dplyr::filter(!is.na(longitude) & !is.na(latitude))
+    dplyr::mutate("obs" = paste0(!!dplyr::ensym(lon_), !!dplyr::ensym(lat_))) %>%
+    dplyr::distinct("obs", .keep_all = TRUE) %>%
+    dplyr::select(-"obs") %>%
+    dplyr::filter(!is.na(!!dplyr::ensym(lon_)) & !is.na(!!dplyr::ensym(lat_)))
 
 
   if (coord_system == "projected") {
-    if(!hasArg(crs))stop("Specify the crs")
+    if(!methods::hasArg(crs))stop("Specify the crs")
     coord <- data %>%
       sf::st_as_sf(coords = c(lon_, lat_), crs = crs) %>%
       sf::st_transform(crs = 4326) %>%
       sf::st_coordinates() %>%
       as.data.frame()
+
   }else{
     coord <- data %>%
-      dplyr::rename(X = lon_, Y = lat_)
+      dplyr::rename("X" = lon_, "Y" = lat_)
   }
 
+  coord <- coord %>% dplyr::select("X", "Y")
+  print(coord)
+
   # data to use
   if (paste0(dplyr::ensym(location_name)) == "") {
     stop("location_name can not be empty")
@@ -88,7 +99,7 @@ mm_check_location <- function(data,
     dplyr::select(- dplyr::all_of(c(lon_, lat_))) %>%
     dplyr::bind_cols(coord) %>%
     # add location_name column
-    dplyr::rename('location_name' = plc_)
+    dplyr::rename("location_name" = plc_)
 
 
   # shiny
@@ -104,32 +115,34 @@ mm_check_location <- function(data,
         leaflet::addProviderTiles("OpenTopoMap", group = "OpenTopoMap") %>%
         leaflet::addLayersControl(
           baseGroups = c("OSM", "OSM France", "Natural", "OpenTopoMap"),
-          options = layersControlOptions(collapsed = T)
+          options = leaflet::layersControlOptions(collapsed = T)
         ) %>%
         leaflet::addMarkers(lng = data$X,
                             lat = data$Y,
                             layerId = data$location_name,
-                            options = markerOptions(draggable = TRUE),
+                            options = leaflet::markerOptions(draggable = TRUE),
                             popup = data$location_name) %>%
         leaflet::setView(lng = data$X[1], lat = data$Y[1], zoom = 8)
     })
 
     # Capture the updated coordinates after marker drag event
-    observeEvent(input$map_marker_dragend, {
+    shiny::observeEvent(input$map_marker_dragend, {
       new_coords <- input$map_marker_dragend  # Get the coordinates from the input object
       # Update the coordinates in the reactive variable
       updated_coords <- dplyr::tibble(X = new_coords$lng,
                                       Y = new_coords$lat,
                                       location_name = new_coords$id)
 
+
       # Update data replacing new coordinate
       coord <- coord %>% dplyr::mutate(location_name = data[["location_name"]])
       coord[coord[["location_name"]] == new_coords$id, "X"] <- new_coords$lng
       coord[coord[["location_name"]] == new_coords$id, "Y"] <- new_coords$lat
+
+
       coord <- coord %>%
-        dplyr::rename("{plc_}" := "location_name",
-                      "{lon_}" := "X",
-                      "{lat_}" := "Y")
+        dplyr::rename_with(~ c(plc_, lon_, lat_), c("location_name", "X", "Y"))
+
       data_to_return <- data_copy %>%
         dplyr::select(- dplyr::all_of(c(lon_, lat_))) %>%
         dplyr::left_join(y = coord, by = plc_)

R/mm_independence.R

@@ -10,10 +10,7 @@
 #' @param datetime A `character` string specifying the name of the column in `data` that contains
 #'   the datetime values. This argument is required if `data` is provided.
 #' @param format A `character` string defining the format used to parse the datetime values in
-#'   the `datetime` column. This argument is required if `data` is provided and should be
-#'   in the format recognized by [strptime()].
-#' @param deltatime A `numeric` vector of time differences between successive events. This argument
-#'   is used if `data` is not provided. If `data` is provided, this argument is ignored.
+#'   the `datetime` column.
 #' @param threshold A `numeric` value representing the time difference threshold (in seconds) to
 #'   determine whether events are independent. Events are considered independent if the time
 #'   difference between them is greater than or equal to this threshold. The default is 30 minutes
@@ -51,7 +48,7 @@ mm_independence <- function(data = NULL,
       stop("Wrong data provided")
     }
 
-    dt_str_ <- ifelse(hasArg(datetime), paste0(dplyr::ensym(datetime)), "datetime")
+    dt_str_ <- ifelse(methods::hasArg(datetime), paste0(dplyr::ensym(datetime)), "datetime")
 
     if (!any(dt_str_ %in% colnames(data))) {
       stop(sprintf("%s not found in data", dt_str_))
@@ -78,7 +75,7 @@ mm_independence <- function(data = NULL,
 
   }else{
     original_datetime <- datetime
-    data <- dplyr::tibble('datetime' := strptime(datetime, format = format)) %>%
+    data <- dplyr::tibble('{datetime}' := strptime(datetime, format = format)) %>%
       dplyr::arrange(datetime)
   }
 

R/mm_overlap_estimates.R

@@ -3,17 +3,12 @@
 #' @inheritParams overlap::overlapEst
 #'
 #' @examples
-#'# Get example data:
-#' data(simulatedData)
 #'
-#' # Use defaults:
-#' mm_overlap_estimates(tigerObs, pigObs)
-#' #     Dhat1     Dhat4     Dhat5
-#' 0.2908618 0.2692011 0.2275000
-#'
-#' mm_overlap_estimates(tigerObs, pigObs, type="Dhat4")
-#' #    Dhat4
-#'#    0.2692011
+#'set.seed(42)
+#' species_A <- runif(100, 1.2, 2 * pi)
+#' species_B <- runif(100, 0.23, 2 * pi)
+#' mm_overlap_estimates(species_A, species_B)
+#' mm_overlap_estimates(species_A, species_B, type = "Dhat4")
 #'
 #'@export
 

R/mm_plot_overlap_coef.R

@@ -26,6 +26,7 @@
 #' @examples
 #' # Example overlap coefficient matrix
 #' overlap_matrix <- matrix(c(1, 0.8, 0.7, 0.8, 1, 0.9, 0.7, 0.9, 1), ncol = 3)
+#' colnames(overlap_matrix) <- rownames(overlap_matrix) <- c("A", "B", "C")
 #'
 #' # Plot lower triangle with shapes
 #' mm_plot_overlap_coef(overlap_matrix, side = "lower", show = "shape")

R/mm_read.R

@@ -20,7 +20,7 @@ mm_read <- function(file_path,
                     ...) {
 
   if (!hasArg(sep)) {
-    sep <- maimer:::check_sep(file_path = file_path)
+    sep <- check_sep(file_path = file_path)
   }
 
   data_read <- read.table(file = file_path,

R/mm_temporal_shift.R

@@ -30,16 +30,17 @@
 #'
 #'
 #' @examples
+#' library(ggplot2)
 #' # Example 1: Using radians as input
+#'
 #' first_period <- c(1.3, 2.3, 2.5, 5.2, 6.1, 2.3)  # Example timestamps for period 1
 #' second_period <- c(1.8, 2.2, 2.5)  # Example timestamps for period 2
-#' result <- mm_temporal_shift(first_period, second_period, plot = T,
-#'                             xcenter = "noon",
+#' result <- mm_temporal_shift(first_period, second_period, plot = TRUE, xcenter = "noon",
 #'                             linestyle_1 = list(color = "red", linetype = 1, linewidth = 1),
 #'                             linestyle_2 = list(color = "#004c2f", linetype = 5, linewidth = .5))
 #'
 #' result$plot+
-#'   ggplot2::labs(color = "PP")+
+#'   labs(color = "PP")+
 #'   theme(legend.position = "top")
 #'
 #' # Example 2: Using time strings as input

R/mm_to_community.R

@@ -1,6 +1,6 @@
 #' Convert Data to a Community Matrix
 #'
-#' The `to_community` function transforms input data into a community matrix where
+#' The function transforms input data into a community matrix where
 #' rows represent sites, columns represent species, and values indicate the count
 #' or abundance of each species at each site.
 #'
@@ -20,20 +20,20 @@
 #'
 #' @examples
 #' # Example data
-#' df <- tibble(
+#' df <- dplyr::tibble(
 #'   site = c("A", "A", "B", "B", "C"),
 #'   species = c("sp1", "sp2", "sp1", "sp3", "sp2"),
 #'   abundance = c(5, 2, 3, 1, 4)
 #' )
 #'
 #' # Convert to community matrix with counts
-#' to_community(df, site_column = site, species_column = species)
+#' mm_to_community(df, site_column = site, species_column = species)
 #'
 #' # Convert to community matrix with abundance
-#' to_community(df, site_column = site, species_column = species, size_column = abundance)
+#' mm_to_community(df, site_column = site, species_column = species, size_column = abundance)
 #'
 #' # Fill missing cells with 0
-#' to_community(df, site_column = site, species_column = species, values_fill = 0)
+#' mm_to_community(df, site_column = site, species_column = species, values_fill = 0)
 #'
 #' @import dplyr
 #' @import tidyr

R/mm_to_radian.R

@@ -1,4 +1,4 @@
-#' Convert Time to Radians
+#' Convert time to radians
 #'
 #' This function converts time values into radians, which is often used in circular statistics and time-of-day analyses.
 #'

R/mm_to_time.R

@@ -2,11 +2,12 @@
 #'
 #' This function converts an angle in radians (representing a fraction of a full circle)
 #' into a time in the format '%H:%M:%S'. The conversion assumes that the radian value
-#' represents a fraction of a 24-hour day (i.e., 0 radians is midnight and 2π radians is the next midnight).
+#' represents a fraction of a 24-hour day (i.e., 0 radians is midnight
+#' and \eqn{2\pi} radians is the next midnight).
 #'
 #' @param radian A numeric value or vector representing an angle in radians.
-#'   The value must lie within the range [0, 2π], where 0 corresponds to midnight
-#'   (00:00:00) and 2π corresponds to the next midnight (24:00:00).
+#' The value must lie within the range \eqn{[0, 2\pi]}, where 0 corresponds to midnight
+#' (00:00:00) and \eqn{2\pi} corresponds to the next midnight (24:00:00).
 #'
 #' @return A character string representing the time in the format '%H:%M:%S'.
 #'
@@ -28,7 +29,7 @@ mm_to_time <- function(radian) {
   }
 
   if (all(radian < 0 | radian > 2*pi)) {
-    stop(sprintf("%f is out of [0, 2π]", radian))
+    stop(sprintf("%f is out of [0, 2\u03C0]", radian))
   }
 
   fraction_of_day <- radian/(2*pi)

R/utils.R

@@ -115,6 +115,8 @@ magrittr::`%>%`
 magrittr::`%<>%`
 
 
+
+################
 #' Parse datetime
 #'
 #' @description
@@ -146,16 +148,11 @@ parse_datetime <- function (datetime,
   if (all(datetime == "") & allow_empty_output)
     return(NA)
   datetime_char <- as.character(datetime)
+
   if (grepl(pattern = "%", x = format, fixed = TRUE)) {
     out <- as.POSIXct(datetime_char, tz = time_zone, format = format)
   }
-  else {
-    if (!requireNamespace("lubridate", quietly = TRUE))
-      stop(paste("package 'lubridate' is required for the specified format",
-                 format))
-    out <- lubridate::parse_date_time(datetime_char, orders = format,
-                                      tz = time_zone, quiet = quiet)
-  }
+
   if (all(is.na(out)))
     stop(paste0("Cannot read datetime format in ", deparse(substitute(datetime)),
                 ". Output is all NA.\n", "expected:  ", format,
@@ -170,6 +167,9 @@ parse_datetime <- function (datetime,
   return(out)
 }
 
+
+
+########################
 #' Select column
 #'
 #' @description