This function converts a matrix of detections (0, 1) by time stamp and receiver into a dataframe.
make_df_detections(
acoustics,
only_keep_detections = FALSE,
set_names = FALSE,
as_POSIXct = as.POSIXct
)Arguments
- acoustics
A detection matrix (time stamps x receivers) in which the cells define whether (1) or not (0) a detection was made at each time stamp/receiver combination. `Meaningful' time stamps and receiver IDs can be taken from the row and column names of this matrix, if specified (see
set_names).- only_keep_detections
A logical variable that defines whether or not to retain only observations that correspond to detections. (If
only_keep_detections = FALSE, the returned dataframe includes time stamps without detections.)- set_names
A logical variable that defines whether or not to take the row and column names of
acousticsas the time stamps and receiver IDs. (Ifset_names = FALSE, time stamps and receiver IDs are simply given as integer vectors of 1 to the number of rows or columns respectively.)- as_POSIXct
If
set_names = TRUE,as_POSIXctis a function that converts the row names ofacousticsinto POSIXct time stamps.
Value
The function returns a dataframe with time stamps (`timestamp') and receivers (`receiver_id'). If set_names = FALSE, these are integer vectors that match the dimensions of acoustics. Otherwise they are are taken from row and column names of acoustics. In this case, if as_POSIXct is defined, time stamps are returned in POSIXct format and receivers are returned as a factor. If only_keep_detections = FALSE, the dataframe also includes a `detection' column that defines whether (1) or not (0) a detection was made for each observation; otherwise, this column is dropped (mirroring real-world data).
Examples
#### Define detection matrix
# Simulate array
array <- sim_array(
boundaries = raster::extent(-1000, 1000, -1000, 1000),
n_receivers = 24, seed = 1
)
#> flapper::sim_array() called (@ 2023-08-29 15:44:48)...
#> ... Defining area...
#> CRS of area is NA.
#> ... Incorporating receivers...
#> ... ... Simulating receivers...
#> ... Plotting array...
#> NULL
#> class : SpatialPolygons
#> features : 1
#> extent : -1000, 1000, -1000, 1000 (xmin, xmax, ymin, ymax)
#> crs : NA
#> NULL
#> NULL
#> NULL
#> prettyGraphics::pretty_map() CRS taken as: 'NA'.
#> ... Defining outputs...
#> ... flapper::sim_array() call completed (@ 2023-08-29 15:44:48) after ~0 minutes.
# Simulate movement in this area
path <- sim_path_sa(n = 50, area = array$array$area, seed = 1)
#> flapper::sim_path_sa() called (@ 2023-08-29 15:44:48)...
#> ... Setting up simulation...
#> ... Simulating movement path...
#>
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|======================================================================| 100%... Plotting simulated path...
#> Spatial layers do not have identical CRS strings
#> prettyGraphics::pretty_map() CRS taken as: 'NA'.
#> ... flapper::sim_path_sa() call completed (@ 2023-08-29 15:44:48) after ~0 minutes.
# Simulate a detection matrix
detections <- sim_detections(
n = 100,
path = path$xy_mat,
xy = sp::coordinates(array$array$xy),
calc_detection_pr = function(dist) ifelse(dist < 425, 1, 0),
)
#> flapper::sim_detections() called (@ 2023-08-29 15:44:48)...
#> ... Setting up simulation...
#> ... Calculating distances...
#> ... Calculating probabilities...
#> ... Simulating detections...
#> ... Plotting detections...
#> Warning: "n" is not a graphical parameter
#> Warning: "n" is not a graphical parameter
#> Warning: "n" is not a graphical parameter
#> ... flapper::simulate_detections() call completed (@ 2023-08-29 15:44:48) after ~0 minutes.
# Extract matrix
mat <- detections$det_mat
# Define row names
rownames(mat) <-
as.character(
seq(as.POSIXct("2016-01-01"), by = "2 mins", length.out = nrow(mat))
)
#### Examples: convert the matrix to a dataframe
utils::str(mat)
#> int [1:50, 1:24] 0 0 0 0 0 0 0 0 0 0 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:50] "2016-01-01 00:00:00" "2016-01-01 00:02:00" "2016-01-01 00:04:00" "2016-01-01 00:06:00" ...
#> ..$ : NULL
dat <- make_df_detections(mat)
utils::str(dat)
#> 'data.frame': 1200 obs. of 3 variables:
#> $ timestamp : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ receiver_id: int 1 2 3 4 5 6 7 8 9 10 ...
#> $ detection : int 0 0 0 0 0 0 0 0 0 0 ...
dat <- make_df_detections(mat, only_keep_detections = TRUE)
utils::str(dat)
#> 'data.frame': 160 obs. of 2 variables:
#> $ timestamp : int 2 2 3 3 3 4 4 4 4 5 ...
#> $ receiver_id: int 15 17 13 15 17 8 13 15 17 8 ...
dat <- make_df_detections(mat, only_keep_detections = TRUE, set_names = TRUE)
#> 'set_names' not implemented for columns: 'acoustics' does not contain column names.
utils::str(dat)
#> 'data.frame': 160 obs. of 2 variables:
#> $ timestamp : POSIXct, format: "2016-01-01 00:02:00" "2016-01-01 00:02:00" ...
#> $ receiver_id: int 15 17 13 15 17 8 13 15 17 8 ...