listLength.Rd
This function takes in data for a recorder and calculates the list length metrics. These metrics are based around the idea of a 'list', defined as the species recorded at a single location (often a 1km square) on a single day by an individual recorder.
listLength(recorder_name, data, threshold = 10, plot = FALSE, sp_col = "preferred_taxon", date_col = "date_start", recorder_col = "recorders", location_col = "kmsq")
recorder_name | the name of the recorder for whom you want to calculate the metrics |
---|---|
data | the data.frame of recording information |
threshold | how many lists do there need to be before we calculate the metrics? If this is not met NA is reported for all metrics except |
plot | should a plot of a histogram of list lengths be created |
sp_col | the name of the column that contains the species names |
date_col | the name of the column that contains the date. This must be formatted as a date |
recorder_col | the name of the column that contains the recorder names |
location_col | the name of the column that contains the location. This is a character, such as a grid reference and should be representative of the scale at which recording is done over a single day, typically 1km-square is used. |
A data.frame with seven columns
recorder
- The name of the recorder, as given in the recorder_name argument
mean_LL
- The mean number of species recorded across all lists
median_LL
- The median number of species recorded across all lists
variance
- The variance in the number of species recorded across all lists
p1
- The proportion of visits that had a single species recorded
p4
- The proportion of visits that had four or more species recorded
n_lists
- The number of lists this recorder recorded
# NOT RUN { # load example data head(cit_sci_data) # Location might be a site name column in your data or a unique combination of lat and long # Our data is missing a location column so we will use lat and long # It might be more sensible to convert lat long to a grid reference and # use a 1 km square grid reference to represent a site cit_sci_data$location <- paste(round(cit_sci_data$lat, 4), round(cit_sci_data$long, 4)) # run for one recorder LL <- listLength(data = cit_sci_data, recorder_name = 3007, threshold = 10, plot = FALSE, sp_col = 'species', date_col = 'date', recorder_col = 'recorder', location_col = 'location') # Run the metric for all recorders LL_all <- lapply(unique(cit_sci_data$recorder), FUN = listLength, data = cit_sci_data, threshold = 10, plot = FALSE, sp_col = 'species', date_col = 'date', recorder_col = 'recorder', location_col = 'location') # summarise as one table LL_all_sum <- do.call(rbind, LL_all) hist(LL_all_sum$n_lists, breaks = 80) # }