Creates summaries of the Portal data
Description
This package is designed to be an interface to the Portal data, which resides online at https://github.com/weecology/portalData. Its contains a set of functions to download, clean, and summarize the data.
Author(s)
Maintainer: Glenda M. Yenni glenda@weecology.org (ORCID)
Authors:
Hao Ye (ORCID)
Erica M. Christensen (ORCID)
Juniper L. Simonis (ORCID)
Ellen K. Bledsoe (ORCID)
Renata M. Diaz (ORCID)
Shawn D. Taylor (ORCID)
Ethan P, White (ORCID)
S.K. Morgan Ernest (ORCID)
Other contributors:
Weecology [copyright holder]
See Also
Useful links:
Report bugs at https://github.com/weecology/portalr/issues
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Add Seasons
Description
Higher-order data summaries, by 6-month seasons, 3-month seasons, or year. Also applies specified functions to the specified summary level.
yearly generates a table of yearly means
Usage
add_seasons(
data,
level = "site",
season_level = 2,
date_column = "yearmon",
summary_funs = NA,
path = get_default_data_path(),
download_if_missing = TRUE,
clean = TRUE
)
yearly(...)
Arguments
data
data frame containing columns: date, period, newmoonnumber, or year and month
level
summarize by "Plot", "Treatment", or "Site"
season_level
either year, 2: winter = Oct-March summer = April-Sept 4: winter = Dec-Feb spring = March-May summer = Jun-Aug fall = Sep-Nov
date_column
either "date" (must be in format "y-m-d"), "period", "newmoonnumber", or "yearmon" (data must contain "year" and "month")
summary_funs
A function specified by its name (e.g. "mean"). Default is NA (returned with seasons added but not summarized).
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
clean
logical, load only QA/QC rodent data (TRUE) or all data (FALSE)
...
arguments passed to add_seasons
Value
a data.frame with additional "season" and "year" column, and other columns summarized as specified. If no summary function is specified, "season" and "year" columns are added to original dataframe, as well as a "seasonyear" column which correctly assigns months to seasons for grouping (eg December 2000 in winter 2001, rather than winter 2000).
Ant Bait Presence Absence
Description
Get ant species presence/absence by year/plot/stake from bait census data
Bait census data is more consistent over time than the colony census data. This function assumes that all species present in at least one census were censused in all years.
Usage
bait_presence_absence(
path = get_default_data_path(),
level = "Site",
download_if_missing = TRUE,
quiet = FALSE
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
level
level at which to summarize data: 'Site', 'Plot', or 'Stake'
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to run without version messages
Value
data frame with year, species, (plot if applicable), and presence [1, 0]
Manage the default path for downloading Portal Data into
Description
check_default_data_path checks if a default data path is
set, and prompts the user to set it if it is missing.
get_default_data_path gets the value of the data path
environmental variable
use_default_data_path has 3 steps. First, it checks for
the presence of a pre-existing setting for the environmental variable.
Then it checks if the folder exists and creates it, if needed. Then it
provides instructions for setting the environmental variable.
Usage
check_default_data_path(
ENV_VAR = "PORTALR_DATA_PATH",
MESSAGE_FUN = message,
DATA_NAME = "Portal data"
)
get_default_data_path(fallback = "~", ENV_VAR = "PORTALR_DATA_PATH")
use_default_data_path(path = NULL, ENV_VAR = "PORTALR_DATA_PATH")
Arguments
ENV_VAR
the environmental variable to check (by default '"PORTALR_DATA_PATH"“)
MESSAGE_FUN
the function to use to output messages
DATA_NAME
the name of the dataset to use in output messages
fallback
the default value to use if the setting is missing
path
character Folder into which data will be downloaded.
Value
FALSE if there is no path set, TRUE otherwise
None
Check for latest version of data files
Description
Check the latest version against the data that exists on the GitHub repo
Usage
check_for_newer_data(path = get_default_data_path())
Arguments
path
Folder in which data will be checked
Value
bool TRUE if there is a newer version of the data online
Do basic cleaning of Portal plant data
Description
This function does basic quality control of the Portal plant
data. It is mainly called from summarize_plant_data , with
several arguments passed along.
The specific steps it does are, in order: (1) correct species names according to recent vouchers, if requested (2) restrict species to annuals or non-woody (3) remove records for unidentified species (5) exclude the plots that aren't long-term treatments
Usage
clean_plant_data(
data_tables,
type = "All",
unknowns = FALSE,
correct_sp = TRUE
)
Arguments
data_tables
the list of data_tables, returned from calling
load_plant_data
type
specify subset of species; If type=Annuals, removes all non-annual species. If type=Non-woody, removes shrub and subshrub species If type=Perennials, returns all perennial species (includes shrubs and subshrubs) If type=Shrubs, returns only shrubs and subshrubs If type=Winter-annual, returns all annuals found in winter IF type=Summer-annual, returns all annuals found in summer
unknowns
either removes all individuals not identified to species (unknowns = FALSE) or sums them in an additional column (unknowns = TRUE)
correct_sp
T/F whether or not to use likely corrected plant IDs,
passed to rename_species_plants
Do basic cleaning of Portal rodent data
Description
This function does basic quality control of the Portal rodent
data. It is mainly called from summarize_rodent_data , with
several arguments passed along.
The specific steps it does are, in order: (1) add in missing weight data (2) remove records with "bad" period codes or plot numbers (3) remove records for unidentified species (4) exclude non-granivores (5) exclude incomplete trapping sessions (6) exclude the plots that aren't long-term treatments
Usage
clean_rodent_data(
rodent_data,
species_table,
fillweight = FALSE,
type = "Rodents",
unknowns = FALSE
)
Arguments
rodent_data
the raw rodent data table
species_table
the species table
fillweight
specify whether to fill in unknown weights with other records from that individual or species, where possible
type
specify subset of species; either all "Rodents" or only "Granivores"
unknowns
either removes all individuals not identified to species (unknowns = FALSE) or sums them in an additional column (unknowns = TRUE)
Ant Colony Presence Absence
Description
Get ant species presence/absence by year/plot/stake from colony census data
Anomalies in ant colony census protocol over the years means that it can be difficult to discern true absences of all species in all years. This function uses information from Portal_ant_species.csv and Portal_ant_dataflags.csv to predict true presence/absence of species per plot per year. If a more conservative estimate is desired, setting the argument 'rare_sp = T' will only include species we are confident were censused regularly. Setting 'rare_sp = F' may include some false absences, since it is unknown if some rare species were censused in all years. Unknowns may also be excluded from output if desired.
Usage
colony_presence_absence(
path = get_default_data_path(),
level = "Site",
rare_sp = FALSE,
unknowns = FALSE,
download_if_missing = TRUE,
quiet = FALSE
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
level
level at which to summarize data: 'Site', 'Plot', or 'Stake'
rare_sp
include rare species (T) or not (F). Rare species may or may not have been censused in all years. Setting 'rare_sp = FALSE' gives a more conservative estimate of presence/absence
unknowns
include unknown species (TRUE) or not (FALSE). Unknowns include those only identified to genus.
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to run without version messages
Value
data frame with year, species, (plot if applicable), and presence [1, 0, NA]
Download the PortalData repo
Description
Downloads specified version of the Portal data.
Usage
download_observations(
path = get_default_data_path(),
version = "latest",
source = "github",
quiet = FALSE,
verbose = FALSE,
pause = 30,
timeout = getOption("timeout"),
force = FALSE
)
Arguments
path
character Folder into which data will be downloaded.
version
character Version of the data to download (default = "latest"). If NULL, returns.
source
character indicator of the source for the download. Either "github" (default) or "zenodo".
quiet
logical whether to download data silently.
verbose
logical whether to provide details of downloading.
pause
Positive integer or integer numeric seconds for pausing during steps around unzipping that require time delayment.
timeout
Positive integer or integer numeric seconds for timeout on downloads. Temporarily overrides the "timeout" option in options .
force
logical indicator of whether or not existing files or folders (such as the archive) should be over-written if an up-to-date copy exists (most users should leave as FALSE).
Value
NULL invisibly.
Forecast ndvi using a seasonal auto ARIMA
Description
Forecast ndvi using a seasonal auto ARIMA
Usage
fcast_ndvi(hist_ndvi, level, lead, moons = NULL)
Arguments
hist_ndvi
historic ndvi data
level
specify "monthly" or "newmoon"
lead
number of steps forward to forecast
moons
moon data (required if level = "newmoon")
Details
ndvi values are forecast using auto.arima with seasonality (using a Fourier transform)
Value
a data.frame with time and ndvi values
Fill in historic ndvi data to the complete timeseries being fit
Description
Fill in historic ndvi data to the complete timeseries being fit
Usage
fill_missing_ndvi(ndvi, level, last_time, moons = NULL)
Arguments
ndvi
ndvi data
level
specify "monthly" or "newmoon"
last_time
the last time step to have been completed
moons
moon data (required if level = "newmoons" and forecasts are needed)
Details
missing values during the time series are replaced using na.interp, missing values at the end of the time series are forecast using auto.arima with seasonality (using Fourier transform)
Value
a data.frame with time and ndvi values
Period code for incomplete censuses
Description
Determines incomplete censuses by finding dates when some plots were trapped, but others were not.
Usage
find_incomplete_censuses(trapping_table, min_plots, min_traps)
Arguments
trapping_table
Data_table of when plots were censused.
min_plots
minimum number of plots within a period for an observation to be included
min_traps
minimum number of traps for a plot to be included
Value
Data.table of period codes when not all plots were trapped.
Return Citation for Portal Data
Description
Return Citation for Portal Data
Usage
get_dataset_citation()
Value
An object of class "citation". For more details, see 'citation()'
Get future newmoon dates and numbers
Description
Get next newmoon dates and assign newmoon numbers for forecasting
Usage
get_future_newmoons(newmoons, nfuture_newmoons = NULL)
Arguments
newmoons
current newmoon table
nfuture_newmoons
number of future newmoons to get
Value
expected newmoons table for requested future newmoons
read in a raw datafile from the downloaded data or the GitHub repo
Description
does checking for whether a particular datafile exists and then reads it in, using na_strings to determine what gets converted to NA. It can also download the dataset if it's missing locally.
Usage
load_datafile(
datafile,
na.strings = "",
path = get_default_data_path(),
download_if_missing = TRUE,
quiet = TRUE,
...
)
Arguments
datafile
the path to the datafile within the folder for Portal data
na.strings
a character vector of strings which are to be
interpreted as NA values. Blank fields are also
considered to be missing values in logical, integer, numeric and
complex fields. Note that the test happens after
white space is stripped from the input, so na.strings
values may need their own white space stripped in advance.
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to perform operations silently
...
arguments passed to download_observations
Read in the Portal data files
Description
Loads Portal data files from either a user-defined path or the online Github repository. If the user-defined path is un- available, the default option is to download to that location.
load_rodent_data loads the rodent data files
load_plant_data loads the plant data files
load_ant_data loads the ant data files
load_trapping_data loads just the rodent trapping files
Usage
load_rodent_data(
path = get_default_data_path(),
download_if_missing = TRUE,
clean = TRUE,
quiet = FALSE,
...
)
load_plant_data(
path = get_default_data_path(),
download_if_missing = TRUE,
quiet = FALSE,
...
)
load_ant_data(
path = get_default_data_path(),
download_if_missing = TRUE,
quiet = FALSE,
...
)
load_trapping_data(
path = get_default_data_path(),
download_if_missing = TRUE,
clean = TRUE,
quiet = FALSE,
...
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
clean
logical, load only QA/QC rodent data (TRUE) or all data (FALSE)
quiet
logical, whether to run without version messages
...
arguments passed to download_observations
Value
load_rodent_data returns a list of 5 dataframes:
rodent_data raw data on rodent captures
species_table species code, names, types
trapping_table when each plot was trapped
newmoons_table pairs census periods with newmoons
plots_table rodent treatment assignments for each plot
load_plant_data returns a list of 7 dataframes:
quadrat_data raw plant quadrat data
species_table species code, names, types
census_table indicates whether each quadrat was counted in each
census; area of each quadrat
date_table start and end date of each plant census
plots_table rodent treatment assignments for each plot
transect_data raw plant transect data with length and height (2015-present)
oldtransect_data raw plant transect data as point counts (1989-2009)
load_ant_data returns a list of 4 dataframes:
bait_data raw ant bait data
colony_data raw ant colony data
species_table species code, names, types
plots_table treatment assignments for each plot
load_trapping_data returns a list of 2 dataframes:
trapping_table when each plot was trapped
newmoons_table pairs census periods with newmoons
Conform NA entries to "NA" entries
Description
Given the species abbreviation Neotoma albigula (NA), when data are read in, there can be an NA when it should be an "NA". This function conforms the entries to be proper character values.
Usage
na_conformer(dfv, colname = "species")
Arguments
dfv
Either [1] a data.frame containing colname as a column with NAs that need to be conformed to "NA"s or [2] a vector with NAs that need to be conformed to "NA"s.
colname
character value of the column name in tab to conform the NAs to "NA"s.
Value
x with any NA in colname replaced with "NA".
Examples
na_conformer(c("a", "b", NA, "c"))
NDVI by calendar month or lunar month
Description
Summarize NDVI data to monthly or lunar monthly level
Usage
ndvi(
level = "monthly",
sensor = "landsat",
fill = FALSE,
forecast = FALSE,
path = get_default_data_path(),
download_if_missing = TRUE
)
Arguments
level
specify "monthly" or "newmoon"
sensor
specify "landsat", "modis", "gimms", or "all"
fill
specify if missing data should be filled, passed to
fill_missing_ndvi
forecast
specify ndvi should be forecast from the end of the data to the present,
passed to fcast_ndvi
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
Phenocam data products by day, calendar month, or lunar month
Description
Summarize phenocam data products to either daily, monthly, or lunar monthly level.
Usage
phenocam(level = "daily", path = get_default_data_path())
Arguments
level
specify 'monthly', 'daily', or 'newmoon'
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
Prints a portal_data_list object
Description
Prints a portal_data_list object
Usage
## S3 method for class 'portal_data_list'
print(x, ...)
Arguments
x
A portal_data_list object.
...
arguments passed to print
If a Value is NULL, Trigger the Parent Function's Return
Description
If the focal input is NULL, return value from the parent function. Should only be used within a function.
Usage
return_if_null(x, value = NULL)
Arguments
x
Focal input.
value
If x is NULL, return this input from the parent function.
Value
If x is not NULL, NULL is returned. If x is NULL, the result of return with value as its input evaluated within the parent function's environment is returned.
Examples
ff <- function(x = 1, null_return = "hello"){
return_if_null(x, null_return)
x
}
ff()
ff(NULL)
Rodent species abbreviations
Description
Creates a simple character vector of abbreviations for the Portal Rodents.
Usage
rodent_species(
path = get_default_data_path(),
type = "code",
set = "all",
total = FALSE
)
forecasting_species(
path = get_default_data_path(),
total = FALSE,
type = "abbreviation"
)
Arguments
path
character Folder into which data will be downloaded.
type
character value indicating the output type. Current options include 'abbreviation' or 'code' (default, two-letter abbreviation), 'g_species' (abbreviated genus and species), 'Latin' (full scientific names), 'common' (common names), and 'table' (a data.frame of all the options).
set
character input of a specified set of species. Options include "all" (default, all species included) and "forecasting" (the species used in forecating pipelines).
total
logical value indicating if "total" should be added or not.
Value
character vector of species abbreviations.
Generate percent cover from Portal plant transect data
Description
This function calculates percent cover from transect data. It handles the pre-2015 data differently from the current transects, becase they are collected differently. But it returns a single time-series with all years of transect data available. It also returns mean height beginning in 2015.
Usage
shrub_cover(
path = get_default_data_path(),
type = "Shrubs",
plots = "all",
unknowns = FALSE,
correct_sp = TRUE,
download_if_missing = TRUE,
quiet = FALSE
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
type
specify subset of species; If type=Annuals, removes all non-annual species. If type=Summer Annuals, returns all annual species that can be found in the summer If type=Winter Annuals, returns all annual species that can be found in the winter If type=Non-woody, removes shrub and subshrub species If type=Perennials, returns all perennial species (includes shrubs and subshrubs) If type=Shrubs, returns only shrubs and subshrubs
plots
specify subset of plots; can be a vector of plots, or specific sets: "all" plots or "Longterm" plots (plots that have had the same treatment for the entire time series)
unknowns
either removes all individuals not identified to species (unknowns = FALSE) or sums them in an additional column (unknowns = TRUE)
correct_sp
correct species names suspected to be incorrect in early data (T/F)
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to run without version messages
Value
a data.frame of percent cover and mean height
Return cleaned Portal rodent individual data
Description
This function cleans and subsets the data based on a number of arguments. It returns stake number and individual level data.
Usage
summarize_individual_rodents(
path = get_default_data_path(),
clean = TRUE,
type = "Rodents",
length = "all",
unknowns = FALSE,
time = "period",
fillweight = FALSE,
min_plots = 1,
min_traps = 1,
download_if_missing = TRUE,
quiet = FALSE
)
summarise_individual_rodents(
path = get_default_data_path(),
clean = TRUE,
type = "Rodents",
length = "all",
unknowns = FALSE,
time = "period",
fillweight = FALSE,
min_plots = 1,
min_traps = 1,
download_if_missing = TRUE,
quiet = FALSE
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
clean
logical, load only QA/QC rodent data (TRUE) or all data (FALSE)
type
specify subset of species; either all "Rodents" or only "Granivores"
length
specify subset of plots; use "All" plots or only "Longterm" plots (to be deprecated)
unknowns
either removes all individuals not identified to species (unknowns = FALSE) or sums them in an additional column (unknowns = TRUE)
time
specify the format of the time index in the output, either "period" (sequential Portal surveys), "newmoon" (lunar cycle numbering), "date" (calendar date), or "all" (for all time indices)
fillweight
specify whether to fill in unknown weights with other records from that individual or species, where possible
min_plots
minimum number of plots within a period for an observation to be included
min_traps
minimum number of traps for a plot to be included
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to run without producing messages
Value
a data.frame
Generate summaries of Portal plant data
Description
This function is a generic interface into creating summaries of the Portal plant species data. It contains a number of arguments to specify both the kind of data to summarize, at what level of aggregation, various choices for dealing with data quality, and output format.
plant_abundance generates a table of plant abundance
Usage
summarize_plant_data(
path = get_default_data_path(),
level = "Site",
type = "All",
length = "all",
plots = length,
unknowns = FALSE,
correct_sp = TRUE,
shape = "flat",
output = "abundance",
na_drop = switch(tolower(level), quadrat = FALSE, plot = FALSE, treatment = TRUE, site
= TRUE, TRUE),
zero_drop = switch(tolower(level), quadrat = TRUE, plot = FALSE, treatment = TRUE, site
= TRUE, TRUE),
min_quads = 1,
effort = TRUE,
download_if_missing = TRUE,
quiet = FALSE
)
plant_abundance(..., shape = "flat")
summarise_plant_data(
path = get_default_data_path(),
level = "Site",
type = "All",
length = "all",
plots = length,
unknowns = FALSE,
correct_sp = TRUE,
shape = "flat",
output = "abundance",
na_drop = switch(tolower(level), quadrat = FALSE, plot = FALSE, treatment = TRUE, site
= TRUE, TRUE),
zero_drop = switch(tolower(level), quadrat = TRUE, plot = FALSE, treatment = TRUE, site
= TRUE, TRUE),
min_quads = 1,
effort = TRUE,
download_if_missing = TRUE,
quiet = FALSE
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
level
summarize by "Plot", "Treatment", "Site", or "Quadrat"
type
specify subset of species; If type=Annuals, removes all non-annual species. If type=Summer Annuals, returns all annual species that can be found in the summer If type=Winter Annuals, returns all annual species that can be found in the winter If type=Non-woody, removes shrub and subshrub species If type=Perennials, returns all perennial species (includes shrubs and subshrubs) If type=Shrubs, returns only shrubs and subshrubs
length
specify subset of plots; use "All" plots or only "Longterm" plots (to be deprecated)
plots
specify subset of plots; can be a vector of plots, or specific sets: "all" plots or "Longterm" plots (plots that have had the same treatment for the entire time series)
unknowns
either removes all individuals not identified to species (unknowns = FALSE) or sums them in an additional column (unknowns = TRUE)
correct_sp
correct species names suspected to be incorrect in early data (T/F)
shape
return data as a "crosstab" or "flat" list
output
specify whether to return "abundance", or "cover" [cover data starts in summer 2015]
na_drop
logical, drop NA values (representing insufficient sampling) filling missing combinations of year-month-treatment/plot-species with NA could represent one of a few slightly different meanings: 1) that combo doesn't exist 2) that combo was skipped that month, or 3) that combo was trapped, but is unusable (a negative period code))
zero_drop
logical, drop 0s (representing sufficient sampling, but no detection)
min_quads
numeric [1:16], minimum number of quadrats (out of 16) for a plot to be included
effort
logical as to whether or not the effort columns should be included in the output
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to run without version messages
...
arguments passed to summarize_plant_data
Value
a data.frame in either "long" or "wide" format, depending on the value of 'shape'
Generate summaries of Portal rodent data
Description
This function is a generic interface into creating summaries of the Portal rodent species data. It contains a number of arguments to specify the kind of data to summarize (at what level of aggregation) and various choices for dealing with data quality, and output format.
abundance generates a table of rodent abundance
* biomass() generates a table of rodent biomass
* energy() generates a table of rodent energy
(computed as 5.69 * (biomass ^ 0.75) after White et al 2004)
* rates() generates a table of rodent growth rates
(computed as r=log(N[t+1]/N[t])
Usage
summarize_rodent_data(
path = get_default_data_path(),
clean = TRUE,
level = "Site",
type = "Rodents",
length = "all",
plots = length,
unknowns = FALSE,
shape = "crosstab",
time = "period",
output = "abundance",
fillweight = (output != "abundance"),
na_drop = TRUE,
zero_drop = switch(tolower(level), plot = FALSE, treatment = TRUE, site = TRUE),
min_traps = 1,
min_plots = 24,
effort = FALSE,
download_if_missing = TRUE,
quiet = FALSE,
include_unsampled = FALSE
)
abundance(...)
biomass(...)
energy(...)
rates(...)
summarise_rodent_data(
path = get_default_data_path(),
clean = TRUE,
level = "Site",
type = "Rodents",
length = "all",
plots = length,
unknowns = FALSE,
shape = "crosstab",
time = "period",
output = "abundance",
fillweight = (output != "abundance"),
na_drop = TRUE,
zero_drop = switch(tolower(level), plot = FALSE, treatment = TRUE, site = TRUE),
min_traps = 1,
min_plots = 24,
effort = FALSE,
download_if_missing = TRUE,
quiet = FALSE,
include_unsampled = FALSE
)
Arguments
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository
clean
logical, load only QA/QC rodent data (TRUE) or all data (FALSE)
level
summarize by "Plot", "Treatment", or "Site"
type
specify subset of species; either all "Rodents" or only "Granivores"
length
specify subset of plots; use "All" plots or only "Longterm" plots (to be deprecated)
plots
specify subset of plots; can be a vector of plots, or specific sets: "all" plots or "Longterm" plots (plots that have had the same treatment for the entire time series)
unknowns
either removes all individuals not identified to species (unknowns = FALSE) or sums them in an additional column (unknowns = TRUE)
shape
return data as a "crosstab" or "flat" list
time
specify the format of the time index in the output, either "period" (sequential Portal surveys), "newmoon" (lunar cycle numbering), "date" (calendar date), or "all" (for all time indices)
output
specify whether to return "abundance", or "biomass", or "energy", or "rates"
fillweight
specify whether to fill in unknown weights with other records from that individual or species, where possible
na_drop
logical, drop NA values (representing insufficient sampling) filling missing combinations of year-month-treatment/plot-species with NA could represent one of a few slightly different meanings: 1) that combo doesn't exist 2) that combo was skipped that month, or 3) that combo was trapped, but is unusable (a negative period code))
zero_drop
logical, drop 0s (representing sufficient sampling, but no detection)
min_traps
minimum number of traps for a plot to be included
min_plots
minimum number of plots within a period for an observation to be included
effort
logical as to whether or not the effort columns should be included in the output
download_if_missing
if the specified file path doesn't have the PortalData folder, then download it
quiet
logical, whether to run without producing messages
include_unsampled
logical, overrides settings for 'na_drop' and 'zero_drop', setting both to FALSE
...
arguments passed to summarize_rodent_data
Value
a data.frame in either "long" or "wide" format, depending on the value of 'shape'
Weather by day, calendar month, or lunar month
Description
Summarize hourly weather data to either daily, monthly, or lunar monthly level.
Usage
weather(
level = "daily",
fill = FALSE,
horizon = 365,
temperature_limit = 4,
path = get_default_data_path()
)
Arguments
level
specify 'monthly', 'daily', or 'newmoon'
fill
specify if missing data should be filled, passed to fill_missing_weather
horizon
Horizon (number of days) to use when calculating cumulative values (eg warm weather precip)
temperature_limit
Temperature limit (in C) to use when calculating cumulative values (eg warm weather precip)
path
either the file path that contains the PortalData folder or "repo", which then pulls data from the PortalData GitHub repository