General functions and methods to concatenate climate data across a time series
get_timeseries(object, day.one, ...)
# S3 method for default
get_timeseries(
object,
day.one,
span = NULL,
last.day = NULL,
as.matrix = FALSE,
data.from = "nasapower",
...
)
# S3 method for matrix
get_timeseries(object, day.one, span = NULL, last.day = NULL, ...)
# S3 method for array
get_timeseries(object, day.one, span = NULL, last.day = NULL, ...)
a data.frame
(or any other object that can be coerced to
data.frame) with geographical coordinates (lonlat), or an object of class
sf
with geometry 'POINT' or 'POLYGON', or a named matrix
with
climate data, or an array with two dimensions for max and min temperature.
See details.
a vector of class Date
or any other object that can be
coerced to Date
(e.g. integer, character YYYY-MM-DD) for the starting
day to capture the climate data
additional arguments passed to methods. See details.
an integer or a vector with integers (optional if last.day is given) for the length of the time series to be captured
optional to span, an object of class Date
or
any other object that can be coerced to Date
(e.g. integer, character
YYYY-MM-DD) for the last day of the time series
logical, optional, to return a matrix or array instead of a data.frame
character, for the source of climate data. See details.
A list with class clima_ls
with data.frame(s) with
the class clima_df
The default
method and the sf
method assumes that the climate
data will be fetched from an remote (cloud) data.from.
The matrix
method assumes that the climate data was previously handled
and will be inputted in the format of a named matrix.
See help("modis", "climatrends") for examples.
Available remote sources to pass data.from: "nasapower"
Additional arguments:
pars
: character vector of solar, meteorological or climatology parameters
to download. See help("parameters", "nasapower") when data.from = "nasapower".
days.before
: an integer for the number of days before day.one to be
included in the timespan.
if (FALSE) { # interactive()
# Using local sources
# an array with temperature data
data("temp_dat", package = "climatrends")
set.seed(9271)
span <- as.integer(runif(10, 6, 15))
get_timeseries(temp_dat, "2013-10-28", span = span)
# matrix with precipitation data
data("rain_dat", package = "climatrends")
get_timeseries(rain_dat, "2013-10-28", span = span)
# \donttest{
# data can be returned as matrix
library("sf")
# Fetch data from NASA POWER using 'sf' method
data("lonlatsf", package = "climatrends")
g <- get_timeseries(object = lonlatsf,
day.one = "2018-05-16",
last.day = "2018-05-30",
pars = c("PRECTOTCORR", "T2M"),
as.matrix = TRUE)
# }
}