A function that communicates with the the AIMS Data Platform via the AIMS Data Platform API
aims_data(target, filters = NULL, summary = NA, ...)A character vector of length 1 specifying
the dataset. Only weather or temp_loggers are currently
allowed.
A list containing a set of
filters for the data query (see Details).
Should summary tables ("summary-by-series" or
"summary-by-deployment") or daily aggregated data ("daily") be
returned instead of full data (see Details)?
Currently unused. Additional arguments to be passed to non-exported internal functions.
aims_data returns a data.frame of class
aimsdf.
If summary %in% c("summary-by-series", "summary-by-deployment"),
the output shows the summary information for the target dataset (i.e.
weather or temperature loggers)
(NB: currently, summary only works for the temperature logger
database). If summary is not passed as an additional argument, then
the output contains raw monitoring data. If summary = "daily",
then the output contains mean daily aggregated monitoring data.
The output also contains five attributes (empty strings if
summary is passed as an additional argument):
metadataa DOI link
containing the metadata record for the data series.
citationthe citation information for the particular
dataset.
parametersThe measured parameters comprised in the
output.
typeThe type of dataset. Either "monitoring" if
summary is not specified, "monitoring (daily aggregation)" if
summary = "daily", or a "summary-by-" otherwise.
targetThe input target.
The AIMS Data Platform R Client provides easy access to
data sets for R applications to the
AIMS Data Platform API.
The AIMS Data Platform requires an API Key for requests, which can
be obtained at this
link.
It is preferred that API Keys are not stored in code. We recommend
storing the environment variable AIMS_DATAPLATFORM_API_KEY
permanently under the user's .Renviron file in order to load
the API Key automatically.
There are two types of data currently available through the
AIMS Data Platform API:
Weather and
Sea Water Temperature Loggers.
They are searched internally via unique DOI identifiers.
Only one data type at a time can be passed to the argument target.
A list of arguments for filters can be exposed for both
Weather and
Sea Water Temperature Loggers
using function aims_expose_attributes.
Note that at present the user can inspect the range of dates for
the temperature loggers data only (see usage of argument summary in
the examples below). For that, the argument summary must be either
the string "summary-by-series" or "summary-by-deployment".
In those cases, time filters will be ignored.
Details about available dates for each dataset and time series can be accessed via Metadata on AIMS Data Platform API. We raise this caveat here because these time boundaries are very important; data are collected at very small time intervals, a window of just a few days can yield very large datasets. The query will return and error if it reaches the system's memory capacity.
For that same reason, from version 1.1.0 onwards, we are offering the
possibility of downloading a mean daily aggregated version. For that, the
user must set summary = "daily". In this particular case, query filter
will be taken into account.
if (FALSE) {
library(dataaimsr)
# assumes that user already has API key saved to
# .Renviron
# start downloads:
# 1. downloads weather data from
# site Yongala
# within a defined date range
wdf_a <- aims_data("weather", api_key = NULL,
filters = list(site = "Yongala",
from_date = "2018-01-01",
thru_date = "2018-01-02"))
# 2. downloads weather data from all sites
# under series_id 64 from Davies Reef
# within a defined date range
wdf_b <- aims_data("weather", api_key = NULL,
filters = list(series_id = 64,
from_date = "1991-10-18",
thru_date = "1991-10-19"))
head(wdf_b)
range(wdf_b$time)
# 3. downloads weather data from all sites
# under series_id 64 from Davies Reef
# within defined date AND time range
wdf_c <- aims_data("weather", api_key = NULL,
filters = list(series_id = 64,
from_date = "1991-10-18T06:00:00",
thru_date = "1991-10-18T12:00:00"))
head(wdf_c)
range(wdf_c$time)
# 4. downloads all parameters from all sites
# within a defined date range
wdf_d <- aims_data("weather", api_key = NULL,
filters = list(from_date = "2003-01-01",
thru_date = "2003-01-02"))
# note that there are multiple sites and series
# so in this case, because we did not specify a specific
# parameter, series within sites could differ by both
# parameter and depth
head(wdf_d)
unique(wdf_d[, c("site", "series_id", "series")])
unique(wdf_d$parameter)
range(wdf_d$time)
# 5. downloads chlorophyll from all sites
# within a defined date range
wdf_e <- aims_data("weather", api_key = NULL,
filters = list(parameter = "Chlorophyll",
from_date = "2018-01-01",
thru_date = "2018-01-02"))
# note again that there are multiple sites and series
# however in this case because we did specify a specific
# parameter, series within sites differ by depth only
head(wdf_e)
unique(wdf_e[, c("site", "series_id", "series", "depth")])
unique(wdf_e$parameter)
range(wdf_e$time)
# 6. downloads temperature data
# summarised by series
sdf_a <- aims_data("temp_loggers", api_key = NULL,
summary = "summary-by-series")
head(sdf_a)
dim(sdf_a)
# 7. downloads temperature data
# summarised by series
# for all sites that contain data
# within a defined date range
sdf_b <- aims_data("temp_loggers", api_key = NULL,
summary = "summary-by-series",
filters = list("from_date" = "2018-01-01",
"thru_date" = "2018-12-31"))
head(sdf_b)
dim(sdf_b) # a subset of sdf_a
# 8. downloads temperature data
# summarised by deployment
sdf_c <- aims_data("temp_loggers", api_key = NULL,
summary = "summary-by-deployment")
head(sdf_c)
dim(sdf_c)
# 9. downloads temperature data
# within a defined date range, averaged by day
sdf_d <- aims_data("temp_loggers", api_key = NULL, summary = "daily",
filters = list(series = "DAVFL1",
from_date = "2018-01-01",
thru_date = "2018-01-10"))
# note again that there are multiple sites and series
# however in this case because we did specify a specific
# parameter, series within sites differ by depth only
head(sdf_d)
unique(sdf_d[, c("site", "series_id", "series", "depth")])
unique(sdf_d$parameter)
range(sdf_d$time)
}