| Title: | AIMS Data Platform API Client |
|---|---|
| Description: | AIMS Data Platform API Client which provides easy access to AIMS Data Platform scientific data and information. |
| Authors: | Diego R. Barneche [aut, cre], AIMS Datacentre [aut], Greg Coleman [aut], Duncan Fermor [aut], Eduardo Klein [aut], Tobias Robinson [aut], Jason Smith [aut], Jeffrey L. Sheehan [aut], Shannon Dowley [aut], Dean Ditton [aut], Kevin Gunn [aut], Gavin Ericson [aut], Murray Logan [aut], Mark Rehbein [aut], Sam Albers [rev], Elizabeth Stark [rev], Laura DeCicco [rev] |
| Maintainer: | Diego R. Barneche <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.0 |
| Built: | 2024-11-27 03:55:01 UTC |
| Source: | https://github.com/ropensci/dataaimsr |
dataaimsr is the Australian Institute of Marine Science (AIMS) Data Platform R package, and provides the user with easy access to datasets from the AIMS Data Platform API. Please see ?aims_data for more details.
Australian Institute of Marine Science (AIMS). (2017). AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program) https://doi.org/10.25845/5b4eb0f9bb848
Australian Institute of Marine Science (AIMS). (2017). Northern Australia Automated Marine Weather and Oceanographic Stations, https://doi.org/10.25845/5c09bf93f315d
Extracts citation attribute from object of class aimsdf
aims_citation(df_)aims_citation(df_)
df_ |
This function retrieves the citation attribute from an
aimsdf object. If the input aimsdf object is
a summary data.frame (see ?aims_data), then output will be
an empty string.
A character vector.
AIMS Datacentre [email protected]
A function that communicates with the the AIMS Data Platform via the AIMS Data Platform API
aims_data(target, filters = NULL, summary = NA, ...)aims_data(target, filters = NULL, summary = NA, ...)
target |
A |
filters |
A |
summary |
Should summary tables ( |
... |
Currently unused. Additional arguments to be passed to non-exported internal functions. |
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.
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.
AIMS Datacentre [email protected]
aims_citation, aims_metadata,
aims_parameters
## Not run: 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) ## End(Not run)## Not run: 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) ## End(Not run)
Expose available query filters which are allowed to be parsed either
via argument summary or filters in aims_data
aims_expose_attributes(target)aims_expose_attributes(target)
target |
A |
Use this function to learn which summary modes and filters are allowed.
We are working on implementing summary visualisation methods for weather station data. So, for the moment, the options below are only available for temperature logger data. Three options are available:
summary-by-seriesExpose summary for all available series; a series is a continuing time-series, i.e. a collection of deployments measuring the same parameter at the same site. For temperature loggers, series is synonymous with sub-site. For weather stations, it is the combination of sub-site and parameter.
summary-by-deploymentExpose summary for all available deployments.
dailyReturn mean daily aggregated monitoring data .
We offer a list of valid filter names:
siteFilter by a particular site.
subsiteFilter by a particular subsite.
seriesFilter by a particular series.
series_idA unique identifier for the series - it should be unique within a dataset. An alternative to looking up a series by name.
parameterParameter of interest. Only relevant for weather station data because temperature logger is always water temperature.
min_latMinimum latitude; used to filter by a lat-lon box.
max_latMaximum latitude; used to filter by a lat-lon box.
min_lonMinimum longitude; used to filter by a lat-lon box.
max_lonMaximum longitude; used to filter by a lat-lon box.
from_dateFilter from time (string of format YYYY-MM-DD).
thru_dateFilter until time (string of format YYYY-MM-DD).
Some additional options for the actual download, which should be passed as additional arguments to the function, are:
sizeSet a page size for large queries
(only for the data and data-no-key endpoints).
cursorUsed for pagination on / data").
versionRequest the data as recorded at a particular time (a version history).
A list of two character
vectors: one detailing summary modes, another detailing filters.
AIMS Datacentre [email protected]
## Not run: library(dataaimsr) aims_expose_attributes("weather") aims_expose_attributes("temp_loggers") ## End(Not run)## Not run: library(dataaimsr) aims_expose_attributes("weather") aims_expose_attributes("temp_loggers") ## End(Not run)
This is a utility function which allows to user to query about the existing possibilities of a given filter name
aims_filter_values(target, filter_name)aims_filter_values(target, filter_name)
target |
A |
filter_name |
A |
For a full description of each valid filter_name see
?aims_expose_attributes. In the temperature logger dataset,
"subsite" is equivalent to "series"; moreover, note that there is only one
parameter being measured (i.e. water temperature), so the "parameter" filter
contains one single value.
Either a data.frame if
filter_name = "series", else a character
vector.
AIMS Datacentre [email protected]
aims_data, aims_expose_attributes
## Not run: library(dataaimsr) aims_filter_values("weather", filter_name = "site") aims_filter_values("temp_loggers", filter_name = "subsite") ## End(Not run)## Not run: library(dataaimsr) aims_filter_values("weather", filter_name = "site") aims_filter_values("temp_loggers", filter_name = "subsite") ## End(Not run)
Extracts metadata attribute from object of class aimsdf
aims_metadata(df_)aims_metadata(df_)
df_ |
This function retrieves the metadata attribute from an
aimsdf object. If the input aimsdf object is
a summary data.frame (see ?aims_data), then output will be
an empty string.
A character vector.
AIMS Datacentre [email protected]
Extracts parameters attribute from object of class aimsdf
aims_parameters(df_)aims_parameters(df_)
df_ |
This function retrieves the parameters attribute from an
aimsdf object. If the input aimsdf object is
a summary data.frame (see ?aims_data), then output will be
an empty string.
A character vector.
AIMS Datacentre [email protected]
aimsdf of data.frame downloaded by the dataaimsr packageDatasets downloaded by the
dataaimsr package inherit
the aimsdf class, which is data.frame with three attributes.
See methods(class = "aimsdf") for an overview of available methods.
aimsdf objectChecks if argument is a aimsdf object
is_aimsdf(x)is_aimsdf(x)
x |
An R object |
Similar to page_data, but for cases #' where there are
multiple URLs for data retrieval
next_page_data(url, api_key = NULL, ...)next_page_data(url, api_key = NULL, ...)
url |
A data retrieval URL |
api_key |
An AIMS Data Platform API Key |
... |
Additional arguments to be passed to internal function
|
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.
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.
AIMS Datacentre [email protected]
aims_filter_values, page_data,
aims_data
A function that communicates with the the AIMS Data Platform via the AIMS Data Platform API
page_data( doi, filters = NULL, api_key = NULL, summary = NA, aims_version = NA, verbose = FALSE )page_data( doi, filters = NULL, api_key = NULL, summary = NA, aims_version = NA, verbose = FALSE )
doi |
A Digital Object Identifier for a chosen AIMS data series |
filters |
A |
api_key |
An AIMS Data Platform API Key |
summary |
Should summary tables ( |
aims_version |
A |
verbose |
Should links be printed to screen? Used for debugging only |
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.
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.
AIMS Datacentre [email protected]
aims_expose_attributes,
aims_filter_values, aims_data
Plotting options for aimsdf objects
## S3 method for class 'aimsdf' plot(x, ..., ptype, pars)## S3 method for class 'aimsdf' plot(x, ..., ptype, pars)
x |
|
... |
Not used. |
ptype |
Type of plot. Can either be "time_series" or "map". |
pars |
Which parameters to plot? Only relevant if ptype is "time_series" |
Currently plots cannot be customised. Summary datasets can only be represented by maps.
An object of class ggplot.
## Not run: library(dataaimsr) wdf <- aims_data("weather", api_key = NULL, filters = list(site = "Yongala", from_date = "2018-01-01", thru_date = "2018-01-02")) plot(wdf, ptype = "map") plot(wdf, ptype = "time_series") # summary-by- datasets can only return maps sdf <- aims_data("temp_loggers", api_key = NULL, summary = "summary-by-deployment") plot(sdf, ptype = "map") ## End(Not run)## Not run: library(dataaimsr) wdf <- aims_data("weather", api_key = NULL, filters = list(site = "Yongala", from_date = "2018-01-01", thru_date = "2018-01-02")) plot(wdf, ptype = "map") plot(wdf, ptype = "time_series") # summary-by- datasets can only return maps sdf <- aims_data("temp_loggers", api_key = NULL, summary = "summary-by-deployment") plot(sdf, ptype = "map") ## End(Not run)
print.aimsdf
## S3 method for class 'aimsdf' print(x, ...)## S3 method for class 'aimsdf' print(x, ...)
x |
|
... |
Not used. |
A list containing a summary of the model fit as returned a brmsfit for each model.
summary.aimsdf
## S3 method for class 'aimsdf' summary(object, ...)## S3 method for class 'aimsdf' summary(object, ...)
object |
|
... |
Unused. |
A list containing summary info from the input data.frame.