,
Friedrich Bohn [ctb]| Title: | Downloading and Exploring Data from the PROFOUND Database |
|---|---|
| Description: | Provides an R interface for the PROFOUND database <doi:10.5880/PIK.2019.008>. The PROFOUND database contains a wide range of data to evaluate vegetation models and simulate climate impacts at the forest stand scale. It includes 9 forest sites across Europe, and provides for them a site description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level and remote sensing data. Moreover, for a subset of 5 sites, also time series of carbon fluxes, energy balances and soil water are available. |
| Authors: | Ramiro Silveyra Gonzalez [aut],
Christopher Reyer [aut],
Mahnken Mats [aut],
Florian Hartig [aut, cre] ,
Friedrich Bohn [ctb] |
| Maintainer: | Florian Hartig <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.1 |
| Built: | 2024-11-12 05:58:52 UTC |
| Source: | https://github.com/cost-fp1304-profound/profounddata |
The ProfoundData R package provides functions to explore, visualize and get data from the PROFOUND database. The development of the PROFOUND database and the ProfoundData R package was faciliated by COST Action FP1304 PROFOUND.
A brief description of the PROFOUND database is avalaible in the vignette 'PROFOUNDdatabase' (
you can open it by running this command: vignette('PROFOUNDdatabase')).
Note: before you can use the package, you need to download the database via downloadDatabase and
register the database location via setDB
Below is a list of the package's functions grouped by theme. See the package
vignette for more information and examples (vignette('ProfoundData')).
Functions to explore the database.
browseData To check
available sites
available datasets
available variables for a dataset
available datasets for a site
database version
metadata
policy
source
site description
summarizeData To obtain
data overviews
data summaries
Functions to extract data. Data can be extracted for one site and one dataset at a time.
getData To extract data
Functions to plot data from the database.
plotData To plot data
Miscellanous
downloadDatabase To download the database
setDB To set the connection to the database
getDB To retrieve the filepath to the database
queryDB To pass self-defined queries
reportDB To create a site-by-site report of the database
writeSim2netCDF To write netCDF-files
Except where indicated otherwise, the functions in this package were written by Ramiro Silveyra Gonzalez, Christopher Reyer, Florian Hartig and Mahnken. Ramiro Silveyra Gonzalez was the main developer of the package. Florian Hartig is currently the maintainer.
A function to provide information on available data in the PROFOUND database.
browseData( dataset = NULL, site = NULL, location = NULL, variables = FALSE, collapse = TRUE )browseData( dataset = NULL, site = NULL, location = NULL, variables = FALSE, collapse = TRUE )
dataset |
a character string providing the name of the dataset (optional). |
site |
a character string providing the name of the site (optional). |
location |
deprecated argument. Please use site instead. |
variables |
a boolean indicating whether to return the variables of a dataset, instead of the available sites. |
collapse |
a boolean indicating whether to return the compact (TRUE) or the extended (FALSE) data overview, if neither site nor dataset are passed to the function. |
Besides providing information on available data, this function allows to access the database metadata, policy, data sources and site descriptions.
if no arguments, an overview of available data
if metadata, the requested metadata
if dataset and variables, available variables for a dataset
if dataset, available sites for the dataset
if site, available datasets for a site
if site and dataset, returns an integer. Availability is coded as 0 = no available and 1 = available. This is the quickest option to check availability.
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # See available data of the database overview <- browseData() # Hint: If *collapse* FALSE, full version of the overview table overview <- browseData(collapse = FALSE) # Available datasets tables <- browseData(dataset = "DATASETS") # Available variables for a given dataset variables <- browseData(dataset = "CLIMATE_LOCAL", variables = TRUE) # Available sites for a given dataset available <- browseData(dataset = "CLIMATE_LOCAL") # Available datasets for a given site available <- browseData(site ="le_bray") # Whether a dataset is available for a specific site available <- browseData(site ="le_bray", dataset = "CLIMATE_LOCAL") # See version history version <- browseData(dataset = "VERSION") # See metadata metadata <- browseData(dataset = "METADATA_DATASETS") metadata <- browseData(dataset = "METADATA_CLIMATE_LOCAL") # See metadata of a specific site metadata <- browseData(dataset = "METADATA_TREE", site = "solling_spruce") # See data source source <- browseData(dataset = "SOURCE") # See data source of a specific site source <- browseData(dataset = "SOURCE", site = "solling_spruce") # See data policy source <- browseData(dataset = "POLICY") # See data policy of a specific site policy <- browseData(dataset = "POLICY", site = "solling_spruce") ## End(Not run)# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # See available data of the database overview <- browseData() # Hint: If *collapse* FALSE, full version of the overview table overview <- browseData(collapse = FALSE) # Available datasets tables <- browseData(dataset = "DATASETS") # Available variables for a given dataset variables <- browseData(dataset = "CLIMATE_LOCAL", variables = TRUE) # Available sites for a given dataset available <- browseData(dataset = "CLIMATE_LOCAL") # Available datasets for a given site available <- browseData(site ="le_bray") # Whether a dataset is available for a specific site available <- browseData(site ="le_bray", dataset = "CLIMATE_LOCAL") # See version history version <- browseData(dataset = "VERSION") # See metadata metadata <- browseData(dataset = "METADATA_DATASETS") metadata <- browseData(dataset = "METADATA_CLIMATE_LOCAL") # See metadata of a specific site metadata <- browseData(dataset = "METADATA_TREE", site = "solling_spruce") # See data source source <- browseData(dataset = "SOURCE") # See data source of a specific site source <- browseData(dataset = "SOURCE", site = "solling_spruce") # See data policy source <- browseData(dataset = "POLICY") # See data policy of a specific site policy <- browseData(dataset = "POLICY", site = "solling_spruce") ## End(Not run)
This function downloads the PROFOUND database
downloadDatabase(location = NULL)downloadDatabase(location = NULL)
location |
file system location to store the database. If not provide, the function will use the current working directory. |
This is a convenience function to quickly download the PROFOUND database. The function will query you to ask about the path to store the databse, and will return a string with the location, for use in setDB
a string with the location of the sql database
Florian Hartig
## Not run: # For downloading the data, use dbfile <- downloadDatabase(location = tempdir()) # Set the connection using the setDB function setDB(dbfile) # check if database is available getDB() ## End(Not run)## Not run: # For downloading the data, use dbfile <- downloadDatabase(location = tempdir()) # Set the connection using the setDB function setDB(dbfile) # check if database is available getDB() ## End(Not run)
A function to extract datasets for a site from the PROFOUND database.
getData( dataset, site = NULL, location = NULL, forcingDataset = NULL, forcingCondition = NULL, species = NULL, variables = NULL, period = NULL, collapse = TRUE, quality = NULL, decreasing = TRUE )getData( dataset, site = NULL, location = NULL, forcingDataset = NULL, forcingCondition = NULL, species = NULL, variables = NULL, period = NULL, collapse = TRUE, quality = NULL, decreasing = TRUE )
dataset |
a character string providing the name of a dataset. |
site |
a character string providing the name of a site. |
location |
deprecated argument. Please use site instead. |
forcingDataset |
a character string providing the name of a forcingDataset. Only relevant for ISIMIP datasets. |
forcingCondition |
a character string providing the name of a forcingCondition. Only relevant for ISIMIP datasets. |
species |
a character string providing the species name or species id. |
variables |
a character array holding the variables to be plotted. Default is all variables. |
period |
a character array with either start or start and end of the subset. It must have the format "YYYY-MM-DD", or c("YYYY-MM-DD", "YYYY-MM-DD"). |
collapse |
a boolean indicating whether the returned data should be a single data frame (TRUE) or a list of data frames (FALSE). Relevant when downloading SOIL and ISIMIP datasets. |
quality |
a number indicating the quality threshold to be used. Default is none. |
decreasing |
a boolean indicating whether the quality threshold should be applied up- or downwards. |
When using quality, please be aware that the threshold value is included in the returned data. Thresholding works by removing data values that are greater (decreasing = TRUE) or smaller (decreasing = FALSE) than the given value. The quality parameter is only relevant for datasets that have quality flags. These are ATMOSPHERICHEATCONDUCTION, SOILTS, FLUX, METEOROLOGICAL, and CLIMATE LOCAL. Please check the metadata of each dataset before using this parameter.
a data frame or a list of data frames, depending on collapse.
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Get SITES data sites <- getData(dataset = "SITES") # Get SITES data of a specific site site <- getData(dataset = "SITES", site = "soro") # Get SITEDESCRIPTION sites <- getData(dataset = "SITEDESCRIPTION") # Get SITEDESCRIPTION of a specific site site <- getData(dataset = "SITEDESCRIPTION", site = "lebray") # Get any dataset for a site data <- getData(dataset = "CLIMATE_LOCAL", site = "soro") # Get ISIMIP datasets as a list with data frames data <- getData(dataset ="CLIMATE_ISIMIP2A", site = "soro", collapse = TRUE) data <- getData(dataset ="CLIMATE_ISIMIP2B", site = "soro", collapse = TRUE) data <- getData(dataset ="NDEPOSITION_ISIMIP2B", site = "soro", collapse = TRUE) # Get ISIMIP datasets as an unique data frame data <- getData(dataset ="CLIMATE_ISIMIP2A", site = "soro", collapse = FALSE) data <- getData(dataset ="CLIMATE_ISIMIP2B", site = "soro", collapse = FALSE) data <- getData(dataset ="NDEPOSITION_ISIMIP2B", site = "soro", collapse = FALSE) # Get SOIL data. Collapse FALSE is recommended. data <- getData(dataset ="SOIL", site = "soro", collapse = FALSE) # Get specific forcing datasets and/or forcing conditions data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6") # which is equivalent to data <- getData(dataset ="CLIMATE_ISIMIP2B_GFDLESM2M_rcp2p6", site ="soro") # Specify variables data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", variables = "p_mm") data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", variables = c("tmax_degC","p_mm")) # Specify species data <- getData(dataset ="TREE", site ="hyytiala", species = "Pinus sylvestris") data <- getData(dataset ="TREE", site ="hyytiala", species = "pisy") data <- getData(dataset ="STAND", site ="hyytiala", species = "Picea abies") data <- getData(dataset ="STAND", site ="hyytiala", species = "piab") # Specify period data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", period = c("2006-01-01","2006-12-31")) # Specify quality data <- getData(dataset = "CLIMATE_LOCAL", site = "soro", quality = 1, decreasing = FALSE) data <- getData(dataset = "FLUX", site = "soro", quality = 0, decreasing = TRUE) ## End(Not run)# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Get SITES data sites <- getData(dataset = "SITES") # Get SITES data of a specific site site <- getData(dataset = "SITES", site = "soro") # Get SITEDESCRIPTION sites <- getData(dataset = "SITEDESCRIPTION") # Get SITEDESCRIPTION of a specific site site <- getData(dataset = "SITEDESCRIPTION", site = "lebray") # Get any dataset for a site data <- getData(dataset = "CLIMATE_LOCAL", site = "soro") # Get ISIMIP datasets as a list with data frames data <- getData(dataset ="CLIMATE_ISIMIP2A", site = "soro", collapse = TRUE) data <- getData(dataset ="CLIMATE_ISIMIP2B", site = "soro", collapse = TRUE) data <- getData(dataset ="NDEPOSITION_ISIMIP2B", site = "soro", collapse = TRUE) # Get ISIMIP datasets as an unique data frame data <- getData(dataset ="CLIMATE_ISIMIP2A", site = "soro", collapse = FALSE) data <- getData(dataset ="CLIMATE_ISIMIP2B", site = "soro", collapse = FALSE) data <- getData(dataset ="NDEPOSITION_ISIMIP2B", site = "soro", collapse = FALSE) # Get SOIL data. Collapse FALSE is recommended. data <- getData(dataset ="SOIL", site = "soro", collapse = FALSE) # Get specific forcing datasets and/or forcing conditions data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6") # which is equivalent to data <- getData(dataset ="CLIMATE_ISIMIP2B_GFDLESM2M_rcp2p6", site ="soro") # Specify variables data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", variables = "p_mm") data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", variables = c("tmax_degC","p_mm")) # Specify species data <- getData(dataset ="TREE", site ="hyytiala", species = "Pinus sylvestris") data <- getData(dataset ="TREE", site ="hyytiala", species = "pisy") data <- getData(dataset ="STAND", site ="hyytiala", species = "Picea abies") data <- getData(dataset ="STAND", site ="hyytiala", species = "piab") # Specify period data <- getData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", period = c("2006-01-01","2006-12-31")) # Specify quality data <- getData(dataset = "CLIMATE_LOCAL", site = "soro", quality = 1, decreasing = FALSE) data <- getData(dataset = "FLUX", site = "soro", quality = 0, decreasing = TRUE) ## End(Not run)
A function to return the filepath to the database.
getDB()getDB()
a character string with the filepath to the database
Ramiro Silveyra Gonzalez
## Not run: # For downloading the data, use dbfile <- downloadDatabase(location = tempdir()) # Set the connection using the setDB function setDB(dbfile) # check if database is available getDB() ## End(Not run)## Not run: # For downloading the data, use dbfile <- downloadDatabase(location = tempdir()) # Set the connection using the setDB function setDB(dbfile) # check if database is available getDB() ## End(Not run)
Calculates the argument x for par(mfrow = x) for a desired number of panels
getPanels(x)getPanels(x)
x |
the desired number of panels |
Florian Hartig
A function to plot data of a site from the PROFOUND database.
plotData( dataset, site, location = NULL, forcingDataset = NULL, forcingCondition = NULL, species = NULL, variables = NULL, period = NULL, aggregated = NULL, FUN = mean, automaticPanels = T, quality = NULL, decreasing = TRUE )plotData( dataset, site, location = NULL, forcingDataset = NULL, forcingCondition = NULL, species = NULL, variables = NULL, period = NULL, aggregated = NULL, FUN = mean, automaticPanels = T, quality = NULL, decreasing = TRUE )
dataset |
a character string providing the name of a dataset |
site |
a character string providing the name of a site. |
location |
deprecated argument. Please use site instead. |
forcingDataset |
a character string providing the name of a forcingDataset. Only relevant for ISIMIP datasets. |
forcingCondition |
a character string providing the name of a forcingCondition. Only relevant for ISIMIP datasets. |
species |
a character string providing the species name or species id. |
variables |
a character array holding the variables to be plotted. Default is all variables. |
period |
a character array with either start or start and end of the subset. It must have the format "YYYY-MM-DD", or c("YYYY-MM-DD", "YYYY-MM-DD"). |
aggregated |
a boolean indicating whether data should be aggregated for display. Possible values are date, day, month and year. |
FUN |
a function to use for aggregating the data |
automaticPanels |
a boolean indicating whether the function automatically creates panels |
quality |
a number indicating the quality threshold to be used. Default is none. |
decreasing |
a boolean indicating whether the quality threshold should be applied up- or downwards |
Plotting is not supported for the following datasets: OVERVIEW, SITES, SOIL(more). The aggregation of data
relies on the function aggregate for zoo objects. The FUN parameter
is passed to FUN from aggregate. Please check the help files of aggregate
for further information. For handling NAs we recommend to pass self-defined functions (see examples).
plots for the specified dataset, site and variables
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Normal plotting plotData(dataset = "CLIMATE_LOCAL", site = "le_bray", automaticPanels = TRUE) plotData(dataset = "TREE", site = "solling_beech", automaticPanels = TRUE) plotData(dataset = "CLIMATE_LOCAL", site = "le_bray", automaticPanels = FALSE) plotData(dataset = "TREE", site = "solling_beech", automaticPanels = FALSE) # Plot specific forcing datasets and conditions plotData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", automaticPanels = TRUE) # Plot specific variables plotData(dataset ="CLIMATE_ISIMIP2B",site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", variables = "p_mm") plotData(dataset ="TREE",site ="solling_beech", variables = "dbh1_cm") # Plot specific species plotData(dataset ="TREE", site ="hyytiala", species = "Pinus sylvestris", automaticPanels = TRUE) # Plot specific period plotData(dataset = "CLIMATE_LOCAL", site = "soro", period = c("2011-01-01","2012-12-31")) # Set quality threshold plotData(dataset = "CLIMATE_LOCAL", site = "soro", period = c("2011-01-01","2012-12-31"), quality = 1, decreasing = FALSE) plotData(dataset = "FLUX", site = "soro", period = c("2011-01-01","2012-12-31"), quality = 0, decreasing = TRUE) # Plot aggregated data plotData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "tmax_degC", period = c("2020-01-01", "2022-01-01"), aggregate = "month", FUN =median) plotData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "p_mm", period = c("2020-01-01", "2022-01-01"), aggregate = "month", FUN =sum) # Plot aggregated data with self-defined function. In this case, compare month # mean temperature of one year to mean of all period data <- getData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "tmax_degC") meanTemperature <- mean(data$tmax_degC, na.rm = TRUE) difference <- function(x) {mean(x, na.rm = TRUE) -meanTemperature} plotData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "tmax_degC", period = c("2020-01-01", "2021-01-01"), aggregate = "month", FUN =difference) abline(h = 0, col = "red") ## End(Not run)# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Normal plotting plotData(dataset = "CLIMATE_LOCAL", site = "le_bray", automaticPanels = TRUE) plotData(dataset = "TREE", site = "solling_beech", automaticPanels = TRUE) plotData(dataset = "CLIMATE_LOCAL", site = "le_bray", automaticPanels = FALSE) plotData(dataset = "TREE", site = "solling_beech", automaticPanels = FALSE) # Plot specific forcing datasets and conditions plotData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", automaticPanels = TRUE) # Plot specific variables plotData(dataset ="CLIMATE_ISIMIP2B",site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6", variables = "p_mm") plotData(dataset ="TREE",site ="solling_beech", variables = "dbh1_cm") # Plot specific species plotData(dataset ="TREE", site ="hyytiala", species = "Pinus sylvestris", automaticPanels = TRUE) # Plot specific period plotData(dataset = "CLIMATE_LOCAL", site = "soro", period = c("2011-01-01","2012-12-31")) # Set quality threshold plotData(dataset = "CLIMATE_LOCAL", site = "soro", period = c("2011-01-01","2012-12-31"), quality = 1, decreasing = FALSE) plotData(dataset = "FLUX", site = "soro", period = c("2011-01-01","2012-12-31"), quality = 0, decreasing = TRUE) # Plot aggregated data plotData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "tmax_degC", period = c("2020-01-01", "2022-01-01"), aggregate = "month", FUN =median) plotData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "p_mm", period = c("2020-01-01", "2022-01-01"), aggregate = "month", FUN =sum) # Plot aggregated data with self-defined function. In this case, compare month # mean temperature of one year to mean of all period data <- getData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "tmax_degC") meanTemperature <- mean(data$tmax_degC, na.rm = TRUE) difference <- function(x) {mean(x, na.rm = TRUE) -meanTemperature} plotData(dataset = "CLIMATE_ISIMIP2B", site ="soro", forcingDataset= "GFDLESM2M", forcingCondition="rcp2p6", variables = "tmax_degC", period = c("2020-01-01", "2021-01-01"), aggregate = "month", FUN =difference) abline(h = 0, col = "red") ## End(Not run)
A queryDB function to perform self-defined queries on the PROFOUND database.
queryDB(queryItem)queryDB(queryItem)
queryItem |
a character string providing the query |
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Basic query overview <- queryDB("SELECT * FROM OVERVIEW") tree <- queryDB("SELECT * FROM TREE") # Site name or site_id myQuery <- queryDB("SELECT date, tmax_degC FROM CLIMATE_LOCAL WHERE site == 'hyytiala'") myQuery <- queryDB("SELECT date, tmax_degC FROM CLIMATE_LOCAL_12") # Tree species myQuery <- queryDB("SELECT * FROM TREE WHERE species == 'Picea abies'") myQuery <- queryDB("SELECT * FROM TREE_piab") # Specify variables myQuery <- queryDB("SELECT date, tmax_degC FROM CLIMATE_LOCAL WHERE tmax_degC > 20 AND site == 'hyytiala' AND year == 2010") ## End(Not run)# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Basic query overview <- queryDB("SELECT * FROM OVERVIEW") tree <- queryDB("SELECT * FROM TREE") # Site name or site_id myQuery <- queryDB("SELECT date, tmax_degC FROM CLIMATE_LOCAL WHERE site == 'hyytiala'") myQuery <- queryDB("SELECT date, tmax_degC FROM CLIMATE_LOCAL_12") # Tree species myQuery <- queryDB("SELECT * FROM TREE WHERE species == 'Picea abies'") myQuery <- queryDB("SELECT * FROM TREE_piab") # Specify variables myQuery <- queryDB("SELECT date, tmax_degC FROM CLIMATE_LOCAL WHERE tmax_degC > 20 AND site == 'hyytiala' AND year == 2010") ## End(Not run)
This functions creates a site-by-site report of all avalaible data in the PROFOUND database. The summary is created with a rmarkdown document, which is rendered and saved as a html document.
reportDB(outDir = NULL)reportDB(outDir = NULL)
outDir |
a character string indicating the output directory in which the html file will be saved. If no value is provided, the working directory will be used as output directory. |
Please note that creating the report it might take several minutes.
a html file with the database report
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: reportDB(outDir = tempdir()) ## End(Not run)# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: reportDB(outDir = tempdir()) ## End(Not run)
A setDB funtion to create a database connection object
setDB(db_name = NULL)setDB(db_name = NULL)
db_name |
a character string providing the absolute path to the PROFOUND database. |
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
## Not run: # For downloading the data, use dbfile <- downloadDatabase(location = tempdir()) # Set the connection using the setDB function setDB(dbfile) # check if database is available getDB() ## End(Not run)## Not run: # For downloading the data, use dbfile <- downloadDatabase(location = tempdir()) # Set the connection using the setDB function setDB(dbfile) # check if database is available getDB() ## End(Not run)
This function allows to summarize datasets for a site from the PROFOUND database.
summarizeData( dataset, site, location = NULL, forcingDataset = NULL, forcingCondition = NULL, by = "year", period = NULL, mode = "data" )summarizeData( dataset, site, location = NULL, forcingDataset = NULL, forcingCondition = NULL, by = "year", period = NULL, mode = "data" )
dataset |
a character string providing the name of a climatic dataset (CLIMATE_LOCAL, CLIMATE_ISIMIP, ...) or the tree dataset.(change this you ) |
site |
a character string providing the name of a site. |
location |
deprecated argument. Please use site instead. |
forcingDataset |
a character string providing the name of a forcingDataset. Only relevant for ISIMIP datasets. |
forcingCondition |
a character string providing the name of a forcingCondition. Only relevant for ISIMIP datasets. |
by |
a character string indicating how to summarize the data. Currently supports by year and total. The latter refers to the entire available period. |
period |
a character array with either start or start and end of the subset. It must have the format "YYYY-MM-DD", or c("YYYY-MM-DD", "YYYY-MM-DD"). |
mode |
a character string indicating whether to display the data summary (data) or an overview (overview). |
This function is under development and has limited functionality. At the moment, it is possible to summarize daily climate datasets and tree data.
Data are summarized by years. Radiation and precipitation are provided as total yearly values, while the rest of climatic values are year mean values. For ISIMIP datasets a summary for whole period will be returned if the dataset comprises more than one forcing dataset and one forcing condition.
a data frame with the summary values
Summary is calculated by year
Climatic datasets
p_mm and rad_Jcm2day are total yearly values
tmax_degC, tmean_degC, tmin_degC, relhum_percent, airpress_hPa and wind_ms are mean yearly values
TREE dataset
density_treeha is the number of tree per ha
dbhArit_cm is the arithmetic mean diameter
dbhBa_cm is the average diameter weighted by basal area calculated as dbhBA = (ba1*dbh1 + ba2*dbh2 + ... + bak*dbhk) / (ba1 + ba2+ ... + bak), where bai and dbhi are the basal area and dbh, respectively, of the tree i, and i = 1, 2, . . , k
dbhDQ_cm is the mean squared diameter or quadratic mean diameter calculated as dbhDQ = sqrt( (dbh1^2 + dbh2^2+ ... + dbhk^2) / N), where dbhi is the diameter at breast height of tree i, i = 1, 2, . . , k, N is the total number of trees, and sqrt is the square root
heightArith_m is the arithmetic mean height
heightArith_m is the average height weighted by basal area or Loreys height calculated as heightBA = (ba1*h1 + ba2*h2 + ... + bak*hk) / (ba1 + ba2+ ... + bak), where bai and hi are the basal area and height, respectively, of the tree i, and i = 1, 2, . . , k
heightBA_m
ba_m2 is the basal area per hectare
To report errors in the package or the data, please use the issue tracker in the GitHub repository of ProfoundData https://github.com/COST-FP1304-PROFOUND/ProfoundData
# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Summarize by years data <-summarizeData(dataset = "TREE", site = "bily_kriz", by = "year", mode = "data") data <-summarizeData(dataset = "CLIMATE_LOCAL", site = "bily_kriz", by = "year", mode = "data") data <-summarizeData(dataset = "CLIMATE_ISIMIP2A", site = "bily_kriz", by = "year", mode = "data") # Specify forcing dataset or condition data <-summarizeData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6") # Summarize total period data <-summarizeData(dataset = "TREE", site = "bily_kriz", by = "total", mode = "data") data <-summarizeData(dataset = "CLIMATE_LOCAL", site = "bily_kriz", by = "total", mode = "data") # Summarize overview data <-summarizeData(dataset = "CLIMATE_LOCAL", site = "bily_kriz", mode = "overview") data <-summarizeData(dataset = "FLUX", site = "bily_kriz", mode = "overview") data <-summarizeData(dataset = "TREE", site = "bily_kriz", mode = "overview") data <-summarizeData(dataset = "STAND", site = "bily_kriz", mode = "overview") ## End(Not run)# example requires that a sql DB is registered via setDB(dbfile) # when run without a registered DB, you will get a file query (depending on OS) ## Not run: # Summarize by years data <-summarizeData(dataset = "TREE", site = "bily_kriz", by = "year", mode = "data") data <-summarizeData(dataset = "CLIMATE_LOCAL", site = "bily_kriz", by = "year", mode = "data") data <-summarizeData(dataset = "CLIMATE_ISIMIP2A", site = "bily_kriz", by = "year", mode = "data") # Specify forcing dataset or condition data <-summarizeData(dataset ="CLIMATE_ISIMIP2B", site ="soro", forcingDataset="GFDLESM2M", forcingCondition ="rcp2p6") # Summarize total period data <-summarizeData(dataset = "TREE", site = "bily_kriz", by = "total", mode = "data") data <-summarizeData(dataset = "CLIMATE_LOCAL", site = "bily_kriz", by = "total", mode = "data") # Summarize overview data <-summarizeData(dataset = "CLIMATE_LOCAL", site = "bily_kriz", mode = "overview") data <-summarizeData(dataset = "FLUX", site = "bily_kriz", mode = "overview") data <-summarizeData(dataset = "TREE", site = "bily_kriz", mode = "overview") data <-summarizeData(dataset = "STAND", site = "bily_kriz", mode = "overview") ## End(Not run)
This function transforms simulation results into netCDF files following the ISIMIP2 protocol
writeSim2netCDF( df, comment1 = NA, comment2 = NA, institution = "PIK", contact = "[email protected]", modelname = "formind", GCM = "hadgem", RCP = "rcp85", ses = "nat", ss = "co2const", region = "Kroof", start = "1980", folder = "ISI-MIP" )writeSim2netCDF( df, comment1 = NA, comment2 = NA, institution = "PIK", contact = "[email protected]", modelname = "formind", GCM = "hadgem", RCP = "rcp85", ses = "nat", ss = "co2const", region = "Kroof", start = "1980", folder = "ISI-MIP" )
df |
A data.frame containing in the first three columns longitude latitude and time. These columns are followed by columns containing the output variables. The columns have to be named with the output variable name as required by the 2B protocol. See table 21. |
comment1 |
Optional comment regarding your simulation |
comment2 |
Optional comment regarding your simulation |
institution |
Your institution |
contact |
Your mail address |
modelname |
The name of the used forest model |
GCM |
The climate model which created the used climate time series |
RCP |
The RCP scenario |
ses |
The scenario describing forest management. UMsoc equals the "nat" settings and histsoc and 2005soc equal the "man" settings in the ISIMIP2a protocol. Default value: "nat". |
ss |
"co2" for all experiments other than the sensitivity experiments for which 2005co2 is explicitly written. Note: even models in which CO2 has no effect should use the co2 identifier relevant to the experiment. Default value: "co2const". |
region |
the region or site of the simulation |
start |
the start year of the simulation. Default value: 1980. |
folder |
The folder in which all netCDF files will be written |
The function transforms your simulation output data frame into several netCDF -files and writes them into the indicated folder using the naming convention of the ISIMIP2(B)-protocol (https://www.isimip.org/protocol/). Units and long names of variables (table 21) will be created automatically.
Friedrich J. Bohn
# Produce sample data df <- data.frame(lat = rep(20, 10), lon = rep(30, 10), time = seq(1920, 2010, 10), dbh_total = c(10:19), nee_total = rnorm(10, -0.5, 0.25), evap = rnorm(10, 0.001, 0.0001), cwood_fasy = seq(40, 85, 5)) # Convert multi-variable data.frame into single-variable netCDFs using ISIMIP naming conventions writeSim2netCDF(df = df, comment1 = NA, comment2 = NA, institution = 'PIK', contact = '[email protected]', modelname = "formind", GCM = "hadgem", RCP = "rcp85", ses = "nat", ss = "co2const", region = "Kroof", start = '1920', folder = tempdir())# Produce sample data df <- data.frame(lat = rep(20, 10), lon = rep(30, 10), time = seq(1920, 2010, 10), dbh_total = c(10:19), nee_total = rnorm(10, -0.5, 0.25), evap = rnorm(10, 0.001, 0.0001), cwood_fasy = seq(40, 85, 5)) # Convert multi-variable data.frame into single-variable netCDFs using ISIMIP naming conventions writeSim2netCDF(df = df, comment1 = NA, comment2 = NA, institution = 'PIK', contact = '[email protected]', modelname = "formind", GCM = "hadgem", RCP = "rcp85", ses = "nat", ss = "co2const", region = "Kroof", start = '1920', folder = tempdir())