Download data from the observation ("observacao") table of one or more datasets contained in the Free
Brazilian Repository for Open Soil Data -- febr, http://www.ufsm.br/febr. This includes spatial
coordinates, observation date, and variables such as geology, land use and vegetation, local topography, and
much more. Use header if you want to check what are the variables contained in the
observation table of a dataset before downloading it.
observation(dataset, variable, stack = FALSE, missing = list(coord =
"keep", time = "keep", data = "keep"), standardization = list(crs =
NULL, time.format = NULL, units = FALSE, round = FALSE),
harmonization = list(harmonize = FALSE, level = 2), progress = TRUE,
verbose = TRUE)Character vector indicating one or more datasets. Identification codes should be as recorded
in http://www.ufsm.br/febr/catalog/. Use dataset = "all" to download all datasets.
(optional) Character vector indicating one or more variables. Accepts only general
identification codes, e.g. "ferro" and "carbono". If missing, then a set of standard identification
variables is downloaded. Use variable = "all" to download all variables. See ‘Details’ for
more information.
(optional) Logical value indicating if tables from different datasets should be stacked on a
single table for output. Requires standardization = list(units = TRUE) -- see below. Defaults to
stack = FALSE, the output being a list of tables.
(optional) List with named sub-arguments indicating what should be done with an observation
missing spatial coordinates, coord, date of observation, time, or data on variables, data? Options are
"keep" (default) and "drop".
(optional) List with named sub-arguments indicating how to perform data standardization.
crs Character string indicating the EPSG code of the coordinate reference system (CRS) to which
spatial coordinates should be transformed. For example, crs = "EPSG:4674", i.e. SIRGAS 2000, the
standard CRS for Brazil -- see more at http://spatialreference.org/ref/epsg/. Defaults to
crs = NULL, i.e. no transformation is performed.
time.format Character string indicating how to format dates. For example,
time.format = "%d-%m-%Y", i.e. dd-mm-yyyy such as in 31-12-2001. Defaults to
time.format = NULL, i.e. no formatting is performed. See as.Date for more
details.
units Logical value indicating if the measurement units of the continuous variable(s) should
be converted to the standard measurement unit(s). Defaults to units = FALSE, i.e. no conversion is
performed. See standard for more information.
round Logical value indicating if the values of the continuous variable(s) should be rounded
to the standard number of decimal places. Requires units = TRUE. Defaults to round = FALSE, i.e.
no rounding is performed. See standard for more information.
(optional) List with named sub-arguments indicating if and how to perform data harmonization.
harmonize Logical value indicating if data should be harmonized? Defaults to harmonize = FALSE,
i.e. no harmonization is performed.
level Integer value indicating the number of levels of the identification code of the variable(s)
that should be considered for harmonization. Defaults to level = 2. See ‘Details’ for more
information.
(optional) Logical value indicating if a download progress bar should be displayed.
(optional) Logical value indicating if informative messages should be displayed. Generally useful to identify datasets with inconsistent data. Please report to febr-forum@googlegroups.com if you find any issue.
A list of data frames or a data frame with data on the chosen variable(s) of the chosen dataset(s).
Standard identification variables and their content are as follows:
dataset_id. Identification code of the dataset in febr to which an observation belongs.
observacao_id. Identification code of an observation in febr.
sisb_id. Identification code of an observation in the Brazilian Soil Information System
maintained by the Brazilian Agricultural Research Corporation (EMBRAPA) at
https://www.bdsolos.cnptia.embrapa.br/consulta_publica.html.
ibge_id. Identification code of an observation in the database of the Brazilian Institute
of Geography and Statistics (IBGE) at http://www.downloads.ibge.gov.br/downloads_geociencias.htm#.
observacao_data. Date (dd-mm-yyyy) in which an observation was made.
coord_sistema. EPSG code of the coordinate reference system.
coord_x. Longitude (<U+00B0>) or easting (m).
coord_y. Latitude (<U+00B0>) or northing (m).
coord_precisao. Precision with which x- and y-coordinates were determined (m).
coord_fonte. Source of the x- and y-coordinates.
pais_id. Country code (ISO 3166-1 alpha-2).
estado_id. Code of the Brazilian federative unit where an observation was made.
municipio_id. Name of the Brazilian county where as observation was made.
amostra_tipo. Type of sample taken.
amostra_quanti. Number of samples taken.
amostra_area. Sampling area.
Data harmonization consists of converting the values of a variable determined using some method B so that they are (approximately) equivalent to the values that would have been obtained if the standard method A had been used instead. For example, converting carbon content values obtained using a wet digestion method to the standard dry combustion method is data harmonization.
A heuristic data harmonization procedure is implemented in the febr package. It consists of grouping
variables
based on a chosen number of levels of their identification code. For example, consider a variable with an
identification code composed of four levels, aaa_bbb_ccc_ddd, where aaa is the first level and
ddd is the fourth level. Now consider a related variable, aaa_bbb_eee_fff. If the harmonization
is to consider all four coding levels (level = 4), then these two variables will remain coded as
separate variables. But if level = 2, then both variables will be re-coded as aaa_bbb, thus becoming the
same variable.
# NOT RUN {
res <- observation(dataset = "ctb0013", variable = "taxon")
str(res)
# }
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