ds.subsetByClass.RdThe function takes a categorical variable or a data frame as input and generates subset(s) variables or data frames for each category.
ds.subsetByClass(
x = NULL,
subsets = "subClasses",
variables = NULL,
datasources = NULL
)a character, the name of the dataframe or the vector to generate subsets from.
the name of the output object, a list that holds the subset objects. If set to NULL the default name of this list is 'subClasses'.
a vector of string characters, the name(s) of the variables to subset by.
a list of DSConnection-class objects obtained after login. If the <datasources>
the default set of connections will be used: see datashield.connections_default.
a no data are return to the user but messages are printed out.
If the input data object is a data frame it is possible to specify the variables to subset on. If a subset is not 'valid' all its the values are reported as missing (i.e. NA), the name of the subsets is labelled with the suffix '_INVALID'. Subsets are considered invalid if the number of observations it holds are between 1 and the threshold allowed by the data owner. if a subset is empty (i.e. no entries) the name of the subset is labelled with the suffix '_EMPTY'.
ds.meanByClass to compute mean and standard deviation across categories of a factor vectors.
ds.subset to subset by complete cases (i.e. removing missing values), threshold, columns and rows.
if (FALSE) { # \dontrun{
# load the login data
data(logindata)
# login and assign some variables to R
myvar <- list('DIS_DIAB','PM_BMI_CONTINUOUS','LAB_HDL', 'GENDER')
conns <- datashield.login(logins=logindata,assign=TRUE,variables=myvar)
# Example 1: generate all possible subsets from the table assigned above (one subset table
# for each class in each factor)
ds.subsetByClass(x='D', subsets='subclasses')
# display the names of the subset tables that were generated in each study
ds.names('subclasses')
# Example 2: subset the table initially assigned by the variable 'GENDER'
ds.subsetByClass(x='D', subsets='subtables', variables='GENDER')
# display the names of the subset tables that were generated in each study
ds.names('subtables')
# Example 3: generate a new variable 'gender' and split it into two vectors: males
# and females
ds.assign(toAssign='D$GENDER', newobj='gender')
ds.subsetByClass(x='gender', subsets='subvectors')
# display the names of the subset vectors that were generated in each study
ds.names('subvectors')
# clear the Datashield R sessions and logout
datashield.logout(conns)
} # }