ds.subsetByClass {dsBaseClient} | R Documentation |

The 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)

`x` |
a character, the name of the dataframe or the vector to generate subsets from. |

`variables` |
a vector of string characters, the name(s) of the variables to subset by. |

`subsets` |
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'. |

`datasources` |
a list of opal object(s) obtained
after login in to opal servers; these objects hold also
the data assign to R, as |

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'.

a no data are return to the user but messages are printed out.

Gaye, A.

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.

{ # load the login data data(logindata) # login and assign some variables to R myvar <- list('DIS_DIAB','PM_BMI_CONTINUOUS','LAB_HDL', 'GENDER') opals <- 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(opals) }

[Package *dsBaseClient* version 4.1.0 ]