Computes the mean and standard deviation across categories

This function calculates the mean and the standard deviation (SD) of a continuous variable for each class of up to 3 categorical variables.

ds.meanByClass(
  x = NULL,
  outvar = NULL,
  covar = NULL,
  type = "combine",
  datasources = NULL
)

Arguments

x

a character string specifying the name of the dataset or a text formula.

outvar

a character vector specifying the names of the continuous variables.

covar

a character vector specifying the names of up to 3 categorical variables

type

a character string that represents the type of analysis to carry out. type can be set as: 'combine' or 'split'. Default 'combine'. For more information see Details.

datasources

a list of DSConnection-class objects obtained after login. If the datasources argument is not specified the default set of connections will be used: see datashield.connections_default.

Value

ds.meanByClass returns to the client-side a table or a list of tables that hold the length of the numeric variable(s) and their mean and standard deviation in each subgroup (subset).

Details

The function splits the input dataset into subsets (one for each category) and calculates the mean and SD of the specified numeric variables. It is important to note that the process of generating the final table(s) can be time consuming particularly if the subsetting is done across more than one categorical variable and the run-time lengthens if the parameter type is set to 'split' as a table is then produced for each study. It is therefore advisable to run the function only for the studies of the user interested in but including only those studies in the parameter datasources.

Depending on the variable type can be carried out two analysis:
(1) 'combine': a pooled table of results is generated.
(2) 'split': a table of results is generated for each study.

See also

ds.subsetByClass to subset by the classes of factor vector(s).

ds.subset to subset by complete cases (i.e. removing missing values), threshold, columns and rows.

Author

DataSHIELD Development Team

Examples

if (FALSE) { # \dontrun{

 ## Version 6, for version 5 see the Wiki
  
  # connecting to the Opal servers

  require('DSI')
  require('DSOpal')
  require('dsBaseClient')

  builder <- DSI::newDSLoginBuilder()
  builder$append(server = "study1", 
                 url = "http://192.168.56.100:8080/", 
                 user = "administrator", password = "datashield_test&", 
                 table = "CNSIM.CNSIM1", driver = "OpalDriver")
  builder$append(server = "study2", 
                 url = "http://192.168.56.100:8080/", 
                 user = "administrator", password = "datashield_test&", 
                 table = "CNSIM.CNSIM2", driver = "OpalDriver")
  builder$append(server = "study3",
                 url = "http://192.168.56.100:8080/", 
                 user = "administrator", password = "datashield_test&", 
                 table = "CNSIM.CNSIM3", driver = "OpalDriver")
  logindata <- builder$build()
  
  
  connections <- DSI::datashield.login(logins = logindata, assign = TRUE, symbol = "D") 
  
  #Calculate mean by class
  
  ds.meanByClass(x = "D",
                 outvar = c('LAB_HDL','LAB_TSC'),
                 covar = c('PM_BMI_CATEGORICAL'),
                 type = "combine",
                 datasources = connections)
                 
  ds.meanByClass(x = "D$LAB_HDL~D$PM_BMI_CATEGORICAL",
                 type = "combine",
                 datasources = connections[1])#Only the frist server is used ("study1")  
             
  # clear the Datashield R sessions and logout
  datashield.logout(connections)
} # }