ds.subset {dsBaseClient}R Documentation

Generates a valid subset of a table or a vector

Description

The function uses the R classical subsetting with squared brackets '[]' and allows also to subset using a logical oprator and a threshold. The object to subset from must be a vector (factor, numeric or charcater) or a table (data.frame or matrix).

Usage

ds.subset(x = NULL, subset = "subsetObject", completeCases = FALSE,
  rows = NULL, cols = NULL, logicalOperator = NULL, threshold = NULL,
  datasources = NULL)

Arguments

subset

the name of the output object, a list that holds the subset object. If set to NULL the default name of this list is 'subsetObject'

x

a character, the name of the dataframe or the factor vector and the range of the subset.

completeCases

a character that tells if only complete cases should be included or not.

rows

a vector of integers, the indices of the rows to extract.

cols

a vector of integers or a vector of characters; the indices of the columns to extract or their names.

logicalOperator

a boolean, the logical parameter to use if the user wishes to subset a vector using a logical operator. This parameter is ignored if the input data is not a vector.

threshold

a numeric, the threshold to use in conjunction with the logical parameter. This parameter is ignored if the input data is not a vector.

datasources

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

Details

(1) If the input data is a table the user specifies the rows and/or columns to include in the subset; the columns can be refered to by their names. Table subsetting can also be done using the name of a variable and a threshold (see example 3). (2) If the input data is a vector and the parameters 'rows', 'logical' and 'threshold' are all provided the last two are ignored (i.e. 'rows' has precedence over the other two parameters then). IMPORTANT NOTE: If the requested subset is not valid (i.e. contains less than the allowed number of observations) all the values are turned into missing values (NA). Hence an invalid subset is indicated by the fact that all values within it are set to NA.

Value

no data are return to the user, the generated subset dataframe is stored on the server side.

Author(s)

Gaye, A.

See Also

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

ds.meanByClass to compute mean and standard deviation across categories of a factor vectors.

Examples

{

  # 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 a subset of the assigned dataframe (by default the table is named 'D') with complete cases only
  ds.subset(x='D', subset='subD1', completeCases=TRUE)
  # display the dimensions of the initial table ('D') and those of the subset table ('subD1')
  ds.dim('D')
  ds.dim('subD1')

  # Example 2: generate a subset of the assigned table (by default the table is named 'D') with only the variables
  # DIS_DIAB' and'PM_BMI_CONTINUOUS' specified by their name.
  ds.subset(x='D', subset='subD2', cols=c('DIS_DIAB','PM_BMI_CONTINUOUS'))

  # Example 3: generate a subset of the table D with bmi values greater than or equal to 25.
  ds.subset(x='D', subset='subD3', logicalOperator='PM_BMI_CONTINUOUS>=', threshold=25)

  # Example 4: get the variable 'PM_BMI_CONTINUOUS' from the dataframe 'D' and generate a subset bmi
  # vector with bmi values greater than or equal to 25
  ds.assign(toAssign='D$PM_BMI_CONTINUOUS', newobj='BMI')
  ds.subset(x='BMI', subset='BMI25plus', logicalOperator='>=', threshold=25)

  # Example 5: subsetting by rows:
  # get the logarithmic values of the variable 'lab_hdl' and generate a subset with
  # the first 50 observations of that new vector. If the specified number of row is greater than the total
  # number of rows in any of the studies the process will stop.
  ds.assign(toAssign='log(D$LAB_HDL)', newobj='logHDL')
  ds.subset(x='logHDL', subset='subLAB_HDL', rows=c(1:50))
  # now get a subset of the table 'D' with just the 100 first observations
  ds.subset(x='D', subset='subD5', rows=c(1:100))

  # clear the Datashield R sessions and logout
  datashield.logout(opals)

}

[Package dsBaseClient version 4.1.0 ]