read.spss              package:foreign              R Documentation

_R_e_a_d _a_n _S_P_S_S _D_a_t_a _F_i_l_e

_D_e_s_c_r_i_p_t_i_o_n:

     'read.spss' reads a file stored by the SPSS 'save' or 'export'
     commands.

_U_s_a_g_e:

     read.spss(file, use.value.labels = TRUE, to.data.frame = FALSE,
               max.value.labels = Inf, trim.factor.names = FALSE,
               trim_values = TRUE, reencode = NA)

_A_r_g_u_m_e_n_t_s:

    file: Character string: the name of the file to read.

use.value.labels: Convert variables with value labels into R factors
          with those levels?

to.data.frame: return a data frame?

max.value.labels: Only variables with value labels and at most this
          many unique values will be converted to factors if
          'use.value.labels = TRUE'.

trim.factor.names: Logical: trim trailing spaces from factor levels?

trim_values: logical: should values and value labels have trailing
          spaces ignored when matching for 'use.value.labels = TRUE'?

reencode: logical: should character strings be re-encoded to the
          current locale.  The default, 'NA', means to do so in a UTF-*
          locale, only.  Alternatively character, specifying an
          encoding to assume.

_D_e_t_a_i_l_s:

     This uses modified code from the PSPP project (<URL:
     http://www.gnu.org/software/pspp/> for reading the SPSS formats.

     Occasionally in SPSS value labels will be added to some values of
     a continuous variable (eg to distinguish different types of
     missing data), and you will not want these variables converted to
     factors.  By setting 'max.val.labels' you can specify that
     variables with a large number of distinct values are not converted
     to factors even if they have value labels.  In addition, variables
     will not be converted to factors if there are non-missing values
     that have no value label. The value labels are then returned in
     the '"value.labels"' attribute of the variable.

     If SPSS variable labels are present, they are returned as the
     '"variable.labels"' attribute of the answer.

     Fixed length strings (including value labels) are padded on the
     right with spaces by SPSS, and so are read that way by R.  The
     default argument 'trim_values=TRUE' causes trailing spaces to be
     ignored when matching to value labels, as examples have been seen
     where the strings and the value labels had different amounts of
     padding.  See the examples for 'sub' for ways to remove trailing
     spaces in charcter data.

_V_a_l_u_e:

     A list (or data frame) with one component for each variable in the
     saved data set.

     If what looks like a Windows codepage was recorded in the SPSS
     file, it is attached (as a number) as attribute '"codepage"' to
     the result.

     There may be attributes '"label.table"' and '"variable.labels"'. 
     Attribute '"label.table"' is a named list of value labels with one
     element per variable, either 'NULL' or a names character vector.
     Attribute '"variable.labels"' is a named character vector with
     names the short variable names and elements the long names.

_N_o_t_e:

     If SPSS value labels are converted to factors the underlying
     numerical codes will not in general be the same as the SPSS
     numerical values, since the numerical codes in R are always
     1,2,3,...

     You may see warnings about the file encoding for SPSS 'save'
     files: it is possible such files contain non-ASCII character data
     which need re-encoding.  The most common occurrence is Windows
     codepage 1252, a superset of Latin-1.  The encoding is recorded
     (as an integer) in attribute '"codepage"' of the result if it
     looks like a Windows codepage.  Automatic re-encoding is done only
     in UTF-8 locales: see 'reencode'.

_A_u_t_h_o_r(_s):

     Saikat DebRoy and the R Core team

_E_x_a_m_p_l_e_s:

     ## Not run: 
     read.spss("datafile")
     ## don't convert value labels to factor levels
     read.spss("datafile", use.value.labels = FALSE)
     ## convert value labels to factors for variables with at most
     ## ten distinct values.
     read.spss("datafile", max.val.labels = 10)
     ## End(Not run)

