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@c PSPP - a program for statistical analysis.
@c Copyright (C) 2017, 2020 Free Software Foundation, Inc.
@c Permission is granted to copy, distribute and/or modify this document
@c under the terms of the GNU Free Documentation License, Version 1.3
@c or any later version published by the Free Software Foundation;
@c with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
@c A copy of the license is included in the section entitled "GNU
@c Free Documentation License".
@c
@node Data Manipulation
@chapter Data transformations
@cindex transformations

The @pspp{} procedures examined in this chapter manipulate data and
prepare the active dataset for later analyses.  They do not produce output,
as a rule.

@menu
* AGGREGATE::                   Summarize multiple cases into a single case.
* AUTORECODE::                  Automatic recoding of variables.
* COMPUTE::                     Assigning a variable a calculated value.
* COUNT::                       Counting variables with particular values.
* FLIP::                        Exchange variables with cases.
* IF::                          Conditionally assigning a calculated value.
* RECODE::                      Mapping values from one set to another.
* SORT CASES::                  Sort the active dataset.
@end menu

@node AGGREGATE
@section AGGREGATE
@vindex AGGREGATE

@display
AGGREGATE
        OUTFILE=@{*,'@var{file_name}',@var{file_handle}@} [MODE=@{REPLACE, ADDVARIABLES@}]
        /PRESORTED
        /DOCUMENT
        /MISSING=COLUMNWISE
        /BREAK=@var{var_list}
        /@var{dest_var}['@var{label}']@dots{}=@var{agr_func}(@var{src_vars}, @var{args}@dots{})@dots{}
@end display

@cmd{AGGREGATE} summarizes groups of cases into single cases.
Cases are divided into groups that have the same values for one or more
variables called @dfn{break variables}.  Several functions are available
for summarizing case contents.

The @subcmd{OUTFILE} subcommand is required and must appear first.  Specify a
system file or portable file by file name or file
handle (@pxref{File Handles}), or a dataset by its name
(@pxref{Datasets}).
The aggregated cases are written to this file.  If @samp{*} is
specified, then the aggregated cases replace the active dataset's data.
Use of @subcmd{OUTFILE} to write a portable file is a @pspp{} extension.

If @subcmd{OUTFILE=*} is given, then the subcommand @subcmd{MODE} may also be
specified.
The mode subcommand has two possible values: @subcmd{ADDVARIABLES} or @subcmd{REPLACE}.
In @subcmd{REPLACE} mode, the entire active dataset is replaced by a new dataset
which contains just the break variables and the destination varibles.
In this mode, the new file contains as many cases as there are
unique combinations of the break variables.
In @subcmd{ADDVARIABLES} mode, the destination variables are appended to
the existing active dataset.
Cases which have identical combinations of values in their break
variables, receive identical values for the destination variables.
The number of cases in the active dataset remains unchanged.
Note that if @subcmd{ADDVARIABLES} is specified, then the data @emph{must} be
sorted on the break variables.

By default, the active dataset is sorted based on the break variables
before aggregation takes place.  If the active dataset is already sorted
or otherwise grouped in terms of the break variables, specify
@subcmd{PRESORTED} to save time.
@subcmd{PRESORTED} is assumed if @subcmd{MODE=ADDVARIABLES} is used.

Specify @subcmd{DOCUMENT} to copy the documents from the active dataset into the
aggregate file (@pxref{DOCUMENT}).  Otherwise, the aggregate file does
not contain any documents, even if the aggregate file replaces the
active dataset.

Normally, only a single case (for @subcmd{SD} and @subcmd{SD}., two cases) need be
non-missing in each group for the aggregate variable to be
non-missing.  Specifying @subcmd{/MISSING=COLUMNWISE} inverts this behavior, so
that the aggregate variable becomes missing if any aggregated value is
missing.

If @subcmd{PRESORTED}, @subcmd{DOCUMENT}, or @subcmd{MISSING} are specified, they must appear
between @subcmd{OUTFILE} and @subcmd{BREAK}.

At least one break variable must be specified on @subcmd{BREAK}, a
required subcommand.  The values of these variables are used to divide
the active dataset into groups to be summarized.  In addition, at least
one @var{dest_var} must be specified.

One or more sets of aggregation variables must be specified.  Each set
comprises a list of aggregation variables, an equals sign (@samp{=}),
the name of an aggregation function (see the list below), and a list
of source variables in parentheses.  Some aggregation functions expect
additional arguments following the source variable names.

Aggregation variables typically are created with no variable label,
value labels, or missing values.  Their default print and write
formats depend on the aggregation function used, with details given in
the table below.  A variable label for an aggregation variable may be
specified just after the variable's name in the aggregation variable
list.

Each set must have exactly as many source variables as aggregation
variables.  Each aggregation variable receives the results of applying
the specified aggregation function to the corresponding source
variable.  The @subcmd{MEAN}, @subcmd{MEDIAN}, @subcmd{SD}, and @subcmd{SUM}
aggregation functions may only be
applied to numeric variables.  All the rest may be applied to numeric
and string variables.

The available aggregation functions are as follows:

@table @asis
@item @subcmd{FGT(@var{var_name}, @var{value})}
Fraction of values greater than the specified constant.  The default
format is F5.3.

@item @subcmd{FIN(@var{var_name}, @var{low}, @var{high})}
Fraction of values within the specified inclusive range of constants.
The default format is F5.3.

@item @subcmd{FLT(@var{var_name}, @var{value})}
Fraction of values less than the specified constant.  The default
format is F5.3.

@item @subcmd{FIRST(@var{var_name})}
First non-missing value in break group.  The aggregation variable
receives the complete dictionary information from the source variable.
The sort performed by @cmd{AGGREGATE} (and by @cmd{SORT CASES}) is stable.
This means that
the first case with particular values for the break variables before
sorting is also the first case in that break group after sorting.

@item @subcmd{FOUT(@var{var_name}, @var{low}, @var{high})}
Fraction of values strictly outside the specified range of constants.
The default format is F5.3.

@item @subcmd{LAST(@var{var_name})}
Last non-missing value in break group.  The aggregation variable
receives the complete dictionary information from the source variable.
The sort performed by @cmd{AGGREGATE} (and by @cmd{SORT CASES}) is stable.

@item @subcmd{MAX(@var{var_name})}
Maximum value.  The aggregation variable receives the complete
dictionary information from the source variable.

@item @subcmd{MEAN(@var{var_name})}
Arithmetic mean.  Limited to numeric values.  The default format is
F8.2.

@item @subcmd{MEDIAN(@var{var_name})}
The median value.  Limited to numeric values.  The default format is F8.2.

@item @subcmd{MIN(@var{var_name})}
Minimum value.  The aggregation variable receives the complete
dictionary information from the source variable.

@item @subcmd{N(@var{var_name})}
Number of non-missing values.  The default format is F7.0 if weighting
is not enabled, F8.2 if it is (@pxref{WEIGHT}).

@item @subcmd{N}
Number of cases aggregated to form this group.  The default format is
F7.0 if weighting is not enabled, F8.2 if it is (@pxref{WEIGHT}).

@item @subcmd{NMISS(@var{var_name})}
Number of missing values.  The default format is F7.0 if weighting is
not enabled, F8.2 if it is (@pxref{WEIGHT}).

@item @subcmd{NU(@var{var_name})}
Number of non-missing values.  Each case is considered to have a weight
of 1, regardless of the current weighting variable (@pxref{WEIGHT}).
The default format is F7.0.

@item @subcmd{NU}
Number of cases aggregated to form this group.  Each case is considered
to have a weight of 1, regardless of the current weighting variable.
The default format is F7.0.

@item @subcmd{NUMISS(@var{var_name})}
Number of missing values.  Each case is considered to have a weight of
1, regardless of the current weighting variable.  The default format is F7.0.

@item @subcmd{PGT(@var{var_name}, @var{value})}
Percentage between 0 and 100 of values greater than the specified
constant.  The default format is F5.1.

@item @subcmd{PIN(@var{var_name}, @var{low}, @var{high})}
Percentage of values within the specified inclusive range of
constants.  The default format is F5.1.

@item @subcmd{PLT(@var{var_name}, @var{value})}
Percentage of values less than the specified constant.  The default
format is F5.1.

@item @subcmd{POUT(@var{var_name}, @var{low}, @var{high})}
Percentage of values strictly outside the specified range of
constants.  The default format is F5.1.

@item @subcmd{SD(@var{var_name})}
Standard deviation of the mean.  Limited to numeric values.  The
default format is F8.2.

@item @subcmd{SUM(@var{var_name})}
Sum.  Limited to numeric values.  The default format is F8.2.
@end table

Aggregation functions compare string values in terms of internal
character codes.
On most modern computers, this is  @acronym{ASCII} or a superset thereof.

The aggregation functions listed above exclude all user-missing values
from calculations.  To include user-missing values, insert a period
(@samp{.}) at the end of the function name.  (@i{e.g.}@: @samp{SUM.}).
(Be aware that specifying such a function as the last token on a line
causes the period to be interpreted as the end of the command.)

@cmd{AGGREGATE} both ignores and cancels the current @cmd{SPLIT FILE}
settings (@pxref{SPLIT FILE}).

@subsection Aggregate Example

The @file{personnel.sav} dataset provides the occupations and salaries of
many individuals.  For many purposes however such detailed information is
not interesting, but often the aggregated statistics of each occupation are
of interest.  In @ref{aggregate:ex} the @cmd{AGGREGATE} command is used
to calculate the mean, the median and the standard deviation of each
occupation.

@float Example, aggregate:ex
@psppsyntax {aggregate.sps}
@caption {Calculating aggregated statistics from the @file{personnel.sav} file.}
@end float

Since we chose the @samp{MODE = REPLACE} option, in @ref{aggregate:res} cases
for the individual persons are no longer present.  They have each been replaced
by a single case per aggregated value.

@float Results, aggregate:res
@psppoutput {aggregate}
@caption {Aggregated mean, median and standard deviation per @exvar{occupation}.}
@end float

Note that some values for the standard deviation are blank.
This is because there is only one case with the respective
occupation.


@node AUTORECODE
@section AUTORECODE
@vindex AUTORECODE

@display
AUTORECODE VARIABLES=@var{src_vars} INTO @var{dest_vars}
        [ /DESCENDING ]
        [ /PRINT ]
        [ /GROUP ]
        [ /BLANK = @{VALID, MISSING@} ]
@end display

The @cmd{AUTORECODE} procedure considers the @var{n} values that a variable
takes on and maps them onto values 1@dots{}@var{n} on a new numeric
variable.

Subcommand @subcmd{VARIABLES} is the only required subcommand and must come
first.  Specify @subcmd{VARIABLES}, an equals sign (@samp{=}), a list of source
variables, @subcmd{INTO}, and a list of target variables.  There must the same
number of source and target variables.  The target variables must not
already exist.

@cmd{AUTORECODE} ordinarily assigns each increasing non-missing value
of a source variable (for a string, this is based on character code
comparisons) to consecutive values of its target variable.  For
example, the smallest non-missing value of the source variable is
recoded to value 1, the next smallest to 2, and so on.  If the source
variable has user-missing values, they are recoded to
consecutive values just above the non-missing values.  For example, if
a source variables has seven distinct non-missing values, then the
smallest missing value would be recoded to 8, the next smallest to 9,
and so on.

Use @subcmd{DESCENDING} to reverse the sort order for non-missing
values, so that the largest non-missing value is recoded to 1, the
second-largest to 2, and so on.  Even with @subcmd{DESCENDING},
user-missing values are still recoded in ascending order just above
the non-missing values.

The system-missing value is always recoded into the system-missing
variable in target variables.

If a source value has a value label, then that value label is retained
for the new value in the target variable.  Otherwise, the source value
itself becomes each new value's label.

Variable labels are copied from the source to target variables.

@subcmd{PRINT} is currently ignored.

The @subcmd{GROUP} subcommand is relevant only if more than one variable is to be
recoded.   It causes a single mapping between source and target values to
be used, instead of one map per variable.  With @subcmd{GROUP},
user-missing values are taken from the first source variable that has
any user-missing values.

If @subcmd{/BLANK=MISSING} is given, then string variables which contain only
whitespace are recoded as SYSMIS.  If @subcmd{/BLANK=VALID} is specified then they
are allocated a value like any other.  @subcmd{/BLANK} is not relevant
to numeric values. @subcmd{/BLANK=VALID} is the default.

@cmd{AUTORECODE} is a procedure.  It causes the data to be read.

@subsection Autorecode Example

In the file @file{personnel.sav}, the variable @exvar{occupation} is a string
variable.   Except for data of a purely commentary nature, string variables
are generally a bad idea.  One reason is that data entry errors are easily
overlooked.  This has happened in @file{personnel.sav}; one entry which should
read ``Scientist'' has been mistyped as ``Scrientist''.  In @ref{autorecode:ex}
first, this error is corrected by the @cmd{DO IF} clause,
@footnote{One must use care when correcting such data input errors rather than
msimply marking them as missing.  For example, if an occupation has been entered
``Barister'', did the person mean ``Barrister'' or did she mean ``Barista''?}
then we use @cmd{AUTORECODE} to
create a new numeric variable which takes recoded values of @exvar{occupation}.
Finally, we remove the old variable and rename the new variable to
the name of the old variable.

@float Example, autorecode:ex
@psppsyntax {autorecode.sps}
@caption {Changing a string variable to a numeric variable using @cmd{AUTORECODE}
after correcting a data entry error}
@end float


@float Screenshot, autorecode:scr
@psppimage {autorecode}
@caption {Autorecode dialog box set to recode @exvar{occupation} to @exvar{occ}}
@end float

Notice in @ref{autorecode:res}, how  the new variable has been automatically
allocated value labels which correspond to the strings of the old variable.
This means that in future analyses the descriptive strings are reported instead
of the numeric values.

@float Result, autorecode:res
@psppoutput {autorecode}
@caption {The properties of the @exvar{occupation} variable following @cmd{AUTORECODE}}
@end float


@node COMPUTE
@section COMPUTE
@vindex COMPUTE

@display
COMPUTE @var{variable} = @var{expression}.
@end display
  or
@display
COMPUTE vector(@var{index}) = @var{expression}.
@end display

@cmd{COMPUTE} assigns the value of an expression to a target
variable.  For each case, the expression is evaluated and its value
assigned to the target variable.  Numeric and string
variables may be assigned.  When a string expression's width differs
from the target variable's width, the string result of the expression
is truncated or padded with spaces on the right as necessary.  The
expression and variable types must match.

For numeric variables only, the target variable need not already
exist.  Numeric variables created by @cmd{COMPUTE} are assigned an
@code{F8.2} output format.  String variables must be declared before
they can be used as targets for @cmd{COMPUTE}.

The target variable may be specified as an element of a vector
(@pxref{VECTOR}).  In this case, an expression @var{index} must be
specified in parentheses following the vector name.  The expression @var{index}
must evaluate to a numeric value that, after rounding down
to the nearest integer, is a valid index for the named vector.

Using @cmd{COMPUTE} to assign to a variable specified on @cmd{LEAVE}
(@pxref{LEAVE}) resets the variable's left state.  Therefore,
@code{LEAVE} should be specified following @cmd{COMPUTE}, not before.

@cmd{COMPUTE} is a transformation.  It does not cause the active dataset to be
read.

When @cmd{COMPUTE} is specified following @cmd{TEMPORARY}
(@pxref{TEMPORARY}), the @cmd{LAG} function may not be used
(@pxref{LAG}).

@subsection Compute Examples

The dataset @file{physiology.sav} contains the height and weight of persons.
For some purposes, neither height nor weight alone is of interest.
Epidemiologists are often more interested in the @dfn{body mass index} which
can sometimes be used as a predictor for clinical conditions.
The body mass index is defined as the weight of the person in kilograms divided
by the square of the person's height in metres.
@footnote{Since BMI is a quantity with a ratio scale and has units, the term ``index''
is a misnomer, but that is what it is called.}

@float Example, bmi:ex
@psppsyntax {compute.sps}
@caption {Computing the body mass index from @exvar{weight} and @exvar{height}}
@end float

@ref{bmi:ex} shows how you can use @cmd{COMPUTE} to generate a new variable called
@exvar{bmi} and have every case's value calculated from the existing values of
@exvar{weight} and @exvar{height}.
It also shows how you can add a label to this new variable (@pxref{VARIABLE LABELS}),
so that a more descriptive label appears in subsequent analyses, and this can be seen
in the ouput from the @cmd{DESCRIPTIVES} command in @ref{bmi:res}.

@float Screenshot, bmi:scr
@psppimage {compute}
@caption {Using the dialog box to generate a new variable and compute its values}
@end float

The expression which follows the @samp{=} sign can be as complicated as necessary.
@xref{Expressions} for a precise description of the language accepted.
Normally it is easiest to enter the code directly, however there is a dialog box
available if desired.  This is illustrated in @ref{bmi:scr}.
One advantage is that it offers a list of mathematical
functions which can be selected and pasted into the expression.

@float Results, bmi:res
@psppoutput {compute}
@caption {An analysis which includes @exvar{bmi} in its results}
@end float



@node COUNT
@section COUNT
@vindex COUNT

@display
COUNT @var{var_name} = @var{var}@dots{} (@var{value}@dots{})
    [/@var{var_name} = @var{var}@dots{} (@var{value}@dots{})]@dots{}

Each @var{value} takes one of the following forms:
        @var{number}
        @var{string}
        @var{num1} THRU @var{num2}
        MISSING
        SYSMIS
where @var{num1} is a numeric expression or the words @subcmd{LO}  or @subcmd{LOWEST}
      and @var{num2} is a numeric expression  or @subcmd{HI} or @subcmd{HIGHEST}.
@end display

@cmd{COUNT} creates or replaces a numeric @dfn{target} variable that
counts the occurrence of a @dfn{criterion} value or set of values over
one or more @dfn{test} variables for each case.

The target variable values are always nonnegative integers.  They are
never missing.  The target variable is assigned an F8.2 output format.
@xref{Input and Output Formats}.  Any variables, including
string variables, may be test variables.

User-missing values of test variables are treated just like any other
values.  They are @strong{not} treated as system-missing values.
User-missing values that are criterion values or inside ranges of
criterion values are counted as any other values.  However (for numeric
variables), keyword @subcmd{MISSING} may be used to refer to all system-
and user-missing values.

@cmd{COUNT} target variables are assigned values in the order
specified.  In the command @subcmd{COUNT @var{A}=@var{A} @var{B}(1) /@var{B}=@var{A} @var{B}(2).}, the
following actions occur:

@itemize @minus
@item
The number of occurrences of 1 between @var{A} and @var{B} is counted.

@item
@var{A} is assigned this value.

@item
The number of occurrences of 1 between @var{B} and the @strong{new}
value of @var{A} is counted.

@item
@var{B} is assigned this value.
@end itemize

Despite this ordering, all @cmd{COUNT} criterion variables must exist
before the procedure is executed---they may not be created as target
variables earlier in the command!  Break such a command into two
separate commands.

@subsection Count Examples

In the survey results in dataset @file{hotel.sav} a manager wishes
to know how many respondents answered with low valued answers to questions
@exvar{v1}, @exvar{v2} and @exvar{v3}.  This can be found using the code
in @ref{count:ex}.  Specifically, this code creates a new variable, and
populates it with the number of values in @exvar{v1}--@exvar{v2} which
are 2 or lower.

@float Example, count:ex
@psppsyntax {count.sps}
@caption {Counting low values to responses @exvar{v1}, @exvar{v2} and @exvar{v3}}
@end float

In @ref{count:ex} the @cmd{COUNT} transformation creates a new variable, @exvar{low_counts} and
its values are shown using the @cmd{LIST} command.

If using the graphic user interface, a two step process must be used to set
up the @cmd{COUNT} transformation.  The first dialog box (@ref{count:scr}) provides for the
variables to be chosen.
Then, one must click on the button marked ``Define Values...'' to reveal
the dialog box for selecting the values to count.

@float Screenshot, count:scr
@psppimage {count}
@caption {The variables @exvar{v1}, @exvar{v2} and @exvar{v3} selected, ready
to define values to count}
@end float

In this dialog box, you must select the values you wish to count
--- in this case all values up to and including 2 --- as shown in @ref{count-define:scr}
and click ``Add''.  As many ranges or may be added as you desire.
When all desired ranges have been added click ``Continue''.

@float Screenshot, count-define:scr
@psppimage {count-define}
@caption {Count ``Define Values'' dialog with @samp{lowest thru 2} selected}
@end float

In @ref{count:res} we can see the values of @exvar{low_counts} after the @cmd{COUNT}
transformation has completed.  The first value is 1, because there is only one
variable amoung @exvar{v1}, @exvar{v2} and @exvar{3} which has a value of 2 or less.
The second value is 2, because both @exvar{v1} and @exvar{v2} are 2 or less.

@float Result, count:res
@psppoutput {count}
@caption {The values of @exvar{v1}, @exvar{v2}, @exvar{v3} and @exvar{low_counts} after
the @cmd{COUNT} transformation has run}
@end float


@node FLIP
@section FLIP
@vindex FLIP

@display
FLIP /VARIABLES=@var{var_list} /NEWNAMES=@var{var_name}.
@end display

@cmd{FLIP} transposes rows and columns in the active dataset.  It
causes cases to be swapped with variables, and vice versa.

All variables in the transposed active dataset are numeric.  String
variables take on the system-missing value in the transposed file.

@subcmd{N} subcommands are required.  If specified, the @subcmd{VARIABLES} subcommand
selects variables to be transformed into cases, and variables not
specified are discarded.  If the @subcmd{VARIABLES} subcommand is omitted, all
variables are selected for transposition.

The variables specified by @subcmd{NEWNAMES}, which must be a
string variable, is
used to give names to the variables created by @cmd{FLIP}.  Only the
first 8 characters of the variable are used.  If
@subcmd{NEWNAMES} is not
specified then the default is a variable named @exvar{CASE_LBL}, if it exists.
If it does not then the variables created by @cmd{FLIP} are named VAR000
through VAR999, then VAR1000, VAR1001, and so on.

When a @subcmd{NEWNAMES} variable is available, the names must be canonicalized
before becoming variable names.  Invalid characters are replaced by
letter @samp{V} in the first position, or by @samp{_} in subsequent
positions.  If the name thus generated is not unique, then numeric
extensions are added, starting with 1, until a unique name is found or
there are no remaining possibilities.  If the latter occurs then the
@cmd{FLIP} operation aborts.

The resultant dictionary contains a @exvar{CASE_LBL} variable, a string
variable of width 8, which stores the names of the variables in the
dictionary before the transposition.  Variables names longer than 8
characters are truncated.  If @cmd{FLIP} is called again on
this dataset, the @exvar{CASE_LBL} variable can be passed to the @subcmd{NEWNAMES}
subcommand to recreate the original variable names.

@cmd{FLIP} honors @cmd{N OF CASES} (@pxref{N OF CASES}).  It ignores
@cmd{TEMPORARY} (@pxref{TEMPORARY}), so that ``temporary''
transformations become permanent.

@subsection Flip Examples


In @ref{flip:ex}, data has been entered using @cmd{DATA LIST} (@pxref{DATA LIST})
such that the first variable in the dataset is a string variable containing
a description of the other data for the case.
Clearly this is not a convenient arrangement for performing statistical analyses,
so it would have been better to think a little more carefully about how the data
should have been arranged.
However often the data is provided by some third party source, and you have
no control over the form.
Fortunately, we can use @cmd{FLIP} to exchange the variables
and cases in the active dataset.

@float Example, flip:ex
@psppsyntax {flip.sps}
@caption {Using @cmd{FLIP} to exchange variables and cases in a dataset}
@end float

As you can see in @ref{flip:res} before the @cmd{FLIP} command has run there
are seven variables (six containing data and one for the heading) and three cases.
Afterwards there are four variables (one per case, plus the @exvar{CASE_LBL} variable)
and six cases.
You can delete the @exvar{CASE_LBL} variable (@pxref{DELETE VARIABLES}) if you don't need it.

@float Results, flip:res
@psppoutput {flip}
@caption {The results of using @cmd{FLIP} to exchange variables and cases in a dataset}
@end float


@node IF
@section IF
@vindex IF

@display
IF @var{condition} @var{variable}=@var{expression}.
@end display
  or
@display
IF @var{condition} vector(@var{index})=@var{expression}.
@end display

The @cmd{IF} transformation conditionally assigns the value of a target
expression to a target variable, based on the truth of a test
expression.

Specify a boolean-valued expression (@pxref{Expressions}) to be tested
following the @cmd{IF} keyword.  This expression is evaluated for each case.
If the value is true, then the value of the expression is computed and
assigned to the specified variable.  If the value is false or missing,
nothing is done.  Numeric and string variables may be
assigned.  When a string expression's width differs from the target
variable's width, the string result of the expression is truncated or
padded with spaces on the right as necessary.  The expression and
variable types must match.

The target variable may be specified as an element of a vector
(@pxref{VECTOR}).  In this case, a vector index expression must be
specified in parentheses following the vector name.  The index
expression must evaluate to a numeric value that, after rounding down
to the nearest integer, is a valid index for the named vector.

Using @cmd{IF} to assign to a variable specified on @cmd{LEAVE}
(@pxref{LEAVE}) resets the variable's left state.  Therefore,
@code{LEAVE} should be specified following @cmd{IF}, not before.

When @cmd{IF} is specified following @cmd{TEMPORARY}
(@pxref{TEMPORARY}), the @cmd{LAG} function may not be used
(@pxref{LAG}).

@node RECODE
@section RECODE
@vindex RECODE

The @cmd{RECODE} command is used to transform existing values into other,
user specified values.
The general form is:

@display
RECODE @var{src_vars}
        (@var{src_value} @var{src_value} @dots{} = @var{dest_value})
        (@var{src_value} @var{src_value} @dots{} = @var{dest_value})
        (@var{src_value} @var{src_value} @dots{} = @var{dest_value}) @dots{}
         [INTO @var{dest_vars}].
@end display

Following the @cmd{RECODE} keyword itself comes @var{src_vars} which is a list
of variables whose values are to be transformed.
These variables may be string variables or they may be numeric.
However the list must be homogeneous; you may not mix string variables and
numeric variables in the same recoding.

After the list of source variables, there should be one or more @dfn{mappings}.
Each mapping is enclosed in parentheses, and contains the source values and
a destination value separated by a single @samp{=}.
The source values are used to specify the values in the dataset which
need to change, and the destination value specifies the new value
to which they should be changed.
Each @var{src_value} may take one of the following forms:
@table @asis
@item @var{number}
If the source variables are numeric then @var{src_value} may be a literal
number.
@item @var{string}
If the source variables are string variables then @var{src_value} may be a
literal string (like all strings, enclosed in single or double quotes).
@item @var{num1} THRU @var{num2}
This form is valid only when the source variables are numeric.
It specifies all values in the range between @var{num1} and @var{num2},
including both endpoints of the range.  By convention, @var{num1}
should be less than @var{num2}.
Open-ended ranges may be specified using @samp{LO} or @samp{LOWEST}
for @var{num1}
or @samp{HI} or @samp{HIGHEST} for @var{num2}.
@item @samp{MISSING}
The literal keyword @samp{MISSING} matches both system missing and user
missing values.
It is valid for both numeric and string variables.
@item @samp{SYSMIS}
The literal keyword @samp{SYSMIS} matches system missing
values.
It is valid for both numeric variables only.
@item @samp{ELSE}
The @samp{ELSE} keyword may be used to match any values which are
not matched by any other @var{src_value} appearing in the command.
If this keyword appears, it should be used in the last mapping of the
command.
@end table

After the source variables comes an @samp{=} and then the @var{dest_value}.
The @var{dest_value} may take any of the following forms:
@table @asis
@item @var{number}
A literal numeric value to which the source values should be changed.
This implies the destination variable must be numeric.
@item @var{string}
A literal string value (enclosed in quotation marks) to which the source
values should be changed.
This implies the destination variable must be a string variable.
@item @samp{SYSMIS}
The keyword @samp{SYSMIS} changes the value to the system missing value.
This implies the destination variable must be numeric.
@item @samp{COPY}
The special keyword @samp{COPY} means that the source value should not be
modified, but
copied directly to the destination value.
This is meaningful only if @samp{INTO @var{dest_vars}} is specified.
@end table

Mappings are considered from left to right.
Therefore, if a value is matched by a @var{src_value} from more than
one mapping, the first (leftmost) mapping which matches is considered.
Any subsequent matches are ignored.

The clause @samp{INTO @var{dest_vars}} is optional.
The behaviour of the command is slightly different depending on whether it
appears or not.

If @samp{INTO @var{dest_vars}} does not appear, then values are recoded
``in place''.
This means that the recoded values are written back to the
source variables from whence the original values came.
In this case, the @var{dest_value} for every mapping must imply a value which
has the same type as the @var{src_value}.
For example, if the source value is a string value, it is not permissible for
@var{dest_value} to be @samp{SYSMIS} or another forms which implies a numeric
result.
It is also not permissible for @var{dest_value} to be  longer than the width
of the source variable.

The following example two numeric variables @var{x} and @var{y} are recoded
in place.
Zero is recoded to 99, the values 1 to 10 inclusive are unchanged,
values 1000 and higher are recoded to the system-missing value and all other
values are changed to 999:
@example
recode @var{x} @var{y}
        (0 = 99)
        (1 THRU 10 = COPY)
        (1000 THRU HIGHEST = SYSMIS)
        (ELSE = 999).
@end example

If @samp{INTO @var{dest_vars}} is given, then recoded values are written
into the variables specified in @var{dest_vars}, which must therefore
 contain a list of valid variable names.
The number of variables in @var{dest_vars} must be the same as the number
of variables in @var{src_vars}
and the respective order of the variables in @var{dest_vars} corresponds to
the order of @var{src_vars}.
That is to say, the recoded value whose
original value came from the @var{n}th variable in @var{src_vars} is
placed into the @var{n}th variable in @var{dest_vars}.
The source variables are unchanged.
If any mapping implies a string as its destination value, then the respective
destination variable must already exist, or
have been declared using @cmd{STRING} or another transformation.
Numeric variables however are automatically created if they don't already
exist.
The following example deals with two source variables, @var{a} and @var{b}
which contain string values.  Hence there are two destination variables
@var{v1} and @var{v2}.
Any cases where @var{a} or @var{b} contain the values @samp{apple},
@samp{pear} or @samp{pomegranate} result in @var{v1} or @var{v2} being
filled with the string @samp{fruit} whilst cases with
@samp{tomato}, @samp{lettuce} or @samp{carrot} result in @samp{vegetable}.
Any other values produce the result @samp{unknown}:
@example
string @var{v1} (a20).
string @var{v2} (a20).

recode @var{a} @var{b}
        ("apple" "pear" "pomegranate" = "fruit")
        ("tomato" "lettuce" "carrot" = "vegetable")
        (ELSE = "unknown")
        into @var{v1} @var{v2}.
@end example

There is one very special mapping, not mentioned above.
If the source variable is a string variable
then a mapping may be specified as @samp{(CONVERT)}.
This mapping, if it appears must be the last mapping given and
the @samp{INTO @var{dest_vars}} clause must also be given and
must not refer to a string variable.
@samp{CONVERT} causes a number specified as a string to
be converted to a numeric value.
For example it converts the string @samp{"3"} into the numeric
value 3 (note that it does not convert @samp{three} into 3).
If the string cannot be parsed as a number, then the system-missing value
is assigned instead.
In the following example, cases where the value of @var{x} (a string variable)
is the empty string, are recoded to 999 and all others are converted to the
numeric equivalent of the input value.  The results are placed into the
numeric variable @var{y}:
@example
recode @var{x}
       ("" = 999)
        (convert)
        into @var{y}.
@end example

It is possible to specify multiple recodings on a single command.
Introduce additional recodings with a slash (@samp{/}) to
separate them from the previous recodings:
@example
recode
        @var{a}  (2 = 22) (else = 99)
        /@var{b} (1 = 3) into @var{z}
        .
@end example
@noindent Here we have two recodings. The first affects the source variable
@var{a} and recodes in-place the value 2 into 22 and all other values to 99.
The second recoding copies the values of @var{b} into the variable @var{z},
changing any instances of 1 into 3.

@node SORT CASES
@section SORT CASES
@vindex SORT CASES

@display
SORT CASES BY @var{var_list}[(@{D|A@}] [ @var{var_list}[(@{D|A@}] ] ...
@end display

@cmd{SORT CASES} sorts the active dataset by the values of one or more
variables.

Specify @subcmd{BY} and a list of variables to sort by.  By default, variables
are sorted in ascending order.  To override sort order, specify @subcmd{(D)} or
@subcmd{(DOWN)} after a list of variables to get descending order, or @subcmd{(A)}
or @subcmd{(UP)}
for ascending order.  These apply to all the listed variables
up until the preceding @subcmd{(A)}, @subcmd{(D)}, @subcmd{(UP)} or @subcmd{(DOWN)}.

The sort algorithms used by @cmd{SORT CASES} are stable.  This means
records which have equal values of the sort variables have the
same relative order before and after sorting.  Thus,
re-sorting an already sorted file does not affect the ordering of
cases.

@cmd{SORT CASES} is a procedure.  It causes the data to be read.

@cmd{SORT CASES} attempts to sort the entire active dataset in main memory.
If workspace is exhausted, it falls back to a merge sort algorithm which
creates numerous temporary files.

@cmd{SORT CASES} may not be specified following @cmd{TEMPORARY}.

@subsection Sorting Example

In @ref{sort-cases:ex} the data from the file @file {physiology.sav} is sorted
by two variables, @i{viz@:} @exvar{sex} in descending order and @exvar{temperature} in
ascending order.

@float Example, sort-cases:ex
@psppsyntax {sort-cases.sps}
@caption {Sorting cases  by two variables.}
@end float

In @ref{sort-cases:res} you can see that all the cases with a @exvar{sex} of
@samp{1} (female) appear before those with a sex of @samp{0} (male).
This is because they have been sorted in descending order.
Within each sex, the data is sorted on the @exvar{temperature} variable,
this time in ascending order.

@float Results, sort-cases:res
@psppoutput {sort-cases}
@caption {The @file{physiology.sav} file after sorting.}
@end float

Note that @cmd{SORT CASES}, like all other transformations, affects only the active file.
It does not have any effect upon the @file{physiology.sav} file itself. For that, you
would have to rewrite the file using the @cmd{SAVE} command (@pxref{SAVE}).

When using the graphic user interface, it is often simpler to perform a sort
directly from the data view.
To do this, switch to the data view.  Select the column corresponding to the
variable by which you want to sort and click button 1 and then click button 3.
A popup menu will appear like that shown in @ref{sort-simple:scr}.  Select
either ``Sort Ascending'' or ``Sort Descending'' from this menu.

@float Screenshot, sort-simple:scr
@psppimage {sort-simple}
@caption {Sorting the data on a single variable @exvar{height}}
@end float

However, sometimes you will want to sort on two or more variables, and that is
not possible using this method.  In this case, you must either use some code or
the ``Sort Cases'' dialog from the Data menu.  @ref{sort:scr} shows the dialog
box set up to perform a sort on both @exvar{sex} and @exvar{height}.
Note that the order in which you enter the variables is important.  In this case,
the data will be first sorted on @exvar{sex}, and then all cases for which @exvar{sex}
is the same will then be sorted by @exvar{height}.

@float Screenshot, sort:scr
@psppimage {sort}
@caption {Sorting the data on two variables @exvar{sex} and  @exvar{height}}
@end float