如何将SAS数据集转换为数据步骤

如何将我的SAS数据集转换为可以轻松粘贴到论坛或交给他人复制我的数据的数据集。理想情况下，我还希望能够控制包含的记录数量

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Ie我在sashelp库中有sashelp.class，但我想在这里提供它，以便其他人可以使用它作为我问题的起点

要做到这一点，可以使用SAS的Mark Jordan编写的宏，代码也存储在GitHub中

您需要提供数据集名称，包括库和要输出的观测数。这使他们井然有序。然后，代码将出现在SAS日志中

*data set you want to create demo data for;
%let dataSetName = sashelp.Class;
*number of observations you want to keep;
%let obsKeep = 5;


******************************************************
DO NOT CHANGE ANYTHING BELOW THIS LINE
******************************************************;

%let source_path = https://gist.githubusercontent.com/statgeek/bcc55940dd825a13b9c8ca40a904cba9/raw/865d2cf18f5150b8e887218dde0fc3951d0ff15b/data2datastep.sas;

filename reprex url "&source_path";
%include reprex;
filename reprex;

option linesize=max;
%data2datastep(dsn=&dataSetName, obs=&obsKeep);

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如果您没有访问github页面的权限，这可能不起作用，在这种情况下，您可以手动导航到该页面（同一链接）并将其复制/粘贴到SAS中。然后运行程序，只运行最后一步，即

%data2datastep（dsn=，obs=）
这个主题最近出现在SAS社区上，我创建了一个比Reeza链接的宏更健壮的宏。您可以在Github中看到它：

例如，如果您想共享SASHELP.CARS的前5个观察结果，您可以运行以下宏调用：

%ds2post(sashelp.cars,obs=5)

这将在SAS日志中生成以下代码：

data work.cars (label='2004 Car Data');
  infile datalines dsd dlm='|' truncover;
  input Make :$13. Model :$40. Type :$8. Origin :$6. DriveTrain :$5.
    MSRP Invoice EngineSize Cylinders Horsepower MPG_City MPG_Highway
    Weight Wheelbase Length
  ;
  format MSRP dollar8. Invoice dollar8. ;
  label EngineSize='Engine Size (L)' MPG_City='MPG (City)'
    MPG_Highway='MPG (Highway)' Weight='Weight (LBS)'
    Wheelbase='Wheelbase (IN)' Length='Length (IN)'
  ;
datalines4;
Acura|MDX|SUV|Asia|All|36945|33337|3.5|6|265|17|23|4451|106|189
Acura|RSX Type S 2dr|Sedan|Asia|Front|23820|21761|2|4|200|24|31|2778|101|172
Acura|TSX 4dr|Sedan|Asia|Front|26990|24647|2.4|4|200|22|29|3230|105|183
Acura|TL 4dr|Sedan|Asia|Front|33195|30299|3.2|6|270|20|28|3575|108|186
Acura|3.5 RL 4dr|Sedan|Asia|Front|43755|39014|3.5|6|225|18|24|3880|115|197
;;;;

尝试这个小测试来比较这两个宏

首先制作一个带有几个问题的示例数据集

data testit;
  set sashelp.class (obs=5);
  if _n_=1 then name='Le Bron';
  if _n_=2 then age=.;
  if _n_=3 then wt=.;
  if _n_=4 then name='12;34';
run;

然后运行两个宏将代码转储到SAS日志

%ds2post(testit);
%data2datastep(dsn=testit,obs=20);

从日志中复制代码。更改数据语句中的名称以不覆盖原始数据集或彼此覆盖。运行它们并将结果与原始结果进行比较

proc compare data=testit compare=testit1; run;
proc compare data=testit compare=testit2; run;

使用
%DS2POST
的结果：

The COMPARE Procedure
Comparison of WORK.TESTIT with WORK.TESTIT1
(Method=EXACT)

Data Set Summary

Dataset                Created          Modified  NVar    NObs

WORK.TESTIT   02NOV18:17:09:40  02NOV18:17:09:40     6       5
WORK.TESTIT1  02NOV18:17:10:29  02NOV18:17:10:29     6       5

Variables Summary

Number of Variables in Common: 6.

Observation Summary

Observation      Base  Compare

First Obs           1        1
Last  Obs           5        5

Number of Observations in Common: 5.
Total Number of Observations Read from WORK.TESTIT: 5.
Total Number of Observations Read from WORK.TESTIT1: 5.

Number of Observations with Some Compared Variables Unequal: 0.
Number of Observations with All Compared Variables Equal: 5.

使用
%Data2DataStep
的结果摘要：

Comparison of WORK.TESTIT with WORK.TESTIT2
(Method=EXACT)

Data Set Summary

Dataset                Created          Modified  NVar    NObs

WORK.TESTIT   02NOV18:17:09:40  02NOV18:17:09:40     6       5
WORK.TESTIT2  02NOV18:17:10:29  02NOV18:17:10:29     6       3


Variables Summary

Number of Variables in Common: 6.


Observation Summary

Observation      Base  Compare

First Obs           1        1
First Unequal       1        1
Last  Unequal       3        3
Last  Match         3        3
Last  Obs           5        .

Number of Observations in Common: 3.
Number of Observations in WORK.TESTIT but not in WORK.TESTIT2: 2.
Total Number of Observations Read from WORK.TESTIT: 5.
Total Number of Observations Read from WORK.TESTIT2: 3.

Number of Observations with Some Compared Variables Unequal: 3.
Number of Observations with All Compared Variables Equal: 0.

变量值摘要

Values Comparison Summary

Number of Variables Compared with All Observations Equal: 1.
Number of Variables Compared with Some Observations Unequal: 5.
Number of Variables with Missing Value Differences: 4.
Total Number of Values which Compare Unequal: 12.
Maximum Difference: 0.


Variables with Unequal Values

Variable  Type  Len  Ndif   MaxDif  MissDif

Name      CHAR    8     1                 0
Sex       CHAR    1     3                 3
Age       NUM     8     2        0        2
Height    NUM     8     3        0        3
Weight    NUM     8     3        0        3

请注意，我确信有些值也会给我的宏带来麻烦。但希望它们是由比空格或分号更不可能出现的数据引起的。
谢谢@reeza，这应该可以帮助很多提问的人。您介意我将SAS社区上的我的重定向到您的GitHub页面吗？这两个代码实际上都不是我的：）
Values Comparison Summary

Number of Variables Compared with All Observations Equal: 1.
Number of Variables Compared with Some Observations Unequal: 5.
Number of Variables with Missing Value Differences: 4.
Total Number of Values which Compare Unequal: 12.
Maximum Difference: 0.


Variables with Unequal Values

Variable  Type  Len  Ndif   MaxDif  MissDif

Name      CHAR    8     1                 0
Sex       CHAR    1     3                 3
Age       NUM     8     2        0        2
Height    NUM     8     3        0        3
Weight    NUM     8     3        0        3