Convert multiple variables between character and numeric formats in SAS

Introduction

I often get data that are coded as character, but are actually meant to be numeric.  Thus, converting them into the correct variable types is a common task, and SAS Note #24590 shows how to do so.  However, I recently needed to do hundreds of these conversions, so I wanted some code to accomplish this quickly and accurately.  This tutorial shows how to do so.

Let’s consider this small data set in SAS as an example.  They are hypothetical statistics of 3 players from a basketball game.

data basketball1;
     input jersey points $ rebounds $ assists $;
     datalines;
21 10 14 1
4  11 3  12
23 29 4  5
;
run;

The 3 performance metrics (points, rebounds, and assists) are clearly numeric, but they are currently coded as character.  (You can use PROC CONTENTS to confirm this if needed.)

The jersey number is really a character variable, because its magnitude has no real-life meaning.  The National Basketball Association (NBA) allows “00” as a possible jersey number.  (Robert Parish wore this jersey number; he won 4 NBA championships and reached the Naismith Basketball Hall of Fame.)  If you code “00” as a numeric variable, then it will render as “0”.  Thus, for NBA jersey numbers, it is best to save it as a character variable.

I can convert these variables into the correct types using the following code.  Note that I chose “2.” for the length of “JERSEY”, because I know that jersey numbers in the NBA have, at most, 2 digits.

data basketball2;
     set basketball1;
 
     jersey2 = put(jersey, 2.);
     drop jersey;
     rename jersey2 = jersey;

     points2 = input(points, 8.);
     drop points;
     rename points2 = points;

     rebounds2 = input(rebounds, 8.);
     drop rebounds;
     rename rebounds2 = rebounds;

     assists2 = input(assists, 8.);
     drop assists;
     rename assists2 = assists;
run;

Despite this success, the above code can be very cumbersome when I need to do this for many variables, and this situation arose in my job recently.  In this tutorial, I will show a fast way of doing these conversions for many variables at once.  I will use this BASKETBALL1 data set as an example, and I will convert POINTS, REBOUNDS, and ASSISTS from character to numeric simultaneously.

A macro to automate the creation of indicator variables in SAS

In a recent blog post, I introduced an easy and efficient way to create indicator variables from categorical variables in SAS.  This method pretends to run logistic regression, but it really is using PROC LOGISTIC to get the design matrix based on dummy-variable coding.  I shared SAS code for how to do so, step-by-step.

I write this follow-up post to provide a macro that you can use to execute all of those steps in one line.  If you have not read my previous post on this topic, then I strongly encourage you to do that first.  Don’t use this macro blindly.

Here is the macro.  The key steps are

1. Run PROC LOGISTIC to get the design matrix (which has the indicator variables)
2. Merge the original data with the newly created indicator variables
3. Delete the “INDICATORS” data set, which was created in an intermediate step

%macro create_indicators(input_data, target, covariates, output_data);

proc logistic
     data = &input_data
          noprint
          outdesign = indicators;
     class &covariates / param = glm;
     model &target = &covariates;
run;


data &output_data;
      merge    &input_data
               indicators (drop = Intercept &target);
run;


proc datasets 
     library = work
          noprint;
     delete indicators;
run;

%mend;

I will use the built-in data set SASHELP.CARS to illustrate the use of my macro.  As you can see, my macro can accept multiple categorical variables as inputs for creating indicator variables.  I will do that here for the variables TYPE, MAKE, and ORIGIN.

An easy and efficient way to create indicator variables (a.k.a. dummy variables) from a categorical variable in SAS

Introduction

In statistics and biostatistics, the creation of binary indicators is a very useful practice.

• They can be useful predictor variables in statistical models.
• They can reduce the amount of memory required to store the data set.
• They can treat a categorical covariate as a continuous covariate in regression, which has certain mathematical conveniences.

However, the creation of indicator variables can be a long, tedious, and error-prone process.  This is especially true if there are many categorical variables, or if a categorical variable has many categories.  In this tutorial, I will show an easy and efficient way to create indicator variables in SAS.  I learned this technique from SAS usage note #23217: Saving the coded design matrix of a model to a data set.

The Example Data Set

Let’s consider the PRDSAL2 data set that is built into the SASHELP library.  Here are the first 5 observations; due to a width constraint, I will show the first 5 columns and the last 6 columns separately.  (I encourage you to view this data set using PROC PRINT in SAS by yourself.)

COUNTRY	STATE	COUNTY	ACTUAL	PREDICT
U.S.A.	California		$987.36	$692.24
U.S.A.	California		$1,782.96	$568.48
U.S.A.	California		$32.64	$16.32
U.S.A.	California		$1,825.12	$756.16
U.S.A.	California		$750.72	$723.52

 

PRODTYPE	PRODUCT	YEAR	QUARTER	MONTH	MONYR
FURNITURE	SOFA	1995	1	Jan	JAN95
FURNITURE	SOFA	1995	1	Feb	FEB95
FURNITURE	SOFA	1995	1	Mar	MAR95
FURNITURE	SOFA	1995	2	Apr	APR95
FURNITURE	SOFA	1995	2	May	MAY95

Communication Tip – Write the message of the email BEFORE the subject and the recipients’ email addresses

In every email service that I have used so far,

1) the address fields are on the top

2) the subject field is in the middle

3) and then the text editor for the message is at the end.

However, when I write most emails, I usually write these 3 things in reverse.  This has several important advantages.

Image courtesy of Pixabay on Pexels.

Sort a data set by ascending or descending variables using PROC SORT in SAS

Consider the built-in data set SASHELP.CLASS in SAS.  Here are the first 5 observations from PROC PRINT.

Obs	Name	Sex	Age	Height	Weight
1	Joyce	F	11	51.3	50.5
2	Thomas	M	11	57.5	85.0
3	James	M	12	57.3	83.0
4	Jane	F	12	59.8	84.5
5	John	M	12	59.0	99.5

As you can clearly see, they are NOT sorted by weight.  Here is how you can sort the data set by weight using PROC SORT.