## Store multiple strings of text as a macro variable in SAS with PROC SQL and the INTO statement

I often need to work with many variables at a time in SAS, but I don’t like to type all of their names manually – not only is it messy to read, it also induces errors in transcription, even when copying and pasting.  I recently learned of an elegant and efficient way to store multiple variable names into a macro variable that overcomes those problems.  This technique uses the INTO statement in PROC SQL.

To illustrate how this storage method can be applied in a practical context, suppose that we want to determine the factors that contribute to a baseball player’s salary in the built-in SASHELP.BASEBALL data setI will consider all continuous variables other than “Salary” and “logSalary”, but I don’t want to write them explicitly in any programming statements.  To do this, I first obtain the variable names and types of a data set using PROC CONTENTS.

* create a data set of the variable names;
proc contents
data = sashelp.baseball
noprint
out = bvars (keep = name type);
run;

## Sorting correlation coefficients by their magnitudes in a SAS macro

#### Theoretical Background

Many statisticians and data scientists use the correlation coefficient to study the relationship between 2 variables.  For 2 random variables, $X$ and $Y$, the correlation coefficient between them is defined as their covariance scaled by the product of their standard deviations.  Algebraically, this can be expressed as

$\rho_{X, Y} = \frac{Cov(X, Y)}{\sigma_X \sigma_Y} = \frac{E[(X - \mu_X)(Y - \mu_Y)]}{\sigma_X \sigma_Y}$.

In real life, you can never know what the true correlation coefficient is, but you can estimate it from data.  The most common estimator for $\rho$ is the Pearson correlation coefficient, which is defined as the sample covariance between $X$ and $Y$ divided by the product of their sample standard deviations.  Since there is a common factor of

$\frac{1}{n - 1}$

in the numerator and the denominator, they cancel out each other, so the formula simplifies to

$r_P = \frac{\sum_{i = 1}^{n}(x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum_{i = 1}^{n}(x_i - \bar{x})^2 \sum_{i = 1}^{n}(y_i - \bar{y})^2}}$.

In predictive modelling, you may want to find the covariates that are most correlated with the response variable before building a regression model.  You can do this by

1. computing the correlation coefficients
2. obtaining their absolute values
3. sorting them by their absolute values.

## How to Extract a String Between 2 Characters in R and SAS

#### Introduction

I recently needed to work with date values that look like this:

 mydate Jan 23/2 Aug 5/20 Dec 17/2

I wanted to extract the day, and the obvious strategy is to extract the text between the space and the slash.  I needed to think about how to program this carefully in both R and SAS, because

1. the length of the day could be 1 or 2 characters long
2. I needed a code that adapted to this varying length from observation to observation
3. there is no function in either language that is suited exactly for this purpose.

In this tutorial, I will show you how to do this in both R and SAS.  I will write a function in R and a macro program in SAS to do so, and you can use the function and the macro program as you please!