**SAS**

In SAS, Rick Wicklin offers an IML solution and links to a macro with the same function. But if you're not an IML coder, and you don't want to investigate a macro solution, it's simple enough to do with data steps. We'll begin by making some fake data.

data test; do i = 1 to 100; cat = "meow"; if i gt 30 then cat = "Purr"; if i gt 70 then cat = "Hiss"; output; end; run;To make the new variable, we'll just sort (section 1.5.6) the data on the categorical variable we want to convert, then use the

`set ds; by x;`syntax to keep track of when a new value is encountered in the data. It's hard to believe that we've never demonstrated this useful syntax before-- perhaps we just can't find it today. The

`set ds; by x;`syntax makes new temporary variables

`first.x`and

`last.x`that are equal to 1 for the first and last observations of each new level of

`x`, respectively, and 0 otherwise. When we find a new value, we'll increase a counter by 1; the counter is our new numeric-valued variable.

proc sort data = test; by cat; run; data catize; set test; by cat; retain catnum 0; if first.cat then catnum = catnum + 1; run; /* check the result */ proc freq data = catize; tables cat * catnum; run;The table also shows the recoding values.

Table of cat by catnum cat catnum Frequency| Percent | Row Pct | Col Pct | 1| 2| 3| Total ---------+--------+--------+--------+ Hiss | 30 | 0 | 0 | 30 | 30.00 | 0.00 | 0.00 | 30.00 | 100.00 | 0.00 | 0.00 | | 100.00 | 0.00 | 0.00 | ---------+--------+--------+--------+ Purr | 0 | 40 | 0 | 40 | 0.00 | 40.00 | 0.00 | 40.00 | 0.00 | 100.00 | 0.00 | | 0.00 | 100.00 | 0.00 | ---------+--------+--------+--------+ meow | 0 | 0 | 30 | 30 | 0.00 | 0.00 | 30.00 | 30.00 | 0.00 | 0.00 | 100.00 | | 0.00 | 0.00 | 100.00 | ---------+--------+--------+--------+ Total 30 40 30 100 30.00 40.00 30.00 100.00

**R**

We begin by making the data. To convert to numbers, we use the

`labels`option to the

`factor()`function, feeding it the sequences of numbers between 1 and however many different values there are. Note that we find this using the

`factor()`function again. There's probably a better way of doing this, but it's a little bit amusing to code it this way. Then we have numbers, but they're store as a factor. We can get them out with a call to

`as.numeric()`.

cat = c(rep("meow",30),rep("Hiss",30), rep("Purr", 40)) catn1 = factor(cat, labels=(1:length(levels(factor(cat))))) catn = as.numeric(catn1) table(catn,cat) cat catn Hiss meow Purr 1 30 0 0 2 0 30 0 3 0 0 40There's a warning in the documentation for

`factor()`that the values are assigned in location-specific fashion, so the table should be used to establish how the codes were assigned. For the record, the use cases for this kind of recoding in R may be more strained than the SAS example given above.

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## 3 comments:

The 'labels' argument is not needed.

cat <- c(rep("meow",30),rep("Hiss",30), rep("Purr", 40))

catn = as.numeric(factor(cat))

table(catn,cat)

If you want to be certain the categories are assigned to integers 1-3 in a certain order, then use 'levels'.

cat <- c(rep("meow",30),rep("Hiss",30), rep("Purr", 40))

catn = as.numeric(factor(cat, levels=c("Hiss", "Purr", "meow")))

table(catn,cat)

cat

catn Hiss meow Purr

1 30 0 0

2 0 0 40

3 0 30 0

Thanks, Chris. That was nice thinking on the part of the R designers!

It's worth noting that with a minor modification of the SAS DATA step code, you can also count the number of each category, thus acheiving a "poor man's PROC FREQ" for the (sorted) categories.

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