(This article was first published on

**SAS and R**, and kindly contributed to R-bloggers)**R**

There are often times when it is useful to create an individual level dataset from aggregated data (such as a table). While this can be done using the

`expand.table()`function within the

`epitools`package, it is also straightforward to do directly within R.

Imagine that instead of the individual level data, we had only the 2x2 table for the association between homeless status and gender within the HELP RCT:

> HELPrct = read.csv("http://www.math.smith.edu/r/data/help.csv")

> xtabs(~ homeless + female, data=HELPrct)

female

homeless 0 1

0 177 67

1 169 40

We can use this information to create an analytic dataset using just the four rows of a new dataset:

> female = c(0, 1, 0, 1)

> homeless = c(1, 1, 0, 0)

> count = c(169, 40, 177, 67)

> ds=data.frame(cbind(female, homeless, count))

> ds

female homeless count

1 0 1 169

2 1 1 40

3 0 0 177

4 1 0 67

Next we use the

`rep()`function to generate a vector of indices to repeat. The

`index`object repeats each row number

`count`times.

> index = rep(seq_len(nrow(ds)), times=ds$count)

> newds = ds[index,]

> newds$count = NULL

> xtabs(~ homeless + female, data=newds)

female

homeless 0 1

0 177 67

1 169 40

The resulting data set is identical to the summarized input data set.

**SAS**

Many SAS procedures offer a

`weight`option (as a statement within the proc) which will duplicate each observation

*varname**varname*times. So, for example, we can make a data set such as that shown above, then use, e.g.,

`proc freq`to produce a table.

data ds;

female = 0; homeless = 1; count = 169; output;

female = 1; homeless = 1; count = 40; output;

female = 0; homeless = 0; count = 177; output;

female = 1; homeless = 0; count = 67; output;

run;

proc freq data = ds;

table homeless * female;

weight count;

run;

homeless female

Frequency|

Percent |

Row Pct |

Col Pct | 0| 1| Total

---------+--------+--------+

0 | 177 | 67 | 244

| 39.07 | 14.79 | 53.86

| 72.54 | 27.46 |

| 51.16 | 62.62 |

---------+--------+--------+

1 | 169 | 40 | 209

| 37.31 | 8.83 | 46.14

| 80.86 | 19.14 |

| 48.84 | 37.38 |

---------+--------+--------+

Total 346 107 453

76.38 23.62 100.00

However, some procedures lack this option, and/or it may be difficult to arrange your data appropriately to take advantage of it. In such cases, it's useful to be able to expand the data manually, as we show for R above. We demonstrate this below, assuming the

`count`variable can be constructed. The explicit

`output`statement puts a line into the

`newds`data set

`count`times.

data newds;

set ds;

do i = 1 to count;

output;

end;

run;

proc freq data = newds;

table homeless * female;

run;

homeless female

Frequency|

Percent |

Row Pct |

Col Pct | 0| 1| Total

---------+--------+--------+

0 | 177 | 67 | 244

| 39.07 | 14.79 | 53.86

| 72.54 | 27.46 |

| 51.16 | 62.62 |

---------+--------+--------+

1 | 169 | 40 | 209

| 37.31 | 8.83 | 46.14

| 80.86 | 19.14 |

| 48.84 | 37.38 |

---------+--------+--------+

Total 346 107 453

76.38 23.62 100.00

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