## Content

### Why did you write the **procs** package?

**Q:** There are already many statistical functions and
packages in R. Why did you write **procs**?

**A:** I wrote the **procs** package to
help SAS® programmers. There are many SAS® programmers trying to learn R
and struggling with the many differences between these two languages.
The **procs** package provides them with a set of functions
that are conceptually similar to SAS® procedures. The aim is to make
them more productive and comfortable working in R in a shorter time
frame.

### Are these functions validated?

**Q:** My company requires that all software be
validated before using in production. Is the **procs**
package validated?

**A:** Yes. The functions were validated by comparing to
SAS®. The validation documentation is here.

### Why is the output different?

**Q:** The output dataset columns and column names are a
little bit different from SAS®. Why?

**A:** The output datasets and column names have been
standardized. They should be more predictable and easier to manipulate
programmatically. They were changed intentionally as an improvement over
the corresponding SAS® procedures.

### Can you do N-way tables?

**Q:** I see you can do one-way and two-way frequency
tables. What about N-way tables? Does that package support them?

**A:** No. N-way tables are less common, and were not
seen as a priority. These types of tables are on the list for a future
enhancement.

### Can these procedures create plots?

**Q:** I’m trying to find the option to produce a plot,
and can’t find it. Does the package support plots or not?

**A:** Not yet. Plots are expected for a future release.
In the meantime, you can send the output results into ggplot2 and create the plots
on your own.

### Does the package support Cochran-Mantel-Haenszel Statistics?

**Q:** I see the frequency function supports Chi-square
and Fishers’s exact tests. What about Cochran-Mantel-Haenszel?

**A:** CMH statistics were left out of the of the
**procs** package because the corresponding R function does
not match SAS® reliably. You can run CMH statistics yourself using using
the mantelhaen.test
function from the stats
package.

### What about PROC COMPARE?

**Q:** I see you have PROC TRANSPOSE and PROC SORT. How
come you didn’t include PROC COMPARE?

**A:** There are already several dataset comparison
packages in R, such as diffdf and comparedf. The
functionality of these packages is also similar to SAS® PROC COMPARE. If
you wish to do dataset comparisons, please research these existing
packages.

### Are there any other statistics procedures coming?

**Q:** The `proc_freq()`

and
`proc_means()`

functions are helpful. But what about some
other stats procedures? What about PROC GLM and PROC ANOVA?

**A:** These functions are planned for a future release.
In the meantime, please see the excellent package sasLM for functions
that replicate SAS® statistics related to linear modeling.

### How do I get my statistics into a report?

**Q:** I created some frequencies, and need to output
them to a report. Is there a way to create a PDF or RTF directly from
the **procs** package?

**A:** Yes. Put your data frames in a list, and send to
`proc_print()`

. You can use the parameters to assign the
titles, the output location, and the output type. The function supports
PDF, RTF, HTML, DOCX, and TXT. For more advanced reporting features, see
the reporter package.

### How do I order my frequencies?

**Q:** The `proc_freq()`

function seems to
always order the categories alphabetically. But I have a specific order
in mind that is not alphabetical. Is there a way to order the frequency
categories in a specific way?

**A:** Yes. If your frequency variable is defined as a
factor, the `proc_freq()`

function will order the frequency
categories by that factor. Here is an example:

```
library(procs)
# Turn off printing for CRAN checks
options("procs.print" = FALSE)
# Create sample data
df <- as.data.frame(HairEyeColor, stringsAsFactors = FALSE)
# Assign factor for ordering
df$Eye <- factor(df$Eye, c("Green", "Hazel", "Blue", "Brown"))
# Use factor in tables parameter
res <- proc_freq(df,
tables = Eye,
weight = Freq)
# Output is now ordered by the factor
res
# VAR CAT N CNT PCT
# 1 Eye Green 592 64 10.81081
# 2 Eye Hazel 592 93 15.70946
# 3 Eye Blue 592 215 36.31757
# 4 Eye Brown 592 220 37.16216
```