--- title: "The Frequency Function" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{The Frequency Function} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r setup, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ``` The `proc_freq()` function simulates a SAS® PROC FREQ procedure. Below is a short tutorial on the function. Like PROC FREQ, the function is both an interactive function and returns datasets. ## Create Sample Data The first step in our tutorial is to create some sample data: ```{r eval=FALSE, echo=TRUE} # Create sample data dat <- read.table(header = TRUE, text = 'x y z 6 A 60 6 A 70 2 A 100 2 B 10 3 B 67 2 C 81 3 C 63 5 C 55') # View sample data dat # x y z # 1 6 A 60 # 2 6 A 70 # 3 2 A 100 # 4 2 B 10 # 5 3 B 67 # 6 2 C 81 # 7 3 C 63 # 8 5 C 55 ``` ## Get Frequencies Now that we have some data, let's send that data to the `proc_freq()` function to see the frequency distribution. The `options()` statement below turns off printing of all **procs** functions. This statement is necessary so that the sample code below can pass CRAN checks. When running sample code yourself, the options statement can be omitted. ```{r eval=FALSE, echo=TRUE} # Turn off printing for CRAN options("procs.print" = FALSE) # Get frequencies proc_freq(dat, tables = y) ```

The above code illustrates a one-way frequency on the "y" variable. The result shows that the "A" and "C" categories appears three times, and the "B" category appears twice. The "N" column shows that there are eight items in the population. This population is used to get the percent shown for each frequency count. ## Control Columns The `options` parameter can control many aspects of the `proc_freq()` function. For example, if you did not want the cumulative frequency and percent, you could turn off these columns with the option "nocum". ```{r eval=FALSE, echo=TRUE} # Turn off cumulative columns proc_freq(dat, tables = y, options = nocum) ```

Let's say you wanted only the frequency counts, and not the other columns. This result can be achieved with the following options. Use the `v()` function when you are passing multiple options: ```{r eval=FALSE, echo=TRUE} proc_freq(dat, tables = y, options = v(nocum, nonobs, nopercent)) ```

## Cross Tabulation For two-way frequencies, you can cross two variables on the `tables` parameter. This syntax produces a cross-tabulation table by default: ```{r eval=FALSE, echo=TRUE} # Create crosstab proc_freq(dat, tables = y * x) ```

## Cross Tabulation Options If you want the data displayed in a list instead of a cross-tabulation table, you can do that with the "list" option. The "nosparse" option will turn off zero-count categories, which are included by default: ```{r eval=FALSE, echo=TRUE} # Two-way frequency in list form proc_freq(dat, tables = y * x, options = v(list, nosparse)) ```

The following options turn off various features of the cross-tabulation table: ```{r eval=FALSE, echo=TRUE} # View frequencies only proc_freq(dat, tables = y * x, options = v(norow, nocol, nopercent)) ```

## Multiple Tables The `tables` parameter accepts more than one table request. To request multiple tables, pass a quoted or unquoted vector. Note that `proc_freq()` does not accept grouping syntax, such as that allowed by SAS®. You must specify each cross-tab individually: ```{r eval=FALSE, echo=TRUE} # Request two crosstabs proc_freq(dat, tables = v(y * x, y * z), options = v(norow, nocol, nopercent)) ```

## Distinct Values The "nlevels" option can be used to count the number of distinct values in a categorical variable: ```{r eval=FALSE, echo=TRUE} # Turn on nlevels option proc_freq(dat, tables = y, options = nlevels) ```

## Weighted Frequencies The `weight` parameter is used to achieve weighted frequencies. When a weight is specified, `proc_freq()` will use the counts in the indicated variable for all frequency calculations. ```{r eval=FALSE, echo=TRUE} # Add weight variable proc_freq(dat, tables = y, weight = z) ```

## Statistics Options The `options` parameter also accepts statistics options. For two-way tables, you may request either Chi-Square or Fisher's tests of association. Here is an example of the Chi-Square test: ```{r eval=FALSE, echo=TRUE} # Request Chi-Square and Output datasets res <- proc_freq(dat, tables = y * x, options = chisq) ```

```{r eval=FALSE, echo=TRUE} # View results res # $`y * x` # VAR1 VAR2 CAT1 CAT2 N CNT PCT # 1 y x A 2 8 1 12.5 # 2 y x A 3 8 0 0.0 # 3 y x A 5 8 0 0.0 # 4 y x A 6 8 2 25.0 # 5 y x B 2 8 1 12.5 # 6 y x B 3 8 1 12.5 # 7 y x B 5 8 0 0.0 # 8 y x B 6 8 0 0.0 # 9 y x C 2 8 1 12.5 # 10 y x C 3 8 1 12.5 # 11 y x C 5 8 1 12.5 # 12 y x C 6 8 0 0.0 # # $`chisq:y * x` # STAT DF VAL PROB # 1 Chi-Square 6 6.444444 0.3752853 # 2 Continuity Adj. Chi-Square 6 6.444444 0.3752853 ``` ## Output Datasets You may control datasets returned from the `proc_freq()` function using the `output` parameter. This parameter takes three basic values: "out", "report", and "none". The "out" keyword requests datasets meant for output, and is the default. These datasets have standardized column names, and sometimes have additional columns to help with data manipulation. The "report" keyword requests the exact datasets used to create the interactive report. For both keywords, if there is more than one dataset, they will be returned as a list of datasets. The name of the list item will identify the dataset. You may specify the names of the output tables in the list by using a named table request. Here is an example of the "out" option: ```{r eval=FALSE, echo=TRUE} # Request output data res <- proc_freq(dat, tables = v(x, y, MyCross = y * x), output = out) # View results res $x VAR CAT N CNT PCT 1 x 2 8 3 37.5 2 x 3 8 2 25.0 3 x 5 8 1 12.5 4 x 6 8 2 25.0 $y VAR CAT N CNT PCT 1 y A 8 3 37.5 2 y B 8 2 25.0 3 y C 8 3 37.5 $MyCross VAR1 VAR2 CAT1 CAT2 N CNT PCT 1 y x A 2 8 1 12.5 2 y x A 3 8 0 0.0 3 y x A 5 8 0 0.0 4 y x A 6 8 2 25.0 5 y x B 2 8 1 12.5 6 y x B 3 8 1 12.5 7 y x B 5 8 0 0.0 8 y x B 6 8 0 0.0 9 y x C 2 8 1 12.5 10 y x C 3 8 1 12.5 11 y x C 5 8 1 12.5 12 y x C 6 8 0 0.0 ``` Notice that the way output datasets are requested from the `proc_freq()` function is much simpler than the corresponding mechanism in SAS®. With `proc_freq()`, by default, all requested tables and statistics will be returned in a list. No other output parameters are needed. ## Data Shaping The `proc_freq()` function provides three options for shaping data: "wide", "long", and "stacked". These options control how the output data is organized. The options are also passed on the `output` parameter. The shaping options are best illustrated by an example: ```{r eval=FALSE, echo=TRUE} # Shape wide res1 <- proc_freq(dat, tables = y, output = wide) # Wide results res1 # VAR CAT N CNT PCT # 1 y A 8 3 37.5 # 2 y B 8 2 25.0 # 3 y C 8 3 37.5 # Shape long res2 <- proc_freq(dat, tables = y, output = long) # Long results res2 # VAR STAT A B C # 1 y N 8.0 8 8.0 # 2 y CNT 3.0 2 3.0 # 3 y PCT 37.5 25 37.5 # Shape stacked res3 <- proc_freq(dat, tables = y, output = stacked) # Stacked results res3 # VAR CAT STAT VALUES # 1 y A N 8.0 # 2 y A CNT 3.0 # 3 y A PCT 37.5 # 4 y B N 8.0 # 5 y B CNT 2.0 # 6 y B PCT 25.0 # 7 y C N 8.0 # 8 y C CNT 3.0 # 9 y C PCT 37.5 ``` As seen above, the "wide" option places the statistics in columns across the top of the dataset and the categories in rows. This shaping option is the default. The "long" option places the statistics in rows, with each category in columns. The "stacked" option places both the statistics and the categories in rows. These shaping options reduce some of the manipulation needed to get your data in the desired form. These options were added for convenience during the development of the **procs** package, and have no equivalent in SAS®. Next: [The Means Function](procs-means.html)