The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
The Frequency Function
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:
# 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.
# 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”.
# 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:
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:
# 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:
# 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:
# 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:
# 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:
# 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.
# 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:
# Request Chi-Square and Output datasets
res <- proc_freq(dat, tables = y * x, options = chisq)
# 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`
# CHISQ CHISQ.DF CHISQ.P
# 1 6.444444 6 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:
# 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:
# 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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.