Overview

freqlist() is a function meant to produce output similar to SAS’s PROC FREQ procedure when using the /list option of the TABLE statement. freqlist() provides options for handling missing or sparse data and can provide cumulative counts and percentages based on subgroups. It depends on the knitr package for printing.

require(arsenal)

Sample dataset

For our examples, we’ll load the mockstudy data included with this package and use it to create a basic table. Because they have fewer levels, for brevity, we’ll use the variables arm, sex, and mdquality.s to create the example table. We’ll retain NAs in the table creation. See the appendix for notes regarding default NA handling and other useful information regarding tables in R.

# load the data
data(mockstudy)

# retain NAs when creating the table using the useNA argument
tab.ex <- table(mockstudy[, c("arm", "sex", "mdquality.s")], useNA = "ifany")

The `freqlist` object

The freqlist() function is an S3 generic (with methods for tables and formulas) which returns an object of class "freqlist".

example1 <- freqlist(tab.ex)

str(example1)

List of 3
 $ Call   : language freqlist.table(object = tab.ex)
 $ control:List of 5
  ..$ sparse      : logi FALSE
  ..$ single      : logi FALSE
  ..$ dupLabels   : logi FALSE
  ..$ digits.count: int 0
  ..$ digits.pct  : int 2
 $ tables :List of 1
  ..$ :List of 6
  .. ..$ y         :List of 2
  .. .. ..$ term : chr ""
  .. .. ..$ label: chr ""
  .. ..$ strata    :List of 4
  .. .. ..$ term     : chr ""
  .. .. ..$ values   : chr ""
  .. .. ..$ label    : chr ""
  .. .. ..$ hasStrata: logi FALSE
  .. ..$ x         :List of 7
  .. .. ..$ arm        :List of 3
  .. .. .. ..$ variable: chr "arm"
  .. .. .. ..$ label   : chr "arm"
  .. .. .. ..$ term    : chr "arm"
  .. .. ..$ sex        :List of 3
  .. .. .. ..$ variable: chr "sex"
  .. .. .. ..$ label   : chr "sex"
  .. .. .. ..$ term    : chr "sex"
  .. .. ..$ mdquality.s:List of 3
  .. .. .. ..$ variable: chr "mdquality.s"
  .. .. .. ..$ label   : chr "mdquality.s"
  .. .. .. ..$ term    : chr "mdquality.s"
  .. .. ..$ Freq       :List of 3
  .. .. .. ..$ variable: chr "Freq"
  .. .. .. ..$ label   : chr "Freq"
  .. .. .. ..$ term    : chr "Freq"
  .. .. ..$ cumFreq    :List of 3
  .. .. .. ..$ variable: chr "cumFreq"
  .. .. .. ..$ label   : chr "Cumulative Freq"
  .. .. .. ..$ term    : chr "cumFreq"
  .. .. ..$ freqPercent:List of 3
  .. .. .. ..$ variable: chr "freqPercent"
  .. .. .. ..$ label   : chr "Percent"
  .. .. .. ..$ term    : chr "freqPercent"
  .. .. ..$ cumPercent :List of 3
  .. .. .. ..$ variable: chr "cumPercent"
  .. .. .. ..$ label   : chr "Cumulative Percent"
  .. .. .. ..$ term    : chr "cumPercent"
  .. ..$ tables    :List of 1
  .. .. ..$ :'data.frame':  18 obs. of  7 variables:
  .. .. .. ..$ arm        : Factor w/ 3 levels "A: IFL","F: FOLFOX",..: 1 1 1 1 1 1 2 2 2 2 ...
  .. .. .. ..$ sex        : Factor w/ 2 levels "Male","Female": 1 1 1 2 2 2 1 1 1 2 ...
  .. .. .. ..$ mdquality.s: Factor w/ 2 levels "0","1": 1 2 NA 1 2 NA 1 2 NA 1 ...
  .. .. .. ..$ Freq       : int [1:18] 29 214 34 12 118 21 31 285 95 21 ...
  .. .. .. ..$ cumFreq    : int [1:18] 29 243 277 289 407 428 459 744 839 860 ...
  .. .. .. ..$ freqPercent: num [1:18] 1.935 14.276 2.268 0.801 7.872 ...
  .. .. .. ..$ cumPercent : num [1:18] 1.93 16.21 18.48 19.28 27.15 ...
  .. ..$ hasWeights: logi FALSE
  .. ..$ na.options: chr "include"
 - attr(*, "class")= chr [1:2] "freqlist" "arsenal_table"

# view the data frame portion of freqlist output
head(as.data.frame(example1))  ## or use as.data.frame(example1)

     arm    sex mdquality.s Freq cumFreq freqPercent cumPercent
1 A: IFL   Male           0   29      29   1.9346231   1.934623
2 A: IFL   Male           1  214     243  14.2761841  16.210807
3 A: IFL   Male        <NA>   34     277   2.2681788  18.478986
4 A: IFL Female           0   12     289   0.8005337  19.279520
5 A: IFL Female           1  118     407   7.8719146  27.151434
6 A: IFL Female        <NA>   21     428   1.4009340  28.552368

Basic output using `summary()`

The summary method for freqlist() relies on the kable() function (in the knitr package) for printing. knitr::kable() converts the output to markdown which can be printed in the console or easily rendered in Word, PDF, or HTML documents.

Note that you must supply results="asis" to properly format the markdown output.

summary(example1)

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		NA	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		NA	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		NA	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		NA	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		NA	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		NA	17	1499	1.13	100.00

You can print a title for the table using the title= argument.

summary(example1, title = "Basic freqlist output")

Basic freqlist output
arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		NA	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		NA	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		NA	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		NA	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		NA	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		NA	17	1499	1.13	100.00

You can also easily pull out the freqlist data frame for more complicated formatting or manipulation (e.g. with another function such as xtable() or pander()) using as.data.frame(summary()):

head(as.data.frame(summary(example1)))

     arm    sex mdquality.s Freq Cumulative Freq Percent Cumulative Percent
1 A: IFL   Male           0   29              29    1.93               1.93
2                         1  214             243   14.28              16.21
3                      <NA>   34             277    2.27              18.48
4        Female           0   12             289    0.80              19.28
5                         1  118             407    7.87              27.15
6                      <NA>   21             428    1.40              28.55

Using a formula with `freqlist`

Instead of passing a pre-computed table to freqlist(), you can instead pass a formula, which will be in turn passed to the xtabs() function. Additional freqlist() arguments are passed through the ... to the freqlist() table method.

Note that freqlist() sets the addNA=TRUE argument by default:

summary(freqlist(~arm + sex + mdquality.s, data = mockstudy))

Treatment Arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		NA	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		NA	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		NA	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		NA	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		NA	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		NA	17	1499	1.13	100.00

One can also set NAs to an explicit value using includeNA().

summary(freqlist(~arm + sex + includeNA(mdquality.s, "Missing"), data = mockstudy))

Treatment Arm	sex	includeNA(mdquality.s, “Missing”)	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		Missing	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		Missing	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		Missing	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		Missing	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		Missing	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		Missing	17	1499	1.13	100.00

In fact, since xtabs() allows for left-hand-side weights, so does freqlist()!

mockstudy$weights <- c(10000, rep(1, nrow(mockstudy) - 1))
summary(freqlist(weights ~ arm + sex + addNA(mdquality.s), data = mockstudy))

Treatment Arm	sex	addNA(mdquality.s)	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	0.25	0.25
		1	214	243	1.86	2.11
		NA	34	277	0.30	2.41
	Female	0	12	289	0.10	2.51
		1	118	407	1.03	3.54
		NA	21	428	0.18	3.72
F: FOLFOX	Male	0	31	459	0.27	3.99
		1	285	744	2.48	6.47
		NA	10094	10838	87.79	94.26
	Female	0	21	10859	0.18	94.44
		1	198	11057	1.72	96.16
		NA	61	11118	0.53	96.70
G: IROX	Male	0	17	11135	0.15	96.84
		1	187	11322	1.63	98.47
		NA	24	11346	0.21	98.68
	Female	0	14	11360	0.12	98.80
		1	121	11481	1.05	99.85
		NA	17	11498	0.15	100.00

You can also specify multiple weights:

mockstudy$weights2 <- c(rep(1, nrow(mockstudy) - 1), 10000)
summary(freqlist(list(weights, weights2) ~ arm + sex + addNA(mdquality.s), data = mockstudy))

Treatment Arm	sex	addNA(mdquality.s)	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	0.25	0.25
		1	214	243	1.86	2.11
		NA	34	277	0.30	2.41
	Female	0	12	289	0.10	2.51
		1	118	407	1.03	3.54
		NA	21	428	0.18	3.72
F: FOLFOX	Male	0	31	459	0.27	3.99
		1	285	744	2.48	6.47
		NA	10094	10838	87.79	94.26
	Female	0	21	10859	0.18	94.44
		1	198	11057	1.72	96.16
		NA	61	11118	0.53	96.70
G: IROX	Male	0	17	11135	0.15	96.84
		1	187	11322	1.63	98.47
		NA	24	11346	0.21	98.68
	Female	0	14	11360	0.12	98.80
		1	121	11481	1.05	99.85
		NA	17	11498	0.15	100.00

Treatment Arm	sex	addNA(mdquality.s)	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	0.25	0.25
		1	214	243	1.86	2.11
		NA	34	277	0.30	2.41
	Female	0	12	289	0.10	2.51
		1	118	407	1.03	3.54
		NA	21	428	0.18	3.72
F: FOLFOX	Male	0	31	459	0.27	3.99
		1	285	744	2.48	6.47
		NA	95	839	0.83	7.30
	Female	0	21	860	0.18	7.48
		1	198	1058	1.72	9.20
		NA	10060	11118	87.49	96.70
G: IROX	Male	0	17	11135	0.15	96.84
		1	187	11322	1.63	98.47
		NA	24	11346	0.21	98.68
	Female	0	14	11360	0.12	98.80
		1	121	11481	1.05	99.85
		NA	17	11498	0.15	100.00

Rounding percentage digits or changing variable names for printing

The digits.pct= argument takes a single numeric value and controls the number of digits of percentages in the output. The digits.count= argument takes a similar argument and controls the number of digits of the count columns. The labelTranslations= argument is a named character vector or list. Both options are applied in the following example.

example2 <- freqlist(tab.ex, labelTranslations = c(arm = "Treatment Arm", sex = "Gender", 
    mdquality.s = "LASA QOL"), digits.pct = 1, digits.count = 1)
summary(example2)

Treatment Arm	Gender	LASA QOL	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29.0	29.0	1.9	1.9
		1	214.0	243.0	14.3	16.2
		NA	34.0	277.0	2.3	18.5
	Female	0	12.0	289.0	0.8	19.3
		1	118.0	407.0	7.9	27.2
		NA	21.0	428.0	1.4	28.6
F: FOLFOX	Male	0	31.0	459.0	2.1	30.6
		1	285.0	744.0	19.0	49.6
		NA	95.0	839.0	6.3	56.0
	Female	0	21.0	860.0	1.4	57.4
		1	198.0	1058.0	13.2	70.6
		NA	61.0	1119.0	4.1	74.6
G: IROX	Male	0	17.0	1136.0	1.1	75.8
		1	187.0	1323.0	12.5	88.3
		NA	24.0	1347.0	1.6	89.9
	Female	0	14.0	1361.0	0.9	90.8
		1	121.0	1482.0	8.1	98.9
		NA	17.0	1499.0	1.1	100.0

Additional examples

Including combinations with frequencies of zero

The sparse= argument takes a single logical value as input. The default option is FALSE. If set to TRUE, the sparse option will include combinations with frequencies of zero in the list of results. As our initial table did not have any such levels, we create a second table to use in our example.

summary(freqlist(~race + sex + arm, data = mockstudy, sparse = TRUE, digits.pct = 1))

Race	sex	Treatment Arm	Freq	Cumulative Freq	Percent	Cumulative Percent
African-Am	Male	A: IFL	25	25	1.7	1.7
		F: FOLFOX	24	49	1.6	3.3
		G: IROX	16	65	1.1	4.3
	Female	A: IFL	14	79	0.9	5.3
		F: FOLFOX	25	104	1.7	6.9
		G: IROX	11	115	0.7	7.7
Asian	Male	A: IFL	0	115	0.0	7.7
		F: FOLFOX	10	125	0.7	8.3
		G: IROX	1	126	0.1	8.4
	Female	A: IFL	1	127	0.1	8.5
		F: FOLFOX	4	131	0.3	8.7
		G: IROX	2	133	0.1	8.9
Caucasian	Male	A: IFL	240	373	16.0	24.9
		F: FOLFOX	352	725	23.5	48.4
		G: IROX	195	920	13.0	61.4
	Female	A: IFL	131	1051	8.7	70.1
		F: FOLFOX	234	1285	15.6	85.7
		G: IROX	136	1421	9.1	94.8
Hawaii/Pacific	Male	A: IFL	1	1422	0.1	94.9
		F: FOLFOX	1	1423	0.1	94.9
		G: IROX	0	1423	0.0	94.9
	Female	A: IFL	0	1423	0.0	94.9
		F: FOLFOX	2	1425	0.1	95.1
		G: IROX	1	1426	0.1	95.1
Hispanic	Male	A: IFL	8	1434	0.5	95.7
		F: FOLFOX	17	1451	1.1	96.8
		G: IROX	12	1463	0.8	97.6
	Female	A: IFL	4	1467	0.3	97.9
		F: FOLFOX	11	1478	0.7	98.6
		G: IROX	2	1480	0.1	98.7
Native-Am/Alaska	Male	A: IFL	1	1481	0.1	98.8
		F: FOLFOX	0	1481	0.0	98.8
		G: IROX	2	1483	0.1	98.9
	Female	A: IFL	1	1484	0.1	99.0
		F: FOLFOX	1	1485	0.1	99.1
		G: IROX	0	1485	0.0	99.1
Other	Male	A: IFL	2	1487	0.1	99.2
		F: FOLFOX	2	1489	0.1	99.3
		G: IROX	1	1490	0.1	99.4
	Female	A: IFL	0	1490	0.0	99.4
		F: FOLFOX	2	1492	0.1	99.5
		G: IROX	0	1492	0.0	99.5
NA	Male	A: IFL	0	1492	0.0	99.5
		F: FOLFOX	5	1497	0.3	99.9
		G: IROX	1	1498	0.1	99.9
	Female	A: IFL	0	1498	0.0	99.9
		F: FOLFOX	1	1499	0.1	100.0
		G: IROX	0	1499	0.0	100.0

Options for NA handling

The various na.options= allow you to include or exclude data with missing values for one or more factor levels in the counts and percentages, as well as show the missing data but exclude it from the cumulative counts and percentages. The default option is to include all combinations with missing values.

summary(freqlist(tab.ex, na.options = "include"))

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		NA	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		NA	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		NA	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		NA	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		NA	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		NA	17	1499	1.13	100.00

summary(freqlist(tab.ex, na.options = "showexclude"))

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	2.33	2.33
		1	214	243	17.16	19.49
		NA	34	NA	NA	NA
	Female	0	12	255	0.96	20.45
		1	118	373	9.46	29.91
		NA	21	NA	NA	NA
F: FOLFOX	Male	0	31	404	2.49	32.40
		1	285	689	22.85	55.25
		NA	95	NA	NA	NA
	Female	0	21	710	1.68	56.94
		1	198	908	15.88	72.81
		NA	61	NA	NA	NA
G: IROX	Male	0	17	925	1.36	74.18
		1	187	1112	15.00	89.17
		NA	24	NA	NA	NA
	Female	0	14	1126	1.12	90.30
		1	121	1247	9.70	100.00
		NA	17	NA	NA	NA

summary(freqlist(tab.ex, na.options = "remove"))

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	2.33	2.33
		1	214	243	17.16	19.49
	Female	0	12	255	0.96	20.45
		1	118	373	9.46	29.91
F: FOLFOX	Male	0	31	404	2.49	32.40
		1	285	689	22.85	55.25
	Female	0	21	710	1.68	56.94
		1	198	908	15.88	72.81
G: IROX	Male	0	17	925	1.36	74.18
		1	187	1112	15.00	89.17
	Female	0	14	1126	1.12	90.30
		1	121	1247	9.70	100.00

Frequency counts and percentages subset by factor levels

The strata= argument internally subsets the data by the specified factor prior to calculating cumulative counts and percentages. By default, when used each subset will print in a separate table. Using the single = TRUE option when printing will collapse the subsetted result into a single table.

example3 <- freqlist(tab.ex, strata = c("arm", "sex"))
summary(example3)

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	10.47	10.47
		1	214	243	77.26	87.73
		NA	34	277	12.27	100.00

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Female	0	12	12	7.95	7.95
		1	118	130	78.15	86.09
		NA	21	151	13.91	100.00

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
F: FOLFOX	Male	0	31	31	7.54	7.54
		1	285	316	69.34	76.89
		NA	95	411	23.11	100.00

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
F: FOLFOX	Female	0	21	21	7.50	7.50
		1	198	219	70.71	78.21
		NA	61	280	21.79	100.00

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
G: IROX	Male	0	17	17	7.46	7.46
		1	187	204	82.02	89.47
		NA	24	228	10.53	100.00

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
G: IROX	Female	0	14	14	9.21	9.21
		1	121	135	79.61	88.82
		NA	17	152	11.18	100.00

# using the single = TRUE argument will collapse results into a single table for
# printing
summary(example3, single = TRUE)

arm	sex	mdquality.s	Freq	Cumulative Freq	Percent	Cumulative Percent
A: IFL	Male	0	29	29	10.47	10.47
		1	214	243	77.26	87.73
		NA	34	277	12.27	100.00
	Female	0	12	12	7.95	7.95
		1	118	130	78.15	86.09
		NA	21	151	13.91	100.00
F: FOLFOX	Male	0	31	31	7.54	7.54
		1	285	316	69.34	76.89
		NA	95	411	23.11	100.00
	Female	0	21	21	7.50	7.50
		1	198	219	70.71	78.21
		NA	61	280	21.79	100.00
G: IROX	Male	0	17	17	7.46	7.46
		1	187	204	82.02	89.47
		NA	24	228	10.53	100.00
	Female	0	14	14	9.21	9.21
		1	121	135	79.61	88.82
		NA	17	152	11.18	100.00

Show only the “n” most common combinations in each table (`head()` and `sort()`)

You can now sort freqlist() objects, and, by taking the head() of the summary, output the most common frequencies. This looks the prettiest with dupLabels=TRUE.

head(summary(sort(example1, decreasing = TRUE), dupLabels = TRUE))



|arm       |sex    |mdquality.s | Freq| Cumulative Freq| Percent| Cumulative Percent|
|:---------|:------|:-----------|----:|---------------:|-------:|------------------:|
|F: FOLFOX |Male   |1           |  285|             285|   19.01|              19.01|
|A: IFL    |Male   |1           |  214|             499|   14.28|              33.29|
|F: FOLFOX |Female |1           |  198|             697|   13.21|              46.50|
|G: IROX   |Male   |1           |  187|             884|   12.47|              58.97|
|G: IROX   |Female |1           |  121|            1005|    8.07|              67.04|
|A: IFL    |Female |1           |  118|            1123|    7.87|              74.92|

Change labels on the fly

labs <- c(arm = "Arm", sex = "Sex", mdquality.s = "QOL", freqPercent = "%")
labels(example1) <- labs
summary(example1)

Arm	Sex	QOL	Freq	Cumulative Freq	%	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		NA	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		NA	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		NA	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		NA	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		NA	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		NA	17	1499	1.13	100.00

You can also supply labelTranslations= to summary().

summary(example1, labelTranslations = labs)

Arm	Sex	QOL	Freq	Cumulative Freq	%	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
		NA	34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
		NA	21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
		NA	95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
		NA	61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
		NA	24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
		NA	17	1499	1.13	100.00

Using `xtable()` to format and print `freqlist()` results

Fair warning: xtable() has kind of a steep learning curve. These examples are given without explanation, for more advanced users.

require(xtable)

Loading required package: xtable

# set up custom function for xtable text
italic <- function(x) paste0("<i>", x, "</i>")

xftbl <- xtable(as.data.frame(summary(example1)), caption = "xtable formatted output of freqlist data frame", 
    align = "|r|r|r|r|c|c|c|r|")

# change the column names
names(xftbl)[1:3] <- c("Arm", "Gender", "LASA QOL")

print(xftbl, sanitize.colnames.function = italic, include.rownames = FALSE, type = "html", 
    comment = FALSE)

xtable formatted output of freqlist data frame
Arm	Gender	LASA QOL	Freq	Cumulative Freq	%	Cumulative Percent
A: IFL	Male	0	29	29	1.93	1.93
		1	214	243	14.28	16.21
			34	277	2.27	18.48
	Female	0	12	289	0.80	19.28
		1	118	407	7.87	27.15
			21	428	1.40	28.55
F: FOLFOX	Male	0	31	459	2.07	30.62
		1	285	744	19.01	49.63
			95	839	6.34	55.97
	Female	0	21	860	1.40	57.37
		1	198	1058	13.21	70.58
			61	1119	4.07	74.65
G: IROX	Male	0	17	1136	1.13	75.78
		1	187	1323	12.47	88.26
			24	1347	1.60	89.86
	Female	0	14	1361	0.93	90.79
		1	121	1482	8.07	98.87
			17	1499	1.13	100.00

Use `freqlist` in bookdown

Since the backbone of freqlist() is knitr::kable(), tables still render well in bookdown. However, print.summary.freqlist() doesn’t use the caption= argument of kable(), so some tables may not have a properly numbered caption. To fix this, use the method described on the bookdown site to give the table a tag/ID.

summary(freqlist(~sex + age, data = mockstudy), title = "(\\#tab:mytableby) Caption here")

Appendix: Notes regarding table options in R

NAs

There are several widely used options for basic tables in R. The table() function in base R is probably the most common; by default it excludes NA values. You can change NA handling in base::table() using the useNA= or exclude= arguments.

# base table default removes NAs
tab.d1 <- base::table(mockstudy[, c("arm", "sex", "mdquality.s")], useNA = "ifany")
tab.d1

, , mdquality.s = 0

           sex
arm         Male Female
  A: IFL      29     12
  F: FOLFOX   31     21
  G: IROX     17     14

, , mdquality.s = 1

           sex
arm         Male Female
  A: IFL     214    118
  F: FOLFOX  285    198
  G: IROX    187    121

, , mdquality.s = NA

           sex
arm         Male Female
  A: IFL      34     21
  F: FOLFOX   95     61
  G: IROX     24     17

xtabs() is similar to table(), but uses a formula-based syntax. However, NAs must be explicitly added to each factor using the addNA() function or using the argument addNA = TRUE.

# without specifying addNA
tab.d2 <- xtabs(formula = ~arm + sex + mdquality.s, data = mockstudy)
tab.d2

, , mdquality.s = 0

           sex
arm         Male Female
  A: IFL      29     12
  F: FOLFOX   31     21
  G: IROX     17     14

, , mdquality.s = 1

           sex
arm         Male Female
  A: IFL     214    118
  F: FOLFOX  285    198
  G: IROX    187    121

# now with addNA
tab.d3 <- xtabs(~arm + sex + addNA(mdquality.s), data = mockstudy)
tab.d3

, , addNA(mdquality.s) = 0

           sex
arm         Male Female
  A: IFL      29     12
  F: FOLFOX   31     21
  G: IROX     17     14

, , addNA(mdquality.s) = 1

           sex
arm         Male Female
  A: IFL     214    118
  F: FOLFOX  285    198
  G: IROX    187    121

, , addNA(mdquality.s) = NA

           sex
arm         Male Female
  A: IFL      34     21
  F: FOLFOX   95     61
  G: IROX     24     17

Since the formula method of freqlist() uses xtabs(), NAs should be treated in the same way. includeNA() can also be helpful here for setting explicit NA values.

Table dimname names (dnn)

Supplying a data.frame to the table() function without giving columns individually will create a contingency table using all variables in the data.frame.

However, if the columns of a data.frame or matrix are supplied separately (i.e., as vectors), column names will not be preserved.

# providing variables separately (as vectors) drops column names
table(mockstudy$arm, mockstudy$sex, mockstudy$mdquality.s)

, ,  = 0

           
            Male Female
  A: IFL      29     12
  F: FOLFOX   31     21
  G: IROX     17     14

, ,  = 1

           
            Male Female
  A: IFL     214    118
  F: FOLFOX  285    198
  G: IROX    187    121

If desired, you can use the dnn= argument to pass variable names.

# add the column name labels back using dnn option in base::table
table(mockstudy$arm, mockstudy$sex, mockstudy$mdquality.s, dnn = c("Arm", "Sex", 
    "QOL"))

, , QOL = 0

           Sex
Arm         Male Female
  A: IFL      29     12
  F: FOLFOX   31     21
  G: IROX     17     14

, , QOL = 1

           Sex
Arm         Male Female
  A: IFL     214    118
  F: FOLFOX  285    198
  G: IROX    187    121

You can also name the arguments to table():

table(Arm = mockstudy$arm, Sex = mockstudy$sex, QOL = mockstudy$mdquality.s)

, , QOL = 0

           Sex
Arm         Male Female
  A: IFL      29     12
  F: FOLFOX   31     21
  G: IROX     17     14

, , QOL = 1

           Sex
Arm         Male Female
  A: IFL     214    118
  F: FOLFOX  285    198
  G: IROX    187    121

If using freqlist(), you can provide the labels directly to freqlist() or to summary() using labelTranslations=.

The freqlist function

Tina Gunderson and Ethan Heinzen

Overview

Sample dataset

The `freqlist` object

Basic output using `summary()`

Using a formula with `freqlist`

Rounding percentage digits or changing variable names for printing

Additional examples

Including combinations with frequencies of zero

Options for NA handling

Frequency counts and percentages subset by factor levels

Show only the “n” most common combinations in each table (`head()` and `sort()`)

Change labels on the fly

Using `xtable()` to format and print `freqlist()` results

Use `freqlist` in bookdown

Appendix: Notes regarding table options in R

NAs

Table dimname names (dnn)

The freqlist function

Tina Gunderson and Ethan Heinzen

Overview

Sample dataset

The freqlist object

Basic output using summary()

Using a formula with freqlist

Rounding percentage digits or changing variable names for printing

Additional examples

Including combinations with frequencies of zero

Options for NA handling

Frequency counts and percentages subset by factor levels

Show only the “n” most common combinations in each table (head() and sort())

Change labels on the fly

Using xtable() to format and print freqlist() results

Use freqlist in bookdown

Appendix: Notes regarding table options in R

NAs

Table dimname names (dnn)

The `freqlist` object

Basic output using `summary()`

Using a formula with `freqlist`

Show only the “n” most common combinations in each table (`head()` and `sort()`)

Using `xtable()` to format and print `freqlist()` results

Use `freqlist` in bookdown