The freqlist function

Tina Gunderson and Ethan Heinzen

13 June, 2019

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.

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 the addNA= argument was added to xtabs() in R 3.4.0. In previous versions, NAs have to be added to relevant columns using addNA().

### this works in R >= 3.4.0 summary(freqlist(~ arm + sex + mdquality.s, data =
### mockstudy, addNA = TRUE))

### This one is backwards-compatible
summary(freqlist(~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 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.

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 4.4
Female A: IFL 14 79 0.9 5.3
F: FOLFOX 25 104 1.7 7.0
G: IROX 11 115 0.7 7.7
Asian Male A: IFL 0 115 0.0 7.7
F: FOLFOX 10 125 0.7 8.4
G: IROX 1 126 0.1 8.4
Female A: IFL 1 127 0.1 8.5
F: FOLFOX 4 131 0.3 8.8
G: IROX 2 133 0.1 8.9
Caucasian Male A: IFL 240 373 16.1 25.0
F: FOLFOX 352 725 23.6 48.6
G: IROX 195 920 13.1 61.7
Female A: IFL 131 1051 8.8 70.4
F: FOLFOX 234 1285 15.7 86.1
G: IROX 136 1421 9.1 95.2
Hawaii/Pacific Male A: IFL 1 1422 0.1 95.3
F: FOLFOX 1 1423 0.1 95.4
G: IROX 0 1423 0.0 95.4
Female A: IFL 0 1423 0.0 95.4
F: FOLFOX 2 1425 0.1 95.5
G: IROX 1 1426 0.1 95.6
Hispanic Male A: IFL 8 1434 0.5 96.1
F: FOLFOX 17 1451 1.1 97.3
G: IROX 12 1463 0.8 98.1
Female A: IFL 4 1467 0.3 98.3
F: FOLFOX 11 1478 0.7 99.1
G: IROX 2 1480 0.1 99.2
Native-Am/Alaska Male A: IFL 1 1481 0.1 99.3
F: FOLFOX 0 1481 0.0 99.3
G: IROX 2 1483 0.1 99.4
Female A: IFL 1 1484 0.1 99.5
F: FOLFOX 1 1485 0.1 99.5
G: IROX 0 1485 0.0 99.5
Other Male A: IFL 2 1487 0.1 99.7
F: FOLFOX 2 1489 0.1 99.8
G: IROX 1 1490 0.1 99.9
Female A: IFL 0 1490 0.0 99.9
F: FOLFOX 2 1492 0.1 100.0
G: IROX 0 1492 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.

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
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
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.

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
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.



|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

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().

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.

Loading required package: xtable
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.

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.

, , 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 (in R >= 3.4.0) using the argument addNA = TRUE.

, , 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
, , 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.

, ,  = 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.

, , 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():

, , 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=.