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.
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.
freqlist
objectThe freqlist()
function is an S3 generic (with methods
for tables and formulas) which returns an object of class
"freqlist"
.
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
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.
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.
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())
:
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
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:
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()
.
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 |
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 |
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 | 4.3 | ||
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 |
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 |
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 |
#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 |
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|
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()
.
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 |
xtable()
to format and print
freqlist()
resultsFair warning: xtable()
has kind of a steep learning
curve. These examples are given without explanation, for more advanced
users.
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)
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 |
freqlist
in bookdownSince 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.
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
, , 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.
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()
:
, , 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=
.
freqlist
objectsummary()
freqlist
head()
and sort()
)xtable()
to format and print freqlist()
resultsfreqlist
in
bookdown