Introduction

One of the most common tables in medical literature includes summary statistics for a set of variables, often stratified by some group (e.g. treatment arm). Locally at Mayo, the SAS macros %table and %summary were written to create summary tables with a single call. With the increasing interest in R, we have developed the function tableby to create similar tables within the R environment.

In developing the tableby() function, the goal was to bring the best features of these macros into an R function. However, the task was not simply to duplicate all the functionality, but rather to make use of R’s strengths (modeling, method dispersion, flexibility in function definition and output format) and make a tool that fits the needs of R users. Additionally, the results needed to fit within the general reproducible research framework so the tables could be displayed within an R markdown report.

This report provides step-by-step directions for using the functions associated with tableby(). All functions presented here are available within the arsenal package. An assumption is made that users are somewhat familiar with R Markdown documents. For those who are new to the topic, a good initial resource is available at rmarkdown.rstudio.com.

Simple Example

The first step when using the tableby function is to load the arsenal package. All the examples in this report use a dataset called mockstudy made available by Paul Novotny which includes a variety of types of variables (character, numeric, factor, ordered factor, survival) to use as examples.

## Loading required package: knitr
## Loading required package: survival
data(mockstudy) ##load data
dim(mockstudy)  ##look at how many subjects and variables are in the dataset 
## [1] 1499   14
# help(mockstudy) ##learn more about the dataset and variables
str(mockstudy) ##quick look at the data
## 'data.frame':    1499 obs. of  14 variables:
##  $ case       : int  110754 99706 105271 105001 112263 86205 99508 90158 88989 90515 ...
##  $ age        : int  67 74 50 71 69 56 50 57 51 63 ...
##   ..- attr(*, "label")= chr "Age in Years"
##  $ arm        : chr  "F: FOLFOX" "A: IFL" "A: IFL" "G: IROX" ...
##   ..- attr(*, "label")= chr "Treatment Arm"
##  $ sex        : Factor w/ 2 levels "Male","Female": 1 2 2 2 2 1 1 1 2 1 ...
##  $ race       : chr  "Caucasian" "Caucasian" "Caucasian" "Caucasian" ...
##   ..- attr(*, "label")= chr "Race"
##  $ fu.time    : int  922 270 175 128 233 120 369 421 387 363 ...
##  $ fu.stat    : int  2 2 2 2 2 2 2 2 2 2 ...
##  $ ps         : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ hgb        : num  11.5 10.7 11.1 12.6 13 10.2 13.3 12.1 13.8 12.1 ...
##  $ bmi        : num  25.1 19.5 NA 29.4 26.4 ...
##   ..- attr(*, "label")= chr "Body Mass Index (kg/m^2)"
##  $ alk.phos   : int  160 290 700 771 350 569 162 152 231 492 ...
##  $ ast        : int  35 52 100 68 35 27 16 12 25 18 ...
##  $ mdquality.s: int  NA 1 1 1 NA 1 1 1 1 1 ...
##  $ age.ord    : Ord.factor w/ 8 levels "10-19"<"20-29"<..: 6 7 4 7 6 5 4 5 5 6 ...

To create a simple table stratified by treatment arm, use a formula statement to specify the variables that you want summarized. The example below uses age (a continuous variable) and sex (a factor).

tab1 <- tableby(arm ~ sex + age, data=mockstudy)

If you want to take a quick look at the table, you can use summary() on your tableby object and the table will print out as text in your R console window. If you use summary() without any options you will see a number of \(\&nbsp;\) statements which translates to “space” in HTML.

Pretty text version of table

If you want a nicer version in your console window then add the text=TRUE option.

summary(tab1, text=TRUE)
## 
## 
## |             | A: IFL (N=428)  | F: FOLFOX (N=691) | G: IROX (N=380) | Total (N=1499)  | p value|
## |:------------|:---------------:|:-----------------:|:---------------:|:---------------:|-------:|
## |sex          |                 |                   |                 |                 |   0.190|
## |-  Male      |   277 (64.7%)   |    411 (59.5%)    |   228 (60.0%)   |   916 (61.1%)   |        |
## |-  Female    |   151 (35.3%)   |    280 (40.5%)    |   152 (40.0%)   |   583 (38.9%)   |        |
## |Age in Years |                 |                   |                 |                 |   0.614|
## |-  Mean (SD) | 59.673 (11.365) |  60.301 (11.632)  | 59.763 (11.499) | 59.985 (11.519) |        |
## |-  Range     | 27.000 - 88.000 |  19.000 - 88.000  | 26.000 - 85.000 | 19.000 - 88.000 |        |

Pretty Rmarkdown version of table

In order for the report to look nice within an R markdown (knitr) report, you just need to specify results="asis" when creating the R chunk. This changes the layout slightly (compresses it) and bolds the variable names.

summary(tab1)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
sex 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age in Years 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000

Data frame version of table

If you want a data.frame version, simply use as.data.frame.

##   group.term   group.label strata.term variable     term        label variable.type
## 1        arm Treatment Arm                  sex      sex          sex   categorical
## 2        arm Treatment Arm                  sex countpct         Male   categorical
## 3        arm Treatment Arm                  sex countpct       Female   categorical
## 4        arm Treatment Arm                  age      age Age in Years       numeric
## 5        arm Treatment Arm                  age   meansd    Mean (SD)       numeric
## 6        arm Treatment Arm                  age    range        Range       numeric
##                A: IFL           F: FOLFOX            G: IROX              Total
## 1                                                                              
## 2 277.00000, 64.71963 411.00000, 59.47902            228, 60  916.0000, 61.1074
## 3 151.00000, 35.28037 280.00000, 40.52098            152, 40  583.0000, 38.8926
## 4                                                                              
## 5  59.67290, 11.36454  60.30101, 11.63225 59.76316, 11.49930 59.98532, 11.51877
## 6              27, 88              19, 88             26, 85             19, 88
##                         test   p.value
## 1 Pearson's Chi-squared test 0.1904388
## 2 Pearson's Chi-squared test 0.1904388
## 3 Pearson's Chi-squared test 0.1904388
## 4         Linear Model ANOVA 0.6143859
## 5         Linear Model ANOVA 0.6143859
## 6         Linear Model ANOVA 0.6143859

Summaries using standard R code

## base R frequency example
tmp <- table(Gender=mockstudy$sex, "Study Arm"=mockstudy$arm)
tmp
##         Study Arm
## Gender   A: IFL F: FOLFOX G: IROX
##   Male      277       411     228
##   Female    151       280     152
# Note: The continuity correction is applied by default in R (not used in %table)
chisq.test(tmp)
## 
##  Pearson's Chi-squared test
## 
## data:  tmp
## X-squared = 3.3168, df = 2, p-value = 0.1904
## base R numeric summary example
tapply(mockstudy$age, mockstudy$arm, summary)
## $`A: IFL`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   27.00   53.00   61.00   59.67   68.00   88.00 
## 
## $`F: FOLFOX`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    19.0    52.0    61.0    60.3    69.0    88.0 
## 
## $`G: IROX`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   26.00   52.00   61.00   59.76   68.00   85.00
summary(aov(age ~ arm, data=mockstudy))
##               Df Sum Sq Mean Sq F value Pr(>F)
## arm            2    129    64.7   0.487  0.614
## Residuals   1496 198628   132.8

Modifying Output

Add labels

In the above example, age is shown with a label (Age in Years), but sex is listed “as is” with lower case letters. This is because the data was created in SAS and in the SAS dataset, age had a label but sex did not. The label is stored as an attribute within R.

## Look at one variable's label
attr(mockstudy$age,'label')
## [1] "Age in Years"
## See all the variables with a label
unlist(lapply(mockstudy,'attr','label'))
##                        age                        arm                       race 
##             "Age in Years"            "Treatment Arm"                     "Race" 
##                        bmi 
## "Body Mass Index (kg/m^2)"
# Can also use labels(mockstudy)

If you want to add labels to other variables, there are a couple of options. First, you could add labels to the variables in your dataset.

attr(mockstudy$sex,'label')  <- 'Gender'

tab1 <- tableby(arm ~ sex + age, data=mockstudy)
summary(tab1)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age in Years 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000

You can also use the built-in data.frame method for labels<-:

labels(mockstudy)  <- c(age = 'Age, yrs', sex = "Gender")

tab1 <- tableby(arm ~ sex + age, data=mockstudy)
summary(tab1)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000

Another option is to add labels after you have created the table

mylabels <- list(sex = "SEX", age = "Age, yrs")
summary(tab1, labelTranslations = mylabels)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
SEX 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000

Alternatively, you can check the variable labels and manipulate them with a function called labels, which works on the tableby object.

labels(tab1)
##             arm             sex             age 
## "Treatment Arm"        "Gender"      "Age, yrs"
labels(tab1) <- c(arm="Treatment Assignment", age="Baseline Age (yrs)")
labels(tab1)
##                    arm                    sex                    age 
## "Treatment Assignment"               "Gender"   "Baseline Age (yrs)"
summary(tab1)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Baseline Age (yrs) 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000

Change summary statistics globally

Currently the default behavior is to summarize continuous variables with: Number of missing values, Mean (SD), 25th - 75th quantiles, and Minimum-Maximum values with an ANOVA (t-test with equal variances) p-value. For categorical variables the default is to show: Number of missing values and count (column percent) with a chi-square p-value. This behavior can be modified using the tableby.control function. In fact, you can save your standard settings and use that for future tables. Note that test=FALSE and total=FALSE results in the total column and p-value column not being printed.

mycontrols  <- tableby.control(test=FALSE, total=FALSE,
                               numeric.test="kwt", cat.test="chisq",
                               numeric.stats=c("N", "median", "q1q3"),
                               cat.stats=c("countpct"),
                               stats.labels=list(N='Count', median='Median', q1q3='Q1,Q3'))
tab2 <- tableby(arm ~ sex + age, data=mockstudy, control=mycontrols)
summary(tab2)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380)
Gender
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%)
Age, yrs
   Count 428 691 380
   Median 61.000 61.000 61.000
   Q1,Q3 53.000, 68.000 52.000, 69.000 52.000, 68.000

You can also change these settings directly in the tableby call.

tab3 <- tableby(arm ~ sex + age, data=mockstudy, test=FALSE, total=FALSE, 
                numeric.stats=c("median","q1q3"), numeric.test="kwt")
summary(tab3)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380)
Gender
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%)
Age, yrs
   Median 61.000 61.000 61.000
   Q1, Q3 53.000, 68.000 52.000, 69.000 52.000, 68.000

Change summary statistics within the formula

In addition to modifying summary options globally, it is possible to modify the test and summary statistics for specific variables within the formula statement. For example, both the kwt (Kruskal-Wallis rank-based) and anova (asymptotic analysis of variance) tests apply to numeric variables, and we can use one for the variable “age”, another for the variable “bmi”, and no test for the variable “ast”. A list of all the options is shown at the end of the vignette.

The tests function can do a quick check on what tests were performed on each variable in tableby.

tab.test <- tableby(arm ~ kwt(age) + anova(bmi) + notest(ast), data=mockstudy)
tests(tab.test)
##           Group Variable   p.value                       Method
## 1 Treatment Arm      age 0.6390614 Kruskal-Wallis rank sum test
## 2 Treatment Arm      bmi 0.8916552           Linear Model ANOVA
## 3 Treatment Arm      ast        NA                      No test
summary(tab.test)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.639
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.892
   N-Miss 9 20 4 33
   Mean (SD) 27.290 (5.552) 27.210 (5.173) 27.106 (5.751) 27.206 (5.432)
   Range 14.053 - 53.008 16.649 - 49.130 15.430 - 60.243 14.053 - 60.243
ast
   N-Miss 69 141 56 266
   Mean (SD) 37.292 (28.036) 35.202 (26.659) 35.670 (25.807) 35.933 (26.843)
   Range 10.000 - 205.000 7.000 - 174.000 5.000 - 176.000 5.000 - 205.000

Summary statistics for any individual variable can also be modified, but it must be done as secondary arguments to the test function. The function names must be strings that are functions already written for tableby, built-in R functions like mean and range, or user-defined functions.

tab.test <- tableby(arm ~ kwt(ast, "Nmiss2","median") + anova(age, "N","mean") +
                    notest(bmi, "Nmiss","median"), data=mockstudy)
summary(tab.test)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
ast 0.039
   N-Miss 69 141 56 266
   Median 29.000 25.500 27.000 27.000
Age, yrs 0.614
   N 428 691 380 1499
   Mean 59.673 60.301 59.763 59.985
Body Mass Index (kg/m^2)
   N-Miss 9 20 4 33
   Median 26.234 26.525 25.978 26.325

Controlling Options for Categorical Tests (Chisq and Fisher’s)

The formal tests for categorical variables against the levels of the by variable, chisq and fe, have options to simulate p-values. We show how to turn on the simulations for these with 500 replicates for the Fisher’s test (fe).

set.seed(100)
tab.catsim <- tableby(arm ~ sex + race, cat.test="fe", simulate.p.value=TRUE, B=500, data=mockstudy)
tests(tab.catsim)
      Group Variable   p.value

1 Treatment Arm sex 0.2195609 2 Treatment Arm race 0.3093812 Method 1 Fisher’s Exact Test for Count Data with simulated p-valuebased on 500 replicates) 2 Fisher’s Exact Test for Count Data with simulated p-valuebased on 500 replicates)

The chi-square test on 2x2 tables applies Yates’ continuity correction by default, so we provide an option to turn off the correction. We show the results with and without the correction that is applied to treatment arm by sex, if we use subset to ignore one of the three treatment arms.

cat.correct <- tableby(arm ~ sex + race, cat.test="chisq", subset = !grepl("^F", arm), data=mockstudy)
tests(cat.correct)
      Group Variable   p.value                     Method

1 Treatment Arm sex 0.1666280 Pearson’s Chi-squared test 2 Treatment Arm race 0.8108543 Pearson’s Chi-squared test

cat.nocorrect <- tableby(arm ~ sex + race, cat.test="chisq", subset = !grepl("^F", arm),
     chisq.correct=FALSE, data=mockstudy)
tests(cat.nocorrect)
      Group Variable   p.value                     Method

1 Treatment Arm sex 0.1666280 Pearson’s Chi-squared test 2 Treatment Arm race 0.8108543 Pearson’s Chi-squared test

Modifying the look & feel in Word documents

You can easily create Word versions of tableby output via an Rmarkdown report and the default options will give you a reasonable table in Word - just select the “Knit Word” option in RStudio.

The functionality listed in this next paragraph is coming soon but needs an upgraded version of RStudio If you want to modify fonts used for the table, then you’ll need to add an extra line to your header at the beginning of your file. You can take the WordStylesReference01.docx file and modify the fonts (storing the format preferences in your project directory). To see how this works, run your report once using WordStylesReference01.docx and then WordStylesReference02.docx.

output: word_document
  reference_docx: /projects/bsi/gentools/R/lib320/arsenal/doc/WordStylesReference01.docx 

For more information on changing the look/feel of your Word document, see the Rmarkdown documentation website.

Additional Examples

Here are multiple examples showing how to use some of the different options.

1. Summarize without a group/by variable

tab.noby <- tableby(~ bmi + sex + age, data=mockstudy)
summary(tab.noby)
Overall (N=1499)
Body Mass Index (kg/m^2)
   N-Miss 33
   Mean (SD) 27.206 (5.432)
   Range 14.053 - 60.243
Gender
   Male 916 (61.1%)
   Female 583 (38.9%)
Age, yrs
   Mean (SD) 59.985 (11.519)
   Range 19.000 - 88.000

2. Display footnotes indicating which “test” was used

summary(tab.test, pfootnote=TRUE)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
ast 0.0391
   N-Miss 69 141 56 266
   Median 29.000 25.500 27.000 27.000
Age, yrs 0.6142
   N 428 691 380 1499
   Mean 59.673 60.301 59.763 59.985
Body Mass Index (kg/m^2)
   N-Miss 9 20 4 33
   Median 26.234 26.525 25.978 26.325
  1. Kruskal-Wallis rank sum test
  2. Linear Model ANOVA

3. Summarize an ordered factor

When comparing groups of ordered data there are a couple of options. The default uses a general independence test available from the coin package. For two-group comparisons, this is essentially the Armitage trend test. The other option is to specify the Kruskal Wallis test. The example below shows both options.

mockstudy$age.ordnew <- ordered(c("a",NA,as.character(mockstudy$age.ord[-(1:2)])))
table(mockstudy$age.ord, mockstudy$sex)
##        
##         Male Female
##   10-19    1      0
##   20-29    8     11
##   30-39   37     30
##   40-49  127     83
##   50-59  257    179
##   60-69  298    170
##   70-79  168    101
##   80-89   20      9
table(mockstudy$age.ordnew, mockstudy$sex)
##        
##         Male Female
##   10-19    1      0
##   20-29    8     11
##   30-39   37     30
##   40-49  127     83
##   50-59  257    179
##   60-69  297    170
##   70-79  168    100
##   80-89   20      9
##   a        1      0
class(mockstudy$age.ord)
## [1] "ordered" "factor"
summary(tableby(sex ~ age.ordnew, data = mockstudy), pfootnote = TRUE)
Male (N=916) Female (N=583) Total (N=1499) p value
age.ordnew 0.0401
   N-Miss 0 1 1
   10-19 1 (0.1%) 0 (0.0%) 1 (0.1%)
   20-29 8 (0.9%) 11 (1.9%) 19 (1.3%)
   30-39 37 (4.0%) 30 (5.2%) 67 (4.5%)
   40-49 127 (13.9%) 83 (14.3%) 210 (14.0%)
   50-59 257 (28.1%) 179 (30.8%) 436 (29.1%)
   60-69 297 (32.4%) 170 (29.2%) 467 (31.2%)
   70-79 168 (18.3%) 100 (17.2%) 268 (17.9%)
   80-89 20 (2.2%) 9 (1.5%) 29 (1.9%)
   a 1 (0.1%) 0 (0.0%) 1 (0.1%)
  1. Trend test for ordinal variables
summary(tableby(sex ~ age.ord, data = mockstudy), pfootnote = TRUE)
Male (N=916) Female (N=583) Total (N=1499) p value
age.ord 0.0491
   10-19 1 (0.1%) 0 (0.0%) 1 (0.1%)
   20-29 8 (0.9%) 11 (1.9%) 19 (1.3%)
   30-39 37 (4.0%) 30 (5.1%) 67 (4.5%)
   40-49 127 (13.9%) 83 (14.2%) 210 (14.0%)
   50-59 257 (28.1%) 179 (30.7%) 436 (29.1%)
   60-69 298 (32.5%) 170 (29.2%) 468 (31.2%)
   70-79 168 (18.3%) 101 (17.3%) 269 (17.9%)
   80-89 20 (2.2%) 9 (1.5%) 29 (1.9%)
  1. Trend test for ordinal variables

4. Summarize a survival variable

First look at the information that is presented by the survfit() function, then see how the same results can be seen with tableby. The default is to show the median survival (time at which the probability of survival = 50%).

survfit(Surv(fu.time, fu.stat)~sex, data=mockstudy)
## Call: survfit(formula = Surv(fu.time, fu.stat) ~ sex, data = mockstudy)
## 
##              n events median 0.95LCL 0.95UCL
## sex=Male   916    829    550     515     590
## sex=Female 583    527    543     511     575
survdiff(Surv(fu.time, fu.stat)~sex, data=mockstudy)
## Call:
## survdiff(formula = Surv(fu.time, fu.stat) ~ sex, data = mockstudy)
## 
##              N Observed Expected (O-E)^2/E (O-E)^2/V
## sex=Male   916      829      830  0.000370  0.000956
## sex=Female 583      527      526  0.000583  0.000956
## 
##  Chisq= 0  on 1 degrees of freedom, p= 1
summary(tableby(sex ~ Surv(fu.time, fu.stat), data=mockstudy))
Male (N=916) Female (N=583) Total (N=1499) p value
Surv(fu.time, fu.stat) 0.975
   Events 829 527 1356
   Median Survival 550.000 543.000 546.000

It is also possible to obtain summaries of the % survival at certain time points (say the probability of surviving 1-year).

summary(survfit(Surv(fu.time/365.25, fu.stat)~sex, data=mockstudy), times=1:5)
## Call: survfit(formula = Surv(fu.time/365.25, fu.stat) ~ sex, data = mockstudy)
## 
##                 sex=Male 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     1    626     286   0.6870  0.0153       0.6576       0.7177
##     2    309     311   0.3437  0.0158       0.3142       0.3761
##     3    152     151   0.1748  0.0127       0.1516       0.2015
##     4     57      61   0.0941  0.0104       0.0759       0.1168
##     5     24      16   0.0628  0.0095       0.0467       0.0844
## 
##                 sex=Female 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##     1    380     202   0.6531  0.0197       0.6155        0.693
##     2    190     189   0.3277  0.0195       0.2917        0.368
##     3     95      90   0.1701  0.0157       0.1420        0.204
##     4     51      32   0.1093  0.0133       0.0861        0.139
##     5     18      12   0.0745  0.0126       0.0534        0.104
summary(tableby(sex ~ Surv(fu.time/365.25, fu.stat), data=mockstudy, times=1:5, surv.stats=c("NeventsSurv","NriskSurv")))
Male (N=916) Female (N=583) Total (N=1499) p value
Surv(fu.time/365.25, fu.stat) 0.975
   time = 1 286 (68.7) 202 (65.3) 488 (67.4)
   time = 2 597 (34.4) 391 (32.8) 988 (33.7)
   time = 3 748 (17.5) 481 (17.0) 1229 (17.3)
   time = 4 809 (9.4) 513 (10.9) 1322 (10.1)
   time = 5 825 (6.3) 525 (7.4) 1350 (6.8)
   time = 1 626 (68.7) 380 (65.3) 1006 (67.4)
   time = 2 309 (34.4) 190 (32.8) 499 (33.7)
   time = 3 152 (17.5) 95 (17.0) 247 (17.3)
   time = 4 57 (9.4) 51 (10.9) 108 (10.1)
   time = 5 24 (6.3) 18 (7.4) 42 (6.8)

5. Summarize date variables

Date variables by default are summarized with the number of missing values, the median, and the range. For example purposes we’ve created a random date. Missing values are introduced for impossible February dates.

set.seed(100)
N <- nrow(mockstudy)
mockstudy$dtentry <- mdy.Date(month=sample(1:12,N,replace=T), day=sample(1:29,N,replace=T), 
                              year=sample(2005:2009,N,replace=T))
summary(tableby(sex ~ dtentry, data=mockstudy))
Male (N=916) Female (N=583) Total (N=1499) p value
dtentry 0.661
   N-Miss 2 3 5
   Median 2007-05-25 2007-05-08 2007-05-22
   Range 2005-01-02 - 2009-12-28 2005-01-01 - 2009-12-25 2005-01-01 - 2009-12-28

6. Summarize multiple variables without typing them out

Often one wants to summarize a number of variables. Instead of typing by hand each individual variable, an alternative approach is to create a formula using the paste command with the collapse="+" option.

## create a vector specifying the variable names
myvars <- names(mockstudy)

## select the 8th through the last variables
## paste them together, separated by the + sign
RHS <- paste(myvars[8:10], collapse="+")
RHS

[1] “ps+hgb+bmi”

## create a formula using the as.formula function
as.formula(paste('arm ~ ', RHS))

arm ~ ps + hgb + bmi

## use the formula in the tableby function
summary(tableby(as.formula(paste('arm ~', RHS)), data=mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
ps 0.903
   N-Miss 69 141 56 266
   Mean (SD) 0.529 (0.597) 0.547 (0.595) 0.537 (0.606) 0.539 (0.598)
   Range 0.000 - 2.000 0.000 - 2.000 0.000 - 2.000 0.000 - 2.000
hgb 0.639
   N-Miss 69 141 56 266
   Mean (SD) 12.276 (1.686) 12.381 (1.763) 12.373 (1.680) 12.348 (1.719)
   Range 9.060 - 17.300 9.000 - 18.200 9.000 - 17.000 9.000 - 18.200
Body Mass Index (kg/m^2) 0.892
   N-Miss 9 20 4 33
   Mean (SD) 27.290 (5.552) 27.210 (5.173) 27.106 (5.751) 27.206 (5.432)
   Range 14.053 - 53.008 16.649 - 49.130 15.430 - 60.243 14.053 - 60.243

These steps can also be done using the formulize function.

## The formulize function does the paste and as.formula steps
tmp <- formulize('arm',myvars[8:10])
tmp

arm ~ ps + hgb + bmi

## More complex formulas could also be written using formulize
tmp2 <- formulize('arm',c('ps','hgb^2','bmi'))

## use the formula in the tableby function
summary(tableby(tmp, data=mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
ps 0.903
   N-Miss 69 141 56 266
   Mean (SD) 0.529 (0.597) 0.547 (0.595) 0.537 (0.606) 0.539 (0.598)
   Range 0.000 - 2.000 0.000 - 2.000 0.000 - 2.000 0.000 - 2.000
hgb 0.639
   N-Miss 69 141 56 266
   Mean (SD) 12.276 (1.686) 12.381 (1.763) 12.373 (1.680) 12.348 (1.719)
   Range 9.060 - 17.300 9.000 - 18.200 9.000 - 17.000 9.000 - 18.200
Body Mass Index (kg/m^2) 0.892
   N-Miss 9 20 4 33
   Mean (SD) 27.290 (5.552) 27.210 (5.173) 27.106 (5.751) 27.206 (5.432)
   Range 14.053 - 53.008 16.649 - 49.130 15.430 - 60.243 14.053 - 60.243

To change summary statistics or statistical tests en masse, consider using paste0() together with formulize():

varlist1 <- c('age','sex','hgb')
varlist2 <- paste0("anova(", c('bmi','alk.phos','ast'), ", 'meansd')")

summary(tableby(formulize("arm", c(varlist1, varlist2)),
                data = mockstudy, numeric.test = "kwt"), pfootnote = TRUE)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.6391
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
Gender 0.1902
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
hgb 0.5701
   N-Miss 69 141 56 266
   Mean (SD) 12.276 (1.686) 12.381 (1.763) 12.373 (1.680) 12.348 (1.719)
   Range 9.060 - 17.300 9.000 - 18.200 9.000 - 17.000 9.000 - 18.200
Body Mass Index (kg/m^2) 0.8923
   Mean (SD) 27.290 (5.552) 27.210 (5.173) 27.106 (5.751) 27.206 (5.432)
alk.phos 0.2263
   Mean (SD) 175.577 (128.608) 161.984 (121.978) 173.506 (138.564) 168.969 (128.492)
ast 0.5073
   Mean (SD) 37.292 (28.036) 35.202 (26.659) 35.670 (25.807) 35.933 (26.843)
  1. Kruskal-Wallis rank sum test
  2. Pearson’s Chi-squared test
  3. Linear Model ANOVA

7. Subset the dataset used in the analysis

Here are two ways to get the same result (limit the analysis to subjects age>5 and in the F: FOLFOX treatment group).

  • The first approach uses the subset function applied to the dataset mockstudy. This example also selects a subset of variables. The tableby function is then applied to this subsetted data.
newdata <- subset(mockstudy, subset=age>50 & arm=='F: FOLFOX', select = c(sex,ps:bmi))
dim(mockstudy)
## [1] 1499   16
table(mockstudy$arm)
## 
##    A: IFL F: FOLFOX   G: IROX 
##       428       691       380
dim(newdata)
## [1] 557   4
names(newdata)
## [1] "sex" "ps"  "hgb" "bmi"
summary(tableby(sex ~ ., data=newdata))
Male (N=333) Female (N=224) Total (N=557) p value
ps 0.652
   N-Miss 64 44 108
   Mean (SD) 0.554 (0.600) 0.528 (0.602) 0.543 (0.600)
   Range 0.000 - 2.000 0.000 - 2.000 0.000 - 2.000
hgb < 0.001
   N-Miss 64 44 108
   Mean (SD) 12.720 (1.925) 12.063 (1.395) 12.457 (1.760)
   Range 9.000 - 18.200 9.100 - 15.900 9.000 - 18.200
bmi 0.650
   N-Miss 9 6 15
   Mean (SD) 27.539 (4.780) 27.337 (5.508) 27.458 (5.081)
   Range 17.927 - 47.458 16.649 - 49.130 16.649 - 49.130
  • The second approach does the same analysis but uses the subset argument within tableby to subset the data.
summary(tableby(sex ~ ps + hgb + bmi, subset=age>50 & arm=="F: FOLFOX", data=mockstudy))
Male (N=333) Female (N=224) Total (N=557) p value
ps 0.652
   N-Miss 64 44 108
   Mean (SD) 0.554 (0.600) 0.528 (0.602) 0.543 (0.600)
   Range 0.000 - 2.000 0.000 - 2.000 0.000 - 2.000
hgb < 0.001
   N-Miss 64 44 108
   Mean (SD) 12.720 (1.925) 12.063 (1.395) 12.457 (1.760)
   Range 9.000 - 18.200 9.100 - 15.900 9.000 - 18.200
Body Mass Index (kg/m^2) 0.650
   N-Miss 9 6 15
   Mean (SD) 27.539 (4.780) 27.337 (5.508) 27.458 (5.081)
   Range 17.927 - 47.458 16.649 - 49.130 16.649 - 49.130

8. Create combinations of variables on the fly

## create a variable combining the levels of mdquality.s and sex
with(mockstudy, table(interaction(mdquality.s,sex)))
## 
##   0.Male   1.Male 0.Female 1.Female 
##       77      686       47      437
summary(tableby(arm ~ interaction(mdquality.s,sex), data=mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
interaction(mdquality.s, sex) 0.493
   N-Miss 55 156 41 252
   0.Male 29 (7.8%) 31 (5.8%) 17 (5.0%) 77 (6.2%)
   1.Male 214 (57.4%) 285 (53.3%) 187 (55.2%) 686 (55.0%)
   0.Female 12 (3.2%) 21 (3.9%) 14 (4.1%) 47 (3.8%)
   1.Female 118 (31.6%) 198 (37.0%) 121 (35.7%) 437 (35.0%)
## create a new grouping variable with combined levels of arm and sex
summary(tableby(interaction(mdquality.s, sex) ~  age + bmi, data=mockstudy, subset=arm=="F: FOLFOX"))
0.Male (N=31) 1.Male (N=285) 0.Female (N=21) 1.Female (N=198) Total (N=535) p value
Age, yrs 0.190
   Mean (SD) 63.065 (11.702) 60.653 (11.833) 60.810 (10.103) 58.924 (11.366) 60.159 (11.612)
   Range 41.000 - 82.000 19.000 - 88.000 42.000 - 81.000 29.000 - 83.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.894
   N-Miss 0 6 1 5 12
   Mean (SD) 26.633 (5.094) 27.387 (4.704) 27.359 (4.899) 27.294 (5.671) 27.307 (5.100)
   Range 20.177 - 41.766 17.927 - 47.458 19.801 - 39.369 16.799 - 44.841 16.799 - 47.458

9. Transform variables on the fly

Certain transformations need to be surrounded by I() so that R knows to treat it as a variable transformation and not some special model feature. If the transformation includes any of the symbols / - + ^ * then surround the new variable by I().

trans <- tableby(arm ~ I(age/10) + log(bmi) + factor(mdquality.s, levels=0:1, labels=c('N','Y')),
                 data=mockstudy)
summary(trans)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.614
   Mean (SD) 5.967 (1.136) 6.030 (1.163) 5.976 (1.150) 5.999 (1.152)
   Range 2.700 - 8.800 1.900 - 8.800 2.600 - 8.500 1.900 - 8.800
Body Mass Index (kg/m^2) 0.811
   N-Miss 9 20 4 33
   Mean (SD) 3.287 (0.197) 3.286 (0.183) 3.279 (0.200) 3.285 (0.192)
   Range 2.643 - 3.970 2.812 - 3.894 2.736 - 4.098 2.643 - 4.098
factor(mdquality.s, levels = 0:1, labels = c(“N”, “Y”)) 0.694
   N-Miss 55 156 41 252
   N 41 (11.0%) 52 (9.7%) 31 (9.1%) 124 (9.9%)
   Y 332 (89.0%) 483 (90.3%) 308 (90.9%) 1123 (90.1%)

The labels for these variables aren’t exactly what we’d like, so we can change modify those after the fact. Instead of typing out the very long variable names, you can modify specific labels by position.

labels(trans)
##                                                           arm 
##                                               "Treatment Arm" 
##                                                     I(age/10) 
##                                                    "Age, yrs" 
##                                                      log(bmi) 
##                                    "Body Mass Index (kg/m^2)" 
##       factor(mdquality.s, levels = 0:1, labels = c("N", "Y")) 
## "factor(mdquality.s, levels = 0:1, labels = c(\"N\", \"Y\"))"
labels(trans)[2:4] <- c('Age per 10 yrs', 'log(BMI)', 'MD Quality')
labels(trans)
##                                                     arm 
##                                         "Treatment Arm" 
##                                               I(age/10) 
##                                        "Age per 10 yrs" 
##                                                log(bmi) 
##                                              "log(BMI)" 
## factor(mdquality.s, levels = 0:1, labels = c("N", "Y")) 
##                                            "MD Quality"
summary(trans)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age per 10 yrs 0.614
   Mean (SD) 5.967 (1.136) 6.030 (1.163) 5.976 (1.150) 5.999 (1.152)
   Range 2.700 - 8.800 1.900 - 8.800 2.600 - 8.500 1.900 - 8.800
log(BMI) 0.811
   N-Miss 9 20 4 33
   Mean (SD) 3.287 (0.197) 3.286 (0.183) 3.279 (0.200) 3.285 (0.192)
   Range 2.643 - 3.970 2.812 - 3.894 2.736 - 4.098 2.643 - 4.098
MD Quality 0.694
   N-Miss 55 156 41 252
   N 41 (11.0%) 52 (9.7%) 31 (9.1%) 124 (9.9%)
   Y 332 (89.0%) 483 (90.3%) 308 (90.9%) 1123 (90.1%)

Note that if we had not changed mdquality.s to a factor, it would have been summarized as though it were a continuous variable.

class(mockstudy$mdquality.s)

[1] “integer”

summary(tableby(arm~mdquality.s, data=mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
mdquality.s 0.695
   N-Miss 55 156 41 252
   Mean (SD) 0.890 (0.313) 0.903 (0.297) 0.909 (0.289) 0.901 (0.299)
   Range 0.000 - 1.000 0.000 - 1.000 0.000 - 1.000 0.000 - 1.000

Another option would be to specify the test and summary statistics. In fact, if I had a set of variables coded 0/1 and that was all I was summarizing, then I could change the global option for continuous variables to use the chi-square test and show countpct.

summary(tableby(arm ~ chisq(mdquality.s, "Nmiss","countpct"), data=mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
mdquality.s 0.694
   N-Miss 55 156 41 252
   0 41 (11.0%) 52 (9.7%) 31 (9.1%) 124 (9.9%)
   1 332 (89.0%) 483 (90.3%) 308 (90.9%) 1123 (90.1%)

10. Subsetting (change the ordering of the variables, delete a variable, sort by p-value, filter by p-value, show only certain by-levels)

mytab <- tableby(arm ~ sex + alk.phos + age, data=mockstudy)
mytab2 <- mytab[c('age','sex','alk.phos')]
summary(mytab2)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
alk.phos 0.226
   N-Miss 69 141 56 266
   Mean (SD) 175.577 (128.608) 161.984 (121.978) 173.506 (138.564) 168.969 (128.492)
   Range 11.000 - 858.000 10.000 - 1014.000 7.000 - 982.000 7.000 - 1014.000
summary(mytab[c('age','sex')], digits = 2)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.614
   Mean (SD) 59.67 (11.36) 60.30 (11.63) 59.76 (11.50) 59.99 (11.52)
   Range 27.00 - 88.00 19.00 - 88.00 26.00 - 85.00 19.00 - 88.00
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
summary(mytab[c(3,1)], digits = 3)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
summary(sort(mytab, decreasing = TRUE))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
alk.phos 0.226
   N-Miss 69 141 56 266
   Mean (SD) 175.577 (128.608) 161.984 (121.978) 173.506 (138.564) 168.969 (128.492)
   Range 11.000 - 858.000 10.000 - 1014.000 7.000 - 982.000 7.000 - 1014.000
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
summary(mytab[mytab < 0.5])
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
alk.phos 0.226
   N-Miss 69 141 56 266
   Mean (SD) 175.577 (128.608) 161.984 (121.978) 173.506 (138.564) 168.969 (128.492)
   Range 11.000 - 858.000 10.000 - 1014.000 7.000 - 982.000 7.000 - 1014.000
head(mytab, 1) # can also use tail()

tableby Object

Function Call: tableby(formula = arm ~ sex + alk.phos + age, data = mockstudy)

Variable(s): arm ~ sex

summary(tableby(list(arm, sex) ~ sex + alk.phos + age, data=mockstudy)[, "sex"])
Male (N=916) Female (N=583) Total (N=1499) p value
alk.phos 0.712
   N-Miss 162 104 266
   Mean (SD) 167.893 (130.754) 170.664 (124.965) 168.969 (128.492)
   Range 10.000 - 1014.000 7.000 - 771.000 7.000 - 1014.000
Age, yrs 0.048
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000
summary(tableby(list(arm, sex) ~ sex + alk.phos + age, data=mockstudy)[, list(sex = "Female", arm = c("F: FOLFOX", "Total"))])
Female (N=583) p value
alk.phos 0.712
   N-Miss 104
   Mean (SD) 170.664 (124.965)
   Range 7.000 - 771.000
Age, yrs 0.048
   Mean (SD) 59.247 (11.722)
   Range 22.000 - 88.000
F: FOLFOX (N=691) Total (N=1499) p value
Gender 0.190
   Male 411 (59.5%) 916 (61.1%)
   Female 280 (40.5%) 583 (38.9%)
alk.phos 0.226
   N-Miss 141 266
   Mean (SD) 161.984 (121.978) 168.969 (128.492)
   Range 10.000 - 1014.000 7.000 - 1014.000
Age, yrs 0.614
   Mean (SD) 60.301 (11.632) 59.985 (11.519)
   Range 19.000 - 88.000 19.000 - 88.000

11. Merge two tableby objects together

It is possible to combine two tableby objects so that they print out together. Overlapping by-variables will have their x-variables concatenated, and (if all=TRUE) non-overlapping by-variables will have their tables printed separately.

## demographics
tab1 <- tableby(arm ~ sex + age, data=mockstudy,
                control=tableby.control(numeric.stats=c("Nmiss","meansd"), total=FALSE))
## lab data
tab2 <- tableby(arm ~ hgb + alk.phos, data=mockstudy,
                control=tableby.control(numeric.stats=c("Nmiss","median","q1q3"),
                                        numeric.test="kwt", total=FALSE))
tab12 <- merge(tab1, tab2)
class(tab12)

[1] “tableby” “arsenal_table”

summary(tab12)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%)
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499)
hgb 0.570
   N-Miss 69 141 56
   Median 12.100 12.200 12.400
   Q1, Q3 11.000, 13.450 11.100, 13.600 11.175, 13.625
alk.phos 0.104
   N-Miss 69 141 56
   Median 133.000 116.000 122.000
   Q1, Q3 89.000, 217.000 85.000, 194.750 87.750, 210.250

For tables with two different outcomes, consider the all=TRUE argument:

summary(merge(
  tableby(sex ~ age, data = mockstudy),
  tableby(arm ~ bmi, data = mockstudy),
  all = TRUE
))
Male (N=916) Female (N=583) Total (N=1499) p value
Age, yrs 0.048
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Body Mass Index (kg/m^2) 0.892
   N-Miss 9 20 4 33
   Mean (SD) 27.290 (5.552) 27.210 (5.173) 27.106 (5.751) 27.206 (5.432)
   Range 14.053 - 53.008 16.649 - 49.130 15.430 - 60.243 14.053 - 60.243

12. Add a title to the table

When creating a pdf the tables are automatically numbered and the title appears below the table. In Word and HTML, the titles appear un-numbered and above the table.

t1 <- tableby(arm ~ sex + age, data=mockstudy)
summary(t1, title='Demographics')
Demographics
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000

With multiple left-hand sides, you can pass a vector or list to determine labels for each table:

summary(tableby(list(arm, sex) ~ age, data = mockstudy), title = c("arm table", "sex table"))
arm table
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 0.614
   Mean (SD) 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519)
   Range 27.000 - 88.000 19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
sex table
Male (N=916) Female (N=583) Total (N=1499) p value
Age, yrs 0.048
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000

13. Modify how missing values are displayed

Depending on the report you are writing you have the following options:

  • Show how many subjects have each variable

  • Show how many subjects are missing each variable

  • Show how many subjects are missing each variable only if there are any missing values

  • Don’t indicate missing values at all

## look at how many missing values there are for each variable
apply(is.na(mockstudy),2,sum)
##        case         age         arm         sex        race     fu.time     fu.stat          ps 
##           0           0           0           0           7           0           0         266 
##         hgb         bmi    alk.phos         ast mdquality.s     age.ord  age.ordnew     dtentry 
##         266          33         266         266         252           0           1           5
## Show how many subjects have each variable (non-missing)
summary(tableby(sex ~ ast + age, data=mockstudy,
                control=tableby.control(numeric.stats=c("N","median"), total=FALSE)))
Male (N=916) Female (N=583) p value
ast 0.921
   N 754 479
   Median 27.000 27.000
Age, yrs 0.048
   N 916 583
   Median 61.000 60.000
## Always list the number of missing values
summary(tableby(sex ~ ast + age, data=mockstudy,
                control=tableby.control(numeric.stats=c("Nmiss2","median"), total=FALSE)))
Male (N=916) Female (N=583) p value
ast 0.921
   N-Miss 162 104
   Median 27.000 27.000
Age, yrs 0.048
   N-Miss 0 0
   Median 61.000 60.000
## Only show the missing values if there are some (default)
summary(tableby(sex ~ ast + age, data=mockstudy, 
                control=tableby.control(numeric.stats=c("Nmiss","mean"),total=FALSE)))
Male (N=916) Female (N=583) p value
ast 0.921
   N-Miss 162 104
   Mean 35.873 36.029
Age, yrs 0.048
   Mean 60.455 59.247
## Don't show N at all
summary(tableby(sex ~ ast + age, data=mockstudy, 
                control=tableby.control(numeric.stats=c("mean"),total=FALSE)))
Male (N=916) Female (N=583) p value
ast 0.921
   Mean 35.873 36.029
Age, yrs 0.048
   Mean 60.455 59.247

One might also consider the use of includeNA() to include NAs in the counts and percents for categorical variables.

mockstudy$ps.cat <- factor(mockstudy$ps)
attr(mockstudy$ps.cat, "label") <- "ps"
summary(tableby(sex ~ includeNA(ps.cat), data = mockstudy, cat.stats = "countpct"))
Male (N=916) Female (N=583) Total (N=1499) p value
ps 0.354
   0 391 (42.7%) 244 (41.9%) 635 (42.4%)
   1 329 (35.9%) 202 (34.6%) 531 (35.4%)
   2 34 (3.7%) 33 (5.7%) 67 (4.5%)
   (Missing) 162 (17.7%) 104 (17.8%) 266 (17.7%)

14. Modify the number of digits used

Within tableby.control function there are 4 options for controlling the number of significant digits shown.

  • digits: controls the number of digits after the decimal place for continuous values

  • digits.count: controls the number of digits after the decimal point for counts

  • digits.pct: controls the number of digits after the decimal point for percents

  • digits.p: controls the number of digits after the decimal point for p-values

summary(tableby(arm ~ sex + age + fu.time, data=mockstudy), digits=4, digits.p=2, digits.pct=1)
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.19
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age, yrs 0.61
   Mean (SD) 59.6729 (11.3645) 60.3010 (11.6323) 59.7632 (11.4993) 59.9853 (11.5188)
   Range 27.0000 - 88.0000 19.0000 - 88.0000 26.0000 - 85.0000 19.0000 - 88.0000
fu.time < 0.01
   Mean (SD) 553.5841 (419.6065) 731.2460 (487.7443) 607.2421 (435.5092) 649.0841 (462.5109)
   Range 9.0000 - 2170.0000 0.0000 - 2472.0000 17.0000 - 2118.0000 0.0000 - 2472.0000

With the exception of digits.p, all of these can be specified on a per-variable basis using the in-formula functions that specify which tests are run:

summary(tableby(arm ~ chisq(sex, digits.pct=1) + anova(age, digits=4) +
                  anova(fu.time, digits = 1), data=mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender 0.190
   Male 277 (64.7%) 411 (59.5%) 228 (60.0%) 916 (61.1%)
   Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%)
Age, yrs 0.614
   Mean (SD) 59.6729 (11.3645) 60.3010 (11.6323) 59.7632 (11.4993) 59.9853 (11.5188)
   Range 27.0000 - 88.0000 19.0000 - 88.0000 26.0000 - 85.0000 19.0000 - 88.0000
fu.time < 0.001
   Mean (SD) 553.6 (419.6) 731.2 (487.7) 607.2 (435.5) 649.1 (462.5)
   Range 9.0 - 2170.0 0.0 - 2472.0 17.0 - 2118.0 0.0 - 2472.0

15. Create a user-defined summary statistic

For purposes of this example, the code below creates a trimmed mean function (trims 10%) and use that to summarize the data. Note the use of the ... which tells R to pass extra arguments on - this is required for user-defined functions. In this case, na.rm=T is passed to myfunc. The weights argument is also required, even though it isn’t passed on to the internal function in this particular example.

trim10 <- function(x, weights=rep(1,length(x)), ...){
  mean(x, trim=.1, ...)
}

summary(tableby(sex ~ hgb, data=mockstudy, 
                control=tableby.control(numeric.stats=c("Nmiss","trim10"), numeric.test="kwt",
                    stats.labels=list(Nmiss='Missing values', trim10="Trimmed Mean, 10%"))))
Male (N=916) Female (N=583) Total (N=1499) p value
hgb < 0.001
   Missing values 162 104 266
   Trimmed Mean, 10% 12.6 11.9 12.3

For statistics to be formatted appropriately, you may want to use as.tbstat() or as.countpct(). For example, suppose you want to create a trimmed mean function that trims by both 5 and 10 percent. The first example shows them separated by a comma; the second puts the 10% trimmed mean in brackets

trim510comma <- function(x, weights=rep(1,length(x)), ...){
  tmp <- c(mean(x, trim = 0.05, ...), mean(x, trim = 0.1, ...))
  as.tbstat(tmp, sep = ", ")
}
trim510bracket <- function(x, weights=rep(1,length(x)), ...){
  tmp <- c(mean(x, trim = 0.05, ...), mean(x, trim = 0.1, ...))
  as.tbstat(tmp, sep = " ", parens = c("[", "]"))
}

summary(tableby(sex ~ hgb, data=mockstudy, numeric.stats=c("Nmiss", "trim510comma"), test = FALSE))
Male (N=916) Female (N=583) Total (N=1499)
hgb
   N-Miss 162 104 266
   trim510comma 12.570, 12.564 11.924, 11.910 12.308, 12.291
summary(tableby(sex ~ hgb, data=mockstudy, numeric.stats=c("Nmiss", "trim510bracket"), test = FALSE))
Male (N=916) Female (N=583) Total (N=1499)
hgb
   N-Miss 162 104 266
   trim510bracket 12.570 [12.564] 11.924 [11.910] 12.308 [12.291]

Or perhaps it’s useful to put the amount of trimming in parentheses. Since it is a percent, we can flag it as such:

trim10pct <- function(x, weights=rep(1,length(x)), ...){
  tmp <- mean(x, trim = 0.05, ...)
  as.countpct(c(tmp, 10), sep = " ", parens = c("(", ")"), which.count = 0, which.pct = 2, pct = "%")
}
summary(tableby(sex ~ hgb, data=mockstudy, numeric.stats=c("Nmiss", "trim10pct"),
                digits = 2, digits.pct = 0, test = FALSE))
Male (N=916) Female (N=583) Total (N=1499)
hgb
   N-Miss 162 104 266
   trim10pct 12.57 (10%) 11.92 (10%) 12.31 (10%)

16. Use case-weights for creating summary statistics

When comparing groups, they are often unbalanced when it comes to nuisances such as age and sex. The tableby function allows you to create weighted summary statistics. If this option us used then p-values are not calculated (test=FALSE).

##create fake group that is not balanced by age/sex 
set.seed(200)
mockstudy$fake_arm <- ifelse(mockstudy$age>60 & mockstudy$sex=='Female',sample(c('A','B'),replace=T, prob=c(.2,.8)),
                            sample(c('A','B'),replace=T, prob=c(.8,.4)))

mockstudy$agegp <- cut(mockstudy$age, breaks=c(18,50,60,70,90), right=FALSE)

## create weights based on agegp and sex distribution
tab1 <- with(mockstudy,table(agegp, sex))
tab2 <- with(mockstudy, table(agegp, sex, fake_arm))
tab2
## , , fake_arm = A
## 
##          sex
## agegp     Male Female
##   [18,50)   73     62
##   [50,60)  128     94
##   [60,70)  139      7
##   [70,90)  102      0
## 
## , , fake_arm = B
## 
##          sex
## agegp     Male Female
##   [18,50)   79     48
##   [50,60)  130     84
##   [60,70)  156    166
##   [70,90)  109    122
gpwts <- rep(tab1, length(unique(mockstudy$fake_arm)))/tab2
gpwts[gpwts>50] <- 30

## apply weights to subjects
index <- with(mockstudy, cbind(as.numeric(agegp), as.numeric(sex), as.numeric(as.factor(fake_arm)))) 
mockstudy$wts <- gpwts[index]

## show weights by treatment arm group
tapply(mockstudy$wts,mockstudy$fake_arm, summary)
## $A
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.774   1.894   2.069   2.276   2.082  24.714 
## 
## $B
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   1.042   1.924   1.677   1.985   2.292
orig <- tableby(fake_arm ~ age + sex + Surv(fu.time/365, fu.stat), data=mockstudy, test=FALSE)
summary(orig, title='No Case Weights used')
No Case Weights used
A (N=605) B (N=894) Total (N=1499)
Age, yrs
   Mean (SD) 57.413 (11.618) 61.726 (11.125) 59.985 (11.519)
   Range 22.000 - 85.000 19.000 - 88.000 19.000 - 88.000
Gender
   Male 442 (73.1%) 474 (53.0%) 916 (61.1%)
   Female 163 (26.9%) 420 (47.0%) 583 (38.9%)
Surv(fu.time/365, fu.stat)
   Events 554 802 1356
   Median Survival 1.504 1.493 1.496
tab1 <- tableby(fake_arm ~ age + sex + Surv(fu.time/365, fu.stat), data=mockstudy, weights=wts)
summary(tab1, title='Case Weights used')
Case Weights used
A (N=1377) B (N=1499) Total (N=2876)
Age, yrs
   Mean (SD) 58.009 (10.925) 60.151 (11.428) 59.126 (11.235)
   Range 22.000 - 85.000 19.000 - 88.000 19.000 - 88.000
Gender
   Male 916 (66.5%) 916 (61.1%) 1832 (63.7%)
   Female 461 (33.5%) 583 (38.9%) 1044 (36.3%)
Surv(fu.time/365, fu.stat)
   Events 1252 1348 2599
   Median Survival 1.534 1.496 1.532

17. Create your own p-value and add it to the table

When using weighted summary statistics, it is often desirable to then show a p-value from a model that corresponds to the weighted analysis. It is possible to add your own p-value and modify the column title for that new p-value. Another use for this would be to add standardized differences or confidence intervals instead of a p-value.

To add the p-value, you simply need to create a data frame and use the function modpval.tableby(). The first few columns in the data.frame are required: (1) the by-variable, (2) the strata value (if the table has a strata term), (3) the x-variable, and (4) the new p-value (or test statistic). Another optional column can be used to indicate what method was used to calculate the p-value. If you specify use.pname=TRUE then the column name indicating the p-value will be also be used in the tableby summary.

mypval <- data.frame(
  byvar = "fake_arm",
  variable = c('age','sex','Surv(fu.time/365, fu.stat)'), 
  adj.pvalue = c(.953,.811,.01), 
  method = c('Age/Sex adjusted model results')
)
tab2 <- modpval.tableby(tab1, mypval, use.pname=TRUE)
summary(tab2, title='Case Weights used, p-values added', pfootnote=TRUE)
Case Weights used, p-values added
A (N=1377) B (N=1499) Total (N=2876) adj.pvalue
Age, yrs 0.9531
   Mean (SD) 58.009 (10.925) 60.151 (11.428) 59.126 (11.235)
   Range 22.000 - 85.000 19.000 - 88.000 19.000 - 88.000
Gender 0.8111
   Male 916 (66.5%) 916 (61.1%) 1832 (63.7%)
   Female 461 (33.5%) 583 (38.9%) 1044 (36.3%)
Surv(fu.time/365, fu.stat) 0.0101
   Events 1252 1348 2599
   Median Survival 1.534 1.496 1.532
  1. Age/Sex adjusted model results

18. For two-level categorical variables or one-line numeric variables, simplify the output.

If the cat.simplify option is set to TRUE, then only the second level of two-level categorical varialbes is shown. In the example below, sex has two levels, and “Female” is the second level, hence only the counts and percents for Female are shown. Similarly, “mdquality.s” was turned to a factor, and “1” is the second level, but since there are missings, the table ignores cat.simplify and displays all levels (since the output can no longer be displayed on one line).

table2 <- tableby(arm~sex + factor(mdquality.s), data=mockstudy, cat.simplify=TRUE)
summary(table2, labelTranslations=c(sex="Female", "factor(mdquality.s)"="MD Quality"))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Female 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%) 0.190
MD Quality 0.694
   N-Miss 55 156 41 252
   0 41 (11.0%) 52 (9.7%) 31 (9.1%) 124 (9.9%)
   1 332 (89.0%) 483 (90.3%) 308 (90.9%) 1123 (90.1%)

Similarly, if numeric.simplify is set to TRUE, then any numerics which only have one row of summary statistics are simplified into a single row. Note again that ast has missing values and so is not simplified to a single row.

summary(tableby(arm ~ age + ast, data = mockstudy,
                numeric.simplify=TRUE, numeric.stats=c("Nmiss", "meansd")))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519) 0.614
ast 0.507
   N-Miss 69 141 56 266
   Mean (SD) 37.292 (28.036) 35.202 (26.659) 35.670 (25.807) 35.933 (26.843)

The in-formula functions to change which tests are run can also be used to specify these options for each variable at a time.

summary(tableby(arm ~ anova(age, "meansd", numeric.simplify=TRUE) +
                  chisq(sex, cat.simplify=TRUE), data = mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Age, yrs 59.673 (11.365) 60.301 (11.632) 59.763 (11.499) 59.985 (11.519) 0.614
Gender 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%) 0.190

The cat.simplify and ord.simplify argument also accept the special string "label", which appends the shown level’s label to the overall label:

summary(tableby(arm ~ sex, cat.simplify = "label", data = mockstudy))
A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380) Total (N=1499) p value
Gender (Female) 151 (35.3%) 280 (40.5%) 152 (40.0%) 583 (38.9%) 0.190

19. Use tableby within an Sweave document

For those users who wish to create tables within an Sweave document, the following code seems to work.

\documentclass{article}

\usepackage{longtable}
\usepackage{pdfpages}

\begin{document}

\section{Read in Data}
<<echo=TRUE>>=
require(arsenal)
require(knitr)
require(rmarkdown)
data(mockstudy)

tab1 <- tableby(arm~sex+age, data=mockstudy)
@

\section{Convert Summary.Tableby to LaTeX}
<<echo=TRUE, results='hide', message=FALSE>>=
capture.output(summary(tab1), file="Test.md")

## Convert R Markdown Table to LaTeX
render("Test.md", pdf_document(keep_tex=TRUE))
@ 

\includepdf{Test.pdf}

\end{document}

20. Export tableby object to a .CSV file

When looking at multiple variables it is sometimes useful to export the results to a csv file. The as.data.frame function creates a data frame object that can be exported or further manipulated within R.

tab1 <- summary(tableby(arm~sex+age, data=mockstudy), text = NULL)
as.data.frame(tab1)
##              A: IFL (N=428) F: FOLFOX (N=691) G: IROX (N=380)  Total (N=1499) p value
## 1    Gender                                                                     0.190
## 2      Male     277 (64.7%)       411 (59.5%)     228 (60.0%)     916 (61.1%)        
## 3    Female     151 (35.3%)       280 (40.5%)     152 (40.0%)     583 (38.9%)        
## 4  Age, yrs                                                                     0.614
## 5 Mean (SD) 59.673 (11.365)   60.301 (11.632) 59.763 (11.499) 59.985 (11.519)        
## 6     Range 27.000 - 88.000   19.000 - 88.000 26.000 - 85.000 19.000 - 88.000
# write.csv(tab1, '/my/path/here/my_table.csv')

21. Write tableby object to a separate Word or HTML file

## write to an HTML document
tab1 <- tableby(arm ~ sex + age, data=mockstudy)
write2html(tab1, "~/trash.html")

## write to a Word document
write2word(tab1, "~/trash.doc", title="My table in Word")

22. Use tableby in R Shiny

The easiest way to output a tableby() object in an R Shiny app is to use the tableOutput() UI in combination with the renderTable() server function and as.data.frame(summary(tableby())):

# A standalone shiny app
library(shiny)
library(arsenal)
data(mockstudy)

shinyApp(
  ui = fluidPage(tableOutput("table")),
  server = function(input, output) {
    output$table <- renderTable({
      as.data.frame(summary(tableby(sex ~ age, data = mockstudy), text = "html"))
    }, sanitize.text.function = function(x) x)
  }
)

This can be especially powerful if you feed the selections from a selectInput(multiple = TRUE) into formulize() to make the table dynamic!

23. Use tableby in bookdown

Since the backbone of tableby() is knitr::kable(), tables still render well in bookdown. However, print.summary.tableby() 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(tableby(sex ~ age, data = mockstudy), title="(\\#tab:mytableby) Caption here")

24. Adjust tableby for multiple p-values

The padjust() function is a new S3 generic piggybacking off of p.adjust(). It works on both tableby and summary.tableby objects:

tab <- summary(tableby(sex ~ age + fu.time + bmi + mdquality.s, data = mockstudy))
tab
Male (N=916) Female (N=583) Total (N=1499) p value
Age, yrs 0.048
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000
fu.time 0.978
   Mean (SD) 649.345 (454.332) 648.674 (475.472) 649.084 (462.511)
   Range 0.000 - 2472.000 9.000 - 2441.000 0.000 - 2472.000
Body Mass Index (kg/m^2) 0.012
   N-Miss 22 11 33
   Mean (SD) 27.491 (5.030) 26.760 (5.984) 27.206 (5.432)
   Range 14.053 - 60.243 15.430 - 53.008 14.053 - 60.243
mdquality.s 0.827
   N-Miss 153 99 252
   Mean (SD) 0.899 (0.301) 0.903 (0.296) 0.901 (0.299)
   Range 0.000 - 1.000 0.000 - 1.000 0.000 - 1.000
padjust(tab, method = "bonferroni")
Male (N=916) Female (N=583) Total (N=1499) p value
Age, yrs 0.191
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000
fu.time 1.000
   Mean (SD) 649.345 (454.332) 648.674 (475.472) 649.084 (462.511)
   Range 0.000 - 2472.000 9.000 - 2441.000 0.000 - 2472.000
Body Mass Index (kg/m^2) 0.048
   N-Miss 22 11 33
   Mean (SD) 27.491 (5.030) 26.760 (5.984) 27.206 (5.432)
   Range 14.053 - 60.243 15.430 - 53.008 14.053 - 60.243
mdquality.s 1.000
   N-Miss 153 99 252
   Mean (SD) 0.899 (0.301) 0.903 (0.296) 0.901 (0.299)
   Range 0.000 - 1.000 0.000 - 1.000 0.000 - 1.000

25. Tabulate multiple endpoints

You can now use list() on the left-hand side of tableby() to give multiple endpoints.

summary(tableby(list(sex, mdquality.s, ps) ~ age + bmi, data = mockstudy))
Male (N=916) Female (N=583) Total (N=1499) p value
Age, yrs 0.048
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.012
   N-Miss 22 11 33
   Mean (SD) 27.491 (5.030) 26.760 (5.984) 27.206 (5.432)
   Range 14.053 - 60.243 15.430 - 53.008 14.053 - 60.243
0 (N=124) 1 (N=1123) Total (N=1247) p value
Age, yrs 0.766
   Mean (SD) 60.089 (11.627) 59.763 (11.537) 59.796 (11.542)
   Range 29.000 - 82.000 19.000 - 88.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.225
   N-Miss 3 18 21
   Mean (SD) 26.684 (6.331) 27.309 (5.274) 27.247 (5.388)
   Range 16.071 - 60.243 14.053 - 53.008 14.053 - 60.243
0 (N=635) 1 (N=531) 2 (N=67) Total (N=1233) p value
Age, yrs 0.335
   Mean (SD) 59.935 (11.261) 60.800 (11.721) 59.254 (12.090) 60.271 (11.507)
   Range 22.000 - 85.000 26.000 - 88.000 28.000 - 80.000 22.000 - 88.000
Body Mass Index (kg/m^2) 0.028
   N-Miss 7 20 1 28
   Mean (SD) 27.539 (5.222) 26.842 (5.436) 26.178 (5.808) 27.169 (5.358)
   Range 14.053 - 48.384 15.430 - 60.243 16.071 - 44.922 14.053 - 60.243

To avoid confusion about which table is which endpoint, you can set term.name=TRUE in summary(). This takes the labels for each by-variable and puts them in the top-left of the table.

summary(tableby(list(sex, mdquality.s, ps) ~ age + bmi, data = mockstudy), term.name = TRUE)
Gender Male (N=916) Female (N=583) Total (N=1499) p value
Age, yrs 0.048
   Mean (SD) 60.455 (11.369) 59.247 (11.722) 59.985 (11.519)
   Range 19.000 - 88.000 22.000 - 88.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.012
   N-Miss 22 11 33
   Mean (SD) 27.491 (5.030) 26.760 (5.984) 27.206 (5.432)
   Range 14.053 - 60.243 15.430 - 53.008 14.053 - 60.243
mdquality.s 0 (N=124) 1 (N=1123) Total (N=1247) p value
Age, yrs 0.766
   Mean (SD) 60.089 (11.627) 59.763 (11.537) 59.796 (11.542)
   Range 29.000 - 82.000 19.000 - 88.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.225
   N-Miss 3 18 21
   Mean (SD) 26.684 (6.331) 27.309 (5.274) 27.247 (5.388)
   Range 16.071 - 60.243 14.053 - 53.008 14.053 - 60.243
ps 0 (N=635) 1 (N=531) 2 (N=67) Total (N=1233) p value
Age, yrs 0.335
   Mean (SD) 59.935 (11.261) 60.800 (11.721) 59.254 (12.090) 60.271 (11.507)
   Range 22.000 - 85.000 26.000 - 88.000 28.000 - 80.000 22.000 - 88.000
Body Mass Index (kg/m^2) 0.028
   N-Miss 7 20 1 28
   Mean (SD) 27.539 (5.222) 26.842 (5.436) 26.178 (5.808) 27.169 (5.358)
   Range 14.053 - 48.384 15.430 - 60.243 16.071 - 44.922 14.053 - 60.243

26. Tabulate data by a non-test group (strata)

You can also specify a second grouping variable that doesn’t get tested (but instead separates results): a strata variable.

summary(tableby(list(sex, ps) ~ age + bmi, strata = arm, data = mockstudy))
Treatment Arm Male (N=916) Female (N=583) Total (N=1499) p value
A: IFL Age, yrs 0.572
   Mean (SD) 59.903 (11.347) 59.252 (11.422) 59.673 (11.365)
   Range 28.000 - 83.000 27.000 - 88.000 27.000 - 88.000
Body Mass Index (kg/m^2) 0.050
   N-Miss 7 2 9
   Mean (SD) 27.685 (5.072) 26.575 (6.287) 27.290 (5.552)
   Range 14.053 - 48.384 16.880 - 53.008 14.053 - 53.008
F: FOLFOX Age, yrs 0.286
   Mean (SD) 60.691 (11.598) 59.729 (11.679) 60.301 (11.632)
   Range 19.000 - 88.000 22.000 - 83.000 19.000 - 88.000
Body Mass Index (kg/m^2) 0.768
   N-Miss 12 8 20
   Mean (SD) 27.259 (4.715) 27.139 (5.789) 27.210 (5.173)
   Range 17.927 - 47.458 16.649 - 49.130 16.649 - 49.130
G: IROX Age, yrs 0.051
   Mean (SD) 60.702 (10.999) 58.355 (12.113) 59.763 (11.499)
   Range 29.000 - 85.000 26.000 - 82.000 26.000 - 85.000
Body Mass Index (kg/m^2) 0.020
   N-Miss 3 1 4
   Mean (SD) 27.672 (5.505) 26.262 (6.021) 27.106 (5.751)
   Range 17.377 - 60.243 15.430 - 45.354 15.430 - 60.243
Treatment Arm 0 (N=635) 1 (N=531) 2 (N=67) Total (N=1233) p value
A: IFL Age, yrs 0.413
   Mean (SD) 60.101 (10.948) 60.579 (12.026) 56.842 (13.226) 60.131 (11.535)
   Range 27.000 - 81.000 28.000 - 88.000 34.000 - 75.000 27.000 - 88.000
Body Mass Index (kg/m^2) 0.023
   N-Miss 1 6 1 8
   Mean (SD) 27.850 (5.318) 26.224 (5.347) 26.954 (5.560) 27.128 (5.385)
   Range 14.053 - 48.384 17.029 - 53.008 17.177 - 37.223 14.053 - 53.008
F: FOLFOX Age, yrs 0.272
   Mean (SD) 60.173 (11.096) 61.342 (11.918) 63.138 (9.303) 60.845 (11.391)
   Range 22.000 - 82.000 26.000 - 88.000 44.000 - 80.000 22.000 - 88.000
Body Mass Index (kg/m^2) 0.225
   N-Miss 5 11 0 16
   Mean (SD) 27.569 (5.004) 27.192 (5.248) 25.904 (5.338) 27.315 (5.134)
   Range 16.649 - 43.867 16.799 - 49.130 20.833 - 44.922 16.649 - 49.130
G: IROX Age, yrs 0.312
   Mean (SD) 59.361 (11.904) 60.081 (11.037) 55.737 (13.523) 59.451 (11.653)
   Range 26.000 - 85.000 28.000 - 84.000 28.000 - 76.000 26.000 - 85.000
Body Mass Index (kg/m^2) 0.642
   N-Miss 1 3 0 4
   Mean (SD) 27.143 (5.462) 26.910 (5.824) 25.861 (6.890) 26.970 (5.694)
   Range 17.615 - 46.204 15.430 - 60.243 16.071 - 44.734 15.430 - 60.243

Available Function Options

Summary statistics

The default summary statistics, by varible type, are:

  • numeric.stats: Continuous variables will show by default Nmiss, meansd, range
  • cat.stats: Categorical and factor variables will show by default Nmiss, countpct
  • ordered.stats: Ordered factors will show by default Nmiss, countpct
  • surv.stats: Survival variables will show by default Nmiss, Nevents, medsurv
  • date.stats: Date variables will show by default Nmiss, median, range

There are a number of extra functions defined specifically for the tableby function.

  • N: a count of the number of observations for a particular group
  • Nmiss: only show the count of the number of missing values if there are some missing values
  • Nmiss2: always show a count of the number of missing values for a variable within each group
  • meansd: print the mean and standard deviation in the format mean(sd)
  • meanse: print the mean and standard error in the format mean(se)
  • meanCI: print the mean and a (t) confidence interval
  • count: print the number of values in a category
  • countN: print the number of values in a category plus the total N for the group in the format N/Total
  • countpct: print the number of values in a category plus the column-percentage in the format N (%)
  • pct: print the column-percentage
  • countrowpct: print the number of values in a category plus the row-percentage in the format N (%)
  • rowpct: print the row-percentage
  • countcellpct: print the number of values in a category plus the cell-percentage in the format N (%)
  • binomCI: print the proportion in a category plus a binomial confidence interval.
  • rowbinomCI: print the row proportion in a category plus a binomial confidence interval.
  • medianq1q3: print the median, 25th, and 75th quantiles median (Q1, Q3)
  • q1q3: print the 25th and 75th quantiles Q1, Q3
  • iqr: print the inter-quartile range.
  • medianrange: print the median, minimum and maximum values median (minimum, maximum)
  • medianmad: print the median and median absolute deviation (mad)
  • Nevents: print number of events for a survival object within each grouping level
  • medSurv: print the median survival
  • NeventsSurv: print number of events and survival at given times
  • NriskSurv: print the number still at risk and survival at given times
  • Nrisk: print the number still at risk at given times
  • medTime: print the median follow-up time
  • sum
  • max
  • min
  • mean
  • sd
  • var
  • median
  • range
  • gmean, gsd, gmeansd, gmeanCI: geometric means, sds, and confidence intervals.

Testing options

The tests used to calculate p-values differ by the variable type, but can be specified explicitly in the formula statement or in the control function.

The following tests are accepted:

  • anova: analysis of variance test; the default test for continuous variables. When the grouping variable has two levels, it is equivalent to the two-sample t-test with equal variance.

  • kwt: Kruskal-Wallis test, optional test for continuous variables. When the grouping variable has two levels, it is equivalent to the Wilcoxon Rank Sum test.

  • wt: An explicit Wilcoxcon test.

  • medtest: Median test test, optional test for continuous variables.

  • chisq: chi-square goodness of fit test for equal counts of a categorical variable across categories; the default for categorical or factor variables

  • fe: Fisher’s exact test for categorical variables; optional

  • logrank: log-rank test, the default test for time-to-event variables

  • trend: The independence_test function from the coin is used to test for trends. Whenthe grouping variable has two levels, it is equivalent to the Armitage trend test. This is the default for ordered factors

  • notest: Don’t perform a test.

tableby.control settings

A quick way to see what arguments are possible to utilize in a function is to use the args() command. Settings involving the number of digits can be set in tableby.control or in summary.tableby.

args(tableby.control)
## function (test = TRUE, total = TRUE, total.pos = c("after", "before"), 
##     test.pname = NULL, numeric.simplify = FALSE, cat.simplify = FALSE, 
##     cat.droplevels = FALSE, ordered.simplify = FALSE, date.simplify = FALSE, 
##     numeric.test = "anova", cat.test = "chisq", ordered.test = "trend", 
##     surv.test = "logrank", date.test = "kwt", selectall.test = "notest", 
##     test.always = FALSE, numeric.stats = c("Nmiss", "meansd", 
##         "range"), cat.stats = c("Nmiss", "countpct"), ordered.stats = c("Nmiss", 
##         "countpct"), surv.stats = c("Nmiss", "Nevents", "medSurv"), 
##     date.stats = c("Nmiss", "median", "range"), selectall.stats = c("Nmiss", 
##         "countpct"), stats.labels = list(), digits = 3L, digits.count = 0L, 
##     digits.pct = 1L, digits.p = 3L, format.p = TRUE, digits.n = 0L, 
##     conf.level = 0.95, wilcox.correct = FALSE, wilcox.exact = NULL, 
##     chisq.correct = FALSE, simulate.p.value = FALSE, B = 2000, 
##     times = 1:5, ...) 
## NULL

summary.tableby settings

The summary.tableby function has options that modify how the table appears (such as adding a title or modifying labels).

## function (object, ..., labelTranslations = NULL, text = FALSE, 
##     title = NULL, pfootnote = FALSE, term.name = "") 
## NULL