compareGroups 4.0: Descriptives by groups

Isaac Subirana, Joan Vila and Héctor Sanz

2018-11-02

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1 Introduction

The compareGroups package (Subirana, Sanz, and Vila 2014) allows users to create tables displaying results of univariate analyses, stratified or not by categorical variable groupings.

Tables can easily be exported to CSV, LaTeX, HTML, PDF, Word or Excel, or inserted in R-markdown files to generate reports automatically.

This package can be used from the R prompt or from a user-friendly graphical user interface for non-R familiarized users.

The compareGroups package is available on CRAN repository. To load the package using the R prompt, enter:

library(compareGroups)

This document provides an overview of the usage of the compareGroups package with a real examples, both using the R syntax and the graphical user interface. It is structure as follows:

2 Package structure: classes and methods

The compareGroups package has three functions:

Figure 1 shows how the package is structured in terms of functions, classes and methods.

Figure 1. Diagram of package structure.

Figure 1. Diagram of package structure.

Since version 4.0, a new function called descrTable has been implemented which is a shortcut of compareGroupsand createTable, i.e. step 1 and step 2 in a single step (see section 4.2.5).

3 Data used as example

To illustrate how this package works we sampled 85% data from the participants in the PREDIMED study (Estruch et al. 2013). PREDIMED is a multicenter trial in Spain, were randomly assigned participants who were at high cardiovascular risk, but with no cardiovascular disease at enrolment, to one of three diets: a Mediterranean diet supplemented with extra-virgin olive oil (MedDiet+VOO), a Mediterranean diet supplemented with mixed nuts (MedDiet+Nuts), or a control diet (advice to reduce dietary fat). Participants received quarterly individual and group educational sessions and, depending on group assignment, free provision of extra-virgin olive oil, mixed nuts, or small non-food gifts. The primary end point was the rate of major cardiovascular events (myocardial infarction, stroke, or death from cardiovascular causes.

First of all, load PREDIMED data typing:

data(predimed)

Variables and labels in this data frame are:

Name Label Codes
group Intervention group Control; MedDiet + Nuts; MedDiet + VOO
sex Sex Male; Female
age Age
smoke Smoking Never; Current; Former
bmi Body mass index
waist Waist circumference
wth Waist-to-height ratio
htn Hypertension No; Yes
diab Type-2 diabetes No; Yes
hyperchol Dyslipidemia No; Yes
famhist Family history of premature CHD No; Yes
hormo Hormone-replacement therapy No; Yes
p14 MeDiet Adherence score
toevent follow-up to main event (years)
event AMI, stroke, or CV Death No; Yes

OBSERVATIONS:

  1. It is important to note that compareGroups is not aimed to perform quality control of the data. Other useful packages such as 2lh (Genolini, Desgraupes, and Franca 2011) are available for this purpose.

  2. It is strongly recommended that the data.frame contain only the variables to be analyzed; the ones not needed in the present analysis should be removed from the list.

  3. The nature of variables to be analyzed should be known, or at least which variables are to be used as categorical. It is important to code categorical variables as factors and the order of their levels is meaningful in this package.

  4. The function label from the Hmisc package could be used to label the variables properly. The tables of results will contain the variable labels (by default).

3.1 Time-to-event variables

A variable of class Surv must be created to deal with time-to-event variables (i.e., time to Cardiovascular event/censored in our example):

predimed$tmain <- with(predimed, Surv(toevent, event == "Yes"))
Hmisc::label(predimed$tmain) <- "AMI, stroke, or CV Death"

Note that variables tmain and tcv are created as time-to-death and time-to-cardiovascular event, respectively, both taking into account censoring (i.e. they are of class Surv).

4 Using syntax

4.1 Computing descriptives

compareGroups is the main function which does all the calculus. It is needed to store results in an object. Later, applying the function createTable (Section 4.2) to this object will create tables of the analysis results.

For example, to perform a univariate analysis with the predimed data between group (“response” variable) and all other variables (“explanatory” variables), this formula is required:

compareGroups(group ~ ., data = predimed)

4.1.1 Selecting response variables

If only a dot occurs on the right side of the ~ all variables in the data frame will be used.

To remove the variable toevent and event from the analysis:

compareGroups(group ~ . - toevent - event, data = predimed)

To select some explanatory variables (e.g., age, sex and waist) and store results in an object of class compareGroups:

res <- compareGroups(group ~ age + sex + smoke + waist + hormo, 
    data = predimed)
res


-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value  method            selection
1 Age                         6324 0.003**  continuous normal ALL      
2 Sex                         6324 <0.001** categorical       ALL      
3 Smoking                     6324 0.444    categorical       ALL      
4 Waist circumference         6324 0.045**  continuous normal ALL      
5 Hormone-replacement therapy 5661 0.850    categorical       ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Note: Although we have full data (n= 6324) for Age, Sex and Waist circumference, there are some missing data in Hormone-replacement therapy (probably male participants).

Diet groups have some differences in Smoking and Hormone-replacement therapy although those don’t reach statistical significance (p-value=0.714 and 0.859, repectively); although Age, Sex and Waist circumference are clearly different.

Age & Waist circumference has been used as continuous and normal distributed. Sex, Smoking & Hormone-replacement therapy as categorical.

No filters have been used (e.g., selecting only treated patients); therefore, the selection column lists “ALL” (for all variables).

4.1.2 Subsetting

To perform the analysis in a subset of participants (e.g., “female” participants):

compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    subset = sex == "Female")


-------- Summary of results by groups of 'group'---------


  var                         N    p.value method            selection      
1 Age                         3645 0.056*  continuous normal sex == "Female"
2 Smoking                     3645 0.907   categorical       sex == "Female"
3 Waist circumference         3645 0.016** continuous normal sex == "Female"
4 Hormone-replacement therapy 3459 0.898   categorical       sex == "Female"
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Note that only results for female participants are shown.

To subset specific variable/s (e.g., hormo and waist):

compareGroups(group ~ age + sex + smoke + waist + hormo, data = predimed, 
    selec = list(hormo = sex == "Female", waist = waist > 20))


-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value  method            selection      
1 Age                         6324 0.003**  continuous normal ALL            
2 Sex                         6324 <0.001** categorical       ALL            
3 Smoking                     6324 0.444    categorical       ALL            
4 Waist circumference         6324 0.045**  continuous normal waist > 20     
5 Hormone-replacement therapy 3459 0.898    categorical       sex == "Female"
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Combinations are also allowed, e.g.:

compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    selec = list(waist = !is.na(hormo)), subset = sex == "Female")


-------- Summary of results by groups of 'group'---------


  var                         N    p.value method            selection                          
1 Age                         3645 0.056*  continuous normal sex == "Female"                    
2 Smoking                     3645 0.907   categorical       sex == "Female"                    
3 Waist circumference         3459 0.007** continuous normal (sex == "Female") & (!is.na(hormo))
4 Hormone-replacement therapy 3459 0.898   categorical       sex == "Female"                    
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

A variable can appear twice in the formula, e.g.:

compareGroups(group ~ age + sex + bmi + bmi + waist + hormo, 
    data = predimed, selec = list(bmi.1 = !is.na(hormo)))


-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value  method            selection    
1 Age                         6324 0.003**  continuous normal ALL          
2 Sex                         6324 <0.001** categorical       ALL          
3 Body mass index             6324 <0.001** continuous normal ALL          
4 Body mass index             5661 <0.001** continuous normal !is.na(hormo)
5 Waist circumference         6324 0.045**  continuous normal ALL          
6 Hormone-replacement therapy 5661 0.850    categorical       ALL          
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

In this case results for bmi will be reported for all participants (n= 6324) and also for only those with no missing in Hormone-replacement therapy (!is.na(hormo)). Note that “bmi.1” in the selec argument refers to the second time that bmi appears in the formula.

4.1.3 Methods for continuous variables

By default continuous variables are analyzed as normal-distributed. When a table is built (see createTable function, Section 4.2), continuous variables will be described with mean and standard deviation. To change default options, e.g., “waist” used as non-normal distributed:

compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    method = c(waist = 2))


-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value method                selection
1 Age                         6324 0.003** continuous normal     ALL      
2 Smoking                     6324 0.444   categorical           ALL      
3 Waist circumference         6324 0.085*  continuous non-normal ALL      
4 Hormone-replacement therapy 5661 0.850   categorical           ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Note that “continuous non-normal” is shown in the method column for the variable Hormone-replacement therapy.

Possible values in methods statement are:

  • 1: forces analysis as normal-distributed

  • 2: forces analysis as continuous non-normal

  • 3: forces analysis as categorical

  • NA: performs a Shapiro-Wilks test to decide between normal or non-normal

If the method argument is stated as NA for a variable, then a Shapiro-Wilk test for normality is used to decide if the variable is normal or non-normal distributed. To change the significance threshold:

compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    method = c(waist = NA), alpha = 0.01)


-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value method                selection
1 Age                         6324 0.003** continuous normal     ALL      
2 Smoking                     6324 0.444   categorical           ALL      
3 Waist circumference         6324 0.085*  continuous non-normal ALL      
4 Hormone-replacement therapy 5661 0.850   categorical           ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

According to Shapiro-Wilk test, stating the cutpoint at 0.01 level, “Hormone-replacement therapy” departed significantly from the normal distribution and therefore the method for this variable will be “continuous non-normal”.

All non factor variables are considered as continuous. Exception is made (by default) for those that have fewer than 5 different values. This threshold can be changed in the min.dis statement:

predimed$age7gr <- as.integer(cut(predimed$age, breaks = c(-Inf, 
    55, 60, 65, 70, 75, 80, Inf), right = TRUE))
compareGroups(group ~ age7gr, data = predimed, method = c(age7gr = NA))


-------- Summary of results by groups of 'Intervention group'---------


  var    N    p.value method                selection
1 age7gr 6324 0.007** continuous non-normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 
compareGroups(group ~ age7gr, data = predimed, method = c(age7gr = NA), 
    min.dis = 8)


-------- Summary of results by groups of 'Intervention group'---------


  var    N    p.value method      selection
1 age7gr 6324 0.009** categorical ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

To avoid errors the maximum categories for the response variable is set at 5 in this example (default value). If this variable has more than 5 different values, the function compareGroups returns an error message. For example:

compareGroups(age7gr ~ sex + bmi + waist, data = predimed)
Error en compareGroups.default(X = X, y = y, include.label = include.label,  :
number of groups must be less or equal to 5

Defaults setting can be changed with the max.ylev statement:

compareGroups(age7gr ~ sex + bmi + waist, data = predimed, max.ylev = 7)


-------- Summary of results by groups of 'age7gr'---------


  var                 N    p.value  method            selection
1 Sex                 6324 <0.001** categorical       ALL      
2 Body mass index     6324 0.021**  continuous normal ALL      
3 Waist circumference 6324 0.034**  continuous normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Similarly, by default there is a limit for the maximum number of levels for an explanatory variable. If this level is exceeded, the variable is removed from the analysis and a warning message is printed:

compareGroups(group ~ sex + age7gr, method = (age7gr = 3), data = predimed, 
    max.xlev = 5)
Warning in compareGroups.default(X = X, y = y, include.label = include.label,  :
Variables 'age7gr' have been removed since some errors ocurred

4.1.4 Dressing up the output

Although the options described in this section correspond to compareGroups function, results of changing/setting them won’t be visible until the table is created with the createTable function (explained later).

  • include.label: By default the variable labels are shown in the output (if there is no label the name will be printed). Changing the statement include.label from “= TRUE” (default) to “= FALSE” will cause variable names to be printed instead.
compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    include.label = FALSE)


-------- Summary of results by groups of 'group'---------


  var   N    p.value method            selection
1 age   6324 0.003** continuous normal ALL      
2 smoke 6324 0.444   categorical       ALL      
3 waist 6324 0.045** continuous normal ALL      
4 hormo 5661 0.850   categorical       ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 
  • Q1, Q3: When the method for a variable is stated as “2” (i.e., to be analyzed as continuous non-normal; see section 4.1.3), by default the median and quartiles 1 and 3 will be shown in the final results, after applying the function createTable (see Section 4.2).
resu1 <- compareGroups(group ~ age + waist, data = predimed, 
    method = c(waist = 2))
createTable(resu1)

--------Summary descriptives table by 'Intervention group'---------

__________________________________________________________________________ 
                       Control     MedDiet + Nuts MedDiet + VOO  p.overall 
                        N=2042         N=2100         N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                  67.3 (6.28)    66.7 (6.02)    67.0 (6.21)     0.003   
Waist circumference 101 [94.0;108] 100 [93.0;107] 100 [93.0;107]   0.085   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: percentiles 25 and 75 are calculated for “Waist circumference”.

To get instead percentile 2.5% and 97.5%:

resu2 <- compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    method = c(waist = 2), Q1 = 0.025, Q3 = 0.975)
createTable(resu2)

--------Summary descriptives table by 'Intervention group'---------

___________________________________________________________________________________ 
                                Control     MedDiet + Nuts MedDiet + VOO  p.overall 
                                 N=2042         N=2100         N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                           67.3 (6.28)    66.7 (6.02)    67.0 (6.21)     0.003   
Smoking:                                                                    0.444   
    Never                     1282 (62.8%)   1259 (60.0%)   1351 (61.9%)            
    Current                   270 (13.2%)    296 (14.1%)    292 (13.4%)             
    Former                    490 (24.0%)    545 (26.0%)    539 (24.7%)             
Waist circumference          101 [80.0;123] 100 [80.0;121] 100 [80.0;121]   0.085   
Hormone-replacement therapy:                                                0.850   
    No                        1811 (98.3%)   1835 (98.4%)   1918 (98.2%)            
    Yes                        31 (1.68%)     30 (1.61%)     36 (1.84%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: percentiles 2.5% and 97.5% are calculated for Follow-up.

To get minimum and maximum:

compareGroups(group ~ age + smoke + waist + hormo, data = predimed, 
    method = c(waist = 2), Q1 = 0, Q3 = 1)
  • simplify: Sometimes a categorical variable has no individuals for a specific group. For example, smoker has 3 levels. As an example and to illustrate this problem, we have created a new variable smk with a new category (“Unknown”):
predimed$smk <- predimed$smoke
levels(predimed$smk) <- c("Never smoker", "Current or former < 1y", 
    "Never or former >= 1y", "Unknown")
Hmisc::label(predimed$smk) <- "Smoking 4 cat."
cbind(table(predimed$smk))
                       [,1]
Never smoker           3892
Current or former < 1y  858
Never or former >= 1y  1574
Unknown                   0

Note that this new category (“unknown”) has no individuals:

compareGroups(group ~ age + smk + waist + hormo, data = predimed)

-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value method            selection
1 Age                         6324 0.001** continuous normal ALL      
2 Smoking 4 cat.              6324 0.714   categorical       ALL      
3 Waist circumference         6324 0.019** continuous normal ALL      
4 Hormone-replacement therapy 5650 0.859   categorical       ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Warning message:
In compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i],  :
  Some levels of 'smk' are removed since no observation in that/those levels

Note that a “Warning” message is printed related to the problem with smk.

To avoid using empty categories, simplify must be stated as TRUE (Default value).

compareGroups(group ~ age + smk, data = predimed, simplify = FALSE)

-------- Summary of results by groups of 'Intervention group'---------


  var            N    p.value method            selection
1 Age            6324 0.001** continuous normal ALL      
2 Smoking 4 cat. 6324 .       categorical       ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Warning messages:
1: In chisq.test(obj, simulate.p.value = TRUE) :
  cannot compute simulated p-value with zero marginals
2: In chisq.test(obj, simulate.p.value = TRUE) :
  Chi-squared approximation may be incorrect

Nota that a “warning” message is shown and no p-values are calculated for “Smoking”.

4.1.5 Summary

Applying the summary function to an object of class createTable will obtain a more detailed output:

res <- compareGroups(group ~ age + sex + smoke + waist + hormo, 
    method = c(waist = 2), data = predimed)
summary(res[c(1, 2, 4)])

 --- Descriptives of each row-variable by groups of 'Intervention group' ---

------------------- 
row-variable: Age 

               N    mean     sd       p.overall p.trend  p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO p.MedDiet + Nuts vs MedDiet + VOO
[ALL]          6324 67.0117  6.17499                                                                                                             
Control        2042 67.34231 6.27992  0.002666  0.101163 0.001672                    0.20596                    0.172672                         
MedDiet + Nuts 2100 66.6819  6.016395                                                                                                            
MedDiet + VOO  2182 67.01971 6.212578                                                                                                            

------------------- 
row-variable: Sex 

               Male Female Male (row%) Female (row%) p.overall p.trend  p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO
[ALL]          2679 3645   42.36243    57.63757                                                                               
Control        812  1230   39.76494    60.23506      8.1e-05   0.388386 0.000133                    0.358324                  
MedDiet + Nuts 968  1132   46.09524    53.90476                                                                               
MedDiet + VOO  899  1283   41.20073    58.79927                                                                               
               p.MedDiet + Nuts vs MedDiet + VOO
[ALL]                                           
Control        0.002076                         
MedDiet + Nuts                                  
MedDiet + VOO                                   

------------------- 
row-variable: Waist circumference 

               N    med Q1 Q3  p.overall p.trend  p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO p.MedDiet + Nuts vs MedDiet + VOO
[ALL]          6324 100 93 107                                                                                                            
Control        2042 101 94 108 0.084601  0.039557 0.125792                    0.110639                   0.743479                         
MedDiet + Nuts 2100 100 93 107                                                                                                            
MedDiet + VOO  2182 100 93 107                                                                                                            

Note that because only variables 1, 3 and 4 are selected, only results for Age, Sex and Waist circumference are shown. Age is summarized by the mean and the standard deviation, Sex by frequencies and percentage, and Waist circumference (method =2) by the median and quartiles.

4.1.6 Plotting

Variables can be plotted to see their distribution. Plots differ according to whether the variable is continuous or categorical. Plots can be seen on-screen or saved in different formats (BMP, JPG’, PNG, TIF or PDF). To specify the format use the argument `type’.

plot(res[c(1, 2)], file = "./figures/univar/", type = "png")

alt text alt text

Plots also can be done according to grouping variable. In this case only a boxplot is shown for continuous variables:

plot(res[c(1, 2)], bivar = TRUE, file = "./figures/bivar/", type = "png")

alt text alt text

4.1.7 Updating

The object from compareGroups can later be updated. For example:

res <- compareGroups(group ~ age + sex + smoke + waist + hormo, 
    data = predimed)
res


-------- Summary of results by groups of 'Intervention group'---------


  var                         N    p.value  method            selection
1 Age                         6324 0.003**  continuous normal ALL      
2 Sex                         6324 <0.001** categorical       ALL      
3 Smoking                     6324 0.444    categorical       ALL      
4 Waist circumference         6324 0.045**  continuous normal ALL      
5 Hormone-replacement therapy 5661 0.850    categorical       ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

The object res is updated using:

res <- update(res, . ~ . - sex + bmi + toevent, subset = sex == 
    "Female", method = c(waist = 2, tovent = 2), selec = list(bmi = !is.na(hormo)))
res

Note that “Sex” is removed as an explanatory variable but used as a filter, subsetting only “Female” participants. Variable “Waist circumference” has been changed to “continuous non-normal”. Two new variables have been added: Body mass index and Follow-up (stated continuous non-normal). For Body mass index is stated to show only data of participants with non-missing values in Hormone-replacement therapy.

4.1.8 Substracting results

Since version 3.0, there is a new function called getResults to retrieve some specific results computed by compareGroups, such as p-values, descriptives (means, proportions, …), etc.

For example, it may be interesting to recover the p-values for each variable as a vector to further manipulate it in R, like adjusting for multiple comparison with p.adjust. For example, lets take the example data from SNPassoc package that contains information of dozens of SNPs (genetic variants) from a sample of cases and controls. In this case we analize five of them:

library(SNPassoc)
data(SNPs)
tab <- createTable(compareGroups(casco ~ snp10001 + snp10002 + 
    snp10005 + snp10008 + snp10009, SNPs))
pvals <- getResults(tab, "p.overall")
p.adjust(pvals, method = "BH")
 snp10001  snp10002  snp10005  snp10008  snp10009 
0.7051300 0.7072158 0.7583432 0.7583432 0.7072158 

4.1.9 Odds Ratios and Hazard Ratios

When the response variable is binary, the Odds Ratio (OR) can be printed in the final table. If the response variable is time-to-event (see Section 3.1), the Hazard Ratio (HR) can be printed instead.

  • ref: This statement can be used to change the reference category:
res1 <- compareGroups(htn ~ age + sex + bmi + smoke, data = predimed, 
    ref = 1)
createTable(res1, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

___________________________________________________________________________ 
                    No          Yes             OR        p.ratio p.overall 
                  N=1089       N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             65.9 (6.19) 67.2 (6.15)  1.04 [1.03;1.05] <0.001   <0.001   
Sex:                                                               <0.001   
    Male        595 (54.6%) 2084 (39.8%)       Ref.        Ref.             
    Female      494 (45.4%) 3151 (60.2%) 1.82 [1.60;2.08]  0.000            
Body mass index 28.9 (3.69) 30.2 (3.80)  1.10 [1.08;1.12] <0.001   <0.001   
Smoking:                                                           <0.001   
    Never       536 (49.2%) 3356 (64.1%)       Ref.        Ref.             
    Current     233 (21.4%) 625 (11.9%)  0.43 [0.36;0.51]  0.000            
    Former      320 (29.4%) 1254 (24.0%) 0.63 [0.54;0.73] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that for categorical response variables the reference category is the first one in the statement:

res2 <- compareGroups(htn ~ age + sex + bmi + smoke, data = predimed, 
    ref = c(smoke = 1, sex = 2))
createTable(res2, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

___________________________________________________________________________ 
                    No          Yes             OR        p.ratio p.overall 
                  N=1089       N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             65.9 (6.19) 67.2 (6.15)  1.04 [1.03;1.05] <0.001   <0.001   
Sex:                                                               <0.001   
    Male        595 (54.6%) 2084 (39.8%) 0.55 [0.48;0.63]  0.000            
    Female      494 (45.4%) 3151 (60.2%)       Ref.        Ref.             
Body mass index 28.9 (3.69) 30.2 (3.80)  1.10 [1.08;1.12] <0.001   <0.001   
Smoking:                                                           <0.001   
    Never       536 (49.2%) 3356 (64.1%)       Ref.        Ref.             
    Current     233 (21.4%) 625 (11.9%)  0.43 [0.36;0.51]  0.000            
    Former      320 (29.4%) 1254 (24.0%) 0.63 [0.54;0.73] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that the reference category for Smoking status is the first and for Sex the second.

  • ref.no: Similarly to the ref statement, ref.no is used to state “no” as the reference category for all variables with this category:
res <- compareGroups(htn ~ age + sex + bmi + hormo + hyperchol, 
    data = predimed, ref.no = "NO")
createTable(res, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

________________________________________________________________________________________ 
                                 No          Yes             OR        p.ratio p.overall 
                               N=1089       N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          65.9 (6.19) 67.2 (6.15)  1.04 [1.03;1.05] <0.001   <0.001   
Sex:                                                                            <0.001   
    Male                     595 (54.6%) 2084 (39.8%)       Ref.        Ref.             
    Female                   494 (45.4%) 3151 (60.2%) 1.82 [1.60;2.08]  0.000            
Body mass index              28.9 (3.69) 30.2 (3.80)  1.10 [1.08;1.12] <0.001   <0.001   
Hormone-replacement therapy:                                                     0.856   
    No                       928 (98.4%) 4636 (98.3%)       Ref.        Ref.             
    Yes                      15 (1.59%)   82 (1.74%)  1.08 [0.64;1.97]  0.773            
Dyslipidemia:                                                                   <0.001   
    No                       409 (37.6%) 1337 (25.5%)       Ref.        Ref.             
    Yes                      680 (62.4%) 3898 (74.5%) 1.75 [1.53;2.01] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: “no”, “No” or “NO” will produce the same results; the coding is not case sensitive.

  • fact.ratio: By default OR or HR for continuous variables are calculated for each unit increase. It can be changed by the fact.or statement:
res <- compareGroups(htn ~ age + bmi, data = predimed)
createTable(res, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

__________________________________________________________________________ 
                    No          Yes            OR        p.ratio p.overall 
                  N=1089      N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             65.9 (6.19) 67.2 (6.15) 1.04 [1.03;1.05] <0.001   <0.001   
Body mass index 28.9 (3.69) 30.2 (3.80) 1.10 [1.08;1.12] <0.001   <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Here the OR is for the increase of one unit for Age and Systolic blood pressure.

res <- compareGroups(htn ~ age + bmi, data = predimed, fact.ratio = c(age = 10, 
    bmi = 2))
createTable(res, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

__________________________________________________________________________ 
                    No          Yes            OR        p.ratio p.overall 
                  N=1089      N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             65.9 (6.19) 67.2 (6.15) 1.43 [1.28;1.59] <0.001   <0.001   
Body mass index 28.9 (3.69) 30.2 (3.80) 1.22 [1.17;1.26] <0.001   <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Here the OR is for the increase of 10 years for Age and 2 units for “Body mass index”.

  • ref.y: By default when OR or HR are calculated, the reference category for the response variable is the first. The reference category could be changed using the ref.y statement:
res <- compareGroups(htn ~ age + sex + bmi + hyperchol, data = predimed)
createTable(res, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

___________________________________________________________________________ 
                    No          Yes             OR        p.ratio p.overall 
                  N=1089       N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             65.9 (6.19) 67.2 (6.15)  1.04 [1.03;1.05] <0.001   <0.001   
Sex:                                                               <0.001   
    Male        595 (54.6%) 2084 (39.8%)       Ref.        Ref.             
    Female      494 (45.4%) 3151 (60.2%) 1.82 [1.60;2.08]  0.000            
Body mass index 28.9 (3.69) 30.2 (3.80)  1.10 [1.08;1.12] <0.001   <0.001   
Dyslipidemia:                                                      <0.001   
    No          409 (37.6%) 1337 (25.5%)       Ref.        Ref.             
    Yes         680 (62.4%) 3898 (74.5%) 1.75 [1.53;2.01] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: This output shows the OR of having hypertension. Therefore, “Non-hypertension” is the reference category.

res <- compareGroups(htn ~ age + sex + bmi + hyperchol, data = predimed, 
    ref.y = 2)
createTable(res, show.ratio = TRUE)

--------Summary descriptives table by 'Hypertension'---------

___________________________________________________________________________ 
                    No          Yes             OR        p.ratio p.overall 
                  N=1089       N=5235                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             65.9 (6.19) 67.2 (6.15)  0.96 [0.98;0.95] <0.001   <0.001   
Sex:                                                               <0.001   
    Male        595 (54.6%) 2084 (39.8%)       Ref.        Ref.             
    Female      494 (45.4%) 3151 (60.2%) 0.55 [0.48;0.63]  0.000            
Body mass index 28.9 (3.69) 30.2 (3.80)  0.91 [0.92;0.89] <0.001   <0.001   
Dyslipidemia:                                                      <0.001   
    No          409 (37.6%) 1337 (25.5%)       Ref.        Ref.             
    Yes         680 (62.4%) 3898 (74.5%) 0.57 [0.50;0.65] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: This output shows the OR of having No hypertension, and ‘Hypertension’ is now the reference category.

When the response variable is of class Surv, the bivariate plot function returns a Kaplan-Meier figure if the explanatory variable is categorical. For continuous variables the function returns a line for each individual, ending with a circle for censored and with a plus sign for uncensored.

plot(compareGroups(tmain ~ sex, data = predimed), bivar = TRUE, 
    file = "./figures/bivarsurv/", type = "png")
plot(compareGroups(tmain ~ age, data = predimed), bivar = TRUE, 
    file = "./figures/bivarsurv/", type = "png")

alt text alt text

4.1.10 Time-to-event explanatory variables

When a variable of class Surv (see Section 3.1) is used as explanatory it will be described with the probability of event, computed by Kaplan-Meier, up to a stated time.

  • timemax: By default probability is calculated at the median of the follow-up period. timemax option allows us to change at what time probability is calculated.
res <- compareGroups(sex ~ age + tmain, timemax = c(tmain = 3), 
    data = predimed)
res

Note that tmain is calculated at 3 years (see section 3.1).

The plot function applied to a variable of class Surv returns a Kaplan-Meier figure. The figure can be stratified by the grouping variable.

plot(res[2], file = "./figures/univar/", type = "png")
plot(res[2], bivar = TRUE, file = "./figures/bivar/", type = "png")

alt text alt text

4.2 Performing the descritive table

The createTable function, applied to an object of compareGroups class, returns tables with descriptives that can be displayed on-screen or exported to CSV, LaTeX, HTML, Word or Excel.

res <- compareGroups(group ~ age + sex + smoke + waist + hormo, 
    data = predimed, selec = list(hormo = sex == "Female"))
restab <- createTable(res)

Two tables are created with the createTable function: one with the descriptives and the other with the available data. The print method applied to an object of class createTable prints one or both tables on the R console:

print(restab, which.table = "descr")

--------Summary descriptives table by 'Intervention group'---------

________________________________________________________________________________ 
                               Control    MedDiet + Nuts MedDiet + VOO p.overall 
                                N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                                    <0.001   
    Male                     812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female                   1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Smoking:                                                                 0.444   
    Never                    1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current                  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Former                   490 (24.0%)   545 (26.0%)    539 (24.7%)            
Waist circumference           101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Hormone-replacement therapy:                                             0.898   
    No                       1143 (97.4%)  1036 (97.2%)  1183 (97.0%)            
    Yes                       31 (2.64%)    30 (2.81%)    36 (2.95%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that the option “descr” prints descriptive tables.

print(restab, which.table = "avail")



---Available data----

________________________________________________________________________________________________________ 
                            [ALL] Control MedDiet + Nuts MedDiet + VOO      method           select      
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                         6324   2042        2100          2182      continuous-normal       ALL       
Sex                         6324   2042        2100          2182         categorical          ALL       
Smoking                     6324   2042        2100          2182         categorical          ALL       
Waist circumference         6324   2042        2100          2182      continuous-normal       ALL       
Hormone-replacement therapy 3459   1174        1066          1219         categorical    sex == "Female" 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

While the option “avail” prints the available data, as well as methods and selections.

By default, only the descriptives table is shown. Stating “both” in which.table argument prints both tables.

4.2.1 Dressing up tables

  • hide: If the explanatory variable is dichotomous, one of the categories often is hidden in the results displayed (i.e., if 42.4% are male, obviously 57.6% are female). To hide some category, e.g., “Male”:
update(restab, hide = c(sex = "Male"))

--------Summary descriptives table by 'Intervention group'---------

________________________________________________________________________________ 
                               Control    MedDiet + Nuts MedDiet + VOO p.overall 
                                N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex: Female                  1230 (60.2%)  1132 (53.9%)  1283 (58.8%)   <0.001   
Smoking:                                                                 0.444   
    Never                    1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current                  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Former                   490 (24.0%)   545 (26.0%)    539 (24.7%)            
Waist circumference           101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Hormone-replacement therapy:                                             0.898   
    No                       1143 (97.4%)  1036 (97.2%)  1183 (97.0%)            
    Yes                       31 (2.64%)    30 (2.81%)    36 (2.95%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that the percentage of males is hidden.

  • hide.no: Similarly, as explained above, if the category “no” is to be hidden for all variables:
res <- compareGroups(group ~ age + sex + htn + diab, data = predimed)
createTable(res, hide.no = "no", hide = c(sex = "Male"))

--------Summary descriptives table by 'Intervention group'---------

___________________________________________________________________ 
                  Control    MedDiet + Nuts MedDiet + VOO p.overall 
                   N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex: Female     1230 (60.2%)  1132 (53.9%)  1283 (58.8%)   <0.001   
Hypertension    1711 (83.8%)  1738 (82.8%)  1786 (81.9%)    0.249   
Type-2 diabetes 970 (47.5%)   950 (45.2%)   1082 (49.6%)    0.017   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: “no”, “No” or “NO” will produce the same results; the coding is not case sensitive.

  • digits: The number of digits that appear in the results can be changed, e.g:
createTable(res, digits = c(age = 2, sex = 3))

--------Summary descriptives table by 'Intervention group'---------

_______________________________________________________________________ 
                    Control     MedDiet + Nuts MedDiet + VOO  p.overall 
                     N=2042         N=2100         N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age               67.34 (6.28)   66.68 (6.02)   67.02 (6.21)    0.003   
Sex:                                                           <0.001   
    Male         812 (39.765%)  968 (46.095%)  899 (41.201%)            
    Female       1230 (60.235%) 1132 (53.905%) 1283 (58.799%)           
Hypertension:                                                   0.249   
    No            331 (16.2%)    362 (17.2%)    396 (18.1%)             
    Yes           1711 (83.8%)   1738 (82.8%)   1786 (81.9%)            
Type-2 diabetes:                                                0.017   
    No            1072 (52.5%)   1150 (54.8%)   1100 (50.4%)            
    Yes           970 (47.5%)    950 (45.2%)    1082 (49.6%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that mean and standard deviation has two decimal places for age, while percentage in sex has been set to three decimal places.

  • type: By default categorical variables are summarized by frequencies and percentages. This can be changed by the type argument:
createTable(res, type = 1)

--------Summary descriptives table by 'Intervention group'---------

___________________________________________________________________ 
                   Control   MedDiet + Nuts MedDiet + VOO p.overall 
                   N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.3 (6.28)  66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                       <0.001   
    Male            39.8%        46.1%          41.2%               
    Female          60.2%        53.9%          58.8%               
Hypertension:                                               0.249   
    No              16.2%        17.2%          18.1%               
    Yes             83.8%        82.8%          81.9%               
Type-2 diabetes:                                            0.017   
    No              52.5%        54.8%          50.4%               
    Yes             47.5%        45.2%          49.6%               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that only percentages are displayed.

createTable(res, type = 3)

--------Summary descriptives table by 'Intervention group'---------

___________________________________________________________________ 
                   Control   MedDiet + Nuts MedDiet + VOO p.overall 
                   N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.3 (6.28)  66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                       <0.001   
    Male             812          968            899                
    Female          1230          1132          1283                
Hypertension:                                               0.249   
    No               331          362            396                
    Yes             1711          1738          1786                
Type-2 diabetes:                                            0.017   
    No              1072          1150          1100                
    Yes              970          950           1082                
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that only frequencies are displayed.

Value 2 or NA return the same results, i.e., the default option.

  • show.n: If option show.n is set to TRUE a column with available data for each variable appears in the results:
createTable(res, show.n = TRUE)

--------Summary descriptives table by 'Intervention group'---------

_________________________________________________________________________ 
                   Control    MedDiet + Nuts MedDiet + VOO p.overall  N   
                    N=2042        N=2100        N=2182                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   6324 
Sex:                                                        <0.001   6324 
    Male         812 (39.8%)   968 (46.1%)    899 (41.2%)                 
    Female       1230 (60.2%)  1132 (53.9%)  1283 (58.8%)                 
Hypertension:                                                0.249   6324 
    No           331 (16.2%)   362 (17.2%)    396 (18.1%)                 
    Yes          1711 (83.8%)  1738 (82.8%)  1786 (81.9%)                 
Type-2 diabetes:                                             0.017   6324 
    No           1072 (52.5%)  1150 (54.8%)  1100 (50.4%)                 
    Yes          970 (47.5%)   950 (45.2%)   1082 (49.6%)                 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • show.descr: If argument show.descr is set to FALSE only p-values are displayed:
createTable(res, show.descr = FALSE)

--------Summary descriptives table by 'Intervention group'---------

__________________________ 
                 p.overall 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                0.003   
Sex:                       
    Male          <0.001   
    Female                 
Hypertension:              
    No             0.249   
    Yes                    
Type-2 diabetes:           
    No             0.017   
    Yes                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • show.all: If show.all argument is set to TRUE a column is displayed with descriptives for all data:
createTable(res, show.all = TRUE)

--------Summary descriptives table by 'Intervention group'---------

_________________________________________________________________________________ 
                    [ALL]       Control    MedDiet + Nuts MedDiet + VOO p.overall 
                    N=6324       N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.0 (6.17)  67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                                     <0.001   
    Male         2679 (42.4%) 812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female       3645 (57.6%) 1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Hypertension:                                                             0.249   
    No           1089 (17.2%) 331 (16.2%)   362 (17.2%)    396 (18.1%)            
    Yes          5235 (82.8%) 1711 (83.8%)  1738 (82.8%)  1786 (81.9%)            
Type-2 diabetes:                                                          0.017   
    No           3322 (52.5%) 1072 (52.5%)  1150 (54.8%)  1100 (50.4%)            
    Yes          3002 (47.5%) 970 (47.5%)   950 (45.2%)   1082 (49.6%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • show.p.overall: If show.p.overall argument is set to FALSE p-values are omitted from the table:
createTable(res, show.p.overall = FALSE)

--------Summary descriptives table by 'Intervention group'---------

__________________________________________________________ 
                   Control    MedDiet + Nuts MedDiet + VOO 
                    N=2042        N=2100        N=2182     
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.3 (6.28)   66.7 (6.02)    67.0 (6.21)  
Sex:                                                       
    Male         812 (39.8%)   968 (46.1%)    899 (41.2%)  
    Female       1230 (60.2%)  1132 (53.9%)  1283 (58.8%)  
Hypertension:                                              
    No           331 (16.2%)   362 (17.2%)    396 (18.1%)  
    Yes          1711 (83.8%)  1738 (82.8%)  1786 (81.9%)  
Type-2 diabetes:                                           
    No           1072 (52.5%)  1150 (54.8%)  1100 (50.4%)  
    Yes          970 (47.5%)   950 (45.2%)   1082 (49.6%)  
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • show.p.trend: If the response variable has more than two categories a p-value for trend can be calculated. Results are displayed if the show.p.trend argument is set to TRUE:
createTable(res, show.p.trend = TRUE)

--------Summary descriptives table by 'Intervention group'---------

____________________________________________________________________________ 
                   Control    MedDiet + Nuts MedDiet + VOO p.overall p.trend 
                    N=2042        N=2100        N=2182                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003    0.101  
Sex:                                                        <0.001    0.388  
    Male         812 (39.8%)   968 (46.1%)    899 (41.2%)                    
    Female       1230 (60.2%)  1132 (53.9%)  1283 (58.8%)                    
Hypertension:                                                0.249    0.096  
    No           331 (16.2%)   362 (17.2%)    396 (18.1%)                    
    Yes          1711 (83.8%)  1738 (82.8%)  1786 (81.9%)                    
Type-2 diabetes:                                             0.017    0.160  
    No           1072 (52.5%)  1150 (54.8%)  1100 (50.4%)                    
    Yes          970 (47.5%)   950 (45.2%)   1082 (49.6%)                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: The p-value for trend is computed from the Pearson test when row-variable is normal and from the Spearman test when it is continuous non-normal. If row-variable is of class Surv, the test score is computed from a Cox model where the grouping variable is introduced as an integer variable predictor. If the row-variable is categorical, the p-value for trend is computed as 1-pchisq(cor(as.integer(x),as.integer(y))^2*(length(x)-1),1)

  • show.p.mul: For a response variable with more than two categories a pairwise comparison of p-values, corrected for multiple comparisons, can be calculated. Results are displayed if the show.p.mul argument is set to TRUE:
createTable(res, show.p.mul = TRUE)

--------Summary descriptives table by 'Intervention group'---------

_____________________________________________________________________________________________________________________________________________________________ 
                   Control    MedDiet + Nuts MedDiet + VOO p.overall p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO p.MedDiet + Nuts vs MedDiet + VOO 
                    N=2042        N=2100        N=2182                                                                                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age              67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003              0.002                      0.206                          0.173               
Sex:                                                        <0.001             <0.001                      0.358                          0.002               
    Male         812 (39.8%)   968 (46.1%)    899 (41.2%)                                                                                                     
    Female       1230 (60.2%)  1132 (53.9%)  1283 (58.8%)                                                                                                     
Hypertension:                                                0.249              0.459                      0.311                          0.459               
    No           331 (16.2%)   362 (17.2%)    396 (18.1%)                                                                                                     
    Yes          1711 (83.8%)  1738 (82.8%)  1786 (81.9%)                                                                                                     
Type-2 diabetes:                                             0.017              0.185                      0.185                          0.014               
    No           1072 (52.5%)  1150 (54.8%)  1100 (50.4%)                                                                                                     
    Yes          970 (47.5%)   950 (45.2%)   1082 (49.6%)                                                                                                     
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: Tukey method is used when explanatory variable is normal-distributed and Benjamini & Hochberg (Benjamini and Hochberg 1995) method otherwise.

  • show.ratio: If response variable is dichotomous or has been defined as class survival (see Section 3.1), Odds Ratios and Hazard Ratios can be displayed in the results by stating TRUE at the show.ratio option:
createTable(update(res, subset = group != "Control"), show.ratio = TRUE)

--------Summary descriptives table by 'group'---------

________________________________________________________________________________ 
                 MedDiet + Nuts MedDiet + VOO        OR        p.ratio p.overall 
                     N=2100        N=2182                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age               66.7 (6.02)    67.0 (6.21)  1.01 [1.00;1.02]  0.071    0.071   
Sex:                                                                     0.001   
    Male          968 (46.1%)    899 (41.2%)        Ref.        Ref.             
    Female        1132 (53.9%)  1283 (58.8%)  1.22 [1.08;1.38]  0.001            
Hypertension:                                                            0.459   
    No            362 (17.2%)    396 (18.1%)        Ref.        Ref.             
    Yes           1738 (82.8%)  1786 (81.9%)  0.94 [0.80;1.10]  0.436            
Type-2 diabetes:                                                         0.005   
    No            1150 (54.8%)  1100 (50.4%)        Ref.        Ref.             
    Yes           950 (45.2%)   1082 (49.6%)  1.19 [1.06;1.34]  0.004            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that category “Control diet” of the response variable has been omitted in order to have only two categories (i.e., a dichotomous variable). No Odds Ratios would be calculated if response variable has more than two categories.

Note that when response variable is of class Surv, Hazard Ratios are calculated instead of Odds Ratios.

createTable(compareGroups(tmain ~ group + age + sex, data = predimed), 
    show.ratio = TRUE)

--------Summary descriptives table by 'AMI, stroke, or CV Death'---------

_______________________________________________________________________________ 
                      No event      Event           HR        p.ratio p.overall 
                       N=6072       N=252                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Intervention group:                                                     0.011   
    Control         1945 (32.0%) 97 (38.5%)        Ref.        Ref.             
    MedDiet + Nuts  2030 (33.4%) 70 (27.8%)  0.66 [0.48;0.89]  0.008            
    MedDiet + VOO   2097 (34.5%) 85 (33.7%)  0.70 [0.53;0.94]  0.018            
Age                 66.9 (6.14)  69.4 (6.65) 1.06 [1.04;1.09] <0.001   <0.001   
Sex:                                                                   <0.001   
    Male            2528 (41.6%) 151 (59.9%)       Ref.        Ref.             
    Female          3544 (58.4%) 101 (40.1%) 0.49 [0.38;0.63] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • digits.ratio: The number of decimal places for Odds/Hazard ratios can be changed by the digits.ratio argument:
createTable(compareGroups(tmain ~ group + age + sex, data = predimed), 
    show.ratio = TRUE, digits.ratio = 3)

--------Summary descriptives table by 'AMI, stroke, or CV Death'---------

__________________________________________________________________________________ 
                      No event      Event            HR          p.ratio p.overall 
                       N=6072       N=252                                          
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Intervention group:                                                        0.011   
    Control         1945 (32.0%) 97 (38.5%)         Ref.          Ref.             
    MedDiet + Nuts  2030 (33.4%) 70 (27.8%)  0.658 [0.484;0.894]  0.008            
    MedDiet + VOO   2097 (34.5%) 85 (33.7%)  0.703 [0.525;0.941]  0.018            
Age                 66.9 (6.14)  69.4 (6.65) 1.065 [1.043;1.086] <0.001   <0.001   
Sex:                                                                      <0.001   
    Male            2528 (41.6%) 151 (59.9%)        Ref.          Ref.             
    Female          3544 (58.4%) 101 (40.1%) 0.488 [0.379;0.628] <0.001            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • header.labels: Change some key table header, such as the p.overall, etc. Note that this is done when printing the table changing the argument in the print function and not in the createTable function. This argument is also present in other function that exports the table to pdf, plain text, etc.
tab <- createTable(compareGroups(tmain ~ group + age + sex, data = predimed), 
    show.all = TRUE)
print(tab, header.labels = c(p.overall = "p-value", all = "All"))

--------Summary descriptives table by 'AMI, stroke, or CV Death'---------

_________________________________________________________________ 
                        All        No event      Event    p-value 
                       N=6324       N=6072       N=252            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Intervention group:                                        0.011  
    Control         2042 (32.3%) 1945 (32.0%) 97 (38.5%)          
    MedDiet + Nuts  2100 (33.2%) 2030 (33.4%) 70 (27.8%)          
    MedDiet + VOO   2182 (34.5%) 2097 (34.5%) 85 (33.7%)          
Age                 67.0 (6.17)  66.9 (6.14)  69.4 (6.65) <0.001  
Sex:                                                      <0.001  
    Male            2679 (42.4%) 2528 (41.6%) 151 (59.9%)         
    Female          3645 (57.6%) 3544 (58.4%) 101 (40.1%)         
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

4.2.2 Combining tables by row (groups of variable)

Tables made with the same response variable can be combined by row:

restab1 <- createTable(compareGroups(group ~ age + sex, data = predimed))
restab2 <- createTable(compareGroups(group ~ bmi + smoke, data = predimed))
rbind(`Non-modifiable risk factors` = restab1, `Modifiable risk factors` = restab2)

--------Summary descriptives table by 'Intervention group'---------

_______________________________________________________________________ 
                      Control    MedDiet + Nuts MedDiet + VOO p.overall 
                       N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Non-modifiable risk factors:
    Age             67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
    Sex:                                                       <0.001   
        Male        812 (39.8%)   968 (46.1%)    899 (41.2%)            
        Female      1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Modifiable risk factors:
    Body mass index 30.3 (3.96)   29.7 (3.77)    29.9 (3.71)   <0.001   
    Smoking:                                                    0.444   
        Never       1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
        Current     270 (13.2%)   296 (14.1%)    292 (13.4%)            
        Former      490 (24.0%)   545 (26.0%)    539 (24.7%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note how variables are grouped under “Non-modifiable” and “Modifiable”" risk factors because of an epigraph defined in the rbind command in the example.

The resulting object is of class rbind.createTable, which can be subset but not updated. It inherits the class createTable. Therefore, columns and other arguments from the createTable function cannot be modified:

To select only Age and Smoking:

x <- rbind(`Non-modifiable` = restab1, Modifiable = restab2)
rbind(`Non-modifiable` = restab1, Modifiable = restab2)[c(1, 
    4)]

--------Summary descriptives table by 'Intervention group'---------

___________________________________________________________________ 
                  Control    MedDiet + Nuts MedDiet + VOO p.overall 
                   N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Non-modifiable:
    Age         67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Modifiable:
    Smoking:                                                0.444   
        Never   1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
        Current 270 (13.2%)   296 (14.1%)    292 (13.4%)            
        Former  490 (24.0%)   545 (26.0%)    539 (24.7%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

To change the order:

rbind(Modifiable = restab1, `Non-modifiable` = restab2)[c(4, 
    3, 2, 1)]

--------Summary descriptives table by 'Intervention group'---------

_______________________________________________________________________ 
                      Control    MedDiet + Nuts MedDiet + VOO p.overall 
                       N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Non-modifiable:
    Smoking:                                                    0.444   
        Never       1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
        Current     270 (13.2%)   296 (14.1%)    292 (13.4%)            
        Former      490 (24.0%)   545 (26.0%)    539 (24.7%)            
    Body mass index 30.3 (3.96)   29.7 (3.77)    29.9 (3.71)   <0.001   
Modifiable:
    Sex:                                                       <0.001   
        Male        812 (39.8%)   968 (46.1%)    899 (41.2%)            
        Female      1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
    Age             67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

4.2.3 Combining tables by column (strata)

Columns from tables built with the same explanatory and response variables but done with a different subset (i.e. “ALL”, “Male” and “Female”, strata) can be combined:

res <- compareGroups(group ~ age + smoke + bmi + htn, data = predimed)
alltab <- createTable(res, show.p.overall = FALSE)
femaletab <- createTable(update(res, subset = sex == "Female"), 
    show.p.overall = FALSE)
maletab <- createTable(update(res, subset = sex == "Male"), show.p.overall = FALSE)
cbind(ALL = alltab, FEMALE = femaletab, MALE = maletab)

--------Summary descriptives table ---------

______________________________________________________________________________________________________________________________________________
                                   ALL                                      FEMALE                                      MALE                  
                _________________________________________  _________________________________________  ________________________________________
                  Control    MedDiet + Nuts MedDiet + VOO    Control    MedDiet + Nuts MedDiet + VOO    Control   MedDiet + Nuts MedDiet + VOO 
                   N=2042        N=2100        N=2182         N=1230        N=1132        N=1283         N=812        N=968          N=899     
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age             67.3 (6.28)   66.7 (6.02)    67.0 (6.21)   68.0 (5.96)   67.4 (5.57)    67.7 (5.84)   66.4 (6.62)  65.8 (6.40)    66.1 (6.61)  
Smoking:                                                                                                                                       
    Never       1282 (62.8%)  1259 (60.0%)  1351 (61.9%)   1077 (87.6%)  993 (87.7%)   1115 (86.9%)   205 (25.2%)  266 (27.5%)    236 (26.3%)  
    Current     270 (13.2%)   296 (14.1%)    292 (13.4%)    66 (5.37%)    54 (4.77%)    71 (5.53%)    204 (25.1%)  242 (25.0%)    221 (24.6%)  
    Former      490 (24.0%)   545 (26.0%)    539 (24.7%)    87 (7.07%)    85 (7.51%)    97 (7.56%)    403 (49.6%)  460 (47.5%)    442 (49.2%)  
Body mass index 30.3 (3.96)   29.7 (3.77)    29.9 (3.71)   30.8 (4.20)   30.2 (4.08)    30.4 (3.91)   29.6 (3.45)  29.1 (3.28)    29.2 (3.28)  
Hypertension:                                                                                                                                  
    No          331 (16.2%)   362 (17.2%)    396 (18.1%)   168 (13.7%)   147 (13.0%)    179 (14.0%)   163 (20.1%)  215 (22.2%)    217 (24.1%)  
    Yes         1711 (83.8%)  1738 (82.8%)  1786 (81.9%)   1062 (86.3%)  985 (87.0%)   1104 (86.0%)   649 (79.9%)  753 (77.8%)    682 (75.9%)  
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

With the argument caption set to NULL no name is displayed for columns.

cbind(alltab, femaletab, maletab, caption = NULL)

--------Summary descriptives table ---------

______________________________________________________________________________________________________________________________________________
                          By Intervention group                            By group                                   By group                
                _________________________________________  _________________________________________  ________________________________________
                  Control    MedDiet + Nuts MedDiet + VOO    Control    MedDiet + Nuts MedDiet + VOO    Control   MedDiet + Nuts MedDiet + VOO 
                   N=2042        N=2100        N=2182         N=1230        N=1132        N=1283         N=812        N=968          N=899     
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age             67.3 (6.28)   66.7 (6.02)    67.0 (6.21)   68.0 (5.96)   67.4 (5.57)    67.7 (5.84)   66.4 (6.62)  65.8 (6.40)    66.1 (6.61)  
Smoking:                                                                                                                                       
    Never       1282 (62.8%)  1259 (60.0%)  1351 (61.9%)   1077 (87.6%)  993 (87.7%)   1115 (86.9%)   205 (25.2%)  266 (27.5%)    236 (26.3%)  
    Current     270 (13.2%)   296 (14.1%)    292 (13.4%)    66 (5.37%)    54 (4.77%)    71 (5.53%)    204 (25.1%)  242 (25.0%)    221 (24.6%)  
    Former      490 (24.0%)   545 (26.0%)    539 (24.7%)    87 (7.07%)    85 (7.51%)    97 (7.56%)    403 (49.6%)  460 (47.5%)    442 (49.2%)  
Body mass index 30.3 (3.96)   29.7 (3.77)    29.9 (3.71)   30.8 (4.20)   30.2 (4.08)    30.4 (3.91)   29.6 (3.45)  29.1 (3.28)    29.2 (3.28)  
Hypertension:                                                                                                                                  
    No          331 (16.2%)   362 (17.2%)    396 (18.1%)   168 (13.7%)   147 (13.0%)    179 (14.0%)   163 (20.1%)  215 (22.2%)    217 (24.1%)  
    Yes         1711 (83.8%)  1738 (82.8%)  1786 (81.9%)   1062 (86.3%)  985 (87.0%)   1104 (86.0%)   649 (79.9%)  753 (77.8%)    682 (75.9%)  
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

By default the name of the table is displayed for each set of columns.

cbind(alltab, femaletab, maletab)

--------Summary descriptives table ---------

______________________________________________________________________________________________________________________________________________
                                 alltab                                    femaletab                                  maletab                 
                _________________________________________  _________________________________________  ________________________________________
                  Control    MedDiet + Nuts MedDiet + VOO    Control    MedDiet + Nuts MedDiet + VOO    Control   MedDiet + Nuts MedDiet + VOO 
                   N=2042        N=2100        N=2182         N=1230        N=1132        N=1283         N=812        N=968          N=899     
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age             67.3 (6.28)   66.7 (6.02)    67.0 (6.21)   68.0 (5.96)   67.4 (5.57)    67.7 (5.84)   66.4 (6.62)  65.8 (6.40)    66.1 (6.61)  
Smoking:                                                                                                                                       
    Never       1282 (62.8%)  1259 (60.0%)  1351 (61.9%)   1077 (87.6%)  993 (87.7%)   1115 (86.9%)   205 (25.2%)  266 (27.5%)    236 (26.3%)  
    Current     270 (13.2%)   296 (14.1%)    292 (13.4%)    66 (5.37%)    54 (4.77%)    71 (5.53%)    204 (25.1%)  242 (25.0%)    221 (24.6%)  
    Former      490 (24.0%)   545 (26.0%)    539 (24.7%)    87 (7.07%)    85 (7.51%)    97 (7.56%)    403 (49.6%)  460 (47.5%)    442 (49.2%)  
Body mass index 30.3 (3.96)   29.7 (3.77)    29.9 (3.71)   30.8 (4.20)   30.2 (4.08)    30.4 (3.91)   29.6 (3.45)  29.1 (3.28)    29.2 (3.28)  
Hypertension:                                                                                                                                  
    No          331 (16.2%)   362 (17.2%)    396 (18.1%)   168 (13.7%)   147 (13.0%)    179 (14.0%)   163 (20.1%)  215 (22.2%)    217 (24.1%)  
    Yes         1711 (83.8%)  1738 (82.8%)  1786 (81.9%)   1062 (86.3%)  985 (87.0%)   1104 (86.0%)   649 (79.9%)  753 (77.8%)    682 (75.9%)  
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

NOTE: The resulting object is of class cbind.createTable and inherits also the class createTable. This cannot be updated. It can be nicely printed on the R console and also exported to LaTeX but it cannot be exported to CSV or HTML.


Since version 4.0, it exists the function strataTable to build tables within stratas defined by the values or levels defined of a variable. Notice that the syntax is much simpler than using cbind method. For example, to perform descriptives by groups, and stratified per gender:

  1. first build the table with descriptives by groups:
res <- compareGroups(group ~ . - sex, predimed)
restab <- createTable(res, hide.no = "no")
  1. and then apply the strataTable function on the table:
strataTable(restab, "sex")

--------Summary descriptives table ---------

_______________________________________________________________________________________________________________________________________
                                                       Male                                               Female                       
                                __________________________________________________  ___________________________________________________
                                  Control   MedDiet + Nuts MedDiet + VOO p.overall    Control    MedDiet + Nuts MedDiet + VOO p.overall 
                                   N=812        N=968          N=899                   N=1230        N=1132        N=1283               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age                             66.4 (6.62)  65.8 (6.40)    66.1 (6.61)    0.215    68.0 (5.96)   67.4 (5.57)    67.7 (5.84)    0.056   
Smoking:                                                                   0.851                                                0.907   
    Never                       205 (25.2%)  266 (27.5%)    236 (26.3%)             1077 (87.6%)  993 (87.7%)   1115 (86.9%)            
    Current                     204 (25.1%)  242 (25.0%)    221 (24.6%)              66 (5.37%)    54 (4.77%)    71 (5.53%)             
    Former                      403 (49.6%)  460 (47.5%)    442 (49.2%)              87 (7.07%)    85 (7.51%)    97 (7.56%)             
Body mass index                 29.6 (3.45)  29.1 (3.28)    29.2 (3.28)    0.018    30.8 (4.20)   30.2 (4.08)    30.4 (3.91)    0.002   
Waist circumference             104 (9.82)    103 (9.36)    103 (9.65)     0.289    99.0 (11.0)   97.8 (11.0)    98.0 (10.5)    0.016   
Waist-to-height ratio           0.62 (0.06)  0.62 (0.06)    0.62 (0.06)    0.191    0.64 (0.07)   0.63 (0.07)    0.63 (0.07)    0.002   
Hypertension                    649 (79.9%)  753 (77.8%)    682 (75.9%)    0.130    1062 (86.3%)  985 (87.0%)   1104 (86.0%)    0.780   
Type-2 diabetes                 430 (53.0%)  496 (51.2%)    486 (54.1%)    0.468    540 (43.9%)   454 (40.1%)    596 (46.5%)    0.007   
Dyslipidemia                    531 (65.4%)  653 (67.5%)    589 (65.5%)    0.575    948 (77.1%)   886 (78.3%)    971 (75.7%)    0.319   
Family history of premature CHD 135 (16.6%)  171 (17.7%)    156 (17.4%)    0.841    327 (26.6%)   289 (25.5%)    351 (27.4%)    0.596   
Hormone-replacement therapy      0 (0.00%)    0 (0.00%)      0 (0.00%)       .       31 (2.64%)    30 (2.81%)    36 (2.95%)     0.898   
MeDiet Adherence score          8.57 (1.94)  8.86 (1.96)    8.91 (2.02)    0.001    8.36 (1.94)   8.77 (1.86)    8.68 (1.92)   <0.001   
follow-up to main event (years) 4.05 (1.78)  4.38 (1.74)    4.53 (1.64)   <0.001    4.12 (1.71)   4.26 (1.67)    4.72 (1.56)   <0.001   
AMI, stroke, or CV Death        58 (7.14%)    41 (4.24%)    52 (5.78%)     0.029     39 (3.17%)    29 (2.56%)    33 (2.57%)     0.576   
AMI, stroke, or CV Death           8.48%        4.42%          5.71%       0.008       3.84%         2.66%          2.16%       0.269   
age7gr                          3.66 (1.36)  3.57 (1.32)    3.63 (1.33)    0.353    4.00 (1.25)   3.87 (1.16)    3.91 (1.20)    0.043   
Smoking 4 cat.:                                                              .                                                    .     
    Never smoker                205 (25.2%)  266 (27.5%)    236 (26.3%)             1077 (87.6%)  993 (87.7%)   1115 (86.9%)            
    Current or former < 1y      204 (25.1%)  242 (25.0%)    221 (24.6%)              66 (5.37%)    54 (4.77%)    71 (5.53%)             
    Never or former >= 1y       403 (49.6%)  460 (47.5%)    442 (49.2%)              87 (7.07%)    85 (7.51%)    97 (7.56%)             
    Unknown                      0 (0.00%)    0 (0.00%)      0 (0.00%)               0 (0.00%)     0 (0.00%)      0 (0.00%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

4.2.4 Miscellaneous

In this section some other createTable options and methods are discussed:

  • print: By default only the table with the descriptives is printed. With which.table argument it can be changed: “avail” returns data available and “both” returns both tables:
print(createTable(compareGroups(group ~ age + sex + smoke + waist + 
    hormo, data = predimed)), which.table = "both")

--------Summary descriptives table by 'Intervention group'---------

________________________________________________________________________________ 
                               Control    MedDiet + Nuts MedDiet + VOO p.overall 
                                N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                                    <0.001   
    Male                     812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female                   1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Smoking:                                                                 0.444   
    Never                    1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current                  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Former                   490 (24.0%)   545 (26.0%)    539 (24.7%)            
Waist circumference           101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Hormone-replacement therapy:                                             0.850   
    No                       1811 (98.3%)  1835 (98.4%)  1918 (98.2%)            
    Yes                       31 (1.68%)    30 (1.61%)    36 (1.84%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 



---Available data----

_______________________________________________________________________________________________ 
                            [ALL] Control MedDiet + Nuts MedDiet + VOO      method       select 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                         6324   2042        2100          2182      continuous-normal  ALL   
Sex                         6324   2042        2100          2182         categorical     ALL   
Smoking                     6324   2042        2100          2182         categorical     ALL   
Waist circumference         6324   2042        2100          2182      continuous-normal  ALL   
Hormone-replacement therapy 5661   1842        1865          1954         categorical     ALL   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

With the print method setting nmax argument to FALSE, the total maximum “n” in the available data is omitted in the first row.

print(createTable(compareGroups(group ~ age + sex + smoke + waist + 
    hormo, data = predimed)), nmax = FALSE)

--------Summary descriptives table by 'Intervention group'---------

________________________________________________________________________________ 
                               Control    MedDiet + Nuts MedDiet + VOO p.overall 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                                    <0.001   
    Male                     812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female                   1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Smoking:                                                                 0.444   
    Never                    1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current                  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Former                   490 (24.0%)   545 (26.0%)    539 (24.7%)            
Waist circumference           101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Hormone-replacement therapy:                                             0.850   
    No                       1811 (98.3%)  1835 (98.4%)  1918 (98.2%)            
    Yes                       31 (1.68%)    30 (1.61%)    36 (1.84%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • summary: returns the same table as that generated with print method setting which.table='avail':
summary(createTable(compareGroups(group ~ age + sex + smoke + 
    waist + hormo, data = predimed)))



---Available data----

_______________________________________________________________________________________________ 
                            [ALL] Control MedDiet + Nuts MedDiet + VOO      method       select 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                         6324   2042        2100          2182      continuous-normal  ALL   
Sex                         6324   2042        2100          2182         categorical     ALL   
Smoking                     6324   2042        2100          2182         categorical     ALL   
Waist circumference         6324   2042        2100          2182      continuous-normal  ALL   
Hormone-replacement therapy 5661   1842        1865          1954         categorical     ALL   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • update: An object of class createTable can be updated:
res <- compareGroups(group ~ age + sex + smoke + waist + hormo, 
    data = predimed)
restab <- createTable(res, type = 1, show.ratio = TRUE)
restab

--------Summary descriptives table by 'Intervention group'---------

_______________________________________________________________________________ 
                               Control   MedDiet + Nuts MedDiet + VOO p.overall 
                               N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)  66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                                   <0.001   
    Male                        39.8%        46.1%          41.2%               
    Female                      60.2%        53.9%          58.8%               
Smoking:                                                                0.444   
    Never                       62.8%        60.0%          61.9%               
    Current                     13.2%        14.1%          13.4%               
    Former                      24.0%        26.0%          24.7%               
Waist circumference          101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Hormone-replacement therapy:                                            0.850   
    No                          98.3%        98.4%          98.2%               
    Yes                         1.68%        1.61%          1.84%               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
update(restab, show.n = TRUE)

--------Summary descriptives table by 'Intervention group'---------

____________________________________________________________________________________ 
                               Control   MedDiet + Nuts MedDiet + VOO p.overall  N   
                               N=2042        N=2100        N=2182                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)  66.7 (6.02)    67.0 (6.21)    0.003   6324 
Sex:                                                                   <0.001   6324 
    Male                        39.8%        46.1%          41.2%                    
    Female                      60.2%        53.9%          58.8%                    
Smoking:                                                                0.444   6324 
    Never                       62.8%        60.0%          61.9%                    
    Current                     13.2%        14.1%          13.4%                    
    Former                      24.0%        26.0%          24.7%                    
Waist circumference          101 (10.8)    100 (10.6)    100 (10.4)     0.045   6324 
Hormone-replacement therapy:                                            0.850   5661 
    No                          98.3%        98.4%          98.2%                    
    Yes                         1.68%        1.61%          1.84%                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

In just one statement it is possible to update an object of class compareGroups and createTable:

update(restab, x = update(res, subset = c(sex == "Female")), 
    show.n = TRUE)

--------Summary descriptives table by 'group'---------

____________________________________________________________________________________ 
                               Control   MedDiet + Nuts MedDiet + VOO p.overall  N   
                               N=1230        N=1132        N=1283                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          68.0 (5.96)  67.4 (5.57)    67.7 (5.84)    0.056   3645 
Sex: Female                     100%          100%          100%          .     3645 
Smoking:                                                                0.907   3645 
    Never                       87.6%        87.7%          86.9%                    
    Current                     5.37%        4.77%          5.53%                    
    Former                      7.07%        7.51%          7.56%                    
Waist circumference          99.0 (11.0)  97.8 (11.0)    98.0 (10.5)    0.016   3645 
Hormone-replacement therapy:                                            0.898   3459 
    No                          97.4%        97.2%          97.0%                    
    Yes                         2.64%        2.81%          2.95%                    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that the compareGroups object (res) is updated, selecting only “Female”" participants, and the createTable class object (restab) is updated to add a column with the maximum available data for each explanatory variable.

  • subsetting: Objects from createTable function can also be subsetted using “[”:
createTable(compareGroups(group ~ age + sex + smoke + waist + 
    hormo, data = predimed))

--------Summary descriptives table by 'Intervention group'---------

________________________________________________________________________________ 
                               Control    MedDiet + Nuts MedDiet + VOO p.overall 
                                N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                          67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                                    <0.001   
    Male                     812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female                   1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Smoking:                                                                 0.444   
    Never                    1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current                  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Former                   490 (24.0%)   545 (26.0%)    539 (24.7%)            
Waist circumference           101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Hormone-replacement therapy:                                             0.850   
    No                       1811 (98.3%)  1835 (98.4%)  1918 (98.2%)            
    Yes                       31 (1.68%)    30 (1.61%)    36 (1.84%)             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
createTable(compareGroups(group ~ age + sex + bmi, data = predimed))[1:2, 
    ]

--------Summary descriptives table by 'Intervention group'---------

______________________________________________________________ 
             Control    MedDiet + Nuts MedDiet + VOO p.overall 
              N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age        67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Sex:                                                  <0.001   
    Male   812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female 1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

4.2.5 Building tables in one step

Making use of descrTable the user can build descriptive table in one step. This function takes all the arguments of compareGroups function plus the ones from createTable function. The result is the same as if the user first call compareGroups and then createTable. Therefore one can use the same methods and functions avaiable for createTable objects (subsetting, ploting, printing, exporting, etc.)

To describe all varaible from predimed, just type:

descrTable(predimed)

--------Summary descriptives table ---------

__________________________________________________ 
                                    [ALL]      N   
                                    N=6324         
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Intervention group:                           6324 
    Control                      2042 (32.3%)      
    MedDiet + Nuts               2100 (33.2%)      
    MedDiet + VOO                2182 (34.5%)      
Sex:                                          6324 
    Male                         2679 (42.4%)      
    Female                       3645 (57.6%)      
Age                              67.0 (6.17)  6324 
Smoking:                                      6324 
    Never                        3892 (61.5%)      
    Current                      858 (13.6%)       
    Former                       1574 (24.9%)      
Body mass index                  30.0 (3.82)  6324 
Waist circumference               100 (10.6)  6324 
Waist-to-height ratio            0.63 (0.07)  6324 
Hypertension:                                 6324 
    No                           1089 (17.2%)      
    Yes                          5235 (82.8%)      
Type-2 diabetes:                              6324 
    No                           3322 (52.5%)      
    Yes                          3002 (47.5%)      
Dyslipidemia:                                 6324 
    No                           1746 (27.6%)      
    Yes                          4578 (72.4%)      
Family history of premature CHD:              6324 
    No                           4895 (77.4%)      
    Yes                          1429 (22.6%)      
Hormone-replacement therapy:                  5661 
    No                           5564 (98.3%)      
    Yes                           97 (1.71%)       
MeDiet Adherence score           8.68 (1.94)  6324 
follow-up to main event (years)  4.36 (1.69)  6324 
AMI, stroke, or CV Death:                     6324 
    No                           6072 (96.0%)      
    Yes                          252 (3.98%)       
AMI, stroke, or CV Death            4.22%     6324 
age7gr                           3.80 (1.27)  6324 
Smoking 4 cat.:                               6324 
    Never smoker                 3892 (61.5%)      
    Current or former < 1y       858 (13.6%)       
    Never or former >= 1y        1574 (24.9%)      
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

To describe some varaibles, make use of formula argument (just as using compareGroups function)

descrTable(~age + sex, predimed)

--------Summary descriptives table ---------

____________________________ 
              [ALL]      N   
              N=6324         
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age        67.0 (6.17)  6324 
Sex:                    6324 
    Male   2679 (42.4%)      
    Female 3645 (57.6%)      
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

To hide “no” category from yes-no variables, use the hide.no argument from createTable:

descrTable(predimed, hide.no = "no")

--------Summary descriptives table ---------

_________________________________________________ 
                                   [ALL]      N   
                                   N=6324         
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Intervention group:                          6324 
    Control                     2042 (32.3%)      
    MedDiet + Nuts              2100 (33.2%)      
    MedDiet + VOO               2182 (34.5%)      
Sex:                                         6324 
    Male                        2679 (42.4%)      
    Female                      3645 (57.6%)      
Age                             67.0 (6.17)  6324 
Smoking:                                     6324 
    Never                       3892 (61.5%)      
    Current                     858 (13.6%)       
    Former                      1574 (24.9%)      
Body mass index                 30.0 (3.82)  6324 
Waist circumference              100 (10.6)  6324 
Waist-to-height ratio           0.63 (0.07)  6324 
Hypertension                    5235 (82.8%) 6324 
Type-2 diabetes                 3002 (47.5%) 6324 
Dyslipidemia                    4578 (72.4%) 6324 
Family history of premature CHD 1429 (22.6%) 6324 
Hormone-replacement therapy      97 (1.71%)  5661 
MeDiet Adherence score          8.68 (1.94)  6324 
follow-up to main event (years) 4.36 (1.69)  6324 
AMI, stroke, or CV Death        252 (3.98%)  6324 
AMI, stroke, or CV Death           4.22%     6324 
age7gr                          3.80 (1.27)  6324 
Smoking 4 cat.:                              6324 
    Never smoker                3892 (61.5%)      
    Current or former < 1y      858 (13.6%)       
    Never or former >= 1y       1574 (24.9%)      
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

To report descriptives by group as well as the descriptives of the entire cohort:

descrTable(group ~ ., predimed, hide.no = "no", show.all = TRUE)

--------Summary descriptives table by 'Intervention group'---------

________________________________________________________________________________________________ 
                                   [ALL]       Control    MedDiet + Nuts MedDiet + VOO p.overall 
                                   N=6324       N=2042        N=2100        N=2182               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Sex:                                                                                    <0.001   
    Male                        2679 (42.4%) 812 (39.8%)   968 (46.1%)    899 (41.2%)            
    Female                      3645 (57.6%) 1230 (60.2%)  1132 (53.9%)  1283 (58.8%)            
Age                             67.0 (6.17)  67.3 (6.28)   66.7 (6.02)    67.0 (6.21)    0.003   
Smoking:                                                                                 0.444   
    Never                       3892 (61.5%) 1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current                     858 (13.6%)  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Former                      1574 (24.9%) 490 (24.0%)   545 (26.0%)    539 (24.7%)            
Body mass index                 30.0 (3.82)  30.3 (3.96)   29.7 (3.77)    29.9 (3.71)   <0.001   
Waist circumference              100 (10.6)   101 (10.8)    100 (10.6)    100 (10.4)     0.045   
Waist-to-height ratio           0.63 (0.07)  0.63 (0.07)   0.62 (0.06)    0.63 (0.06)   <0.001   
Hypertension                    5235 (82.8%) 1711 (83.8%)  1738 (82.8%)  1786 (81.9%)    0.249   
Type-2 diabetes                 3002 (47.5%) 970 (47.5%)   950 (45.2%)   1082 (49.6%)    0.017   
Dyslipidemia                    4578 (72.4%) 1479 (72.4%)  1539 (73.3%)  1560 (71.5%)    0.423   
Family history of premature CHD 1429 (22.6%) 462 (22.6%)   460 (21.9%)    507 (23.2%)    0.581   
Hormone-replacement therapy      97 (1.71%)   31 (1.68%)    30 (1.61%)    36 (1.84%)     0.850   
MeDiet Adherence score          8.68 (1.94)  8.44 (1.94)   8.81 (1.90)    8.77 (1.97)   <0.001   
follow-up to main event (years) 4.36 (1.69)  4.09 (1.74)   4.31 (1.70)    4.64 (1.60)   <0.001   
AMI, stroke, or CV Death        252 (3.98%)   97 (4.75%)    70 (3.33%)    85 (3.90%)     0.064   
AMI, stroke, or CV Death           4.22%        5.69%         3.48%          3.66%       0.011   
age7gr                          3.80 (1.27)  3.86 (1.30)   3.74 (1.24)    3.79 (1.26)    0.005   
Smoking 4 cat.:                                                                          0.444   
    Never smoker                3892 (61.5%) 1282 (62.8%)  1259 (60.0%)  1351 (61.9%)            
    Current or former < 1y      858 (13.6%)  270 (13.2%)   296 (14.1%)    292 (13.4%)            
    Never or former >= 1y       1574 (24.9%) 490 (24.0%)   545 (26.0%)    539 (24.7%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Or you can select individuals using the subset argument.

descrTable(predimed, subset = age > 65)

4.3 Exporting tables

Tables can be exported to CSV, HTML, LaTeX, PDF, Markdown, Word or Excel

Note that, since version 3.0, it is necessary write the extension of the file.

4.3.1 General exporting options

  • which.table: By default only the table with the descriptives is exported. This can be changed with the which.table argument: “avail” exports only available data and “both” exports both tables.

  • nmax: By default a first row with the maximum “n” for available data (i.e. the number of participants minus the least missing data) is exported. Stating nmax argument to FALSE this first row is omitted.

  • sep: Only relevant when table is exported to csv. Stating, for example, sep = ";" table will be exported to csv with columns separated by “;”.

4.3.2 Exporting to LaTeX

A special case of exporting is when tables are exported to LaTeX. The function export2latex returns an object with the tex code as a character that can be changed in the R session.

  • file: If file argument in export2latex is missing, the code is printed in the R console. This can be useful when R code is inserted in a LaTeX document chunk to be processed with Sweave package.
restab <- createTable(compareGroups(group ~ age + sex + smoke + 
    waist + hormo, data = predimed))
export2latex(restab)
    
    \begin{longtable}{lcccc}\caption{Summary descriptives table by groups of `Intervention group'}\\
    \hline  
     &   Control    & MedDiet + Nuts & MedDiet + VOO & \multirow{2}{*}{p.overall}\\ 
 &    N=2042    &     N=2100     &    N=2182     &           \\ 
  
    \hline
    \hline     
    \endfirsthead 
    \multicolumn{5}{l}{\tablename\ \thetable{} \textit{-- continued from previous page}}\\ 
    \hline
     &   Control    & MedDiet + Nuts & MedDiet + VOO & \multirow{2}{*}{p.overall}\\ 
 &    N=2042    &     N=2100     &    N=2182     &           \\ 

    \hline
    \hline  
    \endhead   
    \hline
    \multicolumn{5}{l}{\textit{continued on next page}} \\ 
    \endfoot   
    \multicolumn{5}{l}{}  \\ 
    \endlastfoot 
    Age & 67.3 (6.28)  &  66.7 (6.02)   &  67.0 (6.21)  &   0.003  \\ 
Sex: &              &                &               &  $<$0.001  \\ 
$\qquad$Male & 812 (39.8\%)  &  968 (46.1\%)   &  899 (41.2\%)  &          \\ 
$\qquad$Female & 1230 (60.2\%) &  1132 (53.9\%)  & 1283 (58.8\%)  &          \\ 
Smoking: &              &                &               &   0.444  \\ 
$\qquad$Never & 1282 (62.8\%) &  1259 (60.0\%)  & 1351 (61.9\%)  &          \\ 
$\qquad$Current & 270 (13.2\%)  &  296 (14.1\%)   &  292 (13.4\%)  &          \\ 
$\qquad$Former & 490 (24.0\%)  &  545 (26.0\%)   &  539 (24.7\%)  &          \\ 
Waist circumference &  101 (10.8)  &   100 (10.6)   &  100 (10.4)   &   0.045  \\ 
Hormone-replacement therapy: &              &                &               &   0.850  \\ 
$\qquad$No & 1811 (98.3\%) &  1835 (98.4\%)  & 1918 (98.2\%)  &          \\ 
$\qquad$Yes &  31 (1.68\%)  &   30 (1.61\%)   &  36 (1.84\%)   &           \\ 
 
    \hline
    \end{longtable} 
  • size: The font size of exported tables can be changed by this argument. Possible values are “tiny”, “scriptsize”, “footnotesize”, “small”, “normalsize”, “large”, “Large”, “LARGE”,“huge”, “Huge” or “same”. Default is “same”, which means that font size of the table is the same as specified in the main LaTeX document where the table will be inserted.

  • caption: The table caption for descriptives table and available data table. If which.table is set to “both” the first element of “caption” will be assigned to descriptives table and the second to available data table. If it is set to “”, no caption is inserted. Default value is NULL, which writes “Summary descriptives table by groups of ‘y’” for descriptives table and “Available data by groups of ‘y’” for the available data table.

  • loc.caption: Table caption location. Possible values are “top” or “bottom”. Default value is “top”.

  • label: Used to cite tables in a LaTeX document. If which.table is set to “both” the first element of “label” will be assigned to the descriptives table and the second to the available data table. Default value is NULL, which assigns no label to the table/s.

  • landscape: Table is placed in horizontal way. This option is specially usefull when table contains many columns and/or they are too wide to be placed vertically.

4.3.3 Exporting to Rmarkdown

Since 4.0 version, export2md supports cbind.createTable class objects, i.e. when exporting stratified descriptive tables. Also, nicer and more costumizable tables can be reported making use of kableExtra package (such as size, strip rows, etc.).

Following, there are some examples when exporting to HTML.

First create the descriptive table of PREDIMED variables by intervention groups.

res <- compareGroups(group ~ ., predimed)
restab <- createTable(res, hide.no = "no")
  • Default export2md to HTML:
export2md(restab)
Summary descriptives table by groups of `Intervention group’
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
Dyslipidemia 1479 (72.4%) 1539 (73.3%) 1560 (71.5%) 0.423
Family history of premature CHD 462 (22.6%) 460 (21.9%) 507 (23.2%) 0.581
Hormone-replacement therapy 31 (1.68%) 30 (1.61%) 36 (1.84%) 0.850
MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
AMI, stroke, or CV Death 97 (4.75%) 70 (3.33%) 85 (3.90%) 0.064
AMI, stroke, or CV Death 5.69% 3.48% 3.66% 0.011
age7gr 3.86 (1.30) 3.74 (1.24) 3.79 (1.26) 0.005
Smoking 4 cat.: 0.444
Never smoker 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current or former < 1y 270 (13.2%) 296 (14.1%) 292 (13.4%)
Never or former >= 1y 490 (24.0%) 545 (26.0%) 539 (24.7%)
  • Add strip rows, colouring rows by variables:
export2md(restab, strip = TRUE, first.strip = TRUE)
Summary descriptives table by groups of `Intervention group’
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
Dyslipidemia 1479 (72.4%) 1539 (73.3%) 1560 (71.5%) 0.423
Family history of premature CHD 462 (22.6%) 460 (21.9%) 507 (23.2%) 0.581
Hormone-replacement therapy 31 (1.68%) 30 (1.61%) 36 (1.84%) 0.850
MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
AMI, stroke, or CV Death 97 (4.75%) 70 (3.33%) 85 (3.90%) 0.064
AMI, stroke, or CV Death 5.69% 3.48% 3.66% 0.011
age7gr 3.86 (1.30) 3.74 (1.24) 3.79 (1.26) 0.005
Smoking 4 cat.: 0.444
Never smoker 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current or former < 1y 270 (13.2%) 296 (14.1%) 292 (13.4%)
Never or former >= 1y 490 (24.0%) 545 (26.0%) 539 (24.7%)
  • Change size:
export2md(restab, size = 6)
Summary descriptives table by groups of `Intervention group’
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
Dyslipidemia 1479 (72.4%) 1539 (73.3%) 1560 (71.5%) 0.423
Family history of premature CHD 462 (22.6%) 460 (21.9%) 507 (23.2%) 0.581
Hormone-replacement therapy 31 (1.68%) 30 (1.61%) 36 (1.84%) 0.850
MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
AMI, stroke, or CV Death 97 (4.75%) 70 (3.33%) 85 (3.90%) 0.064
AMI, stroke, or CV Death 5.69% 3.48% 3.66% 0.011
age7gr 3.86 (1.30) 3.74 (1.24) 3.79 (1.26) 0.005
Smoking 4 cat.: 0.444
Never smoker 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current or former < 1y 270 (13.2%) 296 (14.1%) 292 (13.4%)
Never or former >= 1y 490 (24.0%) 545 (26.0%) 539 (24.7%)
  • Making variable names column wider:
export2md(restab, width = "400px")
Summary descriptives table by groups of `Intervention group’
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
Dyslipidemia 1479 (72.4%) 1539 (73.3%) 1560 (71.5%) 0.423
Family history of premature CHD 462 (22.6%) 460 (21.9%) 507 (23.2%) 0.581
Hormone-replacement therapy 31 (1.68%) 30 (1.61%) 36 (1.84%) 0.850
MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
AMI, stroke, or CV Death 97 (4.75%) 70 (3.33%) 85 (3.90%) 0.064
AMI, stroke, or CV Death 5.69% 3.48% 3.66% 0.011
age7gr 3.86 (1.30) 3.74 (1.24) 3.79 (1.26) 0.005
Smoking 4 cat.: 0.444
Never smoker 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current or former < 1y 270 (13.2%) 296 (14.1%) 292 (13.4%)
Never or former >= 1y 490 (24.0%) 545 (26.0%) 539 (24.7%)
  • Stratified table:
resMales <- compareGroups(group ~ . - sex, predimed, subset = sex == 
    "Male", simplify = FALSE)
resFemales <- compareGroups(group ~ . - sex, predimed, subset = sex == 
    "Female", simplify = FALSE)
restabMales <- createTable(resMales, hide.no = "no")
restabFemales <- createTable(resFemales, hide.no = "no")
restab <- cbind(Males = restabMales, Females = restabFemales)
export2md(restab, size = 8)
Summary descriptive tables

Males
Females
Control MedDiet + Nuts MedDiet + VOO p.overall Control MedDiet + Nuts MedDiet + VOO p.overall
N=812 N=968 N=899 N=1230 N=1132 N=1283
Age 66.4 (6.62) 65.8 (6.40) 66.1 (6.61) 0.215 68.0 (5.96) 67.4 (5.57) 67.7 (5.84) 0.056
Smoking: 0.851 0.907
Never 205 (25.2%) 266 (27.5%) 236 (26.3%) 1077 (87.6%) 993 (87.7%) 1115 (86.9%)
Current 204 (25.1%) 242 (25.0%) 221 (24.6%) 66 (5.37%) 54 (4.77%) 71 (5.53%)
Former 403 (49.6%) 460 (47.5%) 442 (49.2%) 87 (7.07%) 85 (7.51%) 97 (7.56%)
Body mass index 29.6 (3.45) 29.1 (3.28) 29.2 (3.28) 0.018 30.8 (4.20) 30.2 (4.08) 30.4 (3.91) 0.002
Waist circumference 104 (9.82) 103 (9.36) 103 (9.65) 0.289 99.0 (11.0) 97.8 (11.0) 98.0 (10.5) 0.016
Waist-to-height ratio 0.62 (0.06) 0.62 (0.06) 0.62 (0.06) 0.191 0.64 (0.07) 0.63 (0.07) 0.63 (0.07) 0.002
Hypertension 649 (79.9%) 753 (77.8%) 682 (75.9%) 0.130 1062 (86.3%) 985 (87.0%) 1104 (86.0%) 0.780
Type-2 diabetes 430 (53.0%) 496 (51.2%) 486 (54.1%) 0.468 540 (43.9%) 454 (40.1%) 596 (46.5%) 0.007
Dyslipidemia 531 (65.4%) 653 (67.5%) 589 (65.5%) 0.575 948 (77.1%) 886 (78.3%) 971 (75.7%) 0.319
Family history of premature CHD 135 (16.6%) 171 (17.7%) 156 (17.4%) 0.841 327 (26.6%) 289 (25.5%) 351 (27.4%) 0.596
Hormone-replacement therapy 0 (0.00%) 0 (0.00%) 0 (0.00%) . 31 (2.64%) 30 (2.81%) 36 (2.95%) 0.898
MeDiet Adherence score 8.57 (1.94) 8.86 (1.96) 8.91 (2.02) 0.001 8.36 (1.94) 8.77 (1.86) 8.68 (1.92) <0.001
follow-up to main event (years) 4.05 (1.78) 4.38 (1.74) 4.53 (1.64) <0.001 4.12 (1.71) 4.26 (1.67) 4.72 (1.56) <0.001
AMI, stroke, or CV Death 58 (7.14%) 41 (4.24%) 52 (5.78%) 0.029 39 (3.17%) 29 (2.56%) 33 (2.57%) 0.576
AMI, stroke, or CV Death 8.48% 4.42% 5.71% 0.008 3.84% 2.66% 2.16% 0.269
age7gr 3.66 (1.36) 3.57 (1.32) 3.63 (1.33) 0.353 4.00 (1.25) 3.87 (1.16) 3.91 (1.20) 0.043
Smoking 4 cat.: . .
Never smoker 205 (25.2%) 266 (27.5%) 236 (26.3%) 1077 (87.6%) 993 (87.7%) 1115 (86.9%)
Current or former < 1y 204 (25.1%) 242 (25.0%) 221 (24.6%) 66 (5.37%) 54 (4.77%) 71 (5.53%)
Never or former >= 1y 403 (49.6%) 460 (47.5%) 442 (49.2%) 87 (7.07%) 85 (7.51%) 97 (7.56%)
Unknown 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%) 0 (0.00%)

4.4 Generating an exhaustive report

Since version 2.0 of compareGroups package, there is a function called report which automatically generates a PDF document with the “descriptive” table as well as the corresponding “available”" table. In addition, plots of all analysed variables are shown.

In order to make easier to navigate throught the document, an index with hyperlinks is inserted in the document.

See the help file of this function where you can find an example with the REGICOR data (the other example data set contained in the compareGroups package)

# to know more about report function
`?`(report)
# info about REGICOR data set
`?`(regicor)

Also, you can use the function radiograph that dumps the raw values on a plain text file. This may be usefull to identify possible wrong codes or non-valid values in the data set.

4.5 Dealing with missing values

Many times, it is important to be aware of the missingness contained in each variable, possibly by groups. Althought “available” table shows the number of the non-missing values for each row-variable and in each group, it would be desirable to test whether the frequency of non-available data is different between groups. For this porpose, a new function has been implemented in the compareGroups package, which is called missingTable. This function applies to both compareGroups and createTable class objects. This last option is useful when the table is already created. To illustrate it, we will use the REGICOR data set, comparing missing rates of all variables by year:

# from a compareGroups object
data(regicor)
res <- compareGroups(year ~ . - id, regicor)
missingTable(res)

--------Missingness table by 'year'---------

____________________________________________________ 
            1995       2000        2005    p.overall 
           N=431       N=786      N=1077             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
age      0 (0.00%)   0 (0.00%)  0 (0.00%)      .     
sex      0 (0.00%)   0 (0.00%)  0 (0.00%)      .     
smoker   16 (3.71%) 28 (3.56%)  17 (1.58%)   0.010   
sbp      3 (0.70%)  11 (1.40%)  0 (0.00%)   <0.001   
dbp      3 (0.70%)  11 (1.40%)  0 (0.00%)   <0.001   
histhtn  0 (0.00%)   0 (0.00%)  8 (0.74%)    0.015   
txhtn    0 (0.00%)   0 (0.00%)  43 (3.99%)  <0.001   
chol     28 (6.50%) 71 (9.03%)  2 (0.19%)   <0.001   
hdl      30 (6.96%) 38 (4.83%)  1 (0.09%)   <0.001   
triglyc  28 (6.50%) 34 (4.33%)  1 (0.09%)   <0.001   
ldl      43 (9.98%) 98 (12.5%)  27 (2.51%)  <0.001   
histchol 0 (0.00%)  15 (1.91%)  6 (0.56%)    0.001   
txchol   0 (0.00%)  13 (1.65%)  42 (3.90%)  <0.001   
height   8 (1.86%)  15 (1.91%)  12 (1.11%)   0.318   
weight   8 (1.86%)  15 (1.91%)  12 (1.11%)   0.318   
bmi      8 (1.86%)  15 (1.91%)  12 (1.11%)   0.318   
phyact   64 (14.8%) 22 (2.80%)  2 (0.19%)   <0.001   
pcs      34 (7.89%) 123 (15.6%) 83 (7.71%)  <0.001   
mcs      34 (7.89%) 123 (15.6%) 83 (7.71%)  <0.001   
cv       33 (7.66%) 45 (5.73%)  53 (4.92%)   0.118   
tocv     33 (7.66%) 45 (5.73%)  53 (4.92%)   0.118   
death    44 (10.2%) 48 (6.11%)  54 (5.01%)   0.001   
todeath  44 (10.2%) 48 (6.11%)  54 (5.01%)   0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
# or from createTable objects
restab <- createTable(res, hide.no = "no")
missingTable(restab)

Perhaps a NA value of a categorical variable may mean something different from just non available. For example, patients admitted for “Coronary Acute Syndrome” with NA in “ST elevation” may have a higher risk of in-hospital death than the ones with available data, i.e. “ST elevation” yes or not. If these kind of variables are introduced in the data set as NA, they are removed from the analysis. To avoid the user having to recode NA as a new category for all categorical variables, new argument called include.miss in compareGroups function has been implemented which does it automatically. Let’s see an example with all variables from REGICOR data set by cardiovascular event.

# first create time-to-cardiovascular event
regicor$tcv <- with(regicor, Surv(tocv, cv == "Yes"))
# create the table
res <- compareGroups(tcv ~ . - id - tocv - cv - todeath - death, 
    regicor, include.miss = TRUE)
restab <- createTable(res, hide.no = "no")
restab

--------Summary descriptives table by 'tcv'---------

________________________________________________________________ 
                                No event      Event    p.overall 
                                 N=2071       N=92               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Recruitment year:                                        0.157   
    1995                      388 (18.7%)  10 (10.9%)            
    2000                      706 (34.1%)  35 (38.0%)            
    2005                      977 (47.2%)  47 (51.1%)            
Age                           54.6 (11.1)  57.5 (11.0)   0.021   
Sex:                                                     0.696   
    Male                      996 (48.1%)  46 (50.0%)            
    Female                    1075 (51.9%) 46 (50.0%)            
Smoking status:                                         <0.001   
    Never smoker              1099 (53.1%) 37 (40.2%)            
    Current or former < 1y    506 (24.4%)  47 (51.1%)            
    Former >= 1y              419 (20.2%)   8 (8.70%)            
    'Missing'                  47 (2.27%)   0 (0.00%)            
Systolic blood pressure        131 (20.3)  138 (21.5)    0.001   
Diastolic blood pressure      79.5 (10.4)  82.9 (12.3)   0.002   
History of hypertension:                                 0.118   
    Yes                       647 (31.2%)  38 (41.3%)            
    No                        1418 (68.5%) 54 (58.7%)            
    'Missing'                  6 (0.29%)    0 (0.00%)            
Hypertension treatment:                                  0.198   
    No                        1657 (80.0%) 70 (76.1%)            
    Yes                       382 (18.4%)  22 (23.9%)            
    'Missing'                  32 (1.55%)   0 (0.00%)            
Total cholesterol              218 (44.5)  224 (50.4)    0.207   
HDL cholesterol               52.8 (14.8)  50.4 (13.3)   0.114   
Triglycerides                  113 (68.2)  123 (52.4)    0.190   
LDL cholesterol                143 (39.6)  149 (45.6)    0.148   
History of hyperchol.:                                   0.470   
    Yes                       639 (30.9%)  25 (27.2%)            
    No                        1414 (68.3%) 67 (72.8%)            
    'Missing'                  18 (0.87%)   0 (0.00%)            
Cholesterol treatment:                                   0.190   
    No                        1817 (87.7%) 86 (93.5%)            
    Yes                       213 (10.3%)   6 (6.52%)            
    'Missing'                  41 (1.98%)   0 (0.00%)            
Height (cm)                    163 (9.21)  163 (9.34)    0.692   
Weight (Kg)                   73.4 (13.7)  74.9 (12.8)   0.294   
Body mass index               27.6 (4.56)  28.1 (4.48)   0.299   
Physical activity (Kcal/week)  405 (397)    338 (238)    0.089   
Physical component            49.7 (8.95)  47.4 (9.03)   0.023   
Mental component              48.1 (10.9)  46.3 (12.2)   0.122   
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4.6 Analysis of genetic data

In the version 2.0 of compareGroups, it is possible to analyse genetic data, more concretely Single Nucleotic Polymorphisms (SNPs), using the function compareSNPs. This function takes advantage of SNPassoc (González et al. 2012) and HardyWeinberg (Graffelman 2012) packages to perform quality control of genetic data displaying the Minor Allele Frequencies, Missingness, Hardy Weinberg Equilibrium, etc. of the whole data set or by groups. When groups are considered, it also performs a test to check whether missingness rates is the same among groups.

Following, we illustrate this by an example taking a data set from SNPassoc package.

First of all, load the SNPs data from SNPassoc, and visualize the first rows. Notice how are the SNPs coded, i.e. by the alleles. The alleles separator can be any character. If so, this must be specified in the sep argument of compareSNPs function (type ?compareSNPs for more details).

data(SNPs)
head(SNPs)
  id casco    sex blood.pre  protein snp10001 snp10002 snp10003 snp10004 snp10005 snp10006 snp10007 snp10008 snp10009 snp100010 snp100011 snp100012
1  1     1 Female      13.7 75640.52       TT       CC       GG       GG       GG       AA       CC       CC       AA        TT        GG        GG
2  2     1 Female      12.7 28688.22       TT       AC       GG       GG       AG       AA       CC       CC       AG        TT        GG        CG
3  3     1 Female      12.9 17279.59       TT       CC       GG       GG       GG       AA       CC       CC       AA        TT        CC        GG
4  4     1   Male      14.6 27253.99       CT       CC       GG       GG       GG       AA       CC       CC       AA        TT        GG        GG
5  5     1 Female      13.4 38066.57       TT       AC       GG       GG       GG       AA       CC       CC       AG        TT        GG        GG
6  6     1 Female      11.3  9872.46       TT       CC       GG       GG       GG       AA       CC       CC       AA        TT        GG        GG
  snp100013 snp100014 snp100015 snp100016 snp100017 snp100018 snp100019 snp100020 snp100021 snp100022 snp100023 snp100024 snp100025 snp100026
1        AA        AA        GG        GG        TT        TT        CC        GG        GG        AA        TT        TT        CC        GG
2        AA        AC        GG        GG        CT        CT        CG        GG        GG        AA        AT        TT        CC        GG
3        AA        CC        GG        GG        TT        TT        CC        GG        GG        AA        TT        TT        CC        GG
4        AA        AC        GG        GG        TT        TT        CG        GG        GG        AA        TT        CT        CC        GG
5        AA        AC        GG        GG        CT        CT        CG        GG        GG        AA        AT        TT        CC        GG
6        AA        AA        GG        GG        TT        TT        CC        GG        GG        AA        TT        TT        CC        GG
  snp100027 snp100028 snp100029 snp100030 snp100031 snp100032 snp100033 snp100034 snp100035
1        CC        CC        GG        AA        TT        AA        AA        TT        TT
2        CG        CT        GG        AA        TT        AG        AG        TT        TT
3        CC        CC        GG        AA        TT        AA        AA        TT        TT
4        CC        CT        AG        AA        TT        AG        AG        CT        TT
5        CG        CT        GG        AA        TT        AG        AG        TT        TT
6        CC        CC        GG        AA        TT        AA        AA        TT      <NA>

In this data frame there are some genetic and non-genetic data. Genetic variables are those whose names begin with “snp”. If we want to summarize the first three SNPs by case control status:

res <- compareSNPs(casco ~ snp10001 + snp10002 + snp10003, data = SNPs)
res
*********** Summary of genetic data (SNPs) by groups ***********


  *** casco = '0' ***

_____________________________________________________ 
SNP      Ntyped    MAF Genotypes    Genotypes.p HWE.p 
===================================================== 
snp10001     47  26.6%  TT|TC|CC  51.1|44.7|4.3 0.487 
snp10002     47  26.6%  CC|CA|AA  46.8|53.2|0.0 0.029 
snp10003     44 100.0%        GG 100.0| 0.0|0.0 1.000 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 


  *** casco = '1' ***

_____________________________________________________ 
SNP      Ntyped    MAF Genotypes    Genotypes.p HWE.p 
===================================================== 
snp10001    110  23.6%  TT|TC|CC  61.8|29.1|9.1 0.069 
snp10002    110  28.6%  CC|CA|AA  47.3|48.2|4.5 0.091 
snp10003    100 100.0%        GG 100.0| 0.0|0.0 1.000 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 


  *** Missingness test ***

________________ 
snps     p.value 
================ 
snp10001   1.000 
snp10002   1.000 
snp10003   0.756 
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Note that all variables specified in the right hand side of the formula must be SNPs, i.e. variables whose levels or codes can be interpreted as genotypes (see setupSNPs function from SNPassoc package for more information). Separated summary tables by groups of cases and controls are displayed, and the last table corresponds to missingness test comparing non-available rates among groups.

If summarizing SNPs in the whole data set is desired, without separating by groups, leave the left side of formula in blank, as in compareGroups function. In this case, a single table is displayed and no missingness test is performed.

res <- compareSNPs(~snp10001 + snp10002 + snp10003, data = SNPs)
res
*********** Summary of genetic data (SNPs) ***********
_____________________________________________________ 
SNP      Ntyped    MAF Genotypes    Genotypes.p HWE.p 
===================================================== 
snp10001    157  24.5%  TT|TC|CC  58.6|33.8|7.6 0.353 
snp10002    157  28.0%  CC|CA|AA  47.1|49.7|3.2 0.006 
snp10003    144 100.0%        GG 100.0| 0.0|0.0 1.000 
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5 Using the Graphical User Inferface (GUI) - tcltk

Once the compareGroups package is loaded, a Graphical User Interface (GUI) is displayed in response to typing cGroupsGUI(predimed). The GUI is meant to make it feasible for users who are unfamiliar with R to construct bivariate tables. Note that, since version 3.0, it is necessary to specifiy an existing data.frame as input. So, for example, you can load the PREDIMED data by typing data(predimed) before calling cGroupsGUI function.

In this section we illustrate, step by step, how to construct a bivariate table containing descriptives by groups from the predimed data using the GUI:

Summary descriptives table by groups of `Intervention group’
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%) <0.001
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
Dyslipidemia 1479 (72.4%) 1539 (73.3%) 1560 (71.5%) 0.423
Family history of premature CHD 462 (22.6%) 460 (21.9%) 507 (23.2%) 0.581
Hormone-replacement therapy 31 (1.68%) 30 (1.61%) 36 (1.84%) 0.850
MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
AMI, stroke, or CV Death 97 (4.75%) 70 (3.33%) 85 (3.90%) 0.064