compareGroups 4.0: Descriptives by groups

Isaac Subirana, Joan Vila and Héctor Sanz

2024-01-29

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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 took a sample from REGICOR study. REGICOR is a cross-sectional study with participants from a north-east region of Spain from whom different sets of variables were collected: demographic (age, sex, …), anthropomorphic (height, weight, waist, …), lipid profile (total and cholesterol, triglycerides, …), questionnaires (physical activity, quality of life, …), etc. Also, cardiovascular events and mortality were obtained from hospital and official registries and reports along more than 10 years.

First of all, load REGICOR data typing:

data(regicor)

Variables and labels in this data frame are:

Name Label Codes
id Individual id
year Recruitment year 1995; 2000; 2005
age Age
sex Sex Male; Female
smoker Smoking status Never smoker; Current or former < 1y; Former \(\geq\) 1y
sbp Systolic blood pressure
dbp Diastolic blood pressure
histhtn History of hypertension Yes; No
txhtn Hypertension treatment No; Yes
chol Total cholesterol
hdl HDL cholesterol
triglyc Triglycerides
ldl LDL cholesterol
histchol History of hyperchol. Yes; No
txchol Cholesterol treatment No; Yes
height Height (cm)
weight Weight (Kg)
bmi Body mass index
phyact Physical activity (Kcal/week)
pcs Physical component
mcs Mental component
cv Cardiovascular event No; Yes
tocv Days to cardiovascular event or end of follow-up
death Overall death No; Yes
todeath Days to overall death or end of follow-up

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. To label the variables set the “label” attributes from each of them. 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):

library(survival)
regicor$tmain <- with(regicor, Surv(tocv, cv == 'Yes'))
attr(regicor$tmain,"label") <- "Time to CV event or censoring"

Note that variable tcv are created as time-to-death and time-to-cardiovascular event 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 regicor data between year (“response” variable) and all other variables (“explanatory” variables), this formula is required:

compareGroups(year ~ . , data=regicor)

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 id from the analysis, use - in the formula:

compareGroups(year ~ . - id, data=regicor)

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

res<-compareGroups(year ~ age + sex + bmi, data=regicor)
res


-------- Summary of results by groups of 'Recruitment year'---------


  var             N    p.value  method            selection
1 Age             2294 0.078*   continuous normal ALL      
2 Sex             2294 0.506    categorical       ALL      
3 Body mass index 2259 <0.001** continuous normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Note: Although we have full data (n= 2294) for Age and Sex, there are some missing data in body mass index (bmi).

Mean values of body mass index is statistically different among recruitment years (p-value < 0.05), while Age and Sex are not statistically related to recruitment year (p-value > 0.05).

Age & BMI has been used as continuous and normal distributed, while sex 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(year ~ age + smoker + bmi, data=regicor, subset = sex=='Female')


-------- Summary of results by groups of 'year'---------


  var             N    p.value  method            selection      
1 Age             1193 0.351    continuous normal sex == "Female"
2 Smoking status  1162 <0.001** categorical       sex == "Female"
3 Body mass index 1169 0.084*   continuous normal 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., age and bmi):

compareGroups(year ~ age + bmi + smoker, data=regicor, selec = list(age= sex=="Female", bmi = age>50 ))


-------- Summary of results by groups of 'Recruitment year'---------


  var             N    p.value  method            selection      
1 Age             1193 0.351    continuous normal sex == "Female"
2 Body mass index 1367 0.002**  continuous normal age > 50       
3 Smoking status  2233 <0.001** categorical       ALL            
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

In this case, age distribution are computed among females, while BMI among people older than 50 years.

Combinations are also allowed, e.g.:

compareGroups(year ~ age + smoker + bmi, data=regicor, selec = list(bmi=age>50), subset = sex=="Female")


-------- Summary of results by groups of 'year'---------


  var             N    p.value  method            selection                     
1 Age             1193 0.351    continuous normal sex == "Female"               
2 Smoking status  1162 <0.001** categorical       sex == "Female"               
3 Body mass index  709 0.308    continuous normal (sex == "Female") & (age > 50)
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

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

compareGroups(year ~ age + sex + bmi + bmi, data=regicor, selec = list(bmi.1=txhtn=='Yes'))


-------- Summary of results by groups of 'Recruitment year'---------


  var             N    p.value  method            selection     
1 Age             2294 0.078*   continuous normal ALL           
2 Sex             2294 0.506    categorical       ALL           
3 Body mass index 2259 <0.001** continuous normal ALL           
4 Body mass index  420 0.008**  continuous normal txhtn == "Yes"
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

In this case results for bmi will be reported for all participants (n= 2294) and also for only those recieving no hypertension treatment. 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., “tryglic” used as non-normal distributed:

compareGroups(year ~ age + smoker + triglyc, data=regicor, method = c(triglyc=2))


-------- Summary of results by groups of 'Recruitment year'---------


  var            N    p.value  method                selection
1 Age            2294 0.078*   continuous normal     ALL      
2 Smoking status 2233 <0.001** categorical           ALL      
3 Triglycerides  2231 0.762    continuous non-normal 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-Wilks test for normality is used to decide if the variable is normal or non-normal distributed. To change the significance threshold:

compareGroups(year ~ age + smoker + triglyc, data=regicor, method = c(triglyc=NA), alpha= 0.01)


-------- Summary of results by groups of 'Recruitment year'---------


  var            N    p.value  method                selection
1 Age            2294 0.078*   continuous normal     ALL      
2 Smoking status 2233 <0.001** categorical           ALL      
3 Triglycerides  2231 0.762    continuous non-normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

According to Shapiro-Wilks test, stating the cutpoint at 0.01 significance level, triglycerides 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:

regicor$age7gr<-as.integer(cut(regicor$age, breaks=c(-Inf,40,45,50,55,65,70,Inf), right=TRUE))
compareGroups(year ~ age7gr, data=regicor, method = c(age7gr=NA))


-------- Summary of results by groups of 'Recruitment year'---------


  var    N    p.value method                selection
1 age7gr 2294 0.022** continuous non-normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 
compareGroups(year ~ age7gr, data=regicor, method = c(age7gr=NA), min.dis=8)


-------- Summary of results by groups of 'Recruitment year'---------


  var    N    p.value method      selection
1 age7gr 2294 0.012** 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:

regicor$var6cat <- factor(sample(1:5, nrow(regicor), replace=TRUE))
compareGroups(age7gr ~ sex + bmi + smoker, data=regicor)
Error in compareGroups.fit(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 + smoker, data=regicor, max.ylev=7)


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


  var             N    p.value  method            selection
1 Sex             2294 0.950    categorical       ALL      
2 Body mass index 2259 <0.001** continuous normal ALL      
3 Smoking status  2233 <0.001** categorical       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(year ~ sex + age7gr, method=c(age7gr=3), data=regicor, max.xlev=5)
Warning in compareGroups.fit(X = X, y = y, include.label = include.label,  :
  Variables 'age7gr' have been removed since some errors occurred

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(year ~ age + smoker + bmi, data=regicor, include.label= FALSE)


-------- Summary of results by groups of 'year'---------


  var    N    p.value  method            selection
1 age    2294 0.078*   continuous normal ALL      
2 smoker 2233 <0.001** categorical       ALL      
3 bmi    2259 <0.001** continuous normal 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(year ~ age + triglyc, data=regicor, method = c(triglyc=2))
createTable(resu1)

--------Summary descriptives table by 'Recruitment year'---------

_______________________________________________________________________ 
                   1995            2000            2005       p.overall 
                   N=431           N=786          N=1077                
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             54.1 (11.7)     54.3 (11.2)     55.3 (10.6)     0.078   
Triglycerides 94.0 [71.0;136] 98.0 [72.0;133] 98.0 [72.0;139]   0.762   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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

To get instead percentile 2.5% and 97.5%:

resu2<-compareGroups(year ~ age + triglyc, data=regicor, method = c(triglyc=2), Q1=0.025, Q3=0.975)
createTable(resu2)

--------Summary descriptives table by 'Recruitment year'---------

_______________________________________________________________________ 
                   1995            2000            2005       p.overall 
                   N=431           N=786          N=1077                
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             54.1 (11.7)     54.3 (11.2)     55.3 (10.6)     0.078   
Triglycerides 94.0 [47.0;292] 98.0 [47.0;278] 98.0 [42.0;293]   0.762   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

To get minimum and maximum:

compareGroups(year ~ age + triglyc, data=regicor, method = c(triglyc=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”):
regicor$smk<-regicor$smoker
levels(regicor$smk)<- c("Never smoker", "Current or former < 1y", "Former >= 1y", "Unknown")
attr(regicor$smk,"label")<-"Smoking 4 cat."
cbind(table(regicor$smk))
                       [,1]
Never smoker           1201
Current or former < 1y  593
Former >= 1y            439
Unknown                   0

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

compareGroups(year ~ age + smk + bmi, data=regicor)


-------- Summary of results by groups of 'Recruitment year'---------


  var             N    p.value  method            selection
1 Age             2294 0.078*   continuous normal ALL      
2 Smoking 4 cat.  2233 <0.001** categorical       ALL      
3 Body mass index 2259 <0.001** continuous normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 
Warning 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(year ~ age + smk + bmi, data=regicor, simplify=FALSE)


-------- Summary of results by groups of 'Recruitment year'---------


  var             N    p.value  method            selection
1 Age             2294 0.078*   continuous normal ALL      
2 Smoking 4 cat.  2233 .        categorical       ALL      
3 Body mass index 2259 <0.001** continuous normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

Note that no p-values are calculated for “Smoking” since Chi-squared nor F-Fisher test cannot be computed with a zero row.

4.1.5 Summary

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

res<-compareGroups(year ~ age + sex + smoker + bmi + triglyc, method = c(triglyc=2), data=regicor)
summary(res[c(1, 2, 5)])

 --- Descriptives of each row-variable by groups of 'Recruitment year' ---

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

      N    mean     sd       lower    upper    p.overall p.trend  p.1995 vs 2000 p.1995 vs 2005 p.2000 vs 2005
[ALL] 2294 54.73627 11.04926 54.28388 55.18866                                                                
1995  431  54.09745 11.7172  52.98813 55.20677 0.077837  0.031665 0.930249       0.143499       0.161195      
2000  786  54.33715 11.21814 53.55168 55.12262                                                                
2005  1077 55.28319 10.62606 54.64786 55.91853                                                                

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

      Male Female Male%    Female%  p.overall p.trend  p.1995 vs 2000 p.1995 vs 2005 p.2000 vs 2005
[ALL] 1101 1193   47.99477 52.00523                                                                
1995  206  225    47.79582 52.20418 0.505601  0.543829 0.793746       0.793746       0.791583      
2000  390  396    49.61832 50.38168                                                                
2005  505  572    46.88951 53.11049                                                                

------------------- 
row-variable: Triglycerides 

      N    med Q1 Q3     lower upper p.overall p.trend  p.1995 vs 2000 p.1995 vs 2005 p.2000 vs 2005
[ALL] 2231 97  72 136    95    100                                                                  
1995  403  94  71 135.5  89    99    0.76155   0.524775 0.836094       0.836094       0.859797      
2000  752  98  72 133.25 95    102                                                                  
2005  1076 98  72 139.25 94    102                                                                  

Note that because only variables 1, 3 and 4 are selected, only results for Age, Sex and Triglycerides are shown. Age is summarized by the mean and the standard deviation, Sex by frequencies and percentage, and Triglycerides (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(year ~ age + sex + smoker + bmi, data=regicor)
res


-------- Summary of results by groups of 'Recruitment year'---------


  var             N    p.value  method            selection
1 Age             2294 0.078*   continuous normal ALL      
2 Sex             2294 0.506    categorical       ALL      
3 Smoking status  2233 <0.001** categorical       ALL      
4 Body mass index 2259 <0.001** continuous normal ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

The object res is updated using:

res<-update(res, . ~. - sex + triglyc + cv + tocv, subset = sex=='Female', method = c(triglyc=2, tocv=2), selec = list(triglyc=txchol=='No'))
res

Note that “Sex” is removed as an explanatory variable but used as a filter, subsetting only “Female” participants. Three new variables have been added: Triglycerides, cardiovascular event (yes/no) and time to cardiovascular event or censoring (stated continuous non-normal). For Triglycerides is stated to show only data of participants with no treatment for cholesterol.

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:

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 

Alternatively, since 4.6.0 version, a new function called padjustCompareGroups created by Jordi Real <jordirealgmail.com> can be used to compute p-values considering multiple testing. The methods are the same from p.adjust function, i.e. Bonferroni, False Discovery Rate, etc.

This function takes the compareGroups object and re-computes the p-values. To obtain the same table as above with the p-values correted by “BH” method:

cg <- compareGroups(casco ~ snp10001 + snp10002 + snp10005 + snp10008 + snp10009, SNPs)
createTable(padjustCompareGroups(cg, method="BH"))

--------Summary descriptives table by 'casco'---------

_________________________________________ 
              0          1      p.overall 
             N=47      N=110              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
snp10001:                         0.705   
    CC    2 (4.26%)  10 (9.09%)           
    CT    21 (44.7%) 32 (29.1%)           
    TT    24 (51.1%) 68 (61.8%)           
snp10002:                         0.707   
    AA    0 (0.00%)  5 (4.55%)            
    AC    25 (53.2%) 53 (48.2%)           
    CC    22 (46.8%) 52 (47.3%)           
snp10005:                         0.758   
    AA    0 (0.00%)  3 (2.73%)            
    AG    22 (46.8%) 48 (43.6%)           
    GG    25 (53.2%) 59 (53.6%)           
snp10008:                         0.758   
    CC    30 (63.8%) 74 (67.3%)           
    CG    15 (31.9%) 29 (26.4%)           
    GG    2 (4.26%)  7 (6.36%)            
snp10009:                         0.707   
    AA    21 (45.7%) 51 (46.4%)           
    AG    25 (54.3%) 54 (49.1%)           
    GG    0 (0.00%)  5 (4.55%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(cv ~ age + sex + bmi + smoker, data=regicor, ref=1)
createTable(res1, show.ratio=TRUE)

--------Summary descriptives table by 'Cardiovascular event'---------

______________________________________________________________________________________ 
                                No          Yes            OR        p.ratio p.overall 
                              N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.6 (11.1)  57.5 (11.0) 1.02 [1.00;1.04]  0.017    0.018   
Sex:                                                                           0.801   
    Male                   996 (48.1%)  46 (50.0%)        Ref.        Ref.             
    Female                 1075 (51.9%) 46 (50.0%)  0.93 [0.61;1.41]  0.721            
Body mass index            27.6 (4.56)  28.1 (4.48) 1.02 [0.98;1.07]  0.313    0.307   
Smoking status:                                                               <0.001   
    Never smoker           1099 (54.3%) 37 (40.2%)        Ref.        Ref.             
    Current or former < 1y 506 (25.0%)  47 (51.1%)  2.75 [1.77;4.32] <0.001            
    Former >= 1y           419 (20.7%)   8 (8.70%)  0.58 [0.25;1.19]  0.142            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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

res2<-compareGroups(cv ~ age + sex + bmi + smoker, data=regicor, ref=c(smoker=1, sex=2))
createTable(res2, show.ratio=TRUE)

--------Summary descriptives table by 'Cardiovascular event'---------

______________________________________________________________________________________ 
                                No          Yes            OR        p.ratio p.overall 
                              N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.6 (11.1)  57.5 (11.0) 1.02 [1.00;1.04]  0.017    0.018   
Sex:                                                                           0.801   
    Male                   996 (48.1%)  46 (50.0%)  1.08 [0.71;1.64]  0.721            
    Female                 1075 (51.9%) 46 (50.0%)        Ref.        Ref.             
Body mass index            27.6 (4.56)  28.1 (4.48) 1.02 [0.98;1.07]  0.313    0.307   
Smoking status:                                                               <0.001   
    Never smoker           1099 (54.3%) 37 (40.2%)        Ref.        Ref.             
    Current or former < 1y 506 (25.0%)  47 (51.1%)  2.75 [1.77;4.32] <0.001            
    Former >= 1y           419 (20.7%)   8 (8.70%)  0.58 [0.25;1.19]  0.142            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(cv ~ age + sex + bmi + histhtn + txhtn, data=regicor, ref.no='NO')
createTable(res, show.ratio=TRUE)

--------Summary descriptives table by 'Cardiovascular event'---------

____________________________________________________________________________________ 
                              No          Yes            OR        p.ratio p.overall 
                            N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.6 (11.1)  57.5 (11.0) 1.02 [1.00;1.04]  0.017    0.018   
Sex:                                                                         0.801   
    Male                 996 (48.1%)  46 (50.0%)        Ref.        Ref.             
    Female               1075 (51.9%) 46 (50.0%)  0.93 [0.61;1.41]  0.721            
Body mass index          27.6 (4.56)  28.1 (4.48) 1.02 [0.98;1.07]  0.313    0.307   
History of hypertension:                                                     0.058   
    Yes                  647 (31.3%)  38 (41.3%)  1.54 [1.00;2.36]  0.049            
    No                   1418 (68.7%) 54 (58.7%)        Ref.        Ref.             
Hypertension treatment:                                                      0.270   
    No                   1657 (81.3%) 70 (76.1%)        Ref.        Ref.             
    Yes                  382 (18.7%)  22 (23.9%)  1.37 [0.82;2.21]  0.223            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(cv ~ age + bmi, data=regicor)
createTable(res, show.ratio=TRUE)

--------Summary descriptives table by 'Cardiovascular event'---------

__________________________________________________________________________ 
                    No          Yes            OR        p.ratio p.overall 
                  N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             54.6 (11.1) 57.5 (11.0) 1.02 [1.00;1.04]  0.017    0.018   
Body mass index 27.6 (4.56) 28.1 (4.48) 1.02 [0.98;1.07]  0.313    0.307   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Here the OR is for the increase of one unit for Age and “Body mass index”.

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

--------Summary descriptives table by 'Cardiovascular event'---------

__________________________________________________________________________ 
                    No          Yes            OR        p.ratio p.overall 
                  N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             54.6 (11.1) 57.5 (11.0) 1.26 [1.04;1.53]  0.017    0.018   
Body mass index 27.6 (4.56) 28.1 (4.48) 1.05 [0.96;1.14]  0.313    0.307   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(cv ~ age + sex + bmi + txhtn, data=regicor)
createTable(res, show.ratio=TRUE)

--------Summary descriptives table by 'Cardiovascular event'---------

___________________________________________________________________________________ 
                             No          Yes            OR        p.ratio p.overall 
                           N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                     54.6 (11.1)  57.5 (11.0) 1.02 [1.00;1.04]  0.017    0.018   
Sex:                                                                        0.801   
    Male                996 (48.1%)  46 (50.0%)        Ref.        Ref.             
    Female              1075 (51.9%) 46 (50.0%)  0.93 [0.61;1.41]  0.721            
Body mass index         27.6 (4.56)  28.1 (4.48) 1.02 [0.98;1.07]  0.313    0.307   
Hypertension treatment:                                                     0.270   
    No                  1657 (81.3%) 70 (76.1%)        Ref.        Ref.             
    Yes                 382 (18.7%)  22 (23.9%)  1.37 [0.82;2.21]  0.223            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: This output shows the OR of having a cardiovascular event. Therefore, having no event is the reference category.

res<-compareGroups(cv ~ age + sex + bmi + txhtn, data=regicor, ref.y=2)
createTable(res, show.ratio=TRUE)

--------Summary descriptives table by 'Cardiovascular event'---------

___________________________________________________________________________________ 
                             No          Yes            OR        p.ratio p.overall 
                           N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                     54.6 (11.1)  57.5 (11.0) 0.98 [1.00;0.96]  0.017    0.018   
Sex:                                                                        0.801   
    Male                996 (48.1%)  46 (50.0%)        Ref.        Ref.             
    Female              1075 (51.9%) 46 (50.0%)  1.08 [0.71;1.64]  0.721            
Body mass index         27.6 (4.56)  28.1 (4.48) 0.98 [1.02;0.93]  0.313    0.307   
Hypertension treatment:                                                     0.270   
    No                  1657 (81.3%) 70 (76.1%)        Ref.        Ref.             
    Yes                 382 (18.7%)  22 (23.9%)  0.73 [0.45;1.22]  0.223            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note: This output shows the OR of having no event, and event 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=regicor), bivar=TRUE, file="./figures/bivarsurv/", type="png")
plot(compareGroups(tmain ~ age, data=regicor), 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*365.25), data=regicor)
res

Note that tmain is calculated at 3 years, i.e. 3*365.25 days (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(year ~ age + sex + smoker + bmi + sbp, data=regicor, selec = list(sbp=txhtn=="No"))
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 'Recruitment year'---------

________________________________________________________________________ 
                              1995        2000        2005     p.overall 
                              N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                             0.506   
    Male                   206 (47.8%) 390 (49.6%) 505 (46.9%)           
    Female                 225 (52.2%) 396 (50.4%) 572 (53.1%)           
Smoking status:                                                 <0.001   
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
Systolic blood pressure    129 (17.4)  130 (20.1)  124 (16.9)   <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that the option “descr” prints descriptive tables.

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



---Available data----

____________________________________________________________________________ 
                        [ALL] 1995 2000 2005      method          select     
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                     2294  431  786  1077 continuous-normal      ALL      
Sex                     2294  431  786  1077    categorical         ALL      
Smoking status          2233  415  758  1060    categorical         ALL      
Body mass index         2259  423  771  1065 continuous-normal      ALL      
Systolic blood pressure 1810  357  649  804  continuous-normal txhtn == "No" 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 48% are male, obviously 52% are female). To hide some category, e.g., “Male”:
update(restab, hide = c(sex="Male"))

--------Summary descriptives table by 'Recruitment year'---------

________________________________________________________________________ 
                              1995        2000        2005     p.overall 
                              N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex: Female                225 (52.2%) 396 (50.4%) 572 (53.1%)   0.506   
Smoking status:                                                 <0.001   
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
Systolic blood pressure    129 (17.4)  130 (20.1)  124 (16.9)   <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(year ~ age + sex + histchol + histhtn, data=regicor)
createTable(res, hide.no='no', hide = c(sex="Male"))

--------Summary descriptives table by 'Recruitment year'---------

_____________________________________________________________________ 
                           1995        2000        2005     p.overall 
                           N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                     54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex: Female             225 (52.2%) 396 (50.4%) 572 (53.1%)   0.506   
History of hyperchol.   97 (22.5%)  256 (33.2%) 356 (33.2%)  <0.001   
History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%)  <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 'Recruitment year'---------

____________________________________________________________________________ 
                             1995          2000          2005      p.overall 
                             N=431         N=786        N=1077               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.10 (11.72) 54.34 (11.22) 55.28 (10.63)   0.078   
Sex:                                                                 0.506   
    Male                 206 (47.796%) 390 (49.618%) 505 (46.890%)           
    Female               225 (52.204%) 396 (50.382%) 572 (53.110%)           
History of hyperchol.:                                              <0.001   
    Yes                   97 (22.5%)    256 (33.2%)   356 (33.2%)            
    No                    334 (77.5%)   515 (66.8%)   715 (66.8%)            
History of hypertension:                                            <0.001   
    Yes                   111 (25.8%)   233 (29.6%)   379 (35.5%)            
    No                    320 (74.2%)   553 (70.4%)   690 (64.5%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 'Recruitment year'---------

______________________________________________________________________ 
                            1995        2000        2005     p.overall 
                            N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                           0.506   
    Male                    47.8%       49.6%       46.9%              
    Female                  52.2%       50.4%       53.1%              
History of hyperchol.:                                        <0.001   
    Yes                     22.5%       33.2%       33.2%              
    No                      77.5%       66.8%       66.8%              
History of hypertension:                                      <0.001   
    Yes                     25.8%       29.6%       35.5%              
    No                      74.2%       70.4%       64.5%              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that only percentages are displayed.

createTable(res, type=3)

--------Summary descriptives table by 'Recruitment year'---------

______________________________________________________________________ 
                            1995        2000        2005     p.overall 
                            N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                           0.506   
    Male                     206         390         505               
    Female                   225         396         572               
History of hyperchol.:                                        <0.001   
    Yes                      97          256         356               
    No                       334         515         715               
History of hypertension:                                      <0.001   
    Yes                      111         233         379               
    No                       320         553         690               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 'Recruitment year'---------

___________________________________________________________________________ 
                            1995        2000        2005     p.overall  N   
                            N=431       N=786      N=1077                   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   2294 
Sex:                                                           0.506   2294 
    Male                 206 (47.8%) 390 (49.6%) 505 (46.9%)                
    Female               225 (52.2%) 396 (50.4%) 572 (53.1%)                
History of hyperchol.:                                        <0.001   2273 
    Yes                  97 (22.5%)  256 (33.2%) 356 (33.2%)                
    No                   334 (77.5%) 515 (66.8%) 715 (66.8%)                
History of hypertension:                                      <0.001   2286 
    Yes                  111 (25.8%) 233 (29.6%) 379 (35.5%)                
    No                   320 (74.2%) 553 (70.4%) 690 (64.5%)                
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • show.descr: If argument show.descr is set to FALSE only p-values are displayed:
createTable(res, show.descr=FALSE)

--------Summary descriptives table by 'Recruitment year'---------

__________________________________ 
                         p.overall 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        0.078   
Sex:                               
    Male                   0.506   
    Female                         
History of hyperchol.:             
    Yes                   <0.001   
    No                             
History of hypertension:           
    Yes                   <0.001   
    No                             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • 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 'Recruitment year'---------

___________________________________________________________________________________ 
                            [ALL]        1995        2000        2005     p.overall 
                            N=2294       N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.7 (11.0)  54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                                        0.506   
    Male                 1101 (48.0%) 206 (47.8%) 390 (49.6%) 505 (46.9%)           
    Female               1193 (52.0%) 225 (52.2%) 396 (50.4%) 572 (53.1%)           
History of hyperchol.:                                                     <0.001   
    Yes                  709 (31.2%)  97 (22.5%)  256 (33.2%) 356 (33.2%)           
    No                   1564 (68.8%) 334 (77.5%) 515 (66.8%) 715 (66.8%)           
History of hypertension:                                                   <0.001   
    Yes                  723 (31.6%)  111 (25.8%) 233 (29.6%) 379 (35.5%)           
    No                   1563 (68.4%) 320 (74.2%) 553 (70.4%) 690 (64.5%)           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • 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 'Recruitment year'---------

____________________________________________________________ 
                            1995        2000        2005     
                            N=431       N=786      N=1077    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 
Sex:                                                         
    Male                 206 (47.8%) 390 (49.6%) 505 (46.9%) 
    Female               225 (52.2%) 396 (50.4%) 572 (53.1%) 
History of hyperchol.:                                       
    Yes                  97 (22.5%)  256 (33.2%) 356 (33.2%) 
    No                   334 (77.5%) 515 (66.8%) 715 (66.8%) 
History of hypertension:                                     
    Yes                  111 (25.8%) 233 (29.6%) 379 (35.5%) 
    No                   320 (74.2%) 553 (70.4%) 690 (64.5%) 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • 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 'Recruitment year'---------

______________________________________________________________________________ 
                            1995        2000        2005     p.overall p.trend 
                            N=431       N=786      N=1077                      
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078    0.032  
Sex:                                                           0.506    0.544  
    Male                 206 (47.8%) 390 (49.6%) 505 (46.9%)                   
    Female               225 (52.2%) 396 (50.4%) 572 (53.1%)                   
History of hyperchol.:                                        <0.001   <0.001  
    Yes                  97 (22.5%)  256 (33.2%) 356 (33.2%)                   
    No                   334 (77.5%) 515 (66.8%) 715 (66.8%)                   
History of hypertension:                                      <0.001   <0.001  
    Yes                  111 (25.8%) 233 (29.6%) 379 (35.5%)                   
    No                   320 (74.2%) 553 (70.4%) 690 (64.5%)                   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 'Recruitment year'---------

___________________________________________________________________________________________________________________ 
                            1995        2000        2005     p.overall p.1995 vs 2000 p.1995 vs 2005 p.2000 vs 2005 
                            N=431       N=786      N=1077                                                           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078       0.930          0.143          0.161      
Sex:                                                           0.506       0.794          0.794          0.792      
    Male                 206 (47.8%) 390 (49.6%) 505 (46.9%)                                                        
    Female               225 (52.2%) 396 (50.4%) 572 (53.1%)                                                        
History of hyperchol.:                                        <0.001       <0.001         <0.001         1.000      
    Yes                  97 (22.5%)  256 (33.2%) 356 (33.2%)                                                        
    No                   334 (77.5%) 515 (66.8%) 715 (66.8%)                                                        
History of hypertension:                                      <0.001       0.169          0.001          0.015      
    Yes                  111 (25.8%) 233 (29.6%) 379 (35.5%)                                                        
    No                   320 (74.2%) 553 (70.4%) 690 (64.5%)                                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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= year!=1995), show.ratio=TRUE)

--------Summary descriptives table by 'year'---------

___________________________________________________________________________________ 
                            2000        2005            OR        p.ratio p.overall 
                            N=786      N=1077                                       
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                      54.3 (11.2) 55.3 (10.6) 1.01 [1.00;1.02]  0.064    0.066   
Sex:                                                                        0.264   
    Male                 390 (49.6%) 505 (46.9%)       Ref.        Ref.             
    Female               396 (50.4%) 572 (53.1%) 1.12 [0.93;1.34]  0.245            
History of hyperchol.:                                                      1.000   
    Yes                  256 (33.2%) 356 (33.2%)       Ref.        Ref.             
    No                   515 (66.8%) 715 (66.8%) 1.00 [0.82;1.22]  0.988            
History of hypertension:                                                    0.010   
    Yes                  233 (29.6%) 379 (35.5%)       Ref.        Ref.             
    No                   553 (70.4%) 690 (64.5%) 0.77 [0.63;0.93]  0.008            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Note that recruitment year 1995 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 ~  year + age + sex, data=regicor), show.ratio=TRUE)

--------Summary descriptives table by 'Time to CV event or censoring'---------

_____________________________________________________________________________ 
                    No event      Event           HR        p.ratio p.overall 
                     N=2071       N=92                                        
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Recruitment year:                                                     0.157   
    1995          388 (18.7%)  10 (10.9%)        Ref.        Ref.             
    2000          706 (34.1%)  35 (38.0%)  1.95 [0.96;3.93]  0.063            
    2005          977 (47.2%)  47 (51.1%)  1.82 [0.92;3.59]  0.087            
Age               54.6 (11.1)  57.5 (11.0) 1.02 [1.00;1.04]  0.021    0.021   
Sex:                                                                  0.696   
    Male          996 (48.1%)  46 (50.0%)        Ref.        Ref.             
    Female        1075 (51.9%) 46 (50.0%)  0.92 [0.61;1.39]  0.696            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • digits.ratio: The number of decimal places for Odds/Hazard ratios can be changed by the digits.ratio argument:
createTable(compareGroups(tmain ~  year + age + sex, data=regicor), show.ratio=TRUE, digits.ratio= 3)

--------Summary descriptives table by 'Time to CV event or censoring'---------

________________________________________________________________________________ 
                    No event      Event            HR          p.ratio p.overall 
                     N=2071       N=92                                           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Recruitment year:                                                        0.157   
    1995          388 (18.7%)  10 (10.9%)         Ref.          Ref.             
    2000          706 (34.1%)  35 (38.0%)  1.946 [0.964;3.930]  0.063            
    2005          977 (47.2%)  47 (51.1%)  1.816 [0.918;3.593]  0.087            
Age               54.6 (11.1)  57.5 (11.0) 1.022 [1.003;1.041]  0.021    0.021   
Sex:                                                                     0.696   
    Male          996 (48.1%)  46 (50.0%)         Ref.          Ref.             
    Female        1075 (51.9%) 46 (50.0%)  0.922 [0.613;1.387]  0.696            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • 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 ~  year + age + sex, data=regicor), show.all = TRUE)
print(tab, header.labels = c("p.overall" = "p-value", "all" = "All"))

--------Summary descriptives table by 'Time to CV event or censoring'---------

_______________________________________________________________ 
                      All        No event      Event    p-value 
                     N=2163       N=2071       N=92             
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Recruitment year:                                        0.157  
    1995          398 (18.4%)  388 (18.7%)  10 (10.9%)          
    2000          741 (34.3%)  706 (34.1%)  35 (38.0%)          
    2005          1024 (47.3%) 977 (47.2%)  47 (51.1%)          
Age               54.7 (11.1)  54.6 (11.1)  57.5 (11.0)  0.021  
Sex:                                                     0.696  
    Male          1042 (48.2%) 996 (48.1%)  46 (50.0%)          
    Female        1121 (51.8%) 1075 (51.9%) 46 (50.0%)          
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(year ~ age + sex, data=regicor))
restab2 <- createTable(compareGroups(year ~ bmi + smoker, data=regicor))
rbind("Non-modifiable risk factors"=restab1, "Modifiable risk factors"=restab2)

--------Summary descriptives table by 'Recruitment year'---------

____________________________________________________________________________ 
                                  1995        2000        2005     p.overall 
                                  N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Non-modifiable risk factors:
    Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
    Sex:                                                             0.506   
        Male                   206 (47.8%) 390 (49.6%) 505 (46.9%)           
        Female                 225 (52.2%) 396 (50.4%) 572 (53.1%)           
Modifiable risk factors:
    Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
    Smoking status:                                                 <0.001   
        Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
        Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
        Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 'Recruitment year'---------

____________________________________________________________________________ 
                                  1995        2000        2005     p.overall 
                                  N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Non-modifiable:
    Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Modifiable:
    Smoking status:                                                 <0.001   
        Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
        Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
        Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

To change the order:

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

--------Summary descriptives table by 'Recruitment year'---------

____________________________________________________________________________ 
                                  1995        2000        2005     p.overall 
                                  N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Non-modifiable:
    Smoking status:                                                 <0.001   
        Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
        Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
        Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
    Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
Modifiable:
    Sex:                                                             0.506   
        Male                   206 (47.8%) 390 (49.6%) 505 (46.9%)           
        Female                 225 (52.2%) 396 (50.4%) 572 (53.1%)           
    Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(year ~ age +  smoker + bmi + histhtn , data=regicor)
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                
                           ___________________________________  ___________________________________  ___________________________________
                              1995        2000        2005         1995        2000        2005         1995        2000        2005     
                              N=431       N=786      N=1077        N=225       N=396       N=572        N=206       N=390       N=505    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)  54.1 (11.7) 54.4 (11.2) 55.2 (10.6)  54.1 (11.8) 54.3 (11.2) 55.4 (10.7) 
Smoking status:                                                                                                                          
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)  182 (83.1%) 302 (79.3%) 416 (74.0%)  52 (26.5%)  112 (29.7%) 137 (27.5%) 
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)  32 (14.6%)  68 (17.8%)  83 (14.8%)   77 (39.3%)  199 (52.8%) 134 (26.9%) 
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)   5 (2.28%)  11 (2.89%)  63 (11.2%)   67 (34.2%)  66 (17.5%)  227 (45.6%) 
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  27.2 (4.57) 28.0 (5.25) 27.3 (5.39)  26.9 (3.64) 28.2 (3.89) 27.9 (3.58) 
History of hypertension:                                                                                                                 
    Yes                    111 (25.8%) 233 (29.6%) 379 (35.5%)  61 (27.1%)  123 (31.1%) 198 (34.8%)  50 (24.3%)  110 (28.2%) 181 (36.2%) 
    No                     320 (74.2%) 553 (70.4%) 690 (64.5%)  164 (72.9%) 273 (68.9%) 371 (65.2%)  156 (75.7%) 280 (71.8%) 319 (63.8%) 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

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

cbind(alltab,femaletab,maletab)

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

________________________________________________________________________________________________________________________________________
                                         alltab                              femaletab                             maletab              
                           ___________________________________  ___________________________________  ___________________________________
                              1995        2000        2005         1995        2000        2005         1995        2000        2005     
                              N=431       N=786      N=1077        N=225       N=396       N=572        N=206       N=390       N=505    
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)  54.1 (11.7) 54.4 (11.2) 55.2 (10.6)  54.1 (11.8) 54.3 (11.2) 55.4 (10.7) 
Smoking status:                                                                                                                          
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)  182 (83.1%) 302 (79.3%) 416 (74.0%)  52 (26.5%)  112 (29.7%) 137 (27.5%) 
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)  32 (14.6%)  68 (17.8%)  83 (14.8%)   77 (39.3%)  199 (52.8%) 134 (26.9%) 
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)   5 (2.28%)  11 (2.89%)  63 (11.2%)   67 (34.2%)  66 (17.5%)  227 (45.6%) 
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  27.2 (4.57) 28.0 (5.25) 27.3 (5.39)  26.9 (3.64) 28.2 (3.89) 27.9 (3.58) 
History of hypertension:                                                                                                                 
    Yes                    111 (25.8%) 233 (29.6%) 379 (35.5%)  61 (27.1%)  123 (31.1%) 198 (34.8%)  50 (24.3%)  110 (28.2%) 181 (36.2%) 
    No                     320 (74.2%) 553 (70.4%) 690 (64.5%)  164 (72.9%) 273 (68.9%) 371 (65.2%)  156 (75.7%) 280 (71.8%) 319 (63.8%) 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

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(year ~ age + bmi + smoker + histchol + histhtn, regicor)
restab <- createTable(res, hide.no="no")
  1. and then apply the strataTable function on the table:
strataTable(restab, "sex")

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

_______________________________________________________________________________________________________________________
                                               Male                                          Female                    
                           _____________________________________________  _____________________________________________
                              1995        2000        2005     p.overall     1995        2000        2005     p.overall 
                              N=206       N=390       N=505                  N=225       N=396       N=572              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age                        54.1 (11.8) 54.3 (11.2) 55.4 (10.7)   0.212    54.1 (11.7) 54.4 (11.2) 55.2 (10.6)   0.351   
Body mass index            26.9 (3.64) 28.2 (3.89) 27.9 (3.58)  <0.001    27.2 (4.57) 28.0 (5.25) 27.3 (5.39)   0.084   
Smoking status:                                                 <0.001                                         <0.001   
    Never smoker           52 (26.5%)  112 (29.7%) 137 (27.5%)            182 (83.1%) 302 (79.3%) 416 (74.0%)           
    Current or former < 1y 77 (39.3%)  199 (52.8%) 134 (26.9%)            32 (14.6%)  68 (17.8%)  83 (14.8%)            
    Former >= 1y           67 (34.2%)  66 (17.5%)  227 (45.6%)             5 (2.28%)  11 (2.89%)  63 (11.2%)            
History of hyperchol.      48 (23.3%)  138 (35.8%) 167 (33.2%)   0.007    49 (21.8%)  118 (30.6%) 189 (33.3%)   0.006   
History of hypertension    50 (24.3%)  110 (28.2%) 181 (36.2%)   0.002    61 (27.1%)  123 (31.1%) 198 (34.8%)   0.097   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯

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(year ~ age + sex + smoker + bmi, data=regicor)), which.table='both')

--------Summary descriptives table by 'Recruitment year'---------

________________________________________________________________________ 
                              1995        2000        2005     p.overall 
                              N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                             0.506   
    Male                   206 (47.8%) 390 (49.6%) 505 (46.9%)           
    Female                 225 (52.2%) 396 (50.4%) 572 (53.1%)           
Smoking status:                                                 <0.001   
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 



---Available data----

_____________________________________________________________ 
                [ALL] 1995 2000 2005      method       select 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             2294  431  786  1077 continuous-normal  ALL   
Sex             2294  431  786  1077    categorical     ALL   
Smoking status  2233  415  758  1060    categorical     ALL   
Body mass index 2259  423  771  1065 continuous-normal  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(year ~ age + sex + smoker + bmi, data=regicor)),  nmax=FALSE)

--------Summary descriptives table by 'Recruitment year'---------

________________________________________________________________________ 
                              1995        2000        2005     p.overall 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                             0.506   
    Male                   206 (47.8%) 390 (49.6%) 505 (46.9%)           
    Female                 225 (52.2%) 396 (50.4%) 572 (53.1%)           
Smoking status:                                                 <0.001   
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • summary: returns the same table as that generated with print method setting which.table='avail':
summary(createTable(compareGroups(year ~ age + sex + smoker + bmi, data=regicor)))



---Available data----

_____________________________________________________________ 
                [ALL] 1995 2000 2005      method       select 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age             2294  431  786  1077 continuous-normal  ALL   
Sex             2294  431  786  1077    categorical     ALL   
Smoking status  2233  415  758  1060    categorical     ALL   
Body mass index 2259  423  771  1065 continuous-normal  ALL   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
  • update: An object of class createTable can be updated:
res<-compareGroups(year ~ age + sex + smoker + bmi, data=regicor)
restab<-createTable(res, type=1)
restab

--------Summary descriptives table by 'Recruitment year'---------

________________________________________________________________________ 
                              1995        2000        2005     p.overall 
                              N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                             0.506   
    Male                      47.8%       49.6%       46.9%              
    Female                    52.2%       50.4%       53.1%              
Smoking status:                                                 <0.001   
    Never smoker              56.4%       54.6%       52.2%              
    Current or former < 1y    26.3%       35.2%       20.5%              
    Former >= 1y              17.3%       10.2%       27.4%              
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
update(restab, show.n=TRUE)

--------Summary descriptives table by 'Recruitment year'---------

_____________________________________________________________________________ 
                              1995        2000        2005     p.overall  N   
                              N=431       N=786      N=1077                   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   2294 
Sex:                                                             0.506   2294 
    Male                      47.8%       49.6%       46.9%                   
    Female                    52.2%       50.4%       53.1%                   
Smoking status:                                                 <0.001   2233 
    Never smoker              56.4%       54.6%       52.2%                   
    Current or former < 1y    26.3%       35.2%       20.5%                   
    Former >= 1y              17.3%       10.2%       27.4%                   
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   2259 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 'year'---------

_____________________________________________________________________________ 
                              1995        2000        2005     p.overall  N   
                              N=225       N=396       N=572                   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.4 (11.2) 55.2 (10.6)   0.351   1193 
Sex: Female                   100%        100%        100%         .     1193 
Smoking status:                                                 <0.001   1162 
    Never smoker              83.1%       79.3%       74.0%                   
    Current or former < 1y    14.6%       17.8%       14.8%                   
    Former >= 1y              2.28%       2.89%       11.2%                   
Body mass index            27.2 (4.57) 28.0 (5.25) 27.3 (5.39)   0.084   1169 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 subset using “[”:
createTable(compareGroups(year ~ age + sex + smoker + bmi + histhtn, data=regicor))

--------Summary descriptives table by 'Recruitment year'---------

________________________________________________________________________ 
                              1995        2000        2005     p.overall 
                              N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age                        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                                             0.506   
    Male                   206 (47.8%) 390 (49.6%) 505 (46.9%)           
    Female                 225 (52.2%) 396 (50.4%) 572 (53.1%)           
Smoking status:                                                 <0.001   
    Never smoker           234 (56.4%) 414 (54.6%) 553 (52.2%)           
    Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)           
    Former >= 1y           72 (17.3%)  77 (10.2%)  290 (27.4%)           
Body mass index            27.0 (4.15) 28.1 (4.62) 27.6 (4.63)  <0.001   
History of hypertension:                                        <0.001   
    Yes                    111 (25.8%) 233 (29.6%) 379 (35.5%)           
    No                     320 (74.2%) 553 (70.4%) 690 (64.5%)           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
createTable(compareGroups(year ~ age + sex + smoker + bmi + histhtn, data=regicor))[1:2, ]

--------Summary descriptives table by 'Recruitment year'---------

________________________________________________________ 
              1995        2000        2005     p.overall 
              N=431       N=786      N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age        54.1 (11.7) 54.3 (11.2) 55.3 (10.6)   0.078   
Sex:                                             0.506   
    Male   206 (47.8%) 390 (49.6%) 505 (46.9%)           
    Female 225 (52.2%) 396 (50.4%) 572 (53.1%)           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 regicor dataset, just type:

descrTable(regicor)

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

_____________________________________________________________________________ 
                                                          [ALL]           N   
                                                         N=2294               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Individual id                                    1215817624 (1339538686) 2294 
Recruitment year:                                                        2294 
    1995                                               431 (18.8%)            
    2000                                               786 (34.3%)            
    2005                                              1077 (46.9%)            
Age                                                    54.7 (11.0)       2294 
Sex:                                                                     2294 
    Male                                              1101 (48.0%)            
    Female                                            1193 (52.0%)            
Smoking status:                                                          2233 
    Never smoker                                      1201 (53.8%)            
    Current or former < 1y                             593 (26.6%)            
    Former >= 1y                                       439 (19.7%)            
Systolic blood pressure                                131 (20.3)        2280 
Diastolic blood pressure                               79.7 (10.5)       2280 
History of hypertension:                                                 2286 
    Yes                                                723 (31.6%)            
    No                                                1563 (68.4%)            
Hypertension treatment:                                                  2251 
    No                                                1823 (81.0%)            
    Yes                                                428 (19.0%)            
Total cholesterol                                      219 (45.2)        2193 
HDL cholesterol                                        52.7 (14.7)       2225 
Triglycerides                                          116 (73.9)        2231 
LDL cholesterol                                        143 (39.7)        2126 
History of hyperchol.:                                                   2273 
    Yes                                                709 (31.2%)            
    No                                                1564 (68.8%)            
Cholesterol treatment:                                                   2239 
    No                                                2011 (89.8%)            
    Yes                                                228 (10.2%)            
Height (cm)                                            163 (9.22)        2259 
Weight (Kg)                                            73.4 (13.7)       2259 
Body mass index                                        27.6 (4.56)       2259 
Physical activity (Kcal/week)                           399 (388)        2206 
Physical component                                     49.6 (9.01)       2054 
Mental component                                       48.0 (11.0)       2054 
Cardiovascular event:                                                    2163 
    No                                                2071 (95.7%)            
    Yes                                                92 (4.25%)             
Days to cardiovascular event or end of follow-up       1755 (1081)       2163 
Overall death:                                                           2148 
    No                                                1975 (91.9%)            
    Yes                                                173 (8.05%)            
Days to overall death or end of follow-up              1721 (1051)       2148 
Time to CV event or censoring                             4.49%          2163 
age7gr                                                 4.03 (1.82)       2294 
Smoking 4 cat.:                                                          2233 
    Never smoker                                      1201 (53.8%)            
    Current or former < 1y                             593 (26.6%)            
    Former >= 1y                                       439 (19.7%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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

descrTable(~ age + sex, regicor)

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

____________________________ 
              [ALL]      N   
              N=2294         
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Age        54.7 (11.0)  2294 
Sex:                    2294 
    Male   1101 (48.0%)      
    Female 1193 (52.0%)      
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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

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

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

_____________________________________________________________________________ 
                                                          [ALL]           N   
                                                         N=2294               
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Individual id                                    1215817624 (1339538686) 2294 
Recruitment year:                                                        2294 
    1995                                               431 (18.8%)            
    2000                                               786 (34.3%)            
    2005                                              1077 (46.9%)            
Age                                                    54.7 (11.0)       2294 
Sex:                                                                     2294 
    Male                                              1101 (48.0%)            
    Female                                            1193 (52.0%)            
Smoking status:                                                          2233 
    Never smoker                                      1201 (53.8%)            
    Current or former < 1y                             593 (26.6%)            
    Former >= 1y                                       439 (19.7%)            
Systolic blood pressure                                131 (20.3)        2280 
Diastolic blood pressure                               79.7 (10.5)       2280 
History of hypertension                                723 (31.6%)       2286 
Hypertension treatment                                 428 (19.0%)       2251 
Total cholesterol                                      219 (45.2)        2193 
HDL cholesterol                                        52.7 (14.7)       2225 
Triglycerides                                          116 (73.9)        2231 
LDL cholesterol                                        143 (39.7)        2126 
History of hyperchol.                                  709 (31.2%)       2273 
Cholesterol treatment                                  228 (10.2%)       2239 
Height (cm)                                            163 (9.22)        2259 
Weight (Kg)                                            73.4 (13.7)       2259 
Body mass index                                        27.6 (4.56)       2259 
Physical activity (Kcal/week)                           399 (388)        2206 
Physical component                                     49.6 (9.01)       2054 
Mental component                                       48.0 (11.0)       2054 
Cardiovascular event                                   92 (4.25%)        2163 
Days to cardiovascular event or end of follow-up       1755 (1081)       2163 
Overall death                                          173 (8.05%)       2148 
Days to overall death or end of follow-up              1721 (1051)       2148 
Time to CV event or censoring                             4.49%          2163 
age7gr                                                 4.03 (1.82)       2294 
Smoking 4 cat.:                                                          2233 
    Never smoker                                      1201 (53.8%)            
    Current or former < 1y                             593 (26.6%)            
    Former >= 1y                                       439 (19.7%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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

descrTable(year ~ ., regicor, hide.no="no", show.all=TRUE)

--------Summary descriptives table by 'Recruitment year'---------

__________________________________________________________________________________________________________________________________ 
                                                          [ALL]                1995              2000           2005     p.overall 
                                                         N=2294                N=431             N=786         N=1077              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Individual id                                    1215817624 (1339538686) 1000002914 (1242) 3000104610 (2379) 1996 (1161)   0.000   
Age                                                    54.7 (11.0)          54.1 (11.7)       54.3 (11.2)    55.3 (10.6)   0.078   
Sex:                                                                                                                       0.506   
    Male                                              1101 (48.0%)          206 (47.8%)       390 (49.6%)    505 (46.9%)           
    Female                                            1193 (52.0%)          225 (52.2%)       396 (50.4%)    572 (53.1%)           
Smoking status:                                                                                                           <0.001   
    Never smoker                                      1201 (53.8%)          234 (56.4%)       414 (54.6%)    553 (52.2%)           
    Current or former < 1y                             593 (26.6%)          109 (26.3%)       267 (35.2%)    217 (20.5%)           
    Former >= 1y                                       439 (19.7%)          72 (17.3%)        77 (10.2%)     290 (27.4%)           
Systolic blood pressure                                131 (20.3)           133 (19.2)        133 (21.3)     129 (19.8)   <0.001   
Diastolic blood pressure                               79.7 (10.5)          77.0 (10.5)       80.8 (10.3)    79.9 (10.6)  <0.001   
History of hypertension                                723 (31.6%)          111 (25.8%)       233 (29.6%)    379 (35.5%)  <0.001   
Hypertension treatment                                 428 (19.0%)          71 (16.5%)        127 (16.2%)    230 (22.2%)   0.002   
Total cholesterol                                      219 (45.2)           225 (43.1)        224 (44.4)     213 (45.9)   <0.001   
HDL cholesterol                                        52.7 (14.7)          51.9 (14.5)       52.3 (15.6)    53.2 (14.2)   0.208   
Triglycerides                                          116 (73.9)           114 (74.4)        114 (70.7)     117 (76.0)    0.582   
LDL cholesterol                                        143 (39.7)           152 (38.4)        149 (38.6)     136 (39.7)   <0.001   
History of hyperchol.                                  709 (31.2%)          97 (22.5%)        256 (33.2%)    356 (33.2%)  <0.001   
Cholesterol treatment                                  228 (10.2%)          28 (6.50%)        68 (8.80%)     132 (12.8%)  <0.001   
Height (cm)                                            163 (9.22)           163 (9.21)        162 (9.39)     163 (9.05)    0.003   
Weight (Kg)                                            73.4 (13.7)          72.3 (12.6)       73.8 (14.0)    73.6 (13.9)   0.150   
Body mass index                                        27.6 (4.56)          27.0 (4.15)       28.1 (4.62)    27.6 (4.63)  <0.001   
Physical activity (Kcal/week)                           399 (388)            491 (419)         422 (377)      351 (378)   <0.001   
Physical component                                     49.6 (9.01)          49.3 (8.08)       49.0 (9.63)    50.1 (8.91)   0.032   
Mental component                                       48.0 (11.0)          49.2 (11.3)       48.9 (11.0)    46.9 (10.8)  <0.001   
Cardiovascular event                                   92 (4.25%)           10 (2.51%)        35 (4.72%)     47 (4.59%)    0.161   
Days to cardiovascular event or end of follow-up       1755 (1081)          1784 (1101)       1686 (1080)    1793 (1072)   0.099   
Overall death                                          173 (8.05%)          18 (4.65%)        81 (11.0%)     74 (7.23%)   <0.001   
Days to overall death or end of follow-up              1721 (1051)          1713 (1042)       1674 (1050)    1758 (1055)   0.252   
Time to CV event or censoring                             4.49%                2.69%             4.70%          5.01%      0.157   
age7gr                                                 4.03 (1.82)          3.88 (1.93)       3.95 (1.84)    4.15 (1.77)   0.012   
Smoking 4 cat.:                                                                                                           <0.001   
    Never smoker                                      1201 (53.8%)          234 (56.4%)       414 (54.6%)    553 (52.2%)           
    Current or former < 1y                             593 (26.6%)          109 (26.3%)       267 (35.2%)    217 (20.5%)           
    Former >= 1y                                       439 (19.7%)          72 (17.3%)        77 (10.2%)     290 (27.4%)           
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

Or you can select individuals using the subset argument.

descrTable(regicor, subset=age>65)

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

____________________________________________________________________________ 
                                                          [ALL]           N  
                                                          N=505              
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 
Individual id                                    1160432191 (1328914243) 505 
Recruitment year:                                                        505 
    1995                                               94 (18.6%)            
    2000                                               164 (32.5%)           
    2005                                               247 (48.9%)           
Age                                                    69.8 (2.51)       505 
Sex:                                                                     505 
    Male                                               246 (48.7%)           
    Female                                             259 (51.3%)           
Smoking status:                                                          488 
    Never smoker                                       310 (63.5%)           
    Current or former < 1y                             82 (16.8%)            
    Former >= 1y                                       96 (19.7%)            
Systolic blood pressure                                144 (18.8)        497 
Diastolic blood pressure                               80.8 (10.2)       497 
History of hypertension:                                                 504 
    Yes                                                252 (50.0%)           
    No                                                 252 (50.0%)           
Hypertension treatment:                                                  497 
    No                                                 305 (61.4%)           
    Yes                                                192 (38.6%)           
Total cholesterol                                      219 (43.2)        482 
HDL cholesterol                                        52.9 (14.5)       489 
Triglycerides                                          116 (47.1)        490 
LDL cholesterol                                        143 (39.0)        477 
History of hyperchol.:                                                   495 
    Yes                                                191 (38.6%)           
    No                                                 304 (61.4%)           
Cholesterol treatment:                                                   488 
    No                                                 377 (77.3%)           
    Yes                                                111 (22.7%)           
Height (cm)                                            160 (8.85)        493 
Weight (Kg)                                            73.8 (11.8)       493 
Body mass index                                        28.9 (4.33)       493 
Physical activity (Kcal/week)                           410 (335)        473 
Physical component                                     46.0 (9.94)       442 
Mental component                                       49.3 (11.0)       442 
Cardiovascular event:                                                    479 
    No                                                 453 (94.6%)           
    Yes                                                26 (5.43%)            
Days to cardiovascular event or end of follow-up       1837 (1093)       479 
Overall death:                                                           473 
    No                                                 397 (83.9%)           
    Yes                                                76 (16.1%)            
Days to overall death or end of follow-up              1672 (1026)       473 
Time to CV event or censoring                             5.68%          479 
age7gr                                                 6.40 (0.49)       505 
Smoking 4 cat.:                                                          488 
    Never smoker                                       310 (63.5%)           
    Current or former < 1y                             82 (16.8%)            
    Former >= 1y                                       96 (19.7%)            
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(year ~ age + sex + smoker + bmi + histchol, data=regicor))
export2latex(restab)
    
    \begin{longtable}{lcccc}\caption{Summary descriptives table by groups of `Recruitment year'}\\
    \hline  
     &    1995     &    2000     &    2005     & \multirow{2}{*}{p.overall}\\ 
 &    N=431    &    N=786    &   N=1077    &           \\ 
  
    \hline
    \hline     
    \endfirsthead 
    \multicolumn{5}{l}{\tablename\ \thetable{} \textit{-- continued from previous page}}\\ 
    \hline
     &    1995     &    2000     &    2005     & \multirow{2}{*}{p.overall}\\ 
 &    N=431    &    N=786    &   N=1077    &           \\ 

    \hline
    \hline  
    \endhead   
    \hline
    \multicolumn{5}{l}{\textit{continued on next page}} \\ 
    \endfoot   
    \multicolumn{5}{l}{}  \\ 
    \endlastfoot 
    Age & 54.1 (11.7) & 54.3 (11.2) & 55.3 (10.6) &   0.078  \\ 
Sex: &             &             &             &   0.506  \\ 
$\qquad$Male & 206 (47.8\%) & 390 (49.6\%) & 505 (46.9\%) &          \\ 
$\qquad$Female & 225 (52.2\%) & 396 (50.4\%) & 572 (53.1\%) &          \\ 
Smoking status: &             &             &             &  $<$0.001  \\ 
$\qquad$Never smoker & 234 (56.4\%) & 414 (54.6\%) & 553 (52.2\%) &          \\ 
$\qquad$Current or former $<$ 1y & 109 (26.3\%) & 267 (35.2\%) & 217 (20.5\%) &          \\ 
$\qquad$Former $\geq$ 1y & 72 (17.3\%)  & 77 (10.2\%)  & 290 (27.4\%) &          \\ 
Body mass index & 27.0 (4.15) & 28.1 (4.62) & 27.6 (4.63) &  $<$0.001  \\ 
History of hyperchol.: &             &             &             &  $<$0.001  \\ 
$\qquad$Yes & 97 (22.5\%)  & 256 (33.2\%) & 356 (33.2\%) &          \\ 
$\qquad$No & 334 (77.5\%) & 515 (66.8\%) & 715 (66.8\%) &           \\ 
 
    \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 useful 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 customizable 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 REGICOR variables by recruitment year.

res <- compareGroups(year ~ ., regicor)
restab <- createTable(res, hide.no="no")
  • Default export2md to HTML:
export2md(restab)
Summary descriptives table by groups of `Recruitment year’
1995 2000 2005 p.overall
N=431 N=786 N=1077
Individual id 1000002914 (1242) 3000104610 (2379) 1996 (1161) 0.000
Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
Sex: 0.506
Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
Smoking status: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001
Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001
History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001
Hypertension treatment 71 (16.5%) 127 (16.2%) 230 (22.2%) 0.002
Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001
HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208
Triglycerides 114 (74.4) 114 (70.7) 117 (76.0) 0.582
LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001
History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001
Cholesterol treatment 28 (6.50%) 68 (8.80%) 132 (12.8%) <0.001
Height (cm) 163 (9.21) 162 (9.39) 163 (9.05) 0.003
Weight (Kg) 72.3 (12.6) 73.8 (14.0) 73.6 (13.9) 0.150
Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
Physical activity (Kcal/week) 491 (419) 422 (377) 351 (378) <0.001
Physical component 49.3 (8.08) 49.0 (9.63) 50.1 (8.91) 0.032
Mental component 49.2 (11.3) 48.9 (11.0) 46.9 (10.8) <0.001
Cardiovascular event 10 (2.51%) 35 (4.72%) 47 (4.59%) 0.161
Days to cardiovascular event or end of follow-up 1784 (1101) 1686 (1080) 1793 (1072) 0.099
Overall death 18 (4.65%) 81 (11.0%) 74 (7.23%) <0.001
Days to overall death or end of follow-up 1713 (1042) 1674 (1050) 1758 (1055) 0.252
Time to CV event or censoring 2.69% 4.70% 5.01% 0.157
age7gr 3.88 (1.93) 3.95 (1.84) 4.15 (1.77) 0.012
Smoking 4 cat.: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
  • Add strip rows, colouring rows by variables:
export2md(restab, strip=TRUE, first.strip=TRUE)
Summary descriptives table by groups of `Recruitment year’
1995 2000 2005 p.overall
N=431 N=786 N=1077
Individual id 1000002914 (1242) 3000104610 (2379) 1996 (1161) 0.000
Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
Sex: 0.506
Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
Smoking status: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001
Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001
History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001
Hypertension treatment 71 (16.5%) 127 (16.2%) 230 (22.2%) 0.002
Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001
HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208
Triglycerides 114 (74.4) 114 (70.7) 117 (76.0) 0.582
LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001
History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001
Cholesterol treatment 28 (6.50%) 68 (8.80%) 132 (12.8%) <0.001
Height (cm) 163 (9.21) 162 (9.39) 163 (9.05) 0.003
Weight (Kg) 72.3 (12.6) 73.8 (14.0) 73.6 (13.9) 0.150
Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
Physical activity (Kcal/week) 491 (419) 422 (377) 351 (378) <0.001
Physical component 49.3 (8.08) 49.0 (9.63) 50.1 (8.91) 0.032
Mental component 49.2 (11.3) 48.9 (11.0) 46.9 (10.8) <0.001
Cardiovascular event 10 (2.51%) 35 (4.72%) 47 (4.59%) 0.161
Days to cardiovascular event or end of follow-up 1784 (1101) 1686 (1080) 1793 (1072) 0.099
Overall death 18 (4.65%) 81 (11.0%) 74 (7.23%) <0.001
Days to overall death or end of follow-up 1713 (1042) 1674 (1050) 1758 (1055) 0.252
Time to CV event or censoring 2.69% 4.70% 5.01% 0.157
age7gr 3.88 (1.93) 3.95 (1.84) 4.15 (1.77) 0.012
Smoking 4 cat.: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
  • Change size:
export2md(restab, size=6)
Summary descriptives table by groups of `Recruitment year’
1995 2000 2005 p.overall
N=431 N=786 N=1077
Individual id 1000002914 (1242) 3000104610 (2379) 1996 (1161) 0.000
Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
Sex: 0.506
Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
Smoking status: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001
Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001
History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001
Hypertension treatment 71 (16.5%) 127 (16.2%) 230 (22.2%) 0.002
Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001
HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208
Triglycerides 114 (74.4) 114 (70.7) 117 (76.0) 0.582
LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001
History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001
Cholesterol treatment 28 (6.50%) 68 (8.80%) 132 (12.8%) <0.001
Height (cm) 163 (9.21) 162 (9.39) 163 (9.05) 0.003
Weight (Kg) 72.3 (12.6) 73.8 (14.0) 73.6 (13.9) 0.150
Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
Physical activity (Kcal/week) 491 (419) 422 (377) 351 (378) <0.001
Physical component 49.3 (8.08) 49.0 (9.63) 50.1 (8.91) 0.032
Mental component 49.2 (11.3) 48.9 (11.0) 46.9 (10.8) <0.001
Cardiovascular event 10 (2.51%) 35 (4.72%) 47 (4.59%) 0.161
Days to cardiovascular event or end of follow-up 1784 (1101) 1686 (1080) 1793 (1072) 0.099
Overall death 18 (4.65%) 81 (11.0%) 74 (7.23%) <0.001
Days to overall death or end of follow-up 1713 (1042) 1674 (1050) 1758 (1055) 0.252
Time to CV event or censoring 2.69% 4.70% 5.01% 0.157
age7gr 3.88 (1.93) 3.95 (1.84) 4.15 (1.77) 0.012
Smoking 4 cat.: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
  • Making variable names column wider:
export2md(restab, width="400px")
Summary descriptives table by groups of `Recruitment year’
1995 2000 2005 p.overall
N=431 N=786 N=1077
Individual id 1000002914 (1242) 3000104610 (2379) 1996 (1161) 0.000
Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
Sex: 0.506
Male 206 (47.8%) 390 (49.6%) 505 (46.9%)
Female 225 (52.2%) 396 (50.4%) 572 (53.1%)
Smoking status: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001
Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001
History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001
Hypertension treatment 71 (16.5%) 127 (16.2%) 230 (22.2%) 0.002
Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001
HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208
Triglycerides 114 (74.4) 114 (70.7) 117 (76.0) 0.582
LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001
History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001
Cholesterol treatment 28 (6.50%) 68 (8.80%) 132 (12.8%) <0.001
Height (cm) 163 (9.21) 162 (9.39) 163 (9.05) 0.003
Weight (Kg) 72.3 (12.6) 73.8 (14.0) 73.6 (13.9) 0.150
Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001
Physical activity (Kcal/week) 491 (419) 422 (377) 351 (378) <0.001
Physical component 49.3 (8.08) 49.0 (9.63) 50.1 (8.91) 0.032
Mental component 49.2 (11.3) 48.9 (11.0) 46.9 (10.8) <0.001
Cardiovascular event 10 (2.51%) 35 (4.72%) 47 (4.59%) 0.161
Days to cardiovascular event or end of follow-up 1784 (1101) 1686 (1080) 1793 (1072) 0.099
Overall death 18 (4.65%) 81 (11.0%) 74 (7.23%) <0.001
Days to overall death or end of follow-up 1713 (1042) 1674 (1050) 1758 (1055) 0.252
Time to CV event or censoring 2.69% 4.70% 5.01% 0.157
age7gr 3.88 (1.93) 3.95 (1.84) 4.15 (1.77) 0.012
Smoking 4 cat.: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
  • Stratified table:
restab <- strataTable(descrTable(year ~ . -id, regicor), "sex")
export2md(restab, size=8)
Summary descriptive tables

Male
Female
1995 2000 2005 p.overall 1995 2000 2005 p.overall
N=206 N=390 N=505 N=225 N=396 N=572
Age 54.1 (11.8) 54.3 (11.2) 55.4 (10.7) 0.212 54.1 (11.7) 54.4 (11.2) 55.2 (10.6) 0.351
Sex: . .
Male 206 (100%) 390 (100%) 505 (100%) 0 (0.00%) 0 (0.00%) 0 (0.00%)
Female 0 (0.00%) 0 (0.00%) 0 (0.00%) 225 (100%) 396 (100%) 572 (100%)
Smoking status: <0.001 <0.001
Never smoker 52 (26.5%) 112 (29.7%) 137 (27.5%) 182 (83.1%) 302 (79.3%) 416 (74.0%)
Current or former < 1y 77 (39.3%) 199 (52.8%) 134 (26.9%) 32 (14.6%) 68 (17.8%) 83 (14.8%)
Former >= 1y 67 (34.2%) 66 (17.5%) 227 (45.6%) 5 (2.28%) 11 (2.89%) 63 (11.2%)
Systolic blood pressure 134 (18.4) 137 (19.3) 132 (18.7) 0.002 132 (19.8) 129 (22.6) 127 (20.5) 0.008
Diastolic blood pressure 79.0 (9.27) 83.0 (9.54) 81.7 (10.8) <0.001 75.2 (11.3) 78.6 (10.6) 78.3 (10.0) <0.001
History of hypertension: 0.002 0.097
Yes 50 (24.3%) 110 (28.2%) 181 (36.2%) 61 (27.1%) 123 (31.1%) 198 (34.8%)
No 156 (75.7%) 280 (71.8%) 319 (63.8%) 164 (72.9%) 273 (68.9%) 371 (65.2%)
Hypertension treatment: <0.001 0.446
No 175 (85.0%) 342 (87.7%) 372 (77.2%) 185 (82.2%) 317 (80.1%) 432 (78.3%)
Yes 31 (15.0%) 48 (12.3%) 110 (22.8%) 40 (17.8%) 79 (19.9%) 120 (21.7%)
Total cholesterol 224 (43.9) 224 (43.9) 210 (40.3) <0.001 226 (42.4) 224 (44.9) 216 (50.3) 0.004
HDL cholesterol 46.5 (13.1) 47.3 (12.6) 48.1 (12.4) 0.290 56.9 (13.9) 57.4 (16.7) 57.8 (14.2) 0.783
Triglycerides 131 (91.5) 128 (81.1) 132 (90.3) 0.817 97.8 (47.9) 99.7 (55.1) 104 (57.6) 0.281
LDL cholesterol 153 (39.6) 152 (39.1) 137 (36.0) <0.001 150 (37.3) 146 (38.0) 136 (42.6) <0.001
History of hyperchol.: 0.007 0.006
Yes 48 (23.3%) 138 (35.8%) 167 (33.2%) 49 (21.8%) 118 (30.6%) 189 (33.3%)
No 158 (76.7%) 247 (64.2%) 336 (66.8%) 176 (78.2%) 268 (69.4%) 379 (66.7%)
Cholesterol treatment: 0.256 <0.001
No 189 (91.7%) 348 (90.2%) 425 (87.8%) 214 (95.1%) 357 (92.2%) 478 (86.8%)
Yes 17 (8.25%) 38 (9.84%) 59 (12.2%) 11 (4.89%) 30 (7.75%) 73 (13.2%)
Height (cm) 170 (7.34) 168 (7.17) 170 (7.43) 0.021 158 (6.31) 156 (6.50) 158 (6.24) <0.001
Weight (Kg) 77.6 (11.7) 80.1 (12.3) 80.2 (11.6) 0.023 67.3 (11.3) 67.6 (12.6) 67.7 (13.0) 0.919
Body mass index 26.9 (3.64) 28.2 (3.89) 27.9 (3.58) <0.001 27.2 (4.57) 28.0 (5.25) 27.3 (5.39) 0.084
Physical activity (Kcal/week) 422 (418) 356 (362) 439 (467) 0.014 553 (412) 486 (382) 273 (253) <0.001
Physical component 50.1 (6.71) 50.9 (8.58) 51.5 (8.07) 0.110 48.6 (9.16) 47.1 (10.2) 48.9 (9.45) 0.027
Mental component 52.1 (9.67) 50.9 (10.2) 49.2 (9.67) 0.001 46.5 (12.2) 46.9 (11.3) 44.7 (11.2) 0.017
Cardiovascular event: 0.272 0.139
No 190 (96.9%) 345 (94.3%) 461 (96.0%) 198 (98.0%) 361 (96.3%) 516 (94.9%)
Yes 6 (3.06%) 21 (5.74%) 19 (3.96%) 4 (1.98%) 14 (3.73%) 28 (5.15%)
Days to cardiovascular event or end of follow-up 1718 (1127) 1646 (1076) 1830 (1064) 0.046 1848 (1075) 1724 (1083) 1761 (1079) 0.421
Overall death: 0.002 0.018
No 174 (93.5%) 321 (87.5%) 448 (93.9%) 195 (97.0%) 336 (90.6%) 501 (91.8%)
Yes 12 (6.45%) 46 (12.5%) 29 (6.08%) 6 (2.99%) 35 (9.43%) 45 (8.24%)
Days to overall death or end of follow-up 1690 (1031) 1664 (1034) 1682 (1027) 0.952 1735 (1055) 1684 (1067) 1825 (1075) 0.139
Time to CV event or censoring 2.95% 5.47% 4.29% 0.218 2.42% 3.96% 5.68% 0.142
age7gr 3.89 (1.91) 3.95 (1.83) 4.17 (1.77) 0.091 3.86 (1.94) 3.96 (1.84) 4.13 (1.77) 0.128
Smoking 4 cat.: . .
Never smoker 52 (26.5%) 112 (29.7%) 137 (27.5%) 182 (83.1%) 302 (79.3%) 416 (74.0%)
Current or former < 1y 77 (39.3%) 199 (52.8%) 134 (26.9%) 32 (14.6%) 68 (17.8%) 83 (14.8%)
Former >= 1y 67 (34.2%) 66 (17.5%) 227 (45.6%) 5 (2.28%) 11 (2.89%) 63 (11.2%)
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 through 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 useful 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. Although “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)


-------- Summary of results by groups of 'year'---------


   var      N    p.value  method      selection
1  age      2294 .        categorical ALL      
2  sex      2294 .        categorical ALL      
3  smoker   2294 0.010**  categorical ALL      
4  sbp      2294 <0.001** categorical ALL      
5  dbp      2294 <0.001** categorical ALL      
6  histhtn  2294 0.015**  categorical ALL      
7  txhtn    2294 <0.001** categorical ALL      
8  chol     2294 <0.001** categorical ALL      
9  hdl      2294 <0.001** categorical ALL      
10 triglyc  2294 <0.001** categorical ALL      
11 ldl      2294 <0.001** categorical ALL      
12 histchol 2294 0.001**  categorical ALL      
13 txchol   2294 <0.001** categorical ALL      
14 height   2294 0.318    categorical ALL      
15 weight   2294 0.318    categorical ALL      
16 bmi      2294 0.318    categorical ALL      
17 phyact   2294 <0.001** categorical ALL      
18 pcs      2294 <0.001** categorical ALL      
19 mcs      2294 <0.001** categorical ALL      
20 cv       2294 0.118    categorical ALL      
21 tocv     2294 0.118    categorical ALL      
22 death    2294 0.001**  categorical ALL      
23 todeath  2294 0.001**  categorical ALL      
-----
Signif. codes:  0 '**' 0.05 '*' 0.1 ' ' 1 

--------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   
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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 
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ 

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(regicor). 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 REGICOR data by typing data(regicor) before calling cGroupsGUI function.

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

Summary descriptives table by groups of `Recruitment year’
1995 2000 2005 p.overall
N=431 N=786 N=1077
Age 54.1 (11.7) 54.3 (11.2) 55.3 (10.6) 0.078
Sex: Female 225 (52.2%) 396 (50.4%) 572 (53.1%) 0.506
Smoking status: <0.001
Never smoker 234 (56.4%) 414 (54.6%) 553 (52.2%)
Current or former < 1y 109 (26.3%) 267 (35.2%) 217 (20.5%)
Former >= 1y 72 (17.3%) 77 (10.2%) 290 (27.4%)
Systolic blood pressure 133 (19.2) 133 (21.3) 129 (19.8) <0.001
Diastolic blood pressure 77.0 (10.5) 80.8 (10.3) 79.9 (10.6) <0.001
History of hypertension 111 (25.8%) 233 (29.6%) 379 (35.5%) <0.001
Hypertension treatment 71 (16.5%) 127 (16.2%) 230 (22.2%) 0.002
Total cholesterol 225 (43.1) 224 (44.4) 213 (45.9) <0.001
HDL cholesterol 51.9 (14.5) 52.3 (15.6) 53.2 (14.2) 0.208
Triglycerides 94.0 [71.0;136] 98.0 [72.0;133] 98.0 [72.0;139] 0.762
LDL cholesterol 152 (38.4) 149 (38.6) 136 (39.7) <0.001
History of hyperchol. 97 (22.5%) 256 (33.2%) 356 (33.2%) <0.001
Cholesterol treatment 28 (6.50%) 68 (8.80%) 132 (12.8%) <0.001
Body mass index 27.0 (4.15) 28.1 (4.62) 27.6 (4.63) <0.001

5.1 Computing Odds Ratio

For a case-control study, it may be necessary to report the Odds Ratio between cases and controls for each variable. The table below contains Odds Ratios for cardiovascular event for each row-variable.

Summary descriptives table by groups of `Cardiovascular event’
OR p.ratio p.overall
Age 1.02 [1.00;1.04] 0.017 0.018
Sex: Female 0.93 [0.61;1.41] 0.721 0.801
Smoking status: <0.001
Never smoker Ref. Ref.
Current or former < 1y 2.75 [1.77;4.32] <0.001
Former >= 1y 0.58 [0.25;1.19] 0.142
Body mass index 1.02 [0.98;1.07] 0.313 0.307
Total cholesterol 1.00 [1.00;1.01] 0.208 0.263
Triglycerides 1.00 [1.00;1.00] 0.184 0.004
LDL cholesterol 1.00 [1.00;1.01] 0.151 0.210
Systolic blood pressure 1.02 [1.01;1.02] 0.002 0.004
Diastolic blood pressure 1.03 [1.01;1.05] 0.002 0.009
Hypertension treatment 1.37 [0.82;2.21] 0.223 0.270

To build this table, as illustrated in the screens below, you would select htn variable (Hypertension status) as the factor variable, indicate “no” category on the “reference” pull-down menu, and mark “Show odds/hazard ratio” in the “Report Options” menu before exporting the table.