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.
This package can be used from the R prompt or from a user-friendly GUI.
Since version 3.0, a Web User Interface (WUI) has been implemented based on Shiny package (RStudio and Inc. 2014) which makes the functionality even more friendly. Also, this can be used remotely from compareGroups project webpage. See section 6 for more details.
This document provides an overview of the usage of the compareGroups package.
To load the package using the R prompt, enter:
library(compareGroups)Once the package is loaded, non-R users can follow the GUI instructions in Section 5.
Design: classes and methods
The compareGroups package has three functions:
compareGroupscreates an object of classcompareGroups. This object can be:- printed
- summarized
- plotted
- updated
createTablecreates an object of classcreateTable. This object can be:- printed
- summarized
export2csv,export2html,export2latex,export2pdf,export2md,export2wordandexport2xlswill export results to CSV, HTML, LaTeX, PDF, Markdown, Word or Excel, respectively.
Figure 1 shows how the package is structured in terms of functions, classes and methods.
Figure 1. Diagram of package structure.
Data used as example
To illustrate how this package works we sampled 85% data from the participants in the PREDIMED study (Estruch et al. 2013). PREDIMED is a multicenter trial in Spain, were randomly assigned participants who were at high cardiovascular risk, but with no cardiovascular disease at enrolment, to one of three diets: a Mediterranean diet supplemented with extra-virgin olive oil (MedDiet+VOO), a Mediterranean diet supplemented with mixed nuts (MedDiet+Nuts), or a control diet (advice to reduce dietary fat). Participants received quarterly individual and group educational sessions and, depending on group assignment, free provision of extra-virgin olive oil, mixed nuts, or small non-food gifts. The primary end point was the rate of major cardiovascular events (myocardial infarction, stroke, or death from cardiovascular causes.
First of all, load PREDIMED data typing:
data(predimed)
Variables and labels in this data frame are:
| Name | Label | Codes |
|---|---|---|
| group | Intervention group | Control; MedDiet + Nuts; MedDiet + VOO |
| sex | Sex | Male; Female |
| age | Age | |
| smoke | Smoking | Never; Current; Former |
| bmi | Body mass index | |
| waist | Waist circumference | |
| wth | Waist-to-height ratio | |
| htn | Hypertension | No; Yes |
| diab | Type-2 diabetes | No; Yes |
| hyperchol | Dyslipidemia | No; Yes |
| famhist | Family history of premature CHD | No; Yes |
| hormo | Hormone-replacement therapy | No; Yes |
| p14 | MeDiet Adherence score | |
| toevent | follow-up to main event (years) | |
| event | AMI, stroke, or CV Death | No; Yes |
OBSERVATIONS:
It is important to note that
compareGroupsis not aimed to perform quality control of the data. Other useful packages such as2lh(Genolini, Desgraupes, and Franca 2011) are available for this purpose.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.
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.
The function
labelfrom theHmiscpackage could be used to label the variables properly. The tables of results will contain the variable labels (by default).
Time-to-event variables
A variable of class Surv must be created to deal with time-to-event variables (i.e., time to Cardiovascular event/censored in our example):
predimed$tmain <- with(predimed, Surv(toevent, event == "Yes"))
label(predimed$tmain) <- "AMI, stroke, or CV Death"Note that variables tmain and tcv are created as time-to-death and time-to-cardiovascular event, respectively, both taking into account censoring (i.e. they are of class Surv).
Using R syntax
compareGroups
This is the main function. It does all the calculus. It is needed to store results in an object. Later, applying the function createTable (Section 4.2) to this object will create tables of the analysis results.
For example, to perform a univariate analysis with the predimed data between group (“response” variable) and all other variables (“explanatory” variables), this formula is required:
compareGroups(group ~ ., data = predimed)
Selecting response variables
If only a dot occurs on the right side of the ~ all variables in the data frame will be used.
To remove the variable toevent and event from the analysis:
compareGroups(group ~ . - toevent - event, data = predimed)To select some explanatory variables (e.g., age, sex and waist) and store results in an object of class compareGroups:
res <- compareGroups(group ~ age + sex + smoke + waist + hormo,
data = predimed)
res
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.003** continuous normal ALL
2 Sex 6324 <0.001** categorical ALL
3 Smoking 6324 0.444 categorical ALL
4 Waist circumference 6324 0.045** continuous normal ALL
5 Hormone-replacement therapy 5661 0.850 categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 Note: Although we have full data (n= 6324) for Age, Sex and Waist circumference, there are some missing data in Hormone-replacement therapy (probably male participants).
Diet groups have some differences in Smoking and Hormone-replacement therapy although those don’t reach statistical significance (p-value=0.714 and 0.859, repectively); although Age, Sex and Waist circumference are clearly different.
Age & Waist circumference has been used as continuous and normal distributed. Sex, Smoking & Hormone-replacement therapy as categorical.
No filters have been used (e.g., selecting only treated patients); therefore, the selection column lists “ALL” (for all variables).
Subsetting
To perform the analysis in a subset of participants (e.g., “female” participants):
compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
subset = sex == "Female")
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method
1 Age 3645 0.056* continuous normal
2 Smoking 3645 0.907 categorical
3 Waist circumference 3645 0.016** continuous normal
4 Hormone-replacement therapy 3459 0.898 categorical
selection
1 sex == "Female"
2 sex == "Female"
3 sex == "Female"
4 sex == "Female"
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 Note that only results for female participants are shown.
To subset specific variable/s (e.g., hormo and waist):
compareGroups(group ~ age + sex + smoke + waist + hormo, data = predimed,
selec = list(hormo = sex == "Female", waist = waist > 20))
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method
1 Age 6324 0.003** continuous normal
2 Sex 6324 <0.001** categorical
3 Smoking 6324 0.444 categorical
4 Waist circumference 6324 0.045** continuous normal
5 Hormone-replacement therapy 3459 0.898 categorical
selection
1 ALL
2 ALL
3 ALL
4 waist > 20
5 sex == "Female"
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 Combinations are also allowed, e.g.:
compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
selec = list(waist = !is.na(hormo)), subset = sex == "Female")
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method
1 Age 3645 0.056* continuous normal
2 Smoking 3645 0.907 categorical
3 Waist circumference 3459 0.007** continuous normal
4 Hormone-replacement therapy 3459 0.898 categorical
selection
1 sex == "Female"
2 sex == "Female"
3 (sex == "Female") & (!is.na(hormo))
4 sex == "Female"
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 A variable can appear twice in the formula, e.g.:
compareGroups(group ~ age + sex + bmi + bmi + waist + hormo,
data = predimed, selec = list(bmi.1 = !is.na(hormo)))
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method
1 Age 6324 0.003** continuous normal
2 Sex 6324 <0.001** categorical
3 Body mass index 6324 <0.001** continuous normal
4 Body mass index 5661 <0.001** continuous normal
5 Waist circumference 6324 0.045** continuous normal
6 Hormone-replacement therapy 5661 0.850 categorical
selection
1 ALL
2 ALL
3 ALL
4 !is.na(hormo)
5 ALL
6 ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 In this case results for bmi will be reported for all participants (n= 6324) and also for only those with no missing in Hormone-replacement therapy (!is.na(hormo)). Note that “bmi.1” in the selec argument refers to the second time that bmi appears in the formula.
Methods for continuous variables
By default continuous variables are analyzed as normal-distributed. When a table is built (see createTable function, Section 4.2), continuous variables will be described with mean and standard deviation. To change default options, e.g., “waist” used as non-normal distributed:
compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
method = c(waist = 2))
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.003** continuous normal ALL
2 Smoking 6324 0.444 categorical ALL
3 Waist circumference 6324 0.085* continuous non-normal ALL
4 Hormone-replacement therapy 5661 0.850 categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 Note that “continuous non-normal” is shown in the method column for the variable Hormone-replacement therapy.
Possible values in methods statement are:
1: forces analysis as normal-distributed
2: forces analysis as continuous non-normal
3: forces analysis as categorical
NA: performs a Shapiro-Wilks test to decide between normal or non-normal
If the method argument is stated as NA for a variable, then a Shapiro-Wilk test for normality is used to decide if the variable is normal or non-normal distributed. To change the significance threshold:
compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
method = c(waist = NA), alpha = 0.01)
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.003** continuous normal ALL
2 Smoking 6324 0.444 categorical ALL
3 Waist circumference 6324 0.085* continuous non-normal ALL
4 Hormone-replacement therapy 5661 0.850 categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 According to Shapiro-Wilk test, stating the cutpoint at 0.01 level, “Hormone-replacement therapy” departed significantly from the normal distribution and therefore the method for this variable will be “continuous non-normal”.
All non factor variables are considered as continuous. Exception is made (by default) for those that have fewer than 5 different values. This threshold can be changed in the min.dis statement:
cuts <- "lo:55=1; 56:60=2; 61:65=3; 66:70=4; 71:75=5; 76:80=6; 81:hi=7"
predimed$age7gr <- car::recode(predimed$age, cuts)
compareGroups(group ~ age7gr, data = predimed, method = c(age7gr = NA))
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.007** continuous non-normal ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 compareGroups(group ~ age7gr, data = predimed, method = c(age7gr = NA),
min.dis = 8)
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.009** categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 To avoid errors the maximum categories for the response variable is set at 5 in this example (default value). If this variable has more than 5 different values, the function compareGroups returns an error message. For example:
compareGroups(age7gr ~ sex + bmi + waist , data=predimed))Error en compareGroups.default(X = X, y = y, include.label = include.label, :
number of groups must be less or equal to 5Defaults setting can be changed with the max.ylev statement:
compareGroups(age7gr ~ sex + bmi + waist, data = predimed, max.ylev = 7)
-------- Summary of results by groups of 'Age'---------
var N p.value method selection
1 Sex 6324 <0.001** categorical ALL
2 Body mass index 6324 0.021** continuous normal ALL
3 Waist circumference 6324 0.034** continuous normal ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 Similarly, by default there is a limit for the maximum number of levels for an explanatory variable. If this level is exceeded, the variable is removed from the analysis and a warning message is printed:
compareGroups(group ~ sex + age7gr, method = (age7gr = 3), data = predimed,
max.xlev = 5)Warning in compareGroups.default(X = X, y = y, include.label = include.label, :
Variables 'age7gr' have been removed since some errors ocurred
Dressing up the output
Although the options described in this section correspond to compareGroups function, results of changing/setting them won’t be visible until the table is created with the createTable function (explained later).
include.label: By default the variable labels are shown in the output (if there is no label the name will be printed). Changing the statement include.label from “= TRUE” (default) to “= FALSE” will cause variable names to be printed instead.
compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
include.label = FALSE)
-------- Summary of results by groups of 'group'---------
var N p.value method selection
1 age 6324 0.003** continuous normal ALL
2 smoke 6324 0.444 categorical ALL
3 waist 6324 0.045** continuous normal ALL
4 hormo 5661 0.850 categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 Q1,Q3: When the method for a variable is stated as “2” (i.e., to be analyzed as continuous non-normal; see section 4.1.3), by default the median and quartiles 1 and 3 will be shown in the final results, after applying the functioncreateTable(see Section 4.2).
resu1 <- compareGroups(group ~ age + waist, data = predimed,
method = c(waist = 2))
createTable(resu1)
--------Summary descriptives table by 'Intervention group'---------
__________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Waist circumference 101 [94.0;108] 100 [93.0;107] 100 [93.0;107] 0.085
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: percentiles 25 and 75 are calculated for “Waist circumference”.
To get instead percentile 2.5% and 97.5%:
resu2 <- compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
method = c(waist = 2), Q1 = 0.025, Q3 = 0.975)
createTable(resu2)
--------Summary descriptives table by 'Intervention group'---------
___________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Waist circumference 101 [80.0;123] 100 [80.0;121] 100 [80.0;121] 0.085
Hormone-replacement therapy: 0.850
No 1811 (98.3%) 1835 (98.4%) 1918 (98.2%)
Yes 31 (1.68%) 30 (1.61%) 36 (1.84%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: percentiles 2.5% and 97.5% are calculated for Follow-up.
To get minimum and maximum:
compareGroups(group ~ age + smoke + waist + hormo, data = predimed,
method = c(waist = 2), Q1 = 0, Q3 = 1)simplify: Sometimes a categorical variable has no individuals for a specific group. For example, smoker has 3 levels. As an example and to illustrate this problem, we have created a new variable smk with a new category (“Unknown”):
predimed$smk <- predimed$smoke
levels(predimed$smk) <- c("Never smoker", "Current or former < 1y",
"Never or former >= 1y", "Unknown")
label(predimed$smk) <- "Smoking 4 cat."
cbind(table(predimed$smk)) [,1]
Never smoker 3892
Current or former < 1y 858
Never or former >= 1y 1574
Unknown 0Note that this new category (“unknown”) has no individuals:
compareGroups(group ~ age + smk + waist + hormo, data = predimed)
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.001** continuous normal ALL
2 Smoking 4 cat. 6324 0.714 categorical ALL
3 Waist circumference 6324 0.019** continuous normal ALL
4 Hormone-replacement therapy 5650 0.859 categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1
Warning message:
In compare.i(X[, i], y = y, selec.i = selec[i], method.i = method[i], :
Some levels of 'smk' are removed since no observation in that/those levelsNote that a “Warning” message is printed related to the problem with smk.
To avoid using empty categories, simplify must be stated as TRUE (Default value).
compareGroups(group ~ age + smk, data = predimed, simplify = FALSE)
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.001** continuous normal ALL
2 Smoking 4 cat. 6324 . categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1
Warning messages:
1: In chisq.test(obj, simulate.p.value = TRUE) :
cannot compute simulated p-value with zero marginals
2: In chisq.test(obj, simulate.p.value = TRUE) :
Chi-squared approximation may be incorrectNota that a “warning” message is shown and no p-values are calculated for “Smoking”.
Summary
Applying the summary function to an object of class createTable will obtain a more detailed output:
res <- compareGroups(group ~ age + sex + smoke + waist + hormo,
method = c(waist = 2), data = predimed)
summary(res[c(1, 2, 4)])
--- Descriptives of each row-variable by groups of 'Intervention group' ---
-------------------
row-variable: Age
N mean sd p.overall p.trend
[ALL] 6324 67.0117 6.17499
Control 2042 67.34231 6.27992 0.002666 0.101163
MedDiet + Nuts 2100 66.6819 6.016395
MedDiet + VOO 2182 67.01971 6.212578
p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO
[ALL]
Control 0.001672 0.20596
MedDiet + Nuts
MedDiet + VOO
p.MedDiet + Nuts vs MedDiet + VOO
[ALL]
Control 0.172672
MedDiet + Nuts
MedDiet + VOO
-------------------
row-variable: Sex
Male Female Male (row%) Female (row%) p.overall p.trend
[ALL] 2679 3645 42.36243 57.63757
Control 812 1230 39.76494 60.23506 8.1e-05 0.388386
MedDiet + Nuts 968 1132 46.09524 53.90476
MedDiet + VOO 899 1283 41.20073 58.79927
p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO
[ALL]
Control 0.000133 0.358324
MedDiet + Nuts
MedDiet + VOO
p.MedDiet + Nuts vs MedDiet + VOO
[ALL]
Control 0.002076
MedDiet + Nuts
MedDiet + VOO
-------------------
row-variable: Waist circumference
N med Q1 Q3 p.overall p.trend
[ALL] 6324 100 93 107
Control 2042 101 94 108 0.084601 0.039557
MedDiet + Nuts 2100 100 93 107
MedDiet + VOO 2182 100 93 107
p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO
[ALL]
Control 0.125792 0.110639
MedDiet + Nuts
MedDiet + VOO
p.MedDiet + Nuts vs MedDiet + VOO
[ALL]
Control 0.743479
MedDiet + Nuts
MedDiet + VOO Note that because only variables 1, 3 and 4 are selected, only results for Age, Sex and Waist circumference are shown. Age is summarized by the mean and the standard deviation, Sex by frequencies and percentage, and Waist circumference (method =2) by the median and quartiles.
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")
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")
Updating
The object from compareGroups can later be updated. For example:
res <- compareGroups(group ~ age + sex + smoke + waist + hormo,
data = predimed)
res
-------- Summary of results by groups of 'Intervention group'---------
var N p.value method selection
1 Age 6324 0.003** continuous normal ALL
2 Sex 6324 <0.001** categorical ALL
3 Smoking 6324 0.444 categorical ALL
4 Waist circumference 6324 0.045** continuous normal ALL
5 Hormone-replacement therapy 5661 0.850 categorical ALL
-----
Signif. codes: 0 '**' 0.05 '*' 0.1 ' ' 1 The object res is updated using:
res <- update(res, . ~ . - sex + bmi + toevent, subset = sex ==
"Female", method = c(waist = 2, tovent = 2), selec = list(bmi = !is.na(hormo)))
resNote that “Sex” is removed as an explanatory variable but used as a filter, subsetting only “Female” participants. Variable “Waist circumference” has been changed to “continuous non-normal”. Two new variables have been added: Body mass index and Follow-up (stated continuous non-normal). For Body mass index is stated to show only data of participants with non-missing values in Hormone-replacement therapy.
Substracting results
Since version 3.0, there is a new function called getResults to retrieve some specific results computed by compareGroups, such as p-values, descriptives (means, proportions, …), etc.
For example, it may be interesting to recover the p-values for each variable as a vector to further manipulate it in R, like adjusting for multiple comparison with p.adjust. For example, lets take the example data from SNPassoc package that contains information of dozens of SNPs (genetic variants) from a sample of cases and controls. In this case we analize five of them:
library(SNPassoc)
data(SNPs)
tab <- createTable(compareGroups(casco ~ snp10001 + snp10002 +
snp10005 + snp10008 + snp10009, SNPs))
pvals <- getResults(tab, "p.overall")
p.adjust(pvals, method = "BH") snp10001 snp10002 snp10005 snp10008 snp10009
0.7051300 0.7072158 0.7583432 0.7583432 0.7072158
Odds Ratios and Hazard Ratios
When the response variable is binary, the Odds Ratio (OR) can be printed in the final table. If the response variable is time-to-event (see Section 3.1), the Hazard Ratio (HR) can be printed instead.
ref: This statement can be used to change the reference category:
res1 <- compareGroups(htn ~ age + sex + bmi + smoke, data = predimed,
ref = 1)
createTable(res1, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
___________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 1.04 [1.03;1.05] <0.001 <0.001
Sex: <0.001
Male 595 (54.6%) 2084 (39.8%) Ref. Ref.
Female 494 (45.4%) 3151 (60.2%) 1.82 [1.60;2.08] 0.000
Body mass index 28.9 (3.69) 30.2 (3.80) 1.10 [1.08;1.12] <0.001 <0.001
Smoking: <0.001
Never 536 (49.2%) 3356 (64.1%) Ref. Ref.
Current 233 (21.4%) 625 (11.9%) 0.43 [0.36;0.51] 0.000
Former 320 (29.4%) 1254 (24.0%) 0.63 [0.54;0.73] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that for categorical response variables the reference category is the first one in the statement:
res2 <- compareGroups(htn ~ age + sex + bmi + smoke, data = predimed,
ref = c(smoke = 1, sex = 2))
createTable(res2, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
___________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 1.04 [1.03;1.05] <0.001 <0.001
Sex: <0.001
Male 595 (54.6%) 2084 (39.8%) 0.55 [0.48;0.63] 0.000
Female 494 (45.4%) 3151 (60.2%) Ref. Ref.
Body mass index 28.9 (3.69) 30.2 (3.80) 1.10 [1.08;1.12] <0.001 <0.001
Smoking: <0.001
Never 536 (49.2%) 3356 (64.1%) Ref. Ref.
Current 233 (21.4%) 625 (11.9%) 0.43 [0.36;0.51] 0.000
Former 320 (29.4%) 1254 (24.0%) 0.63 [0.54;0.73] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that the reference category for Smoking status is the first and for Sex the second.
ref.no: Similarly to therefstatement,ref.nois used to state “no” as the reference category for all variables with this category:
res <- compareGroups(htn ~ age + sex + bmi + hormo + hyperchol,
data = predimed, ref.no = "NO")
createTable(res, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
________________________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 1.04 [1.03;1.05] <0.001 <0.001
Sex: <0.001
Male 595 (54.6%) 2084 (39.8%) Ref. Ref.
Female 494 (45.4%) 3151 (60.2%) 1.82 [1.60;2.08] 0.000
Body mass index 28.9 (3.69) 30.2 (3.80) 1.10 [1.08;1.12] <0.001 <0.001
Hormone-replacement therapy: 0.856
No 928 (98.4%) 4636 (98.3%) Ref. Ref.
Yes 15 (1.59%) 82 (1.74%) 1.08 [0.64;1.97] 0.773
Dyslipidemia: <0.001
No 409 (37.6%) 1337 (25.5%) Ref. Ref.
Yes 680 (62.4%) 3898 (74.5%) 1.75 [1.53;2.01] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: “no”, “No” or “NO” will produce the same results; the coding is not case sensitive.
fact.ratio: By default OR or HR for continuous variables are calculated for each unit increase. It can be changed by thefact.orstatement:
res <- compareGroups(htn ~ age + bmi, data = predimed)
createTable(res, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
__________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 1.04 [1.03;1.05] <0.001 <0.001
Body mass index 28.9 (3.69) 30.2 (3.80) 1.10 [1.08;1.12] <0.001 <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Here the OR is for the increase of one unit for Age and Systolic blood pressure.
res <- compareGroups(htn ~ age + bmi, data = predimed, fact.ratio = c(age = 10,
bmi = 2))
createTable(res, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
__________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 1.43 [1.28;1.59] <0.001 <0.001
Body mass index 28.9 (3.69) 30.2 (3.80) 1.22 [1.17;1.26] <0.001 <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Here the OR is for the increase of 10 years for Age and 2 units for “Body mass index”.
ref.y: By default when OR or HR are calculated, the reference category for the response variable is the first. The reference category could be changed using theref.ystatement:
res <- compareGroups(htn ~ age + sex + bmi + hyperchol, data = predimed)
createTable(res, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
___________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 1.04 [1.03;1.05] <0.001 <0.001
Sex: <0.001
Male 595 (54.6%) 2084 (39.8%) Ref. Ref.
Female 494 (45.4%) 3151 (60.2%) 1.82 [1.60;2.08] 0.000
Body mass index 28.9 (3.69) 30.2 (3.80) 1.10 [1.08;1.12] <0.001 <0.001
Dyslipidemia: <0.001
No 409 (37.6%) 1337 (25.5%) Ref. Ref.
Yes 680 (62.4%) 3898 (74.5%) 1.75 [1.53;2.01] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: This output shows the OR of having hypertension. Therefore, “Non-hypertension” is the reference category.
res <- compareGroups(htn ~ age + sex + bmi + hyperchol, data = predimed,
ref.y = 2)
createTable(res, show.ratio = TRUE)
--------Summary descriptives table by 'Hypertension'---------
___________________________________________________________________________
No Yes OR p.ratio p.overall
N=1089 N=5235
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 65.9 (6.19) 67.2 (6.15) 0.96 [0.98;0.95] <0.001 <0.001
Sex: <0.001
Male 595 (54.6%) 2084 (39.8%) Ref. Ref.
Female 494 (45.4%) 3151 (60.2%) 0.55 [0.48;0.63] 0.000
Body mass index 28.9 (3.69) 30.2 (3.80) 0.91 [0.92;0.89] <0.001 <0.001
Dyslipidemia: <0.001
No 409 (37.6%) 1337 (25.5%) Ref. Ref.
Yes 680 (62.4%) 3898 (74.5%) 0.57 [0.50;0.65] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: This output shows the OR of having No hypertension, and ‘Hypertension’ is now the reference category.
When the response variable is of class Surv, the bivariate plot function returns a Kaplan-Meier figure if the explanatory variable is categorical. For continuous variables the function returns a line for each individual, ending with a circle for censored and with a plus sign for uncensored.
plot(compareGroups(tmain ~ sex, data = predimed), bivar = TRUE,
file = "./figures/bivarsurv/", type = "png")
plot(compareGroups(tmain ~ age, data = predimed), bivar = TRUE,
file = "./figures/bivarsurv/", type = "png")
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.timemaxoption allows us to change at what time probability is calculated.
res <- compareGroups(sex ~ age + tmain, timemax = c(tmain = 3),
data = predimed)
resNote that tmain is calculated at 3 years (see section 3.1).
The plot function applied to a variable of class Surv returns a Kaplan-Meier figure. The figure can be stratified by the grouping variable.
plot(res[2], file = "./figures/univar/", type = "png")
plot(res[2], bivar = TRUE, file = "./figures/bivar/", type = "png")
createTable
createTable function, applied to an object of compareGroups class, returns tables with descriptives that can be displayed on-screen or exported to CSV, LaTeX, HTML, Word or Excel.
res <- compareGroups(group ~ age + sex + smoke + waist + hormo,
data = predimed, selec = list(hormo = sex == "Female"))
restab <- createTable(res)Two tables are created with the createTable function: one with the descriptives and the other with the available data. The print method applied to an object of class createTable prints one or both tables on the R console:
print(restab, which.table = "descr")
--------Summary descriptives table by 'Intervention group'---------
________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Hormone-replacement therapy: 0.898
No 1143 (97.4%) 1036 (97.2%) 1183 (97.0%)
Yes 31 (2.64%) 30 (2.81%) 36 (2.95%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that the option “descr” prints descriptive tables.
print(restab, which.table = "avail")
---Available data----
________________________________________________________________________________________________________
[ALL] Control MedDiet + Nuts MedDiet + VOO method select
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 6324 2042 2100 2182 continuous-normal ALL
Sex 6324 2042 2100 2182 categorical ALL
Smoking 6324 2042 2100 2182 categorical ALL
Waist circumference 6324 2042 2100 2182 continuous-normal ALL
Hormone-replacement therapy 3459 1174 1066 1219 categorical sex == "Female"
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ While the option “avail” prints the available data, as well as methods and selections.
By default, only the descriptives table is shown. Stating “both” in which.table argument prints both tables.
Dressing up tables
hide: If the explanatory variable is dichotomous, one of the categories often is hidden in the results displayed (i.e., if 42.4% are male, obviously 57.6% are female). To hide some category, e.g., “Male”:
update(restab, hide = c(sex = "Male"))
--------Summary descriptives table by 'Intervention group'---------
________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%) <0.001
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Hormone-replacement therapy: 0.898
No 1143 (97.4%) 1036 (97.2%) 1183 (97.0%)
Yes 31 (2.64%) 30 (2.81%) 36 (2.95%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that the percentage of males is hidden.
hide.no: Similarly, as explained above, if the category “no” is to be hidden for all variables:
res <- compareGroups(group ~ age + sex + htn + diab, data = predimed)
createTable(res, hide.no = "no", hide = c(sex = "Male"))
--------Summary descriptives table by 'Intervention group'---------
___________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: “no”, “No” or “NO” will produce the same results; the coding is not case sensitive.
digits: The number of digits that appear in the results can be changed, e.g:
createTable(res, digits = c(age = 2, sex = 3))
--------Summary descriptives table by 'Intervention group'---------
_______________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.34 (6.28) 66.68 (6.02) 67.02 (6.21) 0.003
Sex: <0.001
Male 812 (39.765%) 968 (46.095%) 899 (41.201%)
Female 1230 (60.235%) 1132 (53.905%) 1283 (58.799%)
Hypertension: 0.249
No 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes: 0.017
No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that mean and standard deviation has two decimal places for age, while percentage in sex has been set to three decimal places.
type: By default categorical variables are summarized by frequencies and percentages. This can be changed by thetypeargument:
createTable(res, type = 1)
--------Summary descriptives table by 'Intervention group'---------
___________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 39.8% 46.1% 41.2%
Female 60.2% 53.9% 58.8%
Hypertension: 0.249
No 16.2% 17.2% 18.1%
Yes 83.8% 82.8% 81.9%
Type-2 diabetes: 0.017
No 52.5% 54.8% 50.4%
Yes 47.5% 45.2% 49.6%
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that only percentages are displayed.
createTable(res, type = 3)
--------Summary descriptives table by 'Intervention group'---------
___________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 968 899
Female 1230 1132 1283
Hypertension: 0.249
No 331 362 396
Yes 1711 1738 1786
Type-2 diabetes: 0.017
No 1072 1150 1100
Yes 970 950 1082
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that only frequencies are displayed.
Value 2 or NA return the same results, i.e., the default option.
show.n: If optionshow.nis set toTRUEa column with available data for each variable appears in the results:
createTable(res, show.n = TRUE)
--------Summary descriptives table by 'Intervention group'---------
_________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall N
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003 6324
Sex: <0.001 6324
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Hypertension: 0.249 6324
No 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes: 0.017 6324
No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ show.descr: If argumentshow.descris set toFALSEonly p-values are displayed:
createTable(res, show.descr = FALSE)
--------Summary descriptives table by 'Intervention group'---------
__________________________
p.overall
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 0.003
Sex:
Male <0.001
Female
Hypertension:
No 0.249
Yes
Type-2 diabetes:
No 0.017
Yes
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ show.all: Ifshow.allargument is set toTRUEa column is displayed with descriptives for all data:
createTable(res, show.all = TRUE)
--------Summary descriptives table by 'Intervention group'---------
_________________________________________________________________________________
[ALL] Control MedDiet + Nuts MedDiet + VOO p.overall
N=6324 N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.0 (6.17) 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 2679 (42.4%) 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 3645 (57.6%) 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Hypertension: 0.249
No 1089 (17.2%) 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 5235 (82.8%) 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes: 0.017
No 3322 (52.5%) 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 3002 (47.5%) 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ show.p.overall: Ifshow.p.overallargument is set toFALSEp-values are omitted from the table:
createTable(res, show.p.overall = FALSE)
--------Summary descriptives table by 'Intervention group'---------
__________________________________________________________
Control MedDiet + Nuts MedDiet + VOO
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21)
Sex:
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Hypertension:
No 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes:
No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ show.p.trend: If the response variable has more than two categories a p-value for trend can be calculated. Results are displayed if theshow.p.trendargument is set toTRUE:
createTable(res, show.p.trend = TRUE)
--------Summary descriptives table by 'Intervention group'---------
____________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall p.trend
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003 0.101
Sex: <0.001 0.388
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Hypertension: 0.249 0.096
No 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes: 0.017 0.160
No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: The p-value for trend is computed from the Pearson test when row-variable is normal and from the Spearman test when it is continuous non-normal. If row-variable is of class Surv, the test score is computed from a Cox model where the grouping variable is introduced as an integer variable predictor. If the row-variable is categorical, the p-value for trend is computed as 1-pchisq(cor(as.integer(x),as.integer(y))^2*(length(x)-1),1)
show.p.mul: For a response variable with more than two categories a pairwise comparison of p-values, corrected for multiple comparisons, can be calculated. Results are displayed if theshow.p.mulargument is set toTRUE:
createTable(res, show.p.mul = TRUE)
--------Summary descriptives table by 'Intervention group'---------
_____________________________________________________________________________________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall p.Control vs MedDiet + Nuts p.Control vs MedDiet + VOO p.MedDiet + Nuts vs MedDiet + VOO
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003 0.002 0.206 0.173
Sex: <0.001 <0.001 0.358 0.002
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Hypertension: 0.249 0.459 0.311 0.459
No 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes: 0.017 0.185 0.185 0.014
No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note: Tukey method is used when explanatory variable is normal-distributed and Benjamini & Hochberg (Benjamini and Hochberg 1995) method otherwise.
show.ratio: If response variable is dichotomous or has been defined as classsurvival(see Section 3.1), Odds Ratios and Hazard Ratios can be displayed in the results by statingTRUEat the show.ratio option:
createTable(update(res, subset = group != "Control diet"), show.ratio = TRUE)
--------Summary descriptives table by 'Intervention group'---------
____________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Hypertension: 0.249
No 331 (16.2%) 362 (17.2%) 396 (18.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%)
Type-2 diabetes: 0.017
No 1072 (52.5%) 1150 (54.8%) 1100 (50.4%)
Yes 970 (47.5%) 950 (45.2%) 1082 (49.6%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that category “Control diet” of the response variable has been omitted in order to have only two categories (i.e., a dichotomous variable). No Odds Ratios would be calculated if response variable has more than two categories.
createTable(compareGroups(tmain ~ group + age + sex, data = predimed),
show.ratio = TRUE)
--------Summary descriptives table by 'AMI, stroke, or CV Death'---------
_______________________________________________________________________________
No event Event HR p.ratio p.overall
N=6072 N=252
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Intervention group: 0.011
Control 1945 (32.0%) 97 (38.5%) Ref. Ref.
MedDiet + Nuts 2030 (33.4%) 70 (27.8%) 0.66 [0.48;0.89] 0.008
MedDiet + VOO 2097 (34.5%) 85 (33.7%) 0.70 [0.53;0.94] 0.018
Age 66.9 (6.14) 69.4 (6.65) 1.06 [1.04;1.09] <0.001 <0.001
Sex: <0.001
Male 2528 (41.6%) 151 (59.9%) Ref. Ref.
Female 3544 (58.4%) 101 (40.1%) 0.49 [0.38;0.63] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that when response variable is of class Surv, Hazard Ratios are calculated instead of Odds Ratios.
digits.ratio: The number of decimal places for Odds/Hazard ratios can be changed by thedigits.ratioargument:
createTable(compareGroups(tmain ~ group + age + sex, data = predimed),
show.ratio = TRUE, digits.ratio = 3)
--------Summary descriptives table by 'AMI, stroke, or CV Death'---------
__________________________________________________________________________________
No event Event HR p.ratio p.overall
N=6072 N=252
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Intervention group: 0.011
Control 1945 (32.0%) 97 (38.5%) Ref. Ref.
MedDiet + Nuts 2030 (33.4%) 70 (27.8%) 0.658 [0.484;0.894] 0.008
MedDiet + VOO 2097 (34.5%) 85 (33.7%) 0.703 [0.525;0.941] 0.018
Age 66.9 (6.14) 69.4 (6.65) 1.065 [1.043;1.086] <0.001 <0.001
Sex: <0.001
Male 2528 (41.6%) 151 (59.9%) Ref. Ref.
Female 3544 (58.4%) 101 (40.1%) 0.488 [0.379;0.628] <0.001
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ header.labels: Change some key table header, such as the p.overall, etc. Note that this is done when printing the table changing the argument in theprintfunction and not in thecreateTablefunction. This argument is also present in other function that exports the table to pdf, plain text, etc.
tab <- createTable(compareGroups(tmain ~ group + age + sex, data = predimed),
show.all = TRUE)
print(tab, header.labels = c(p.overall = "p-value", all = "All"))
--------Summary descriptives table by 'AMI, stroke, or CV Death'---------
_________________________________________________________________
All No event Event p-value
N=6324 N=6072 N=252
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Intervention group: 0.011
Control 2042 (32.3%) 1945 (32.0%) 97 (38.5%)
MedDiet + Nuts 2100 (33.2%) 2030 (33.4%) 70 (27.8%)
MedDiet + VOO 2182 (34.5%) 2097 (34.5%) 85 (33.7%)
Age 67.0 (6.17) 66.9 (6.14) 69.4 (6.65) <0.001
Sex: <0.001
Male 2679 (42.4%) 2528 (41.6%) 151 (59.9%)
Female 3645 (57.6%) 3544 (58.4%) 101 (40.1%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Combining tables by row
Tables made with the same response variable can be combined by row:
restab1 <- createTable(compareGroups(group ~ age + sex, data = predimed))
restab2 <- createTable(compareGroups(group ~ bmi + smoke, data = predimed))
rbind(`Non-modifiable risk factors` = restab1, `Modifiable risk factors` = restab2)
--------Summary descriptives table by 'Intervention group'---------
_______________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Non-modifiable risk factors:
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Modifiable risk factors:
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note how variables are grouped under “Non-modifiable” and “Modifiable”" risk factors because of an epigraph defined in the rbind command in the example.
The resulting object is of class rbind.createTable, which can be subset but not updated. It inherits the class createTable. Therefore, columns and other arguments from the createTable function cannot be modified:
To select only Age and Smoking:
rbind(`Non-modifiable` = restab1, Modifiable = restab2)[c(1,
4)]
--------Summary descriptives table by 'Intervention group'---------
___________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Non-modifiable:
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Modifiable:
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ To change the order:
rbind(Modifiable = restab1, `Non-modifiable` = restab2)[c(4,
3, 2, 1)]
--------Summary descriptives table by 'Intervention group'---------
_______________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Non-modifiable:
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Modifiable:
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Combining tables by column
Columns from tables built with the same explanatory and response variables but done with a different subset (i.e. “ALL”, “Male” and “Female”) can be combined:
res <- compareGroups(group ~ age + smoke + bmi + htn, data = predimed)
alltab <- createTable(res, show.p.overall = FALSE)
femaletab <- createTable(update(res, subset = sex == "Female"),
show.p.overall = FALSE)
maletab <- createTable(update(res, subset = sex == "Male"), show.p.overall = FALSE)
cbind(ALL = alltab, FEMALE = femaletab, MALE = maletab)
--------Summary descriptives table ---------
______________________________________________________________________________________________________________________________________________
ALL FEMALE MALE
_________________________________________ _________________________________________ ________________________________________
Control MedDiet + Nuts MedDiet + VOO Control MedDiet + Nuts MedDiet + VOO Control MedDiet + Nuts MedDiet + VOO
N=2042 N=2100 N=2182 N=1230 N=1132 N=1283 N=812 N=968 N=899
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 68.0 (5.96) 67.4 (5.57) 67.7 (5.84) 66.4 (6.62) 65.8 (6.40) 66.1 (6.61)
Smoking:
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%) 1077 (87.6%) 993 (87.7%) 1115 (86.9%) 205 (25.2%) 266 (27.5%) 236 (26.3%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%) 66 (5.37%) 54 (4.77%) 71 (5.53%) 204 (25.1%) 242 (25.0%) 221 (24.6%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%) 87 (7.07%) 85 (7.51%) 97 (7.56%) 403 (49.6%) 460 (47.5%) 442 (49.2%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) 30.8 (4.20) 30.2 (4.08) 30.4 (3.91) 29.6 (3.45) 29.1 (3.28) 29.2 (3.28)
Hypertension:
No 331 (16.2%) 362 (17.2%) 396 (18.1%) 168 (13.7%) 147 (13.0%) 179 (14.0%) 163 (20.1%) 215 (22.2%) 217 (24.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 1062 (86.3%) 985 (87.0%) 1104 (86.0%) 649 (79.9%) 753 (77.8%) 682 (75.9%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯With the argument caption set to NULL no name is displayed for columns.
cbind(alltab, femaletab, maletab, caption = NULL)
--------Summary descriptives table ---------
______________________________________________________________________________________________________________________________________________
By Intervention group By Intervention group By Intervention group
_________________________________________ _________________________________________ ________________________________________
Control MedDiet + Nuts MedDiet + VOO Control MedDiet + Nuts MedDiet + VOO Control MedDiet + Nuts MedDiet + VOO
N=2042 N=2100 N=2182 N=1230 N=1132 N=1283 N=812 N=968 N=899
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 68.0 (5.96) 67.4 (5.57) 67.7 (5.84) 66.4 (6.62) 65.8 (6.40) 66.1 (6.61)
Smoking:
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%) 1077 (87.6%) 993 (87.7%) 1115 (86.9%) 205 (25.2%) 266 (27.5%) 236 (26.3%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%) 66 (5.37%) 54 (4.77%) 71 (5.53%) 204 (25.1%) 242 (25.0%) 221 (24.6%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%) 87 (7.07%) 85 (7.51%) 97 (7.56%) 403 (49.6%) 460 (47.5%) 442 (49.2%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) 30.8 (4.20) 30.2 (4.08) 30.4 (3.91) 29.6 (3.45) 29.1 (3.28) 29.2 (3.28)
Hypertension:
No 331 (16.2%) 362 (17.2%) 396 (18.1%) 168 (13.7%) 147 (13.0%) 179 (14.0%) 163 (20.1%) 215 (22.2%) 217 (24.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 1062 (86.3%) 985 (87.0%) 1104 (86.0%) 649 (79.9%) 753 (77.8%) 682 (75.9%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯By default the name of the table is displayed for each set of columns.
cbind(alltab, femaletab, maletab)
--------Summary descriptives table ---------
______________________________________________________________________________________________________________________________________________
alltab femaletab maletab
_________________________________________ _________________________________________ ________________________________________
Control MedDiet + Nuts MedDiet + VOO Control MedDiet + Nuts MedDiet + VOO Control MedDiet + Nuts MedDiet + VOO
N=2042 N=2100 N=2182 N=1230 N=1132 N=1283 N=812 N=968 N=899
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 68.0 (5.96) 67.4 (5.57) 67.7 (5.84) 66.4 (6.62) 65.8 (6.40) 66.1 (6.61)
Smoking:
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%) 1077 (87.6%) 993 (87.7%) 1115 (86.9%) 205 (25.2%) 266 (27.5%) 236 (26.3%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%) 66 (5.37%) 54 (4.77%) 71 (5.53%) 204 (25.1%) 242 (25.0%) 221 (24.6%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%) 87 (7.07%) 85 (7.51%) 97 (7.56%) 403 (49.6%) 460 (47.5%) 442 (49.2%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) 30.8 (4.20) 30.2 (4.08) 30.4 (3.91) 29.6 (3.45) 29.1 (3.28) 29.2 (3.28)
Hypertension:
No 331 (16.2%) 362 (17.2%) 396 (18.1%) 168 (13.7%) 147 (13.0%) 179 (14.0%) 163 (20.1%) 215 (22.2%) 217 (24.1%)
Yes 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 1062 (86.3%) 985 (87.0%) 1104 (86.0%) 649 (79.9%) 753 (77.8%) 682 (75.9%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯NOTE: The resulting object is of class cbind.createTable and inherits also the class createTable. This cannot be updated. It can be nicely printed on the R console and also exported to LaTeX but it cannot be exported to CSV or HTML.
createTable miscellaneous
print: By default only the table with the descriptives is printed. Withwhich.tableargument it can be changed: “avail” returns data available and “both” returns both tables:
print(createTable(compareGroups(group ~ age + sex + smoke + waist +
hormo, data = predimed)), which.table = "both")
--------Summary descriptives table by 'Intervention group'---------
________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Hormone-replacement therapy: 0.850
No 1811 (98.3%) 1835 (98.4%) 1918 (98.2%)
Yes 31 (1.68%) 30 (1.61%) 36 (1.84%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
---Available data----
_______________________________________________________________________________________________
[ALL] Control MedDiet + Nuts MedDiet + VOO method select
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 6324 2042 2100 2182 continuous-normal ALL
Sex 6324 2042 2100 2182 categorical ALL
Smoking 6324 2042 2100 2182 categorical ALL
Waist circumference 6324 2042 2100 2182 continuous-normal ALL
Hormone-replacement therapy 5661 1842 1865 1954 categorical ALL
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ With the print method setting nmax argument to FALSE, the total maximum “n” in the available data is omitted in the first row.
print(createTable(compareGroups(group ~ age + sex + smoke + waist +
hormo, data = predimed)), nmax = FALSE)
--------Summary descriptives table by 'Intervention group'---------
________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Hormone-replacement therapy: 0.850
No 1811 (98.3%) 1835 (98.4%) 1918 (98.2%)
Yes 31 (1.68%) 30 (1.61%) 36 (1.84%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ summary: returns the same table as that generated withprintmethod settingwhich.table='avail':
summary(createTable(compareGroups(group ~ age + sex + smoke +
waist + hormo, data = predimed)))
---Available data----
_______________________________________________________________________________________________
[ALL] Control MedDiet + Nuts MedDiet + VOO method select
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 6324 2042 2100 2182 continuous-normal ALL
Sex 6324 2042 2100 2182 categorical ALL
Smoking 6324 2042 2100 2182 categorical ALL
Waist circumference 6324 2042 2100 2182 continuous-normal ALL
Hormone-replacement therapy 5661 1842 1865 1954 categorical ALL
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ update: An object of classcreateTablecan be updated:
res <- compareGroups(group ~ age + sex + smoke + waist + hormo,
data = predimed)
restab <- createTable(res, type = 1, show.ratio = TRUE)
restab
--------Summary descriptives table by 'Intervention group'---------
_______________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 39.8% 46.1% 41.2%
Female 60.2% 53.9% 58.8%
Smoking: 0.444
Never 62.8% 60.0% 61.9%
Current 13.2% 14.1% 13.4%
Former 24.0% 26.0% 24.7%
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Hormone-replacement therapy: 0.850
No 98.3% 98.4% 98.2%
Yes 1.68% 1.61% 1.84%
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ update(restab, show.n = TRUE)
--------Summary descriptives table by 'Intervention group'---------
____________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall N
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003 6324
Sex: <0.001 6324
Male 39.8% 46.1% 41.2%
Female 60.2% 53.9% 58.8%
Smoking: 0.444 6324
Never 62.8% 60.0% 61.9%
Current 13.2% 14.1% 13.4%
Former 24.0% 26.0% 24.7%
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045 6324
Hormone-replacement therapy: 0.850 5661
No 98.3% 98.4% 98.2%
Yes 1.68% 1.61% 1.84%
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ In just one statement it is possible to update an object of class compareGroups and createTable:
update(restab, x = update(res, subset = c(sex == "Female")),
show.n = TRUE)
--------Summary descriptives table by 'Intervention group'---------
____________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall N
N=1230 N=1132 N=1283
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 68.0 (5.96) 67.4 (5.57) 67.7 (5.84) 0.056 3645
Sex: Female 100% 100% 100% . 3645
Smoking: 0.907 3645
Never 87.6% 87.7% 86.9%
Current 5.37% 4.77% 5.53%
Former 7.07% 7.51% 7.56%
Waist circumference 99.0 (11.0) 97.8 (11.0) 98.0 (10.5) 0.016 3645
Hormone-replacement therapy: 0.898 3459
No 97.4% 97.2% 97.0%
Yes 2.64% 2.81% 2.95%
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ Note that the compareGroups object (res) is updated, selecting only “Female”" participants, and the createTable class object (restab) is updated to add a column with the maximum available data for each explanatory variable.
subsetting: Objects fromcreateTablefunction can also be subsetted using “[”:
createTable(compareGroups(group ~ age + sex + smoke + waist +
hormo, data = predimed))
--------Summary descriptives table by 'Intervention group'---------
________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Hormone-replacement therapy: 0.850
No 1811 (98.3%) 1835 (98.4%) 1918 (98.2%)
Yes 31 (1.68%) 30 (1.61%) 36 (1.84%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯ createTable(compareGroups(group ~ age + sex + bmi, data = predimed))[1:2,
]
--------Summary descriptives table by 'Intervention group'---------
______________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Exporting tables
Tables can be exported to CSV, HTML, LaTeX, PDF, Markdown, Word or Excel
export2csv(restab, file='table1.csv'), exports to CSV formatexport2html(restab, file='table1.html'), exports to HTML formatexport2latex(restab, file='table1.tex'), exports to LaTeX format (to be included in Swaeave documents R chunks)export2pdf(restab, file='table1.pdf'), exports to PDF formatexport2md(restab, file='table1.md'), to be included inside Markdown documents R chunksexport2word(restab, file='table1.docx'), exports to Word formatexport2xls(restab, file='table1.xlsx'), exports to Excel format
Note that, since version 3.0, it is necessary write the extension of the file.
General exporting options
which.table: By default only the table with the descriptives is exported. This can be changed with thewhich.tableargument: “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. Statingnmaxargument 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 “;”.
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: Iffileargument inexport2latexis missing, the code is printed in theRconsole. This can be useful whenRcode is inserted in a LaTeX document chunk to be processed withSweavepackage.
restab <- createTable(compareGroups(group ~ age + sex + smoke +
waist + hormo, data = predimed))
export2latex(restab)
\begin{longtable}{lcccc}\caption{Summary descriptives table by groups of `Intervention group'}\\
\hline
& Control & MedDiet + Nuts & MedDiet + VOO & \multirow{2}{*}{p.overall}\\
& N=2042 & N=2100 & N=2182 & \\
\hline
\hline
\endfirsthead
\multicolumn{5}{l}{\tablename\ \thetable{} \textit{-- continued from previous page}}\\
\hline
& Control & MedDiet + Nuts & MedDiet + VOO & \multirow{2}{*}{p.overall}\\
& N=2042 & N=2100 & N=2182 & \\
\hline
\hline
\endhead
\hline
\multicolumn{5}{l}{\textit{continued on next page}} \\
\endfoot
\multicolumn{5}{l}{} \\
\endlastfoot
Age & 67.3 (6.28) & 66.7 (6.02) & 67.0 (6.21) & 0.003 \\
Sex: & & & & $<$0.001 \\
$\qquad$Male & 812 (39.8\%) & 968 (46.1\%) & 899 (41.2\%) & \\
$\qquad$Female & 1230 (60.2\%) & 1132 (53.9\%) & 1283 (58.8\%) & \\
Smoking: & & & & 0.444 \\
$\qquad$Never & 1282 (62.8\%) & 1259 (60.0\%) & 1351 (61.9\%) & \\
$\qquad$Current & 270 (13.2\%) & 296 (14.1\%) & 292 (13.4\%) & \\
$\qquad$Former & 490 (24.0\%) & 545 (26.0\%) & 539 (24.7\%) & \\
Waist circumference & 101 (10.8) & 100 (10.6) & 100 (10.4) & 0.045 \\
Hormone-replacement therapy: & & & & 0.850 \\
$\qquad$No & 1811 (98.3\%) & 1835 (98.4\%) & 1918 (98.2\%) & \\
$\qquad$Yes & 31 (1.68\%) & 30 (1.61\%) & 36 (1.84\%) & \\
\hline
\end{longtable} size: The font size of exported tables can be changed by this argument. Possible values are “tiny”, “scriptsize”, “footnotesize”, “small”, “normalsize”, “large”, “Large”, “LARGE”,“huge”, “Huge” or “same”. Default is “same”, which means that font size of the table is the same as specified in the main LaTeX document where the table will be inserted.caption: The table caption for descriptives table and available data table. Ifwhich.tableis 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 isNULL, 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. Ifwhich.tableis 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 isNULL, which assigns no label to the table/s.landscape: Table is placed in horizontal way. This option is specially usefull when table contains many columns and/or they are too wide to be placed vertically.
Generating an exhaustive report
Since version 2.0 of compareGroups package, there is a function called report which automatically generates a PDF document with the “descriptive” table as well as the corresponding “available”" table. In addition, plots of all analysed variables are shown.
In order to make easier to navigate throught the document, an index with hyperlinks is inserted in the document.
See the help file of this function where you can find an example with the REGICOR data (the other example data set contained in the compareGroups package)
# to know more about report function
`?`(report)
# info about REGICOR data set
`?`(regicor)Also, you can use the function radiograph that dumps the raw values on a plain text file. This may be usefull to identify possible wrong codes or non-valid values in the data set.
Missing values
Many times, it is important to be aware of the missingness contained in each variable, possibly by groups. Althought “available” table shows the number of the non-missing values for each row-variable and in each group, it would be desirable to test whether the frequency of non-available data is different between groups. For this porpose, a new function has been implemented in the compareGroups package, which is called missingTable. This function applies to both compareGroups and createTable class objects. This last option is useful when the table is already created. To illustrate it, we will use the REGICOR data set, comparing missing rates of all variables by year:
# from a compareGroups object
data(regicor)
res <- compareGroups(year ~ . - id, regicor)
missingTable(res)
--------Missingness table by 'Recruitment 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%) .
Smoking status 16 (3.71%) 28 (3.56%) 17 (1.58%) 0.010
Systolic blood pressure 3 (0.70%) 11 (1.40%) 0 (0.00%) <0.001
Diastolic blood pressure 3 (0.70%) 11 (1.40%) 0 (0.00%) <0.001
History of hypertension 0 (0.00%) 0 (0.00%) 8 (0.74%) 0.015
Hypertension treatment 0 (0.00%) 0 (0.00%) 43 (3.99%) <0.001
Total cholesterol 28 (6.50%) 71 (9.03%) 2 (0.19%) <0.001
HDL cholesterol 30 (6.96%) 38 (4.83%) 1 (0.09%) <0.001
Triglycerides 28 (6.50%) 34 (4.33%) 1 (0.09%) <0.001
LDL cholesterol 43 (9.98%) 98 (12.5%) 27 (2.51%) <0.001
History of hyperchol. 0 (0.00%) 15 (1.91%) 6 (0.56%) 0.001
Cholesterol treatment 0 (0.00%) 13 (1.65%) 42 (3.90%) <0.001
Height (cm) 8 (1.86%) 15 (1.91%) 12 (1.11%) 0.318
Weight (Kg) 8 (1.86%) 15 (1.91%) 12 (1.11%) 0.318
Body mass index 8 (1.86%) 15 (1.91%) 12 (1.11%) 0.318
Physical activity (Kcal/week) 64 (14.8%) 22 (2.80%) 2 (0.19%) <0.001
Physical component 34 (7.89%) 123 (15.6%) 83 (7.71%) <0.001
Mental component 34 (7.89%) 123 (15.6%) 83 (7.71%) <0.001
Cardiovascular event 33 (7.66%) 45 (5.73%) 53 (4.92%) 0.118
Days to cardiovascular event or end of follow-up 33 (7.66%) 45 (5.73%) 53 (4.92%) 0.118
Overall death 44 (10.2%) 48 (6.11%) 54 (5.01%) 0.001
Days to overall death or end of follow-up 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
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
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
1 1 1 Female 13.7 75640.52 TT CC GG GG
2 2 1 Female 12.7 28688.22 TT AC GG GG
3 3 1 Female 12.9 17279.59 TT CC GG GG
4 4 1 Male 14.6 27253.99 CT CC GG GG
5 5 1 Female 13.4 38066.57 TT AC GG GG
6 6 1 Female 11.3 9872.46 TT CC GG GG
snp10005 snp10006 snp10007 snp10008 snp10009 snp100010 snp100011
1 GG AA CC CC AA TT GG
2 AG AA CC CC AG TT GG
3 GG AA CC CC AA TT CC
4 GG AA CC CC AA TT GG
5 GG AA CC CC AG TT GG
6 GG AA CC CC AA TT GG
snp100012 snp100013 snp100014 snp100015 snp100016 snp100017 snp100018
1 GG AA AA GG GG TT TT
2 CG AA AC GG GG CT CT
3 GG AA CC GG GG TT TT
4 GG AA AC GG GG TT TT
5 GG AA AC GG GG CT CT
6 GG AA AA GG GG TT TT
snp100019 snp100020 snp100021 snp100022 snp100023 snp100024 snp100025
1 CC GG GG AA TT TT CC
2 CG GG GG AA AT TT CC
3 CC GG GG AA TT TT CC
4 CG GG GG AA TT CT CC
5 CG GG GG AA AT TT CC
6 CC GG GG AA TT TT CC
snp100026 snp100027 snp100028 snp100029 snp100030 snp100031 snp100032
1 GG CC CC GG AA TT AA
2 GG CG CT GG AA TT AG
3 GG CC CC GG AA TT AA
4 GG CC CT AG AA TT AG
5 GG CG CT GG AA TT AG
6 GG CC CC GG AA TT AA
snp100033 snp100034 snp100035
1 AA TT TT
2 AG TT TT
3 AA TT TT
4 AG CT TT
5 AG TT TT
6 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
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
Using GUI
Once the compareGroups package is loaded, a Graphical User Interface (GUI) is displayed in response to typing cGroupsGUI(predimed). The GUI is meant to make it feasible for users who are unfamiliar with R to construct bivariate tables. Note that, since version 3.0, it is necessary to specifiy an existing data.frame as input. So, for example, you can load the PREDIMED data by typing data(predimed) before calling cGroupsGUI function.
In this section we illustrate, step by step, how to construct a bivariate table containing descriptives by groups from the predimed data using the GUI:
| Var | Control N=2042 | MedDiet + Nuts N=2100 | MedDiet + VOO N=2182 | p.overall |
|---|---|---|---|---|
| Age | 67.3 (6.28) | 66.7 (6.02) | 67.0 (6.21) | 0.003 |
| Sex: Female | 1230 (60.2%) | 1132 (53.9%) | 1283 (58.8%) | <0.001 |
| Smoking: | 0.444 | |||
| Never | 1282 (62.8%) | 1259 (60.0%) | 1351 (61.9%) | |
| Current | 270 (13.2%) | 296 (14.1%) | 292 (13.4%) | |
| Former | 490 (24.0%) | 545 (26.0%) | 539 (24.7%) | |
| Body mass index | 30.3 (3.96) | 29.7 (3.77) | 29.9 (3.71) | <0.001 |
| Waist circumference | 101 (10.8) | 100 (10.6) | 100 (10.4) | 0.045 |
| Waist-to-height ratio | 0.63 (0.07) | 0.62 (0.06) | 0.63 (0.06) | <0.001 |
| Hypertension | 1711 (83.8%) | 1738 (82.8%) | 1786 (81.9%) | 0.249 |
| Type-2 diabetes | 970 (47.5%) | 950 (45.2%) | 1082 (49.6%) | 0.017 |
| Dyslipidemia | 1479 (72.4%) | 1539 (73.3%) | 1560 (71.5%) | 0.423 |
| Family history of premature CHD | 462 (22.6%) | 460 (21.9%) | 507 (23.2%) | 0.581 |
| Hormone-replacement therapy | 31 (1.68%) | 30 (1.61%) | 36 (1.84%) | 0.850 |
| MeDiet Adherence score | 8.44 (1.94) | 8.81 (1.90) | 8.77 (1.97) | <0.001 |
| follow-up to main event (years) | 4.09 (1.74) | 4.31 (1.70) | 4.64 (1.60) | <0.001 |
| AMI, stroke, or CV Death | 97 (4.75%) | 70 (3.33%) | 85 (3.90%) | 0.064 |
- Step 1. Browse for and select the data to be loaded. Valid file types include SPSS or
Rformat, CSV plain text file or a data.frame already existing in the Workspace. By default, the predimed example data is loaded when the GUI is opened.
- Step 2. Choose the variables to be described (row-variables).
- Step 3. If descriptives by group are desired (for example), move the variable group to the GUI top frame, making it the factor variable. To report descriptives for the whole sample (i.e., no groups), click on the “none” button.
- Step 4. It is possible to hide the first, last or no categories of a categorical row-variable. In this example, “Male” levels will be hidden for Sex; conversely, all categories will be shown for other categorical variables.
- Step 5. For each continuous variable, it is possible to specify whether to treat it as normal or non-normal or to transform a numerical variable into a categorical one. This last option can be interesting if a categorical variable has been coded as numerical. By default, all continuous variables are treated as normal. In this example, Waist circumference will be treated as non-normal, i.e., median and quartiles will be reported instead of mean and standard deviation.
- Step 6. For each row-variable, it is possible to select a subset of individuals from the data set to be included. In this example, descriptives of Body mass index, Waist circumference and Waist-to-height ratio will be reported only for Female participants. Also, it is possible to specify criteria to select a subset of individuals to be included for all row-variables: type the logical condition (selection criteria of individuals) on the “Global subset” window instead of “Variable subset”.
- Step 7. Some bivariate table characteristics can be set by clicking on “Report options” from the main menu, such as to report descriptives (mean, frequencies, medians, etc.), display the p-trend, and show only relative frequencies.
- Step 8. Finally, specify the bivariate table format (LaTeX, CVS plain text or HTML). Clicking on “print”" will then display the bivariate table, as well as a summary (available data, etc.), on the
Rconsole. The table can also be exported to the file formats listed.
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 each row-variable by hypertension status.
| Var | OR | p.ratio | p.overall |
|---|---|---|---|
| Age | 1.04 [1.03;1.05] | <0.001 | <0.001 |
| Sex: Female | 1.82 [1.60;2.08] | 0.000 | <0.001 |
| Smoking: | <0.001 | ||
| Never | Ref. | Ref. | |
| Current | 0.43 [0.36;0.51] | 0.000 | |
| Former | 0.63 [0.54;0.73] | <0.001 | |
| Body mass index | 1.10 [1.08;1.12] | <0.001 | <0.001 |
| Waist circumference | 1.01 [1.01;1.02] | <0.001 | <0.001 |
| Waist-to-height ratio | 71.5 [25.6;199] | <0.001 | <0.001 |
| Type-2 diabetes | 0.25 [0.22;0.29] | 0.000 | <0.001 |
| Dyslipidemia | 1.75 [1.53;2.01] | <0.001 | <0.001 |
| Family history of premature CHD | 0.87 [0.75;1.01] | 0.070 | 0.074 |
| Hormone-replacement therapy | 1.08 [0.64;1.97] | 0.773 | 0.856 |
| MeDiet Adherence score | 0.96 [0.93;1.00] | 0.028 | 0.029 |
| follow-up to main event (years) | 0.94 [0.90;0.98] | 0.002 | 0.001 |
| AMI, stroke, or CV Death | 1.04 [0.75;1.48] | 0.826 | 0.879 |
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.
Computing Hazard Ratio
In a cohort study, it may be more informative to compute hazard ratio taking into account time-to-event.
| Var | No event N=6072 | Event N=252 | HR | p.ratio | p.overall |
|---|---|---|---|---|---|
| Intervention group: | 0.011 | ||||
| Control | 1945 (32.0%) | 97 (38.5%) | Ref. | Ref. | |
| MedDiet + Nuts | 2030 (33.4%) | 70 (27.8%) | 0.66 [0.48;0.89] | 0.008 | |
| MedDiet + VOO | 2097 (34.5%) | 85 (33.7%) | 0.70 [0.53;0.94] | 0.018 | |
| Age | 66.9 (6.14) | 69.4 (6.65) | 1.06 [1.04;1.09] | <0.001 | <0.001 |
| Sex: | <0.001 | ||||
| Male | 2528 (41.6%) | 151 (59.9%) | Ref. | Ref. | |
| Female | 3544 (58.4%) | 101 (40.1%) | 0.49 [0.38;0.63] | <0.001 |
To generate this table, select toevent variable and event, indicating the time-to-event and the status, respectively, and select the event category for the status variable. Finally, as for Odds Ratios, mark ‘Show odds/hazard ratio’ in the ‘Report Options’ menu before exporting the table.
To return to the R console, just close the GUI window.
Using WUI
Since version 2.1, compareGropus package incorporates a Web User Interface (WUI) based on shiny R available on CRAN repository (RStudio and Inc. 2014) shiny website to facilitate the use of the package for non R users.
This application includes almost all the options existing in “type on” version. Also, thanks to the power of shiny package, the user can see the results when setting the included variable, the groups, number of decimals, etc almost instantaneously (“reactivity” -see shiny manual and examples-). This is very useful to modify and customize the descriptive table before saving it in the desired format saving a lot of time.
In the following subsections, we list and describe all the options available in the Shiny-compareGroups application, and we illustrate how it works with a real example.
Example
In this section we illustrate how to analyse a data set.
Launch the WUI application
To use the WUI locally (and not on a remote server), first load the compareGroups package and call the cGroupsWUI function:
library(compareGroups)
cGroupsWUI()
Load the data
In this example we will load the PREDIMED data set from the PREDIMED study (Estruch et al. 2013). This data is already available in the compareGroups package.
Select the variables to be analysed
After the data is loaded satisfactory, the “Step 2. Select variable” panel is opened automatically. Using this panel, we will select all variables except the “event” and “toevent” (the time-to-event) variables. They will represent the response and must be removed from the row-variables.
Select the grouping variable
Since PREDIMED is a longitudinal study where the main goal is to check whether the mediterranean diet is related to a less incidence of cardiovascular disease, we will take the response as the time to cardiovascular event. To do so, we will select “Survival” response type, setting the “toevent” as time variable and “event” as indicator variable (taking “yes” as case code).
Take a look at the plots
To see which continuous variables should be treated as normal and which not. By default, compareGroups performs a Shapiro-Wilks normality test to decide which variables are normal. But we may want to check the normality assumption graphically.
Set the options.
- Type: Specify the variables to be treated as normal and the ones as non normal. To specify that all of them will be treated as normal, except `p14’ for which median and quartiles are displayed instead of mean and SD:
- Hide: To hide the “No” category for binary variables such as hypertension, diabetes, etc., we will type “No” in the “hide no” input text window. Also, to hide the “female” category for we will select the “sex” variable and “female” category and press “Update” button afterwards.
- OR: Since we have hidden the females, we have to do the same for the HR. Also, for the waist we will change the scale to 5. Doing this, the HR will represent the change in 5 units.
- Subset: If we want to do the analysis only for male type “sex==1” or “sex==2” in only females must be analysed in the “global subset” text input window.
What to be displayed and how.
- Show: Then, we specify which columns (information) to be displayed in the bivariate table. For instance, if we want to display the Odds Ratio, its p-value, but not the descriptives for the entire cohort “p.overall”:
- Format: Also, we may want to display only the percentages for categorical variables, mean and standard deviation with plus/minus symbol and rounded brackets for first and third quantiles for continuous normal and non-normal variables, respectively:
- Decimals: Moreover, it may be important to report more decimals for the odds ratio. By default, two decimals are reported. If we want to report 3 decimals for all variables:
- Headers: Finally, some “key” words in the header of the descriptive tables, (such as “p.ratio”, …) may be changed:
Visualize the table
To see the output, click on TABLE tab on the right panel.
By pressing “View options” button a slider to customize the font-size of the bivariate table appears, as well as an “info” button. Pressing the “info” button a table containg information about the number of available data by variable and group, type of variable (normal, non-normal or categorical, etc) is displayed on a modal.
Saving the table
Finally, once the table contains all the desired figures and in the appropriate format, it can be downloaded and stored in different formats:
PDF: a LaTeX compiler such as MikTex must be installed to build the PDF document with the descriptive table. The user can select the font size and whether table must be placed vertically or horizontally (landscape option).
CSV: a plain text file with columns separated by commas or semicolons. For Windows users, this format is useful since it can be opened by Excel.
HTML: a web browser is opened and the table can be easily copied and pasted to Word, for instance.
TXT: a plain text file which can be opened by any text editor program and which contains the table as in
Rconsole with a nice format. Once the “download” button is pressed, the file is automatically stored in your PC/Mac.Word: A Word document file (either 2000, 2003 or 2010 version) is created.
Excel: An Excel sheet file (either 200, 2003 or 2010 version) is created.
References
Benjamini, Y., and Y. Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. Roy. Statist. Soc. Ser. B 57: 289–300.
Estruch, R., E. Ros, J. Salas-Salvadó, MI. Covas, D. Corella, F. Arós, E. Gómez-Gracia, et al. 2013. “Primary Prevention of Cardiovascular Disease with a Mediterranean Diet.” N Engl J Med 368 (14): 1279–90.
Genolini, C., B Desgraupes, and Lionel-Riou Franca. 2011. R2lh: R to LaTeX and HTML. http://CRAN.R-project.org/package=r2lh.
González, Juan R, Lluís Armengol, Elisabet Guinó, Xavier Solé, and and Víctor Moreno. 2012. SNPassoc: SNPs-Based Whole Genome Association Studies. http://CRAN.R-project.org/package=SNPassoc.
Graffelman, Jan. 2012. HardyWeinberg: Graphical Tests for Hardy-Weinberg Equilibrium. http://CRAN.R-project.org/package=HardyWeinberg.
RStudio, and Inc. 2014. Shiny: Web Application Framework for R. http://CRAN.R-project.org/package=shiny.
Subirana, Isaac, Héctor Sanz, and Joan Vila. 2014. “Building Bivariate Tables: The compareGroups Package for R.” Journal of Statistical Software 57 (12): 1–16. http://www.jstatsoft.org/v57/i12/.