compareGroups
?The compareGroups
R
meant to build nice format tables with descriptives of several variables from a sample, possibly stratifying by groups.
See examples
compareGroups
for?Among other features compareGroups
creates data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML, LaTeX, PDF, Word or Excel).
performs figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.).
display statistics (mean, median, frequencies, incidences, etc.) depending on the type of variable (continuous, normal-distributed, categorical, etc.).
computes the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, …) depending on the nature of the described variable (normal, non-normal or qualitative).
summarizes genetic data (Single Nucleotids Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.
compareGroups
?compareGroups
R
code to build tables and plots using compareGroups
predimed
data set are displayed by groups. Appropiate descriptives and statistics are performed depending whether the variable is numerical or categorical. Moreover, the table is displayed in ready-to-publish format.library(compareGroups)
data(predimed)
res <- compareGroups(group ~ . , predimed)
createTable(res, hide.no="no")
--------Summary descriptives table by 'Intervention group'---------
___________________________________________________________________________________
Control MedDiet + Nuts MedDiet + VOO p.overall
N=2042 N=2100 N=2182
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Sex: <0.001
Male 812 (39.8%) 968 (46.1%) 899 (41.2%)
Female 1230 (60.2%) 1132 (53.9%) 1283 (58.8%)
Age 67.3 (6.28) 66.7 (6.02) 67.0 (6.21) 0.003
Smoking: 0.444
Never 1282 (62.8%) 1259 (60.0%) 1351 (61.9%)
Current 270 (13.2%) 296 (14.1%) 292 (13.4%)
Former 490 (24.0%) 545 (26.0%) 539 (24.7%)
Body mass index 30.3 (3.96) 29.7 (3.77) 29.9 (3.71) <0.001
Waist circumference 101 (10.8) 100 (10.6) 100 (10.4) 0.045
Waist-to-height ratio 0.63 (0.07) 0.62 (0.06) 0.63 (0.06) <0.001
Hypertension 1711 (83.8%) 1738 (82.8%) 1786 (81.9%) 0.249
Type-2 diabetes 970 (47.5%) 950 (45.2%) 1082 (49.6%) 0.017
Dyslipidemia 1479 (72.4%) 1539 (73.3%) 1560 (71.5%) 0.423
Family history of premature CHD 462 (22.6%) 460 (21.9%) 507 (23.2%) 0.581
Hormone-replacement therapy 31 (1.68%) 30 (1.61%) 36 (1.84%) 0.850
MeDiet Adherence score 8.44 (1.94) 8.81 (1.90) 8.77 (1.97) <0.001
follow-up to main event (years) 4.09 (1.74) 4.31 (1.70) 4.64 (1.60) <0.001
AMI, stroke, or CV Death 97 (4.75%) 70 (3.33%) 85 (3.90%) 0.064
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R
language, a Graphical User Interface has been designed to build and customize descriptive tables as well as perform plots or explore your data writing no code by just “clicking”. This web-based Graphical User Interface (R
installed in your device. compareGroups
?Althought there are several functions in R
to build descriptives (see…), it does not exist any to create tables in the standard format as are presented in many epidemiology studies (see figures). So we…
@@ To be completed @@
compareGroups
?compareGroups is developed and maintained by Isaac Subirana, Joan Vila, Héctor Sanz at the Cardiovascular Epidemiology Research Unit (URLEC), located at Barcelona Biomedical Research Park, and collaborators (Gavin Lucas, Judith Peñafiel and David Giménez).
As the driving force behind the REGICOR study, URLEC has extensive experience in statistical epidemiology, and is a national reference centre for research into cardiovascular diseases and their risk factors.