README.md
In parkseeh/INRAETableOne: Generate the descriptive statistics table
INRAETableOne
An R package for generating tables of descriptive statistics.
Installation
To install directly from Github:
require(devtools)
devtools::install_github("parkseeh/INRAETableOne")
Getting Started
A short introduction to the package with examples is shown below
Example
For this example, you will be using pal dataset, which you can directly load
by using data()
library(INRAETableOne)
data(pal)
In order to use INRAETableOne, the first argument needs to be a formula format.
INRAETableOne(Groupe_ttt ~ Age + Sexe + Poids + BMI + MMSE, data = pal)
Summary descriptives table by 'Groupe_ttt'
______________________________________
Placebo Verum p.value
(n=364) (n=366)
--------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
MMSE 28.7 ± 1.2 28.7 ± 1.2 0.697
--------------------------------------
If you want to include all the variables in the dataset, simply write a formula with ~ .
INRAETableOne(Groupe_ttt ~ ., data = pal)
Summary descriptives table by 'Groupe_ttt'
___________________________________________
Placebo Verum p.value
(n=364) (n=366)
-------------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
Systole 129.1 ± 18.3 128.3 ± 18.8 0.577
Diastole 77.9 ± 11.4 78.2 ± 11.6 0.711
Freq_car 72.2 ± 10.6 72.3 ± 11.2 0.972
PAL_TEA 38.4 ± 26.4 39.3 ± 26.8 0.658
MMSE 28.7 ± 1.2 28.7 ± 1.2 0.697
-------------------------------------------
The show.detail = T will show more summary for the continuous variables such as min, max, and median.
INRAETableOne(Groupe_ttt ~ ., data = pal, show.detail = T)
Summary descriptives table by 'Groupe_ttt'
_____________________________________________________________
Placebo Verum p.value
(n=364) (n=366)
-------------------------------------------------------------
Age 0.442
- Mean ± SD 64.2 ± 2.9 64.1 ± 2.9
- Med [Min;Max] 64.0[60.0;70.0] 64.0[60.0;70.0]
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
Poids 0.781
- Mean ± SD 68.0 ± 12.0 67.7 ± 11.7
- Med [Min;Max] 68.0[40.0;103.0] 67.0[46.0;101.0]
BMI 0.959
- Mean ± SD 24.4 ± 2.6 24.4 ± 2.8
- Med [Min;Max] 24.2[19.5;30.5] 24.3[18.8;31.4]
Systole 0.577
- Mean ± SD 129.1 ± 18.3 128.3 ± 18.8
- Med [Min;Max] 129.0[88.0;214.0] 127.0[82.0;195.0]
Diastole 0.711
- Mean ± SD 77.9 ± 11.4 78.2 ± 11.6
- Med [Min;Max] 77.0[45.0;114.0] 77.0[41.0;111.0]
Freq_car 0.972
- Mean ± SD 72.2 ± 10.6 72.3 ± 11.2
- Med [Min;Max] 72.0[48.0;113.0] 72.0[41.0;111.0]
PAL_TEA 0.658
- Mean ± SD 38.4 ± 26.4 39.3 ± 26.8
- Med [Min;Max] 30.0[3.0;131.0] 30.0[3.0;132.0]
MMSE 0.697
- Mean ± SD 28.7 ± 1.2 28.7 ± 1.2
- Med [Min;Max] 29.0[23.0;30.0] 29.0[23.0;30.0]
-------------------------------------------------------------
You can also choose the maximum level of x level by setting max.x.level any number you want.
For instance, if you put max.x.level = 10, the MMSE which contains only 8 different level of
factor will be considered as categorical variable.
INRAETableOne(Groupe_ttt ~ ., data = pal, max.x.level = 10)
Summary descriptives table by 'Groupe_ttt'
___________________________________________
Placebo Verum p.value
(n=364) (n=366)
-------------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
Systole 129.1 ± 18.3 128.3 ± 18.8 0.577
Diastole 77.9 ± 11.4 78.2 ± 11.6 0.711
Freq_car 72.2 ± 10.6 72.3 ± 11.2 0.972
PAL_TEA 38.4 ± 26.4 39.3 ± 26.8 0.658
MMSE 0.102
- 23 1 (0.3%) 1 (0.3%)
- 24 1 (0.3%) 1 (0.3%)
- 25 7 (2.0%) 4 (1.1%)
- 26 9 (2.5%) 11 (3.1%)
- 27 26 (7.3%) 49 (13.6%)
- 28 84 (23.7%) 69 (19.2%)
- 29 130 (36.6%) 116 (32.3%)
- 30 97 (27.3%) 108 (30.1%)
-------------------------------------------
Also if you want to know whether there exists the missing variable, just put
show.missing = T as other argument.
INRAETableOne(Groupe_ttt ~ ., data = pal, max.x.level = 10, show.missing = T)
Summary descriptives table by 'Groupe_ttt'
_____________________________________________
Placebo Verum p.value
(n=364) (n=366)
---------------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
- Missing 0 (0.0%) 0 (0.0%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
- Missing 0 (0.0%) 0 (0.0%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
Systole 129.1 ± 18.3 128.3 ± 18.8 0.577
Diastole 77.9 ± 11.4 78.2 ± 11.6 0.711
Freq_car 72.2 ± 10.6 72.3 ± 11.2 0.972
PAL_TEA 38.4 ± 26.4 39.3 ± 26.8 0.658
MMSE 0.106
- 23 1 (0.3%) 1 (0.3%)
- 24 1 (0.3%) 1 (0.3%)
- 25 7 (1.9%) 4 (1.1%)
- 26 9 (2.5%) 11 (3.0%)
- 27 26 (7.1%) 49 (13.4%)
- 28 84 (23.1%) 69 (18.9%)
- 29 130 (35.7%) 116 (31.7%)
- 30 97 (26.6%) 108 (29.5%)
- Missing 9 (2.5%) 7 (1.9%)
---------------------------------------------
You can add second y variable using + sign at the left hand side of
~ on the formula.
INRAETableOne(Groupe_ttt + Period ~ ., data = pal)
Descriptive Statistics stratified by 'Groupe_ttt' and 'Period'
_____________________________________________________________________________
Verum Placebo
--------------------------------- ---------------------------------
T0 T4 p.value T0 T4 p.value
(n=183) (n=183) (n=182) (n=182)
-----------------------------------------------------------------------------
Age 64.1 ± 2.9 64.1 ± 2.9 1.000 64.2 ± 2.9 64.2 ± 2.9 1.000
Sexe 1.000 1.000
- F 110 (60.1%) 110 (60.1%) 112 (61.5%) 112 (61.5%)
- M 73 (39.9%) 73 (39.9%) 70 (38.5%) 70 (38.5%)
Poids 67.6 ± 11.5 67.9 ± 11.9 0.833 68.0 ± 11.9 68.0 ± 12.1 0.991
BMI 24.3 ± 2.7 24.5 ± 2.9 0.682 24.4 ± 2.6 24.4 ± 2.6 0.958
Systole 128.9 ± 20.6 127.7 ± 16.9 0.526 128.8 ± 17.2 129.4 ± 19.3 0.792
Diastole 78.3 ± 12.4 78.2 ± 10.8 0.906 77.6 ± 11.3 78.3 ± 11.6 0.566
Freq_car 72.4 ± 10.9 72.1 ± 11.6 0.740 72.5 ± 11.3 72.0 ± 9.7 0.635
PAL_TEA 43.9 ± 28.0 34.5 ± 24.8 0.001 43.6 ± 27.7 33.0 ± 23.9 0.000
MMSE 28.9 ± 1.0 28.4 ± 1.4 0.001 29.0 ± 0.9 28.4 ± 1.4 0.000
-----------------------------------------------------------------------------
Last but not least, the paired = T will perform the two sample paired t-test to compare difference between T0 and T4. However, the
number of sample at T0 and T4 needs to be same.
parkseeh/INRAETableOne documentation built on April 19, 2022, 1:28 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
INRAETableOne
An R package for generating tables of descriptive statistics.
Installation
To install directly from Github:
require(devtools)
devtools::install_github("parkseeh/INRAETableOne")
Getting Started
A short introduction to the package with examples is shown below
Example
For this example, you will be using pal dataset, which you can directly load
by using data()
library(INRAETableOne)
data(pal)
In order to use INRAETableOne, the first argument needs to be a formula format.
INRAETableOne(Groupe_ttt ~ Age + Sexe + Poids + BMI + MMSE, data = pal)
Summary descriptives table by 'Groupe_ttt'
______________________________________
Placebo Verum p.value
(n=364) (n=366)
--------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
MMSE 28.7 ± 1.2 28.7 ± 1.2 0.697
--------------------------------------
If you want to include all the variables in the dataset, simply write a formula with ~ .
INRAETableOne(Groupe_ttt ~ ., data = pal)
Summary descriptives table by 'Groupe_ttt'
___________________________________________
Placebo Verum p.value
(n=364) (n=366)
-------------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
Systole 129.1 ± 18.3 128.3 ± 18.8 0.577
Diastole 77.9 ± 11.4 78.2 ± 11.6 0.711
Freq_car 72.2 ± 10.6 72.3 ± 11.2 0.972
PAL_TEA 38.4 ± 26.4 39.3 ± 26.8 0.658
MMSE 28.7 ± 1.2 28.7 ± 1.2 0.697
-------------------------------------------
The show.detail = T will show more summary for the continuous variables such as min, max, and median.
INRAETableOne(Groupe_ttt ~ ., data = pal, show.detail = T)
Summary descriptives table by 'Groupe_ttt'
_____________________________________________________________
Placebo Verum p.value
(n=364) (n=366)
-------------------------------------------------------------
Age 0.442
- Mean ± SD 64.2 ± 2.9 64.1 ± 2.9
- Med [Min;Max] 64.0[60.0;70.0] 64.0[60.0;70.0]
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
Poids 0.781
- Mean ± SD 68.0 ± 12.0 67.7 ± 11.7
- Med [Min;Max] 68.0[40.0;103.0] 67.0[46.0;101.0]
BMI 0.959
- Mean ± SD 24.4 ± 2.6 24.4 ± 2.8
- Med [Min;Max] 24.2[19.5;30.5] 24.3[18.8;31.4]
Systole 0.577
- Mean ± SD 129.1 ± 18.3 128.3 ± 18.8
- Med [Min;Max] 129.0[88.0;214.0] 127.0[82.0;195.0]
Diastole 0.711
- Mean ± SD 77.9 ± 11.4 78.2 ± 11.6
- Med [Min;Max] 77.0[45.0;114.0] 77.0[41.0;111.0]
Freq_car 0.972
- Mean ± SD 72.2 ± 10.6 72.3 ± 11.2
- Med [Min;Max] 72.0[48.0;113.0] 72.0[41.0;111.0]
PAL_TEA 0.658
- Mean ± SD 38.4 ± 26.4 39.3 ± 26.8
- Med [Min;Max] 30.0[3.0;131.0] 30.0[3.0;132.0]
MMSE 0.697
- Mean ± SD 28.7 ± 1.2 28.7 ± 1.2
- Med [Min;Max] 29.0[23.0;30.0] 29.0[23.0;30.0]
-------------------------------------------------------------
You can also choose the maximum level of x level by setting max.x.level any number you want.
For instance, if you put max.x.level = 10, the MMSE which contains only 8 different level of
factor will be considered as categorical variable.
INRAETableOne(Groupe_ttt ~ ., data = pal, max.x.level = 10)
Summary descriptives table by 'Groupe_ttt'
___________________________________________
Placebo Verum p.value
(n=364) (n=366)
-------------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
Systole 129.1 ± 18.3 128.3 ± 18.8 0.577
Diastole 77.9 ± 11.4 78.2 ± 11.6 0.711
Freq_car 72.2 ± 10.6 72.3 ± 11.2 0.972
PAL_TEA 38.4 ± 26.4 39.3 ± 26.8 0.658
MMSE 0.102
- 23 1 (0.3%) 1 (0.3%)
- 24 1 (0.3%) 1 (0.3%)
- 25 7 (2.0%) 4 (1.1%)
- 26 9 (2.5%) 11 (3.1%)
- 27 26 (7.3%) 49 (13.6%)
- 28 84 (23.7%) 69 (19.2%)
- 29 130 (36.6%) 116 (32.3%)
- 30 97 (27.3%) 108 (30.1%)
-------------------------------------------
Also if you want to know whether there exists the missing variable, just put
show.missing = T as other argument.
INRAETableOne(Groupe_ttt ~ ., data = pal, max.x.level = 10, show.missing = T)
Summary descriptives table by 'Groupe_ttt'
_____________________________________________
Placebo Verum p.value
(n=364) (n=366)
---------------------------------------------
Age 64.2 ± 2.9 64.1 ± 2.9 0.442
Sexe 0.749
- F 224 (61.5%) 220 (60.1%)
- M 140 (38.5%) 146 (39.9%)
- Missing 0 (0.0%) 0 (0.0%)
Period 1.000
- T0 182 (50.0%) 183 (50.0%)
- T4 182 (50.0%) 183 (50.0%)
- Missing 0 (0.0%) 0 (0.0%)
Poids 68.0 ± 12.0 67.7 ± 11.7 0.781
BMI 24.4 ± 2.6 24.4 ± 2.8 0.959
Systole 129.1 ± 18.3 128.3 ± 18.8 0.577
Diastole 77.9 ± 11.4 78.2 ± 11.6 0.711
Freq_car 72.2 ± 10.6 72.3 ± 11.2 0.972
PAL_TEA 38.4 ± 26.4 39.3 ± 26.8 0.658
MMSE 0.106
- 23 1 (0.3%) 1 (0.3%)
- 24 1 (0.3%) 1 (0.3%)
- 25 7 (1.9%) 4 (1.1%)
- 26 9 (2.5%) 11 (3.0%)
- 27 26 (7.1%) 49 (13.4%)
- 28 84 (23.1%) 69 (18.9%)
- 29 130 (35.7%) 116 (31.7%)
- 30 97 (26.6%) 108 (29.5%)
- Missing 9 (2.5%) 7 (1.9%)
---------------------------------------------
You can add second y variable using + sign at the left hand side of
~ on the formula.
INRAETableOne(Groupe_ttt + Period ~ ., data = pal)
Descriptive Statistics stratified by 'Groupe_ttt' and 'Period'
_____________________________________________________________________________
Verum Placebo
--------------------------------- ---------------------------------
T0 T4 p.value T0 T4 p.value
(n=183) (n=183) (n=182) (n=182)
-----------------------------------------------------------------------------
Age 64.1 ± 2.9 64.1 ± 2.9 1.000 64.2 ± 2.9 64.2 ± 2.9 1.000
Sexe 1.000 1.000
- F 110 (60.1%) 110 (60.1%) 112 (61.5%) 112 (61.5%)
- M 73 (39.9%) 73 (39.9%) 70 (38.5%) 70 (38.5%)
Poids 67.6 ± 11.5 67.9 ± 11.9 0.833 68.0 ± 11.9 68.0 ± 12.1 0.991
BMI 24.3 ± 2.7 24.5 ± 2.9 0.682 24.4 ± 2.6 24.4 ± 2.6 0.958
Systole 128.9 ± 20.6 127.7 ± 16.9 0.526 128.8 ± 17.2 129.4 ± 19.3 0.792
Diastole 78.3 ± 12.4 78.2 ± 10.8 0.906 77.6 ± 11.3 78.3 ± 11.6 0.566
Freq_car 72.4 ± 10.9 72.1 ± 11.6 0.740 72.5 ± 11.3 72.0 ± 9.7 0.635
PAL_TEA 43.9 ± 28.0 34.5 ± 24.8 0.001 43.6 ± 27.7 33.0 ± 23.9 0.000
MMSE 28.9 ± 1.0 28.4 ± 1.4 0.001 29.0 ± 0.9 28.4 ± 1.4 0.000
-----------------------------------------------------------------------------
Last but not least, the paired = T will perform the two sample paired t-test to compare difference between T0 and T4. However, the
number of sample at T0 and T4 needs to be same.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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