Ejercicios Día 4
Reportes estadisticos
Carga de Librerias - Arsenal
library(arsenal)require(knitr)
data(mockstudy) # cargar los datos
dim(mockstudy) # número de filas y columnas ## [1] 1499 14Número de filas
## 'data.frame': 1499 obs. of 14 variables:
## $ case : int 110754 99706 105271 105001 112263 86205 99508 90158 88989 90515 ...
## $ age : int 67 74 50 71 69 56 50 57 51 63 ...
## ..- attr(*, "label")= chr "Age in Years"
## $ arm : chr "F: FOLFOX" "A: IFL" "A: IFL" "G: IROX" ...
## ..- attr(*, "label")= chr "Treatment Arm"
## $ sex : Factor w/ 2 levels "Male","Female": 1 2 2 2 2 1 1 1 2 1 ...
## $ race : chr "Caucasian" "Caucasian" "Caucasian" "Caucasian" ...
## ..- attr(*, "label")= chr "Race"
## $ fu.time : int 922 270 175 128 233 120 369 421 387 363 ...
## $ fu.stat : int 2 2 2 2 2 2 2 2 2 2 ...
## $ ps : int 0 1 1 1 0 0 0 0 1 1 ...
## $ hgb : num 11.5 10.7 11.1 12.6 13 10.2 13.3 12.1 13.8 12.1 ...
## $ bmi : num 25.1 19.5 NA 29.4 26.4 ...
## ..- attr(*, "label")= chr "Body Mass Index (kg/m^2)"
## $ alk.phos : int 160 290 700 771 350 569 162 152 231 492 ...
## $ ast : int 35 52 100 68 35 27 16 12 25 18 ...
## $ mdquality.s: int NA 1 1 1 NA 1 1 1 1 1 ...
## $ age.ord : Ord.factor w/ 8 levels "10-19"<"20-29"<..: 6 7 4 7 6 5 4 5 5 6 ...## [1] "F: FOLFOX" "A: IFL" "G: IROX"##
## A: IFL F: FOLFOX G: IROX
## 428 691 380##
## A: IFL F: FOLFOX G: IROX
## 0.2855237 0.4609740 0.2535023Distribución de la varia arm en función del sexo y la edad
## tableby Object
##
## Function Call:
## tableby(formula = arm ~ sex + age, data = mockstudy)
##
## Variable(s):
## arm ~ sex, age##
##
## | | A: IFL (N=428) | F: FOLFOX (N=691) | G: IROX (N=380) | Total (N=1499) | p value|
## |:---------------------------|:---------------:|:-----------------:|:---------------:|:---------------:|-------:|
## |**sex** | | | | | 0.190|
## | Male | 277 (64.7%) | 411 (59.5%) | 228 (60.0%) | 916 (61.1%) | |
## | Female | 151 (35.3%) | 280 (40.5%) | 152 (40.0%) | 583 (38.9%) | |
## |**Age in Years** | | | | | 0.614|
## | Mean (SD) | 59.673 (11.365) | 60.301 (11.632) | 59.763 (11.499) | 59.985 (11.519) | |
## | Range | 27.000 - 88.000 | 19.000 - 88.000 | 26.000 - 85.000 | 19.000 - 88.000 | |## group.term group.label strata.term variable term label
## 1 arm Treatment Arm sex sex sex
## 2 arm Treatment Arm sex countpct Male
## 3 arm Treatment Arm sex countpct Female
## 4 arm Treatment Arm age age Age in Years
## 5 arm Treatment Arm age meansd Mean (SD)
## 6 arm Treatment Arm age range Range
## variable.type A: IFL F: FOLFOX G: IROX
## 1 categorical
## 2 categorical 277.00000, 64.71963 411.00000, 59.47902 228, 60
## 3 categorical 151.00000, 35.28037 280.00000, 40.52098 152, 40
## 4 numeric
## 5 numeric 59.67290, 11.36454 60.30101, 11.63225 59.76316, 11.49930
## 6 numeric 27, 88 19, 88 26, 85
## Total test p.value
## 1 Pearson's Chi-squared test 0.1904388
## 2 916.0000, 61.1074 Pearson's Chi-squared test 0.1904388
## 3 583.0000, 38.8926 Pearson's Chi-squared test 0.1904388
## 4 Linear Model ANOVA 0.6143859
## 5 59.98532, 11.51877 Linear Model ANOVA 0.6143859
## 6 19, 88 Linear Model ANOVA 0.6143859## [1] "" "916" "61.1074049366244" "583"
## [5] "38.8925950633756" "" "59.9853235490327" "11.5187684866331"
## [9] "19" "88"## [1] 916## Nombre_Columna
## 1 916.0000
## 2 61.1074## NULL## NULL## [1] "1" "2"## Nombre_Columna
## Media 916.0000
## SD 61.1074##
##
## | | A: IFL (N=428) | F: FOLFOX (N=691) | G: IROX (N=380) | Total (N=1499) | p value|
## |:---------------------------|:---------------:|:-----------------:|:---------------:|:---------------:|-------:|
## |**sex** | | | | | 0.190|
## | Male | 277 (64.7%) | 411 (59.5%) | 228 (60.0%) | 916 (61.1%) | |
## | Female | 151 (35.3%) | 280 (40.5%) | 152 (40.0%) | 583 (38.9%) | |
## |**Age in Years** | | | | | 0.614|
## | Mean (SD) | 59.673 (11.365) | 60.301 (11.632) | 59.763 (11.499) | 59.985 (11.519) | |
## | Range | 27.000 - 88.000 | 19.000 - 88.000 | 26.000 - 85.000 | 19.000 - 88.000 | |## [1] NA 916.00000 61.10740 583.00000 38.89260 NA 59.98532
## [8] 11.51877 19.00000 88.00000Resumen de variables de tiempo
##
##
## | | Time Point 1 (N=4) | Time Point 2 (N=4) | Difference (N=4) | p value|
## |:---------------------------|:-----------------------:|:-----------------------:|:----------------:|-------:|
## |**Cat** | | | | 1.000|
## | A | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) | |
## | B | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) | |
## |**Fac** | | | | 0.261|
## | A | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) | |
## | B | 1 (25.0%) | 2 (50.0%) | 1 (100.0%) | |
## | C | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) | |
## |**Num** | | | | 0.391|
## | Mean (SD) | 2.750 (1.258) | 3.250 (0.957) | 0.500 (1.000) | |
## | Range | 1.000 - 4.000 | 2.000 - 4.000 | -1.000 - 1.000 | |
## |**Ord** | | | | 0.174|
## | I | 2 (50.0%) | 0 (0.0%) | 2 (100.0%) | |
## | II | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) | |
## | III | 1 (25.0%) | 3 (75.0%) | 0 (0.0%) | |
## |**Lgl** | | | | 1.000|
## | FALSE | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) | |
## | TRUE | 2 (50.0%) | 3 (75.0%) | 1 (50.0%) | |
## |**Dat** | | | | 0.182|
## | Median | 2018-05-03 | 2018-05-04 | 0.500 | |
## | Range | 2018-05-02 - 2018-05-06 | 2018-05-02 - 2018-05-07 | 0.000 - 1.000 | |##
##
## | | Time Point 1 (N=4) | Time Point 2 (N=4) | Difference (N=4) | p value|
## |:---------------------------|:-----------------------:|:-----------------------:|:----------------:|-------:|
## |**Cat** | | | | 1.000|
## | A | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) | |
## | B | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) | |
## |**Fac** | | | | 0.261|
## | A | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) | |
## | B | 1 (25.0%) | 2 (50.0%) | 1 (100.0%) | |
## | C | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) | |
## |**Num** | | | | 0.391|
## | Mean (SD) | 2.750 (1.258) | 3.250 (0.957) | 0.500 (1.000) | |
## | Range | 1.000 - 4.000 | 2.000 - 4.000 | -1.000 - 1.000 | |
## |**Ord** | | | | 0.174|
## | I | 2 (50.0%) | 0 (0.0%) | 2 (100.0%) | |
## | II | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) | |
## | III | 1 (25.0%) | 3 (75.0%) | 0 (0.0%) | |
## |**Lgl** | | | | 1.000|
## | FALSE | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) | |
## | TRUE | 2 (50.0%) | 3 (75.0%) | 1 (50.0%) | |
## |**Dat** | | | | 0.182|
## | Median | 2018-05-03 | 2018-05-04 | 0.500 | |
## | Range | 2018-05-02 - 2018-05-06 | 2018-05-02 - 2018-05-07 | 0.000 - 1.000 | |Variables por puntos de tiempo (ejercicio)
## # A tibble: 5 x 3
## # Groups: tp [2]
## tp Cat `n()`
## <chr> <chr> <int>
## 1 Trimestre1 A 2
## 2 Trimestre1 B 2
## 3 Trimestre1 <NA> 1
## 4 Trimestre2 A 2
## 5 Trimestre2 B 3## # A tibble: 2 x 2
## tp NumeroClientes
## <chr> <int>
## 1 Trimestre1 5
## 2 Trimestre2 5## # A tibble: 10 x 2
## # Groups: tp [2]
## tp Clientes
## <chr> <dbl>
## 1 Trimestre1 1
## 2 Trimestre1 2
## 3 Trimestre1 3
## 4 Trimestre1 4
## 5 Trimestre1 5
## 6 Trimestre2 1
## 7 Trimestre2 2
## 8 Trimestre2 3
## 9 Trimestre2 4
## 10 Trimestre2 6## [1] I II II X III III I III II I <NA>
## Levels: I < II < III < X## [1] 1 2 2 10 3 <NA> 3 1 3 2 1
## Levels: 1 < 2 < 3 < 10## [1] 1 2 2 10 3 3 1 <NA> 3 2 1
## Levels: 10 < 3 < 2 < 1## [1] Primaria Secundaria Secundaria Postgrado Universitaria
## [6] Universitaria Primaria Universitaria Secundaria Primaria
## Levels: Postgrado < Primaria < Secundaria < Universitaria## [1] Primaria Secundaria Secundaria Postgrado Universitaria
## [6] Universitaria Primaria Universitaria Secundaria Primaria
## Levels: Postgrado < Primaria < Secundaria < Universitaria## [1] Primaria Secundaria Secundaria Postgrado Universitaria
## [6] Universitaria Primaria Universitaria Secundaria Primaria
## [11] <NA> <NA>
## Levels: Primaria < Secundaria < Universitaria < Postgrado##
##
## | | Trimestre1 (N=4) | Trimestre2 (N=4) | Difference (N=4) | p value|
## |:---------------------------|:-----------------------:|:-----------------------:|:----------------:|-------:|
## |**Cat** | | | | 1.000|
## | A | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) | |
## | B | 2 (50.0%) | 2 (50.0%) | 1 (50.0%) | |
## |**Fac** | | | | 0.261|
## | A | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) | |
## | B | 1 (25.0%) | 2 (50.0%) | 1 (100.0%) | |
## | C | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) | |
## |**Num** | | | | 0.391|
## | Mean (SD) | 2.750 (1.258) | 3.250 (0.957) | 0.500 (1.000) | |
## | Range | 1.000 - 4.000 | 2.000 - 4.000 | -1.000 - 1.000 | |
## |**Ord** | | | | 0.174|
## | I | 2 (50.0%) | 0 (0.0%) | 2 (100.0%) | |
## | II | 1 (25.0%) | 1 (25.0%) | 1 (100.0%) | |
## | III | 1 (25.0%) | 3 (75.0%) | 0 (0.0%) | |
## |**Lgl** | | | | 1.000|
## | FALSE | 2 (50.0%) | 1 (25.0%) | 2 (100.0%) | |
## | TRUE | 2 (50.0%) | 3 (75.0%) | 1 (50.0%) | |
## |**Dat** | | | | 0.182|
## | Median | 2018-05-03 | 2018-05-04 | 0.500 | |
## | Range | 2018-05-02 - 2018-05-06 | 2018-05-02 - 2018-05-07 | 0.000 - 1.000 | |##
##
## | | Trimestre1 (N=6) | Trimestre2 (N=6) | Difference (N=6) | p value|
## |:---------------------------|:-----------------------:|:-----------------------:|:----------------:|-------:|
## |**Cat** | | | | 1.000|
## | N-Miss | 2 | 1 | 2 | |
## | A | 2 (50.0%) | 2 (40.0%) | 1 (50.0%) | |
## | B | 2 (50.0%) | 3 (60.0%) | 1 (50.0%) | |
## |**Fac** | | | | 0.261|
## | N-Miss | 1 | 1 | 2 | |
## | A | 2 (40.0%) | 2 (40.0%) | 2 (100.0%) | |
## | B | 1 (20.0%) | 2 (40.0%) | 1 (100.0%) | |
## | C | 2 (40.0%) | 1 (20.0%) | 1 (100.0%) | |
## |**Num** | | | | 0.391|
## | N-Miss | 1 | 2 | 2 | |
## | Mean (SD) | 2.200 (1.643) | 3.250 (0.957) | 0.500 (1.000) | |
## | Range | 0.000 - 4.000 | 2.000 - 4.000 | -1.000 - 1.000 | |
## |**Ord** | | | | 0.174|
## | N-Miss | 1 | 1 | 2 | |
## | I | 2 (40.0%) | 1 (20.0%) | 2 (100.0%) | |
## | II | 2 (40.0%) | 1 (20.0%) | 1 (100.0%) | |
## | III | 1 (20.0%) | 3 (60.0%) | 0 (0.0%) | |
## |**Lgl** | | | | 1.000|
## | N-Miss | 1 | 1 | 2 | |
## | FALSE | 3 (60.0%) | 2 (40.0%) | 2 (100.0%) | |
## | TRUE | 2 (40.0%) | 3 (60.0%) | 1 (50.0%) | |
## |**Dat** | | | | 0.182|
## | N-Miss | 1 | 1 | 2 | |
## | Median | 2018-05-04 | 2018-05-05 | 0.500 | |
## | Range | 2018-05-02 - 2018-05-06 | 2018-05-02 - 2018-05-07 | 0.000 - 1.000 | |## id a b c
## rn1 person1 a 1 f
## rn2 person2 b 3 e
## rn3 person3 c 4 d## 'data.frame': 3 obs. of 4 variables:
## $ id: chr "person1" "person2" "person3"
## $ a : chr "a" "b" "c"
## $ b : num 1 3 4
## $ c : chr "f" "e" "d"## id a b d
## rn1 person3 c 1 rn1
## rn3 person2 b 3 rn2
## rn2 person1 a 4 rn3## Compare Object
##
## Function Call:
## comparedf(x = df1, y = df2)
##
## Shared: 3 non-by variables and 3 observations.
## Not shared: 2 variables and 0 observations.
##
## Differences found in 2/3 variables compared.
## 0 variables compared have non-identical attributes.