solution https://github.com/eheinzen/arsenal/issues/143#issuecomment-427933192
library(arsenal)
df <- data.frame(arm = sample(c("Int","Contr"),size=10, replace = T),
sex = sample(c("Male", "Female"),size=10, replace = T),
agegp = sample(c("<15","15-49",">50"),size=10, replace = T))
tab1 <- tableby(arm ~ sex + agegp, data=df)
summary(tab1, text=TRUE)
#>
#>
#> | | Contr (N=7) | Int (N=3) | Total (N=10) | p value|
#> |:---------|:-----------:|:---------:|:------------:|-------:|
#> |sex | | | | 1.000|
#> |- Female | 3 (42.9%) | 1 (33.3%) | 4 (40.0%) | |
#> |- Male | 4 (57.1%) | 2 (66.7%) | 6 (60.0%) | |
#> |agegp | | | | 0.665|
#> |- <15 | 1 (14.3%) | 0 (0.0%) | 1 (10.0%) | |
#> |- >50 | 5 (71.4%) | 2 (66.7%) | 7 (70.0%) | |
#> |- 15-49 | 1 (14.3%) | 1 (33.3%) | 2 (20.0%) | |
summary(tableby(arm ~ sex + agegp, data = df, cat.stats = "countrowpct"), text = TRUE)
#>
#>
#> | | Contr (N=7) | Int (N=3) | Total (N=10) | p value|
#> |:---------|:-----------:|:---------:|:------------:|-------:|
#> |sex | | | | 1.000|
#> |- Female | 3 (75.0%) | 1 (25.0%) | 4 (100.0%) | |
#> |- Male | 4 (66.7%) | 2 (33.3%) | 6 (100.0%) | |
#> |agegp | | | | 0.665|
#> |- <15 | 1 (100.0%) | 0 (0.0%) | 1 (100.0%) | |
#> |- >50 | 5 (71.4%) | 2 (28.6%) | 7 (100.0%) | |
#> |- 15-49 | 1 (50.0%) | 1 (50.0%) | 2 (100.0%) | |
Created on 2018-10-09 by the reprex package (v0.2.1)