Everyone has a funcion like this, str, glimpse, glance etc. This one is
specifically designed for use with finalfit language. It is different
in dividing variables by numeric vs factor.
ff_glimpse(
.data,
dependent = NULL,
explanatory = NULL,
digits = 1,
levels_cut = 5
)
finalfit_glimpse(
.data,
dependent = NULL,
explanatory = NULL,
digits = 1,
levels_cut = 5
)Dataframe.
Optional character vector: name(s) of depdendent variable(s).
Optional character vector: name(s) of explanatory variable(s).
Significant digits for continuous variable summaries
Max number of factor levels to include in factor levels summary (in order to avoid the long printing of variables with many factors).
Dataframe on summary data.
library(finalfit)
dependent = 'mort_5yr'
explanatory = c("age", "nodes", "age.factor", "extent.factor", "perfor.factor")
colon_s %>%
finalfit_glimpse(dependent, explanatory)
#> $Continuous
#> label var_type n missing_n missing_percent mean sd min
#> age Age (years) <dbl> 929 0 0.0 59.8 11.9 18.0
#> nodes nodes <dbl> 911 18 1.9 3.7 3.6 0.0
#> quartile_25 median quartile_75 max
#> age 53.0 61.0 69.0 85.0
#> nodes 1.0 2.0 5.0 33.0
#>
#> $Categorical
#> label var_type n missing_n missing_percent levels_n
#> mort_5yr Mortality 5 year <fct> 915 14 1.5 2
#> age.factor Age <fct> 929 0 0.0 3
#> extent.factor Extent of spread <fct> 929 0 0.0 4
#> perfor.factor Perforation <fct> 929 0 0.0 2
#> levels
#> mort_5yr "Alive", "Died", "(Missing)"
#> age.factor "<40 years", "40-59 years", "60+ years", "(Missing)"
#> extent.factor "Submucosa", "Muscle", "Serosa", "Adjacent structures", "(Missing)"
#> perfor.factor "No", "Yes", "(Missing)"
#> levels_count levels_percent
#> mort_5yr 511, 404, 14 55.0, 43.5, 1.5
#> age.factor 70, 344, 515 7.5, 37.0, 55.4
#> extent.factor 21, 106, 759, 43 2.3, 11.4, 81.7, 4.6
#> perfor.factor 902, 27 97.1, 2.9
#>