Split a data frame into multiple new data frames based on one or
two grouping variables. The surv_group_by() function takes an
existing data frame and converts it into a grouped data frame where
survival analysis are performed "by group".
Value
Returns an object of class surv_group_by which is a
tibble data frame with the following components:
one column for each grouping variables. Contains the levels.
a coumn named "data", which is a named list of data subsets created by the grouping variables. The list names are created by concatening the levels of grouping variables.
Examples
library("survival")
library("magrittr")
# Grouping by one variables: treatment "rx"
#::::::::::::::::::::::::::::::::::::::::::
grouped.d <- colon %>%
surv_group_by("rx")
grouped.d # print
#> # A tibble: 3 × 2
#> # Groups: rx [3]
#> rx data
#> * <fct> <named list>
#> 1 Obs <tibble [630 × 15]>
#> 2 Lev <tibble [620 × 15]>
#> 3 Lev+5FU <tibble [608 × 15]>
grouped.d$data # Access to the data
#> $rx.Obs
#> # A tibble: 630 × 15
#> id study sex age obstruct perfor adhere nodes status differ extent
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3 1 0 71 0 0 1 7 1 2 2
#> 2 3 1 0 71 0 0 1 7 1 2 2
#> 3 5 1 1 69 0 0 0 22 1 2 3
#> 4 5 1 1 69 0 0 0 22 1 2 3
#> 5 8 1 1 54 0 0 0 1 0 2 3
#> 6 8 1 1 54 0 0 0 1 0 2 3
#> 7 13 1 1 64 0 0 0 1 1 2 3
#> 8 13 1 1 64 0 0 0 1 1 2 3
#> 9 15 1 1 46 1 0 0 4 0 2 3
#> 10 15 1 1 46 1 0 0 4 0 2 3
#> # ℹ 620 more rows
#> # ℹ 4 more variables: surg <dbl>, node4 <dbl>, time <dbl>, etype <dbl>
#>
#> $rx.Lev
#> # A tibble: 620 × 15
#> id study sex age obstruct perfor adhere nodes status differ extent
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 1 1 77 0 0 0 5 1 2 3
#> 2 7 1 1 77 0 0 0 5 1 2 3
#> 3 9 1 1 46 0 0 1 2 0 2 3
#> 4 9 1 1 46 0 0 1 2 0 2 3
#> 5 11 1 0 47 0 0 1 1 0 2 3
#> 6 11 1 0 47 0 0 1 1 0 2 3
#> 7 14 1 1 68 1 0 0 3 1 2 3
#> 8 14 1 1 68 1 0 0 3 1 2 3
#> 9 17 1 1 62 1 0 1 6 1 2 3
#> 10 17 1 1 62 1 0 1 6 1 2 3
#> # ℹ 610 more rows
#> # ℹ 4 more variables: surg <dbl>, node4 <dbl>, time <dbl>, etype <dbl>
#>
#> $`rx.Lev+5FU`
#> # A tibble: 608 × 15
#> id study sex age obstruct perfor adhere nodes status differ extent
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 1 43 0 0 0 5 1 2 3
#> 2 1 1 1 43 0 0 0 5 1 2 3
#> 3 2 1 1 63 0 0 0 1 0 2 3
#> 4 2 1 1 63 0 0 0 1 0 2 3
#> 5 4 1 0 66 1 0 0 6 1 2 3
#> 6 4 1 0 66 1 0 0 6 1 2 3
#> 7 6 1 0 57 0 0 0 9 1 2 3
#> 8 6 1 0 57 0 0 0 9 1 2 3
#> 9 10 1 0 68 0 0 0 1 0 2 3
#> 10 10 1 0 68 0 0 0 1 0 2 3
#> # ℹ 598 more rows
#> # ℹ 4 more variables: surg <dbl>, node4 <dbl>, time <dbl>, etype <dbl>
#>
# Grouping by two variables
#::::::::::::::::::::::::::::::::::::::::::
grouped.d <- colon %>%
surv_group_by(grouping.vars = c("rx", "adhere"))
grouped.d
#> # A tibble: 6 × 3
#> # Groups: rx, adhere [6]
#> rx adhere data
#> * <fct> <dbl> <named list>
#> 1 Obs 0 <tibble [536 × 14]>
#> 2 Obs 1 <tibble [94 × 14]>
#> 3 Lev 0 <tibble [522 × 14]>
#> 4 Lev 1 <tibble [98 × 14]>
#> 5 Lev+5FU 0 <tibble [530 × 14]>
#> 6 Lev+5FU 1 <tibble [78 × 14]>