Accumulate kpilists into KPIs per site lists The KPIs themselves are all well and good for e.g. a report where you walk through each individual KPI and present all of the info there, but they're not ideal if you want all of the KPIs for a given site or country or the overall study in a single table. kpi_accumulate does this conversion
kpi_accumulate.RdAccumulate kpilists into KPIs per site lists
The KPIs themselves are all well and good for e.g. a report where you walk
through each individual KPI and present all of the info there, but they're
not ideal if you want all of the KPIs for a given site or country or the
overall study in a single table. kpi_accumulate does this conversion
Examples
kpi1 <- mtcars %>%
kpi(var = "mpg", by = c("am", "cyl"), txt = "MPG",
kpi_fn = kpi_fn_median, breakpoints = c(0, 20, 30, 50))
kpi2 <- mtcars %>%
kpi(var = "drat", by = c("am", "cyl"), txt = "DRAT",
kpi_fn = kpi_fn_median, breakpoints = c(0, 3, 4, 50))
l <- c(kpi1, kpi2)
kpi3 <- mtcars %>%
mutate(cylgt4 = cyl > 4) %>%
kpi(var = "cylgt4", by = c("am", "cyl"), txt = "Cylinders",
kpi_fn = kpi_fn_perc, , breakpoints = c(0, 30, 50, 100))
l2 <- c(l, kpi3)
kpi_accumulate(l2)
#> $overall
#> txt N n_nonmiss stat risk cols n
#> 1 MPG 32 32 19.200 low green NA
#> 2 DRAT 32 32 3.695 moderate yellow NA
#> 3 Cylinders 32 32 65.625 high red 21
#>
#> $am
#> $am$`0`
#> # A tibble: 3 × 8
#> txt am N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 0 19 19 17.3 low green NA
#> 2 DRAT 0 19 19 3.15 moderate yellow NA
#> 3 Cylinders 0 19 19 84.2 high red 16
#>
#> $am$`1`
#> # A tibble: 3 × 8
#> txt am N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 1 13 13 22.8 moderate yellow NA
#> 2 DRAT 1 13 13 4.08 high red NA
#> 3 Cylinders 1 13 13 38.5 moderate yellow 5
#>
#>
#> $cyl
#> $cyl$`4`
#> # A tibble: 3 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 4 11 11 26 moderate yellow NA
#> 2 DRAT 4 11 11 4.08 high red NA
#> 3 Cylinders 4 11 11 0 NA NA 0
#>
#> $cyl$`6`
#> # A tibble: 3 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 6 7 7 19.7 low green NA
#> 2 DRAT 6 7 7 3.9 moderate yellow NA
#> 3 Cylinders 6 7 7 100 high red 7
#>
#> $cyl$`8`
#> # A tibble: 3 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 8 14 14 15.2 low green NA
#> 2 DRAT 8 14 14 3.12 moderate yellow NA
#> 3 Cylinders 8 14 14 100 high red 14
#>
#>
#> attr(,"class")
#> [1] "kpi_accumulate" "list"
# only the cyl level
kpi_accumulate(l2, by = "cyl")
#> $`4`
#> # A tibble: 3 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 4 11 11 26 moderate yellow NA
#> 2 DRAT 4 11 11 4.08 high red NA
#> 3 Cylinders 4 11 11 0 NA NA 0
#>
#> $`6`
#> # A tibble: 3 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 6 7 7 19.7 low green NA
#> 2 DRAT 6 7 7 3.9 moderate yellow NA
#> 3 Cylinders 6 7 7 100 high red 7
#>
#> $`8`
#> # A tibble: 3 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 8 14 14 15.2 low green NA
#> 2 DRAT 8 14 14 3.12 moderate yellow NA
#> 3 Cylinders 8 14 14 100 high red 14
#>
#> attr(,"class")
#> [1] "kpi_accumulate" "list"
# only the study/overall level
kpi_accumulate(l2, by = "overall")
#> txt N n_nonmiss stat risk cols n
#> 1 MPG 32 32 19.200 low green NA
#> 2 DRAT 32 32 3.695 moderate yellow NA
#> 3 Cylinders 32 32 65.625 high red 21
# no splitting
kpi_accumulate(l2, split = FALSE)
#> $overall
#> txt N n_nonmiss stat risk cols n
#> 1 MPG 32 32 19.200 low green NA
#> 2 DRAT 32 32 3.695 moderate yellow NA
#> 3 Cylinders 32 32 65.625 high red 21
#>
#> $am
#> # A tibble: 6 × 8
#> txt am N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 0 19 19 17.3 low green NA
#> 2 MPG 1 13 13 22.8 moderate yellow NA
#> 3 DRAT 0 19 19 3.15 moderate yellow NA
#> 4 DRAT 1 13 13 4.08 high red NA
#> 5 Cylinders 0 19 19 84.2 high red 16
#> 6 Cylinders 1 13 13 38.5 moderate yellow 5
#>
#> $cyl
#> # A tibble: 9 × 8
#> txt cyl N n_nonmiss stat risk cols n
#> <chr> <dbl> <int> <int> <dbl> <fct> <fct> <int>
#> 1 MPG 4 11 11 26 moderate yellow NA
#> 2 MPG 6 7 7 19.7 low green NA
#> 3 MPG 8 14 14 15.2 low green NA
#> 4 DRAT 4 11 11 4.08 high red NA
#> 5 DRAT 6 7 7 3.9 moderate yellow NA
#> 6 DRAT 8 14 14 3.12 moderate yellow NA
#> 7 Cylinders 4 11 11 0 NA NA 0
#> 8 Cylinders 6 7 7 100 high red 7
#> 9 Cylinders 8 14 14 100 high red 14
#>
#> attr(,"class")
#> [1] "kpi_accumulate" "list"