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After installation (using
remotes::install_github("stopsack/rifttable")), load the
package with:
This example uses the cancer dataset from the survival
package.
rifttable’s table1_design() generates the
design table that can then be passed on to
rifttable().
design <- cancer |>
table1_design(
age, ph.ecog, ph.karno, pat.karno, # leave empty to include all variables
by = sex
)
# Print the design for illustration purposes
design
#> # A tibble: 12 × 5
#> label outcome type na_rm exposure
#> <chr> <chr> <chr> <lgl> <chr>
#> 1 "N" "" "total" NA sex
#> 2 "age" "age" "median (iqr)" FALSE sex
#> 3 "ph.ecog" "" "" FALSE sex
#> 4 " 0" "ph.ecog@0" "outcomes (risk)" TRUE sex
#> 5 " 1" "ph.ecog@1" "outcomes (risk)" TRUE sex
#> 6 " 2" "ph.ecog@2" "outcomes (risk)" TRUE sex
#> 7 " 3" "ph.ecog@3" "outcomes (risk)" TRUE sex
#> 8 " Unknown" "ph.ecog@_NA_" "outcomes" FALSE sex
#> 9 "ph.karno" "ph.karno" "median (iqr)" TRUE sex
#> 10 " Unknown" "ph.karno@_NA_" "outcomes" FALSE sex
#> 11 "pat.karno" "pat.karno" "median (iqr)" TRUE sex
#> 12 " Unknown" "pat.karno@_NA_" "outcomes" FALSE sex| sex | Male | Female |
|---|---|---|
| N | 138 | 90 |
| age | 64.00 (57.00, 70.00) | 61.00 (55.00, 68.00) |
| ph.ecog | ||
| 0 | 36 (26%) | 27 (30%) |
| 1 | 71 (52%) | 42 (47%) |
| 2 | 29 (21%) | 21 (23%) |
| 3 | 1 (1%) | 0 (0%) |
| Unknown | 1 | 0 |
| ph.karno | 80.00 (70.00, 90.00) | 80.00 (80.00, 90.00) |
| Unknown | 1 | 0 |
| pat.karno | 80.00 (70.00, 90.00) | 80.00 (70.00, 90.00) |
| Unknown | 2 | 1 |
Of note, the dataset is passed along silently together with the
design. However, the same design can be applied to
different data, if it contains the same variable names, e.g.,
rifttable(design = design, data = otherdata).
Label variables, set custom rounding, change estimands, and add “Overall” column.
# Alternative: set_variable_labels() from the {labelled} package
attr(cancer$age, "label") <- "Age, years" # At diagnosis? Unclear (Loprinzi 94)
attr(cancer$ph.ecog, "label") <- "Physician-rated ECOG score"
attr(cancer$ph.karno, "label") <- "Physician-rated Karnofsky score"
attr(cancer$pat.karno, "label") <- "Patient-rated Karnofsky score"
attr(cancer$sex, "label") <- "Sex"
design <- cancer |>
table1_design(
age, ph.ecog, ph.karno, pat.karno,
by = sex,
continuous_type = "mean (sd)"
) |> # default: "median (iqr)"
mutate( # rounding specifically for the "age" variable
digits = if_else(
outcome == "age",
true = 1,
false = NA
)
)
design |>
rifttable(
diff_digits = 0, # rounding for continuous variables other than age
overall = TRUE
) |> # add unstratified "overall" column
rt_gt() |> # obtain formatted output
gt::tab_footnote(
footnote = "Data shown are count (percent) or mean (standard deviation)."
)| Summary | Overall | Male | Female |
|---|---|---|---|
| N | 228 | 138 | 90 |
| Age, years | 62.4 (9.1) | 63.3 (9.1) | 61.1 (8.8) |
| Physician-rated ECOG score | |||
| 0 | 63 (28%) | 36 (26%) | 27 (30%) |
| 1 | 113 (50%) | 71 (52%) | 42 (47%) |
| 2 | 50 (22%) | 29 (21%) | 21 (23%) |
| 3 | 1 (0%) | 1 (1%) | 0 (0%) |
| Unknown | 1 | 1 | 0 |
| Physician-rated Karnofsky score | 82 (12) | 82 (12) | 82 (12) |
| Unknown | 1 | 1 | 0 |
| Patient-rated Karnofsky score | 80 (15) | 79 (14) | 81 (15) |
| Unknown | 3 | 2 | 1 |
| Data shown are count (percent) or mean (standard deviation). | |||
For the age variable, do not just display mean and standard
deviation, but also the range. To this end, edit the design
just like a regular dataset.
design_new <- design |>
mutate( # create three rows, not one, for "age"
copy = if_else(
outcome == "age",
true = 3,
false = 1
)
) |>
tidyr::uncount(copy, .id = "copy") |>
mutate( # set new labels and types for the three rows on age
label = case_when(
outcome == "age" & copy == 2 ~ " Mean (SD)",
outcome == "age" & copy == 3 ~ " Range",
TRUE ~ label
),
type = case_when(
outcome == "age" & copy == 1 ~ "",
outcome == "age" & copy == 2 ~ "mean (sd)",
outcome == "age" & copy == 3 ~ "range",
TRUE ~ type
)
)
design_new
#> # A tibble: 14 × 7
#> label outcome type na_rm exposure digits copy
#> <chr> <chr> <chr> <lgl> <chr> <dbl> <int>
#> 1 "N" "" "tot… NA sex NA 1
#> 2 "Age, years" "age" "" FALSE sex 1 1
#> 3 " Mean (SD)" "age" "mea… FALSE sex 1 2
#> 4 " Range" "age" "ran… FALSE sex 1 3
#> 5 "Physician-rated ECOG score" "" "" FALSE sex NA 1
#> 6 " 0" "ph.ecog… "out… TRUE sex NA 1
#> 7 " 1" "ph.ecog… "out… TRUE sex NA 1
#> 8 " 2" "ph.ecog… "out… TRUE sex NA 1
#> 9 " 3" "ph.ecog… "out… TRUE sex NA 1
#> 10 " Unknown" "ph.ecog… "out… FALSE sex NA 1
#> 11 "Physician-rated Karnofsky score" "ph.karn… "mea… TRUE sex NA 1
#> 12 " Unknown" "ph.karn… "out… FALSE sex NA 1
#> 13 "Patient-rated Karnofsky score" "pat.kar… "mea… TRUE sex NA 1
#> 14 " Unknown" "pat.kar… "out… FALSE sex NA 1
design_new |>
rifttable(
diff_digits = 0,
overall = TRUE
) |>
rt_gt() |>
gt::tab_footnote(
footnote = "Data shown are count (percent), unless indicated otherwise."
)| Summary | Overall | Male | Female |
|---|---|---|---|
| N | 228 | 138 | 90 |
| Age, years | |||
| Mean (SD) | 62.4 (9.1) | 63.3 (9.1) | 61.1 (8.8) |
| Range | 39.0, 82.0 | 39.0, 82.0 | 41.0, 77.0 |
| Physician-rated ECOG score | |||
| 0 | 63 (28%) | 36 (26%) | 27 (30%) |
| 1 | 113 (50%) | 71 (52%) | 42 (47%) |
| 2 | 50 (22%) | 29 (21%) | 21 (23%) |
| 3 | 1 (0%) | 1 (1%) | 0 (0%) |
| Unknown | 1 | 1 | 0 |
| Physician-rated Karnofsky score | 82 (12) | 82 (12) | 82 (12) |
| Unknown | 1 | 1 | 0 |
| Patient-rated Karnofsky score | 80 (15) | 79 (14) | 81 (15) |
| Unknown | 3 | 2 | 1 |
| Data shown are count (percent), unless indicated otherwise. | |||
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.