Loading the package
After installation (using
remotes::install_github("stopsack/rifttable")), load the
package with:
Generate a table design
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 sexObtain Table 1
| 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).
Customize the table
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). | |||
Add more statistics per variable
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. | |||