README.md
In emilelatour/tidytableone: Tidy construction of "Table 1"
tidytableone
The goal of tidytableone is to enable a tidy approach to creating the
“Table 1” of study characteristics common in bimoedical research.
Installation
You can install the released version of tidytableone from
CRAN with:
# install.packages("devtools")
devtools::install_github("emilelatour/tidytableone)
Example
Load the packages for the example
library(tidytableone)
library(tidyverse)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
#> ✔ tibble 2.1.3 ✔ dplyr 0.8.3
#> ✔ tidyr 1.0.0 ✔ stringr 1.4.0
#> ✔ readr 1.3.1 ✔ forcats 0.4.0
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(knitr)
Take a glimpse at the data that comes in the tidytableone package:
dplyr::glimpse(pbc_mayo)
#> Observations: 418
#> Variables: 20
#> $ id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
#> $ time <int> 400, 4500, 1012, 1925, 1504, 2503, 1832, 2466, 2400, 51…
#> $ status <fct> Dead, Alive, Dead, Dead, Liver transplant, Dead, Alive,…
#> $ trt <fct> D-penicillamine, D-penicillamine, D-penicillamine, D-pe…
#> $ age <dbl> 58.76523, 56.44627, 70.07255, 54.74059, 38.10541, 66.25…
#> $ sex <fct> Female, Female, Male, Female, Female, Female, Female, F…
#> $ ascites <fct> Yes, No, No, No, No, No, No, No, No, Yes, No, No, No, Y…
#> $ hepato <fct> Yes, Yes, No, Yes, Yes, Yes, Yes, No, No, No, Yes, No, …
#> $ spiders <fct> Yes, Yes, No, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes,…
#> $ edema <fct> "Edema despite diuretic therapy", "No edema and no diur…
#> $ bili <dbl> 14.5, 1.1, 1.4, 1.8, 3.4, 0.8, 1.0, 0.3, 3.2, 12.6, 1.4…
#> $ chol <int> 261, 302, 176, 244, 279, 248, 322, 280, 562, 200, 259, …
#> $ albumin <dbl> 2.60, 4.14, 3.48, 2.54, 3.53, 3.98, 4.09, 4.00, 3.08, 2…
#> $ copper <int> 156, 54, 210, 64, 143, 50, 52, 52, 79, 140, 46, 94, 40,…
#> $ alk_phos <dbl> 1718.0, 7394.8, 516.0, 6121.8, 671.0, 944.0, 824.0, 465…
#> $ ast <dbl> 137.95, 113.52, 96.10, 60.63, 113.15, 93.00, 60.45, 28.…
#> $ trig <int> 172, 88, 55, 92, 72, 63, 213, 189, 88, 143, 79, 95, 130…
#> $ platelet <int> 190, 221, 151, 183, 136, NA, 204, 373, 251, 302, 258, 7…
#> $ protime <dbl> 12.2, 10.6, 12.0, 10.3, 10.9, 11.0, 9.7, 11.0, 11.0, 11…
#> $ stage <chr> "4", "3", "4", "4", "3", "3", "3", "3", "2", "4", "4", …
Use the main function of this package to make a tidy table one
tab1 <- tidytableone::create_tidy_table_one(data = pbc_mayo,
strata = "trt",
.vars = c("time", "status", "trt", "age", "sex", "ascites", "hepato",
"spiders", "edema", "bili", "chol", "albumin", "copper", "alk_phos",
"ast", "trig", "platelet", "protime", "stage"))
Use the dplyr::glimpse command to see the tidy table one
dplyr::glimpse(tab1)
#> Observations: 132
#> Variables: 27
#> $ strata <fct> Overall, D-penicillamine, Placebo, (Missi…
#> $ var <fct> time, time, time, time, status, status, s…
#> $ n <int> 418, 158, 154, 106, NA, NA, NA, NA, NA, N…
#> $ n_distinct <int> 399, 155, 151, 106, NA, NA, NA, NA, NA, N…
#> $ complete <int> 418, 158, 154, 106, NA, NA, NA, NA, NA, N…
#> $ missing <int> 0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, N…
#> $ mean <dbl> 1917.78230, 2015.62025, 1996.86364, 1657.…
#> $ sd <dbl> 1104.672992, 1094.123315, 1155.928911, 10…
#> $ p0 <dbl> 41.00000, 41.00000, 51.00000, 41.00000, N…
#> $ p25 <dbl> 1092.75000, 1231.00000, 1153.00000, 998.0…
#> $ p50 <dbl> 1730.00000, 1895.00000, 1811.00000, 1397.…
#> $ p75 <dbl> 2613.50000, 2632.50000, 2771.25000, 2262.…
#> $ p100 <dbl> 4795.00000, 4556.00000, 4523.00000, 4795.…
#> $ cv <dbl> 0.5760158, 0.5428221, 0.5788722, 0.608644…
#> $ shapiro_test <dbl> 8.291488e-08, 3.809631e-03, 1.249396e-03,…
#> $ level <chr> NA, NA, NA, NA, "Alive", "Dead", "Liver t…
#> $ n_level <dbl> NA, NA, NA, NA, 232, 161, 25, 83, 65, 10,…
#> $ n_strata <dbl> NA, NA, NA, NA, 418, 418, 418, 158, 158, …
#> $ chisq_test <dbl> NA, NA, NA, NA, 0.8935097, 0.8935097, 0.8…
#> $ fisher_test <dbl> NA, NA, NA, NA, 0.8842219, 0.8842219, 0.8…
#> $ check_categorical_test <chr> NA, NA, NA, NA, "ok", "ok", "ok", "ok", "…
#> $ oneway_test <dbl> 0.88312949, 0.88312949, 0.88312949, 0.883…
#> $ kruskal_test <dbl> 0.82661809, 0.82661809, 0.82661809, 0.826…
#> $ bartlett_test <dbl> 0.4946294, 0.4946294, 0.4946294, 0.494629…
#> $ smd <dbl> 0.01666588, 0.01666588, 0.01666588, 0.016…
#> $ class <chr> "integer", "integer", "integer", "integer…
#> $ var_type <chr> "continuous", "continuous", "continuous",…
Some information is only available for categorical variables and some
information for continuous variables.
tab1 %>%
dplyr::filter(var_type == "categorical") %>%
dplyr::select(strata, var, level:check_categorical_test, smd:var_type)
#> # A tibble: 88 x 11
#> strata var level n_level n_strata chisq_test fisher_test
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Overa… stat… Alive 232 418 0.894 0.884
#> 2 Overa… stat… Dead 161 418 0.894 0.884
#> 3 Overa… stat… Live… 25 418 0.894 0.884
#> 4 D-pen… stat… Alive 83 158 0.894 0.884
#> 5 D-pen… stat… Dead 65 158 0.894 0.884
#> 6 D-pen… stat… Live… 10 158 0.894 0.884
#> 7 Place… stat… Alive 85 154 0.894 0.884
#> 8 Place… stat… Dead 60 154 0.894 0.884
#> 9 Place… stat… Live… 9 154 0.894 0.884
#> 10 (Miss… stat… Alive 64 106 0.894 0.884
#> # … with 78 more rows, and 4 more variables: check_categorical_test <chr>,
#> # smd <dbl>, class <chr>, var_type <chr>
tab1 %>%
dplyr::filter(var_type == "continuous") %>%
dplyr::select(strata, var, n:shapiro_test, oneway_test:bartlett_test, smd:var_type)
#> # A tibble: 44 x 21
#> strata var n n_distinct complete missing mean sd p0
#> <fct> <fct> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 Overa… time 418 399 418 0 1.92e3 1.10e3 41
#> 2 D-pen… time 158 155 158 0 2.02e3 1.09e3 41
#> 3 Place… time 154 151 154 0 2.00e3 1.16e3 51
#> 4 (Miss… time 106 106 106 0 1.66e3 1.01e3 41
#> 5 Overa… age 418 344 418 0 5.07e1 1.04e1 26.3
#> 6 D-pen… age 158 156 158 0 5.14e1 1.10e1 26.3
#> 7 Place… age 154 154 154 0 4.86e1 9.96e0 30.6
#> 8 (Miss… age 106 37 106 0 5.29e1 9.78e0 33.0
#> 9 Overa… bili 418 98 418 0 3.22e0 4.41e0 0.3
#> 10 D-pen… bili 158 58 158 0 2.87e0 3.63e0 0.3
#> # … with 34 more rows, and 12 more variables: p25 <dbl>, p50 <dbl>,
#> # p75 <dbl>, p100 <dbl>, cv <dbl>, shapiro_test <dbl>,
#> # oneway_test <dbl>, kruskal_test <dbl>, bartlett_test <dbl>, smd <dbl>,
#> # class <chr>, var_type <chr>
Then with some simple data manipulation we can reshape to make a table
one…
tab1 %>%
mutate(strata = forcats::fct_inorder(strata),
n_pct = scales::percent(n_level / n_strata),
mean = scales::number(mean, accuracy = 0.1),
value = dplyr::if_else(mean == "NA", n_pct, mean),
p_value = dplyr::coalesce(oneway_test, chisq_test),
p_value = scales::pvalue(p_value)) %>%
dplyr::distinct(strata,
var,
level,
value,
p_value) %>%
tidyr::spread(.,
key = strata,
value = value) %>%
dplyr::select(-p_value, dplyr::everything(), p_value) %>%
knitr::kable()
| var | level | Overall | D-penicillamine | Placebo | (Missing) | p_value |
| :-------- | :-------------------------------------------------------------- | :------ | :-------------- | :------ | :-------- | :------- |
| time | NA | 1 917.8 | 2 015.6 | 1 996.9 | 1 657.1 | 0.883 |
| status | Alive | 56% | 53% | 55% | 60% | 0.894 |
| status | Dead | 39% | 41% | 39% | 34% | 0.894 |
| status | Liver transplant | 6% | 6% | 6% | 6% | 0.894 |
| age | NA | 50.7 | 51.4 | 48.6 | 52.9 | 0.018 |
| sex | Female | 89% | 87% | 90% | 92% | 0.421 |
| sex | Male | 11% | 13% | 10% | 8% | 0.421 |
| ascites | No | 69% | 91% | 94% | 0% | 0.567 |
| ascites | Yes | 6% | 9% | 6% | 0% | 0.567 |
| ascites | NA | 25% | 0% | 0% | 100% | 0.567 |
| hepato | No | 36% | 54% | 44% | 0% | 0.088 |
| hepato | Yes | 38% | 46% | 56% | 0% | 0.088 |
| hepato | NA | 25% | 0% | 0% | 100% | 0.088 |
| spiders | No | 53% | 72% | 71% | 0% | 0.985 |
| spiders | Yes | 22% | 28% | 29% | 0% | 0.985 |
| spiders | NA | 25% | 0% | 0% | 100% | 0.985 |
| edema | Edema despite diuretic therapy | 5% | 6% | 6% | 0% | 0.877 |
| edema | Edema present without diuretics, or edema resolved by diuretics | 11% | 10% | 8% | 14% | 0.877 |
| edema | No edema and no diuretic therapy for edema | 85% | 84% | 85% | 86% | 0.877 |
| bili | NA | 3.2 | 2.9 | 3.6 | 3.1 | 0.133 |
| chol | NA | 369.5 | 365.0 | 373.9 | NaN | 0.747 |
| albumin | NA | 3.5 | 3.5 | 3.5 | 3.4 | 0.874 |
| copper | NA | 97.6 | 97.6 | 97.7 | NaN | 0.999 |
| alk_phos | NA | 1 982.7 | 2 021.3 | 1 943.0 | NaN | 0.747 |
| ast | NA | 122.6 | 120.2 | 125.0 | NaN | 0.460 |
| trig | NA | 124.7 | 124.1 | 125.3 | NaN | 0.886 |
| platelet | NA | 257.0 | 258.8 | 265.2 | 241.7 | 0.554 |
| protime | NA | 10.7 | 10.7 | 10.8 | 10.8 | 0.199 |
| stage | 1 | 5% | 8% | 3% | 5% | 0.201 |
| stage | 2 | 22% | 22% | 21% | 24% | 0.201 |
| stage | 3 | 37% | 35% | 42% | 33% | 0.201 |
| stage | 4 | 34% | 35% | 35% | 33% | 0.201 |
| stage | NA | 1% | 0% | 0% | 6% | 0.201 |
This is rough, but you get the idea. The plan is to next create a
function to make the table based on specifications.
Or possibly with some nesting…
tab1 %>%
tidyr::nest(con_res = c(n:shapiro_test, oneway_test:bartlett_test)) %>%
tidyr::nest(cat_res = c(n_level:check_categorical_test)) %>%
mutate(mean_sd = purrr::map_chr(.x = con_res,
.f = ~ paste0(round(.x$mean, 1),
" (",
round(.x$sd, 1),
")"))) %>%
mutate(n_pct = purrr::map_chr(.x = cat_res,
.f = ~ tryCatch(suppressWarnings(paste0(.x$n_level,
" (",
scales::percent(.x$n_level / .x$n_strata),
")")),
error = function(err) NA))) %>%
mutate(value = dplyr::if_else(var_type == "continuous", mean_sd, n_pct)) %>%
dplyr::select(-mean_sd, -n_pct, -class)
#> # A tibble: 132 x 8
#> strata var level smd var_type con_res cat_res value
#> <fct> <fct> <chr> <dbl> <chr> <list<df[> <list<df> <chr>
#> 1 Overall time <NA> 0.0167 continu… [1 × 16] [1 × 5] 1917.8 …
#> 2 D-penici… time <NA> 0.0167 continu… [1 × 16] [1 × 5] 2015.6 …
#> 3 Placebo time <NA> 0.0167 continu… [1 × 16] [1 × 5] 1996.9 …
#> 4 (Missing) time <NA> 0.0167 continu… [1 × 16] [1 × 5] 1657.1 …
#> 5 Overall status Alive 0.0538 categor… [1 × 16] [1 × 5] 232 (55…
#> 6 Overall status Dead 0.0538 categor… [1 × 16] [1 × 5] 161 (38…
#> 7 Overall status Liver tr… 0.0538 categor… [1 × 16] [1 × 5] 25 (5.9…
#> 8 D-penici… status Alive 0.0538 categor… [1 × 16] [1 × 5] 83 (52.…
#> 9 D-penici… status Dead 0.0538 categor… [1 × 16] [1 × 5] 65 (41.…
#> 10 D-penici… status Liver tr… 0.0538 categor… [1 × 16] [1 × 5] 10 (6.3…
#> # … with 122 more rows
emilelatour/tidytableone documentation built on Jan. 6, 2025, 9:20 a.m.
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tidytableone
The goal of tidytableone is to enable a tidy approach to creating the
“Table 1” of study characteristics common in bimoedical research.
Installation
You can install the released version of tidytableone from CRAN with:
# install.packages("devtools")
devtools::install_github("emilelatour/tidytableone)
Example
Load the packages for the example
library(tidytableone)
library(tidyverse)
#> ── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.2.1 ✔ purrr 0.3.2
#> ✔ tibble 2.1.3 ✔ dplyr 0.8.3
#> ✔ tidyr 1.0.0 ✔ stringr 1.4.0
#> ✔ readr 1.3.1 ✔ forcats 0.4.0
#> ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
library(knitr)
Take a glimpse at the data that comes in the tidytableone package:
dplyr::glimpse(pbc_mayo)
#> Observations: 418
#> Variables: 20
#> $ id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, …
#> $ time <int> 400, 4500, 1012, 1925, 1504, 2503, 1832, 2466, 2400, 51…
#> $ status <fct> Dead, Alive, Dead, Dead, Liver transplant, Dead, Alive,…
#> $ trt <fct> D-penicillamine, D-penicillamine, D-penicillamine, D-pe…
#> $ age <dbl> 58.76523, 56.44627, 70.07255, 54.74059, 38.10541, 66.25…
#> $ sex <fct> Female, Female, Male, Female, Female, Female, Female, F…
#> $ ascites <fct> Yes, No, No, No, No, No, No, No, No, Yes, No, No, No, Y…
#> $ hepato <fct> Yes, Yes, No, Yes, Yes, Yes, Yes, No, No, No, Yes, No, …
#> $ spiders <fct> Yes, Yes, No, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes,…
#> $ edema <fct> "Edema despite diuretic therapy", "No edema and no diur…
#> $ bili <dbl> 14.5, 1.1, 1.4, 1.8, 3.4, 0.8, 1.0, 0.3, 3.2, 12.6, 1.4…
#> $ chol <int> 261, 302, 176, 244, 279, 248, 322, 280, 562, 200, 259, …
#> $ albumin <dbl> 2.60, 4.14, 3.48, 2.54, 3.53, 3.98, 4.09, 4.00, 3.08, 2…
#> $ copper <int> 156, 54, 210, 64, 143, 50, 52, 52, 79, 140, 46, 94, 40,…
#> $ alk_phos <dbl> 1718.0, 7394.8, 516.0, 6121.8, 671.0, 944.0, 824.0, 465…
#> $ ast <dbl> 137.95, 113.52, 96.10, 60.63, 113.15, 93.00, 60.45, 28.…
#> $ trig <int> 172, 88, 55, 92, 72, 63, 213, 189, 88, 143, 79, 95, 130…
#> $ platelet <int> 190, 221, 151, 183, 136, NA, 204, 373, 251, 302, 258, 7…
#> $ protime <dbl> 12.2, 10.6, 12.0, 10.3, 10.9, 11.0, 9.7, 11.0, 11.0, 11…
#> $ stage <chr> "4", "3", "4", "4", "3", "3", "3", "3", "2", "4", "4", …
Use the main function of this package to make a tidy table one
tab1 <- tidytableone::create_tidy_table_one(data = pbc_mayo,
strata = "trt",
.vars = c("time", "status", "trt", "age", "sex", "ascites", "hepato",
"spiders", "edema", "bili", "chol", "albumin", "copper", "alk_phos",
"ast", "trig", "platelet", "protime", "stage"))
Use the dplyr::glimpse command to see the tidy table one
dplyr::glimpse(tab1)
#> Observations: 132
#> Variables: 27
#> $ strata <fct> Overall, D-penicillamine, Placebo, (Missi…
#> $ var <fct> time, time, time, time, status, status, s…
#> $ n <int> 418, 158, 154, 106, NA, NA, NA, NA, NA, N…
#> $ n_distinct <int> 399, 155, 151, 106, NA, NA, NA, NA, NA, N…
#> $ complete <int> 418, 158, 154, 106, NA, NA, NA, NA, NA, N…
#> $ missing <int> 0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, N…
#> $ mean <dbl> 1917.78230, 2015.62025, 1996.86364, 1657.…
#> $ sd <dbl> 1104.672992, 1094.123315, 1155.928911, 10…
#> $ p0 <dbl> 41.00000, 41.00000, 51.00000, 41.00000, N…
#> $ p25 <dbl> 1092.75000, 1231.00000, 1153.00000, 998.0…
#> $ p50 <dbl> 1730.00000, 1895.00000, 1811.00000, 1397.…
#> $ p75 <dbl> 2613.50000, 2632.50000, 2771.25000, 2262.…
#> $ p100 <dbl> 4795.00000, 4556.00000, 4523.00000, 4795.…
#> $ cv <dbl> 0.5760158, 0.5428221, 0.5788722, 0.608644…
#> $ shapiro_test <dbl> 8.291488e-08, 3.809631e-03, 1.249396e-03,…
#> $ level <chr> NA, NA, NA, NA, "Alive", "Dead", "Liver t…
#> $ n_level <dbl> NA, NA, NA, NA, 232, 161, 25, 83, 65, 10,…
#> $ n_strata <dbl> NA, NA, NA, NA, 418, 418, 418, 158, 158, …
#> $ chisq_test <dbl> NA, NA, NA, NA, 0.8935097, 0.8935097, 0.8…
#> $ fisher_test <dbl> NA, NA, NA, NA, 0.8842219, 0.8842219, 0.8…
#> $ check_categorical_test <chr> NA, NA, NA, NA, "ok", "ok", "ok", "ok", "…
#> $ oneway_test <dbl> 0.88312949, 0.88312949, 0.88312949, 0.883…
#> $ kruskal_test <dbl> 0.82661809, 0.82661809, 0.82661809, 0.826…
#> $ bartlett_test <dbl> 0.4946294, 0.4946294, 0.4946294, 0.494629…
#> $ smd <dbl> 0.01666588, 0.01666588, 0.01666588, 0.016…
#> $ class <chr> "integer", "integer", "integer", "integer…
#> $ var_type <chr> "continuous", "continuous", "continuous",…
Some information is only available for categorical variables and some information for continuous variables.
tab1 %>%
dplyr::filter(var_type == "categorical") %>%
dplyr::select(strata, var, level:check_categorical_test, smd:var_type)
#> # A tibble: 88 x 11
#> strata var level n_level n_strata chisq_test fisher_test
#> <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Overa… stat… Alive 232 418 0.894 0.884
#> 2 Overa… stat… Dead 161 418 0.894 0.884
#> 3 Overa… stat… Live… 25 418 0.894 0.884
#> 4 D-pen… stat… Alive 83 158 0.894 0.884
#> 5 D-pen… stat… Dead 65 158 0.894 0.884
#> 6 D-pen… stat… Live… 10 158 0.894 0.884
#> 7 Place… stat… Alive 85 154 0.894 0.884
#> 8 Place… stat… Dead 60 154 0.894 0.884
#> 9 Place… stat… Live… 9 154 0.894 0.884
#> 10 (Miss… stat… Alive 64 106 0.894 0.884
#> # … with 78 more rows, and 4 more variables: check_categorical_test <chr>,
#> # smd <dbl>, class <chr>, var_type <chr>
tab1 %>%
dplyr::filter(var_type == "continuous") %>%
dplyr::select(strata, var, n:shapiro_test, oneway_test:bartlett_test, smd:var_type)
#> # A tibble: 44 x 21
#> strata var n n_distinct complete missing mean sd p0
#> <fct> <fct> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 Overa… time 418 399 418 0 1.92e3 1.10e3 41
#> 2 D-pen… time 158 155 158 0 2.02e3 1.09e3 41
#> 3 Place… time 154 151 154 0 2.00e3 1.16e3 51
#> 4 (Miss… time 106 106 106 0 1.66e3 1.01e3 41
#> 5 Overa… age 418 344 418 0 5.07e1 1.04e1 26.3
#> 6 D-pen… age 158 156 158 0 5.14e1 1.10e1 26.3
#> 7 Place… age 154 154 154 0 4.86e1 9.96e0 30.6
#> 8 (Miss… age 106 37 106 0 5.29e1 9.78e0 33.0
#> 9 Overa… bili 418 98 418 0 3.22e0 4.41e0 0.3
#> 10 D-pen… bili 158 58 158 0 2.87e0 3.63e0 0.3
#> # … with 34 more rows, and 12 more variables: p25 <dbl>, p50 <dbl>,
#> # p75 <dbl>, p100 <dbl>, cv <dbl>, shapiro_test <dbl>,
#> # oneway_test <dbl>, kruskal_test <dbl>, bartlett_test <dbl>, smd <dbl>,
#> # class <chr>, var_type <chr>
Then with some simple data manipulation we can reshape to make a table one…
tab1 %>%
mutate(strata = forcats::fct_inorder(strata),
n_pct = scales::percent(n_level / n_strata),
mean = scales::number(mean, accuracy = 0.1),
value = dplyr::if_else(mean == "NA", n_pct, mean),
p_value = dplyr::coalesce(oneway_test, chisq_test),
p_value = scales::pvalue(p_value)) %>%
dplyr::distinct(strata,
var,
level,
value,
p_value) %>%
tidyr::spread(.,
key = strata,
value = value) %>%
dplyr::select(-p_value, dplyr::everything(), p_value) %>%
knitr::kable()
| var | level | Overall | D-penicillamine | Placebo | (Missing) | p_value | | :-------- | :-------------------------------------------------------------- | :------ | :-------------- | :------ | :-------- | :------- | | time | NA | 1 917.8 | 2 015.6 | 1 996.9 | 1 657.1 | 0.883 | | status | Alive | 56% | 53% | 55% | 60% | 0.894 | | status | Dead | 39% | 41% | 39% | 34% | 0.894 | | status | Liver transplant | 6% | 6% | 6% | 6% | 0.894 | | age | NA | 50.7 | 51.4 | 48.6 | 52.9 | 0.018 | | sex | Female | 89% | 87% | 90% | 92% | 0.421 | | sex | Male | 11% | 13% | 10% | 8% | 0.421 | | ascites | No | 69% | 91% | 94% | 0% | 0.567 | | ascites | Yes | 6% | 9% | 6% | 0% | 0.567 | | ascites | NA | 25% | 0% | 0% | 100% | 0.567 | | hepato | No | 36% | 54% | 44% | 0% | 0.088 | | hepato | Yes | 38% | 46% | 56% | 0% | 0.088 | | hepato | NA | 25% | 0% | 0% | 100% | 0.088 | | spiders | No | 53% | 72% | 71% | 0% | 0.985 | | spiders | Yes | 22% | 28% | 29% | 0% | 0.985 | | spiders | NA | 25% | 0% | 0% | 100% | 0.985 | | edema | Edema despite diuretic therapy | 5% | 6% | 6% | 0% | 0.877 | | edema | Edema present without diuretics, or edema resolved by diuretics | 11% | 10% | 8% | 14% | 0.877 | | edema | No edema and no diuretic therapy for edema | 85% | 84% | 85% | 86% | 0.877 | | bili | NA | 3.2 | 2.9 | 3.6 | 3.1 | 0.133 | | chol | NA | 369.5 | 365.0 | 373.9 | NaN | 0.747 | | albumin | NA | 3.5 | 3.5 | 3.5 | 3.4 | 0.874 | | copper | NA | 97.6 | 97.6 | 97.7 | NaN | 0.999 | | alk_phos | NA | 1 982.7 | 2 021.3 | 1 943.0 | NaN | 0.747 | | ast | NA | 122.6 | 120.2 | 125.0 | NaN | 0.460 | | trig | NA | 124.7 | 124.1 | 125.3 | NaN | 0.886 | | platelet | NA | 257.0 | 258.8 | 265.2 | 241.7 | 0.554 | | protime | NA | 10.7 | 10.7 | 10.8 | 10.8 | 0.199 | | stage | 1 | 5% | 8% | 3% | 5% | 0.201 | | stage | 2 | 22% | 22% | 21% | 24% | 0.201 | | stage | 3 | 37% | 35% | 42% | 33% | 0.201 | | stage | 4 | 34% | 35% | 35% | 33% | 0.201 | | stage | NA | 1% | 0% | 0% | 6% | 0.201 |
This is rough, but you get the idea. The plan is to next create a function to make the table based on specifications.
Or possibly with some nesting…
tab1 %>%
tidyr::nest(con_res = c(n:shapiro_test, oneway_test:bartlett_test)) %>%
tidyr::nest(cat_res = c(n_level:check_categorical_test)) %>%
mutate(mean_sd = purrr::map_chr(.x = con_res,
.f = ~ paste0(round(.x$mean, 1),
" (",
round(.x$sd, 1),
")"))) %>%
mutate(n_pct = purrr::map_chr(.x = cat_res,
.f = ~ tryCatch(suppressWarnings(paste0(.x$n_level,
" (",
scales::percent(.x$n_level / .x$n_strata),
")")),
error = function(err) NA))) %>%
mutate(value = dplyr::if_else(var_type == "continuous", mean_sd, n_pct)) %>%
dplyr::select(-mean_sd, -n_pct, -class)
#> # A tibble: 132 x 8
#> strata var level smd var_type con_res cat_res value
#> <fct> <fct> <chr> <dbl> <chr> <list<df[> <list<df> <chr>
#> 1 Overall time <NA> 0.0167 continu… [1 × 16] [1 × 5] 1917.8 …
#> 2 D-penici… time <NA> 0.0167 continu… [1 × 16] [1 × 5] 2015.6 …
#> 3 Placebo time <NA> 0.0167 continu… [1 × 16] [1 × 5] 1996.9 …
#> 4 (Missing) time <NA> 0.0167 continu… [1 × 16] [1 × 5] 1657.1 …
#> 5 Overall status Alive 0.0538 categor… [1 × 16] [1 × 5] 232 (55…
#> 6 Overall status Dead 0.0538 categor… [1 × 16] [1 × 5] 161 (38…
#> 7 Overall status Liver tr… 0.0538 categor… [1 × 16] [1 × 5] 25 (5.9…
#> 8 D-penici… status Alive 0.0538 categor… [1 × 16] [1 × 5] 83 (52.…
#> 9 D-penici… status Dead 0.0538 categor… [1 × 16] [1 × 5] 65 (41.…
#> 10 D-penici… status Liver tr… 0.0538 categor… [1 × 16] [1 × 5] 10 (6.3…
#> # … with 122 more rows
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