Examples taken from: https://r-graph-gallery.com/package/gtsummary.html#:~:text=The%20gtsummary%20package%20in%20R,code%20and%20publication%2Dready%20tables!
data(iris)
library(gtsummary)
iris %>%
tbl_summary()| Characteristic | N = 1501 |
|---|---|
| Sepal.Length | 5.80 (5.10, 6.40) |
| Sepal.Width | 3.00 (2.80, 3.30) |
| Petal.Length | 4.35 (1.60, 5.10) |
| Petal.Width | 1.30 (0.30, 1.80) |
| Species | |
| setosa | 50 (33%) |
| versicolor | 50 (33%) |
| virginica | 50 (33%) |
| 1 Median (Q1, Q3); n (%) | |
# load dataset
data(Titanic)
df = as.data.frame(Titanic)
# load library
library(gtsummary)
# create the model
model = glm(Survived ~ Age + Class + Sex + Freq, family=binomial, data=df)
# generate table
model %>%
tbl_regression() %>% # regression summary function
add_global_p() %>% # add p-values
bold_labels() %>% # make label in bold
italicize_levels() # make categories in label in italic| Characteristic | log(OR) | 95% CI | p-value |
|---|---|---|---|
| Age | 0.5 | ||
| Child | — | — | |
| Adult | 0.62 | -1.0, 2.4 | |
| Class | >0.9 | ||
| 1st | — | — | |
| 2nd | -0.03 | -2.0, 2.0 | |
| 3rd | 0.25 | -1.8, 2.4 | |
| Crew | 0.27 | -1.8, 2.4 | |
| Sex | 0.6 | ||
| Male | — | — | |
| Female | -0.37 | -1.9, 1.1 | |
| Freq | -0.01 | -0.02, 0.00 | 0.2 |
| Abbreviations: CI = Confidence Interval, OR = Odds Ratio | |||
# load dataset and filter to keep just a few columns
data(mtcars)
mtcars = mtcars %>%
select(vs, mpg, drat, hp, gear)
# load package
library(gtsummary)
# create summary table
mtcars %>%
tbl_summary(
by=vs, # group by the `vs` variable (dichotomous: 0 or 1)
statistic = list(
all_continuous() ~ "{mean} ({sd})", # will display: mean (standard deviation)
all_categorical() ~ "{n} / {N} ({p}%)" # will display: n / N (percentage)
)
) %>%
add_overall() %>% # statistics for all observations
add_p() %>% # add p-values
bold_labels() %>% # make label in bold
italicize_levels() # make categories in label in italic## The following warnings were returned during `add_p()`:
## ! For variable `drat` (`vs`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: cannot compute exact p-value with
## ties
## ! For variable `drat` (`vs`) and "estimate", "statistic", "p.value",
## "conf.low", and "conf.high" statistics: cannot compute exact confidence
## intervals with ties
## ! For variable `hp` (`vs`) and "estimate", "statistic", "p.value", "conf.low",
## and "conf.high" statistics: cannot compute exact p-value with ties
## ! For variable `hp` (`vs`) and "estimate", "statistic", "p.value", "conf.low",
## and "conf.high" statistics: cannot compute exact confidence intervals with
## ties
## ! For variable `mpg` (`vs`) and "estimate", "statistic", "p.value", "conf.low",
## and "conf.high" statistics: cannot compute exact p-value with ties
## ! For variable `mpg` (`vs`) and "estimate", "statistic", "p.value", "conf.low",
## and "conf.high" statistics: cannot compute exact confidence intervals with
## ties| Characteristic | Overall N = 321 |
0 N = 181 |
1 N = 141 |
p-value2 |
|---|---|---|---|---|
| mpg | 20.1 (6.0) | 16.6 (3.9) | 24.6 (5.4) | <0.001 |
| drat | 3.60 (0.53) | 3.39 (0.47) | 3.86 (0.51) | 0.013 |
| hp | 147 (69) | 190 (60) | 91 (24) | <0.001 |
| gear | 0.001 | |||
| 3 | 15 / 32 (47%) | 12 / 18 (67%) | 3 / 14 (21%) | |
| 4 | 12 / 32 (38%) | 2 / 18 (11%) | 10 / 14 (71%) | |
| 5 | 5 / 32 (16%) | 4 / 18 (22%) | 1 / 14 (7.1%) | |
| 1 Mean (SD); n / N (%) | ||||
| 2 Wilcoxon rank sum test; Fisher’s exact test | ||||
data(iris)
library(gtsummary)
library(gt)
iris %>%
tbl_summary(by=Species) %>%
add_overall() %>% # info ignoring the `by` argument
add_n() %>% # number of observations
modify_header(label ~ "**Variables from the dataset**") %>% # title of the variables
modify_spanning_header(c("stat_0", "stat_1", "stat_2", "stat_3") ~ "*Descriptive statistics of the iris flowers*, grouped by Species") %>%
as_gt() %>%
gt::tab_source_note(gt::md("*The iris dataset is probably the **most famous** dataset in the world*"))| Variables from the dataset | N |
Descriptive statistics of the iris flowers, grouped by Species
|
|||
|---|---|---|---|---|---|
| Overall N = 1501 |
setosa N = 501 |
versicolor N = 501 |
virginica N = 501 |
||
| Sepal.Length | 150 | 5.80 (5.10, 6.40) | 5.00 (4.80, 5.20) | 5.90 (5.60, 6.30) | 6.50 (6.20, 6.90) |
| Sepal.Width | 150 | 3.00 (2.80, 3.30) | 3.40 (3.20, 3.70) | 2.80 (2.50, 3.00) | 3.00 (2.80, 3.20) |
| Petal.Length | 150 | 4.35 (1.60, 5.10) | 1.50 (1.40, 1.60) | 4.35 (4.00, 4.60) | 5.55 (5.10, 5.90) |
| Petal.Width | 150 | 1.30 (0.30, 1.80) | 0.20 (0.20, 0.30) | 1.30 (1.20, 1.50) | 2.00 (1.80, 2.30) |
| The iris dataset is probably the most famous dataset in the world | |||||
| 1 Median (Q1, Q3) | |||||