This package helps to create journal style descriptive tables. Currently it automate table outputs as:
- Descriptive Tables (Table1)
- Model output table (Table2)
devtools::install_github('acmilannesta/JReport')
Table1(data, numcol = NULL, catcol = NULL, exp_var, output = NULL, overall = TRUE,
esdigits = 1, pdigits = 3, eps = 0.001)
Table2(model, data, catcols = NULL, esdigits = 2, output = NULL, pdigits = 2, eps = 0.001)
data: A dataframe including the exposure variable.
numcol: A vector of numerical column names in character. Default to NULL.
catcol: A vector of categorical column names in character. Default to NULL.
exp_var: String of main exposure variable name
output: String of path to store the output word file. E.g., 'Table1.rtf' or 'Table1.doc'
overall: Whether to add a column for overall subjects. Default to TRUE
model: Object output from lm, glm or coxph
esdigits: Controlling the effect size, mean, SD and percent digits.
pdigits: Controlling the significant p-value digits. Default to 2.
eps: P-value tolerane. Those less than eps are formatted as "< [eps]". Default to 0.001.
If output is not specified, a dataframe will be returned. Otherwise, a rtf file will be saved in the specified path.
Table1
For numerical/continuous columns, the results will be mean(SD). P-value is computed from Kruskal-Wallis Rank Sum test statistic.
For categorical columns, the results will be n(%). P-value is computed from Chi-square test statistic.
Table2
Currently the function only supports lm, glm and coxph objects.
The output include effect size (95% CI) along with p-values.
df = data.frame(
a = sample(1:100, 100, TRUE),
b = sample(c('Y', 'N', 'UNK'), 100, TRUE, prob=c(0.5, 0.3, 0.2)),
c = sample(1:100, 100, TRUE),
d = sample(c('Exposed', 'Unexposed'), 100, TRUE, prob=c(0.6, 0.4)))
Table1(df, c('a', 'c'), 'b', 'd')
| Name | Overall (n=100) | Exposed (n=60) | Unexposed (n=40) | P_val |
|---|---|---|---|---|
| b | 0.804 | |||
| N | 28 (28) | 18 (30) | 10 (25) | |
| UNK | 27 (27) | 15 (25) | 12 (30) | |
| Y | 45 (45) | 27 (45) | 18 (45) | |
| a | 54.2 (28.6) | 52.2 (28.3) | 57.2 (29.2) | 0.408 |
| c | 50.1 (31) | 50.1 (29.7) | 50.1 (33.3) | 0.935 |
data(mtcars)
mtcars$am = factor(mtcars$am)
log = glm(vs==1~mpg+am, family='binomial', data=mtcars)
Table2(log, mtcars, 'am')
| Variable | ES_CI | P_val |
|---|---|---|
| (Intercept) | 0 (0, 0.03) | 0.006 |
| mpg | 1.98 (1.2, 3.24) | 0.007 |
| am | ||
| 0 | Ref | Ref |
| 1 | 0.05 (0, 1.14) | 0.060 |