README.md

In acmilannesta/JReport: Create journal style tables

Journal style reporting (R-package "JReport")

Description

This package helps to create journal style descriptive tables. Currently it automate table outputs as: 1. Descriptive Tables (Table1) 2. Model output table (Table2)

Install

devtools::install_github('acmilannesta/JReport')

Usage

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)

Arguments

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.

Return Value

If output is not specified, a dataframe will be returned. Otherwise, a rtf file will be saved in the specified path.

Details

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.

Examples

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

acmilannesta/JReport documentation built on Feb. 19, 2020, 4:24 a.m.