easytable R package

Create MS word friendly regression tables

easytable is an R package designed to simplify the process of creating visually appealing and informative tables for summarizing model results.

Introduction to easytable: Creating Beautiful Tables for Model Summaries

Overview

easytable is an R package designed to simplify the process of creating visually appealing and informative tables for summarizing model results. Whether you’re working with linear regression, logistic regression, or any other type of model, easytable provides a user-friendly interface to generate tables with ease.

In this tutorial, we’ll walk through the basic usage of easytable using a sample dataset and a few example models.

Installation

You can install easytable from GitHub using the devtools package:

# If you don't have devtools already
install.packages("devtools")

# You can install easytable from GitHub
devtools::install_github("alhdzsz/easytable")

Once installed, you can load the package into your R session:

library(easytable)

Model Input

The easytable package has one function (easytable()). The main input to use this function is a named list of statistical objects.

data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)

# Fit models
m1 <- lm(am ~ cyl + hp + wt, data = mtcars)
m2 <- glm(am ~ cyl + hp + wt, data = mtcars, family = "binomial")
m3 <- glm(am ~ wt + disp, data = mtcars, family = binomial(link = "probit"))

# Create a list of models
model_list <- list(
  LPM = m1,
  Logit = m2,
  Probit = m3
)

Models are tidied using the broom::tidy() function and thus must be statistical objects.

Example Usage

Let’s dive into an example to see how easytable works. We’ll use the famous mtcars dataset and fit three different models to it: a linear probability model (LPM), a logistic regression, and a probit regression.

# Generate the table
easy_table(model_list)

term	LPM	Logit	Probit
(Intercept)	1.18 *** (0.28)	18.09 * (9.46)	8.64 *** (3.1)
cyl6	-0.18 (0.19)	2.77 (3.16)
cyl8	-0.57 * (0.3)	-8.39 (13.17)
hp	0 *** (0)	0.1 (0.1)
wt	-0.38 *** (0.1)	-10.68 ** (5.44)	-3.34 *** (1.27)
disp			0.01 (0.01)
N	32	32	32
R sq.	0.61
Adj. R sq.	0.56
AIC		17.26	23.85
Significance: ***p < .01; **p < .05; *p < .1

Other options within the function include: a) highlighting statistically significant results, b) adding average marginal effects coefficients, c) adding robust standard errors, d) printing a .csv file of the table, and e) omitting control variables from the output.

# Generate the table
easy_table(model_list,
           highlight = TRUE,
           csv = "model_summaries",  # Export as CSV
           control.var = c("cyl", "disp"),  # Control variables
           margins = TRUE,  # Include row/column margins
           robust.se = TRUE  # Calculate robust standard errors
)

term	LPM	Logit	Probit
hp	0 *** (0)	0 * (0.06)
wt	-0.38 *** (0.09)	-0.34 *** (2.88)	-0.52 *** (1.08)
disp			Y
cyl	Y	Y
N	32	32	32
R sq.	0.61
Adj. R sq.	0.56
AIC		17.26	23.85
Significance: ***p < .01; **p < .05; *p < .1
Note: Marginal Effects and Robust Standard Errors

With easytable, creating informative model summaries in R has never been easier. Whether you’re conducting data analysis, writing research papers, or preparing presentations, easytable empowers you to generate elegant and insightful tables with just a few lines of code.

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