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Getting Started with Report Creation

Source: vignettes/making-reports.Rmd
making-reports.Rmd

Introduction

This vignette explains how to make publication-grade DAGassist reports in LaTeX, Word, Excel, and plaintext. Since this package’s primary utility is as a robustness check for observational studies, most of its functionality revolves around creating reports.

Install 

install.packages("pak")
pak::pak("grahamgoff/DAGassist")

Setup

In addition to loading DAGassist, we will load the following baseline packages:

  • modelsummary to build the model comparison table for LaTeX, Word, Excel, and plaintext.
    • LaTeX uses broom as a fallback for report generation
  • knitr to build intermediate .md for Word and plaintext report generation.
  • rmarkdown to convert .md files to .docx files for Word report generation.
  • writexl to export Excel files.

Essentially, to export:

  • LaTeX only needs modelsummary
  • Excel needs modelsummary and writexl
  • plaintext needs modelsummary and knitr
  • Word needs modelsummary, knitr, and rmarkdown 
#load DAGassist
library(DAGassist) 
#load libraries to help export
library(modelsummary)
library(writexl)
library(knitr)
library(rmarkdown)

Example DAG

Example DAG

The data, which simulates the DAG relationships:

head(df)
#>            Y          D          H          A           G          F
#> 1 -4.3598990 -1.7293707 -1.2908129 -0.3906503 -0.15030748 -0.5116037
#> 2  1.6129050  1.0748492  0.2956294  0.6771872 -0.32775713  0.2369379
#> 3  1.5096451  1.0922811 -1.1613337  1.1254048 -1.44816529 -0.5415892
#> 4  1.1554071 -0.5867292  1.3752785 -0.5173670 -0.69728458  1.2192276
#> 5  0.2920729 -2.0225129 -1.1232830 -1.8569842  2.59849023  0.1741359
#> 6  3.1428430  3.0900447  1.1675898  2.6936976 -0.03741501 -0.6152683
#>             C           B
#> 1 -0.99579872 -0.56047565
#> 2 -1.03995504 -0.23017749
#> 3 -0.01798024  1.55870831
#> 4 -0.13217513  0.07050839
#> 5 -2.54934277  0.12928774
#> 6  1.04057346  1.71506499

Creating a report

Suppose you wrote an article uses model original as its main finding, and you want to ensure that your specification captures the total effect of D on Y, without mediator or collider bias.

original <- lm(Y ~ D + G + H + F + A + B + C, data = df)

DAGassist can generate a report assessing the causal roles of the variables in original.

DAGassist(dag = dag_model, #specify a dagitty or ggdag object
          formula = lm(Y ~ D + G + H + F + A + B + C, data = df), #provide your formula
          type = "text", #output type
          out = out_txt) #a temporary directory, for the purpose of this vignette

cat(readLines(out_txt), sep = "\n") # show the output

DAGassist Report:

Roles

Variable Role X Y CON MED COL IO dMed dCol
A confounder x
B confounder x
C confounder x
D exposure x
F confounder x
G other
H other
Y outcome x

Models

Term Original Minimal 1 Minimal 2 Minimal 3 Minimal 4 Canonical
D 1.034*** 0.977*** 0.969*** 1.038*** 1.024*** 1.034***
  (0.032) (0.020) (0.023) (0.036) (0.041) (0.032)
G 0.397*** 0.397***
  (0.032) (0.032)
H 0.486*** 0.522*** 0.495*** 0.486***
  (0.032) (0.028) (0.031) (0.032)
F 0.618*** 0.606*** 0.593*** 0.618***
  (0.037) (0.036) (0.040) (0.037)
A -0.111** 0.158*** 0.169*** -0.111**
  (0.043) (0.047) (0.051) (0.043)
B -0.008 -0.161*** -0.162*** -0.008
  (0.039) (0.045) (0.049) (0.039)
C 0.037 0.420*** 0.391*** 0.037
  (0.042) (0.040) (0.045) (0.042)
Num.Obs. 1000 1000 1000 1000 1000 1000
R2 0.836 0.809 0.769 0.762 0.724 0.836

Notes

  • p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Controls (minimal): {F, H} Controls (canonical): {A, B, C, F, G, H}.