Initial commit

Author: malcolmbarrett

.DS_Store

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+.Rproj.user
+.Rhistory
+.RData

.gitignore

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+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+## user2020! Causal Inference in R Workshop
+
+### Slides
+* [00 Intro](https://user2020.lucymcgowan.com/00-intro.html)
+* [01 Whole Game](https://user2020.lucymcgowan.com/01-causal_modeling_whole_game.html)
+* [02 Causal Diagrams](https://user2020.lucymcgowan.com/02-dags.html)
+* [03 Introduction to Propensity Scores](https://user2020.lucymcgowan.com/03-pscores.html)
+* [04 Using Propensity Scores](https://user2020.lucymcgowan.com/04-pscore-weighting.html)
+* [05 Checking Propensity Scores](https://user2020.lucymcgowan.com/05-pscore-diagnostics.html)
+* [06 Fitting the outcome model](https://user2020.lucymcgowan.com/06-outcome-model.html)
+
+### Installing materials locally
+
+We will be using RStudio Cloud for the workshop, but if you would like to install the required packages and course materials, we have an R package called {[useRcausal2020](https://github.com/malcolmbarrett/useRcausal2020)} to help you do that! You can install {[useRcausal2020](https://github.com/malcolmbarrett/useRcausal2020)} from GitHub with:
+
+``` r
+install.packages("remotes")
+remotes::install_github("malcolmbarrett/useRcausal2020")
+```
+
+Once you've installed the package, install the workshop with
+
+``` r
+useRcausal2020::install_workshop("path/to/your/computer")
+```
+
+Replace "path/to/your/computer" with where on your computer you want the workshop installed.

README.Rmd

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+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+## user2020\! Causal Inference in R Workshop
+
+### Slides
+
+  - [00 Intro](https://user2020.lucymcgowan.com/00-intro.html)
+  - [01 Whole
+    Game](https://user2020.lucymcgowan.com/01-causal_modeling_whole_game.html)
+  - [02 Causal Diagrams](https://user2020.lucymcgowan.com/02-dags.html)
+  - [03 Introduction to Propensity
+    Scores](https://user2020.lucymcgowan.com/03-pscores.html)
+  - [04 Using Propensity
+    Scores](https://user2020.lucymcgowan.com/04-pscore-weighting.html)
+  - [05 Checking Propensity
+    Scores](https://user2020.lucymcgowan.com/05-pscore-diagnostics.html)
+  - [06 Fitting the outcome
+    model](https://user2020.lucymcgowan.com/06-outcome-model.html)
+
+### Installing materials locally
+
+We will be using RStudio Cloud for the workshop, but if you would like
+to install the required packages and course materials, we have an R
+package called
+{[useRcausal2020](https://github.com/malcolmbarrett/useRcausal2020)} to
+help you do that\! You can install
+{[useRcausal2020](https://github.com/malcolmbarrett/useRcausal2020)}
+from GitHub with:
+
+``` r
+install.packages("remotes")
+remotes::install_github("malcolmbarrett/useRcausal2020")
+```
+
+Once you’ve installed the package, install the workshop with
+
+``` r
+useRcausal2020::install_workshop("path/to/your/computer")
+```
+
+Replace “path/to/your/computer” with where on your computer you want the
+workshop installed.

README.md

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+Version: 1.0
+
+RestoreWorkspace: No
+SaveWorkspace: No
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+LineEndingConversion: Posix
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace

causal_inference_r_workshop.Rproj

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+library(survey)
+library(tableone)
+library(tidyverse)
+library(broom)
+# remotes::install_github("malcolmbarrett/cidata")
+library(cidata)
+
+propensity_model <- glm(
+  qsmk ~ sex + 
+    race + age + I(age^2) + education + 
+    smokeintensity + I(smokeintensity^2) + 
+    smokeyrs + I(smokeyrs^2) + exercise + active + 
+    wt71 + I(wt71^2), 
+  family = binomial(), 
+  data = nhefs_complete
+)
+
+df <- propensity_model %>% 
+  augment(type.predict = "response", data = nhefs_complete) %>% 
+  mutate(wts = 1 / ifelse(qsmk == 0, 1 - .fitted, .fitted))
+
+svy_des <- svydesign(
+  ids = ~ 1,
+  data = df,
+  weights = ~ wts)
+
+smd_table_unweighted <- CreateTableOne(
+  vars = c("sex", "race", "age", "education", "smokeintensity", "smokeyrs", 
+           "exercise", "active", "wt71"),
+  strata = "qsmk",
+  data = df,
+  test = FALSE)
+
+smd_table <- svyCreateTableOne(
+  vars = c("sex", "race", "age", "education", "smokeintensity", "smokeyrs", 
+           "exercise", "active", "wt71"),
+  strata = "qsmk",
+  data = svy_des,
+  test = FALSE)
+
+
+plot_df <- data.frame(
+  var = rownames(ExtractSmd(smd_table)),                        
+  Unadjusted = as.numeric(ExtractSmd(smd_table_unweighted)),                      
+  Weighted = as.numeric(ExtractSmd(smd_table))) %>%
+  pivot_longer(-var, names_to = "Method", values_to = "SMD")
+
+ggplot(
+  data = plot_df,
+  mapping = aes(x = var, y = SMD, group = Method, color = Method)
+) +
+  geom_line() +
+  geom_point() + 
+  geom_hline(yintercept = 0.1, color = "black", size = 0.1) +  
+  coord_flip()