Lightweight Meta-Analysis.R

## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
library(metafor)
library(DT)
library(knitr)
library(RCurl)
library(dplyr)
library(rmarkdown)
library(kableExtra)

output <- default_output_format(knitr::current_input()) ## This will fail if run manually. Don't worry!

## These lines of code download and run the metafor_tidiers functions that implement broom type tidy data functions for rma objects
source("metafor_tidiers.R")

# Set so that long lines in R will be wrapped:
opts_chunk$set(tidy.opts = list(width.cutoff = 80), tidy = TRUE)


## ----read_data-----------------------------------------------------------
dat <- read.csv("data_ostracism.csv")

## ----calculate_ES--------------------------------------------------------
if (!"vi" %in% colnames(dat))
{
  dat_ES <-
    escalc(
    measure = "SMD",
    m1i = Intervention.Mean,
    sd1i = Intervention.SD,
    n1i = Intervention.N,
    m2i = Control.Mean,
    sd2i = Control.SD,
    n2i = Control.N,
    data = dat
    )
} else {
  dat_ES <- dat
  attrs <- NULL
  attrs$measure <- "SMD"
  attrs$ni <- dat$Intervention.N + dat$Control.N
  attributes(dat_ES$yi) <- attrs
}


## ----dat_es_html, eval = (output$name == "html_document"), echo = FALSE, warning = FALSE----
datatable(dat_ES %>% 
            select(-one_of(c("X", "Timestamp"))), rownames = FALSE)  %>% 
                formatRound('yi', 3) %>% 
                  formatRound('vi', 3)

## ----dat_es_pdf, eval = (output$name == "pdf_document"), echo = FALSE, warning = FALSE----
kable(dat_ES %>% 
        select(-one_of(c("X", "Timestamp"))), booktabs = T, format = "latex") %>%
            kable_styling(latex_options = c("striped", "scale_down"))


## ----dat_es_word,eval = (output$name == "word_document"), echo = FALSE, warning = FALSE----
kable(dat_ES %>% 
        select(-one_of(c("X", "Timestamp"))))

## ----run_MA--------------------------------------------------------------
dat_MA <- rma(yi, vi, data = dat_ES, slab = Study.ID)

model <- tidy.rma(dat_MA)

het.small <- glance.rma(dat_MA) %>% 
  select(one_of(c("k", "tau2", "se.tau2", "QE", "QEp", "I2")))

eggers <- regtest(dat_MA)

## ----forest, warning = FALSE, fig.height = (het.small$k*0.5)-------------
forest(dat_MA)

## ----funnel--------------------------------------------------------------
funnel(dat_MA, back="white")


## ----include=FALSE-------------------------------------------------------
citPkgs <- names(sessionInfo()$otherPkgs)
write_bib(citPkgs, file="R-Pckgs.bib")