Lightweight Meta-Analysis.Rmd

---
title: "Light Weight Meta-Analysis Example"
output:
  word_document: default
  pdf_document: default
  html_document:
    code_folding: show
always_allow_html: yes
csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
bibliography: R-Pckgs.bib
nocite: |
  @*
---
```{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)

```

```{r read_data}
dat <- read.csv("data_ostracism.csv")
```

## Caluclate effect sizes for standarised mean differences
```{r 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
}

```

```{r 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)
```

```{r 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"))

```

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



## Run meta-analysis
```{r 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)
```
### Summary

A random-effects meta-analysis (k = `r dat_MA$k`) was conducted using the `r dat_MA$method` estimator.

`r kable(model, col.names=c("*g*", "se", "z", "*p*", "95% CI LB", "95% CI UB"), row.names=FALSE, digits = 3, caption="Effect Size")`

`r kable(het.small, col.names=c("k", "$\\tau$^2^", "se", "Q", "*p*", "I^2^"), digits = 3, caption="Heterogeneity")`

## Plots
###Forest plot
```{r forest, warning = FALSE, fig.height = (het.small$k*0.5)}
forest(dat_MA)
```

###Funnel plot
```{r funnel}
funnel(dat_MA, back="white")

```

## Packages used in this document
```{r include=FALSE}
citPkgs <- names(sessionInfo()$otherPkgs)
write_bib(citPkgs, file="R-Pckgs.bib")
```