EFAshiny_RCodeDemo.Rmd

---
title: "EFAshiny : R Code Demo"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
### Overview
This tutorial aims to demonstrate how to perform the fundamental analyses in [`EFAshiny`](https://psychilin.shinyapps.io/EFAshiny/) using R code. By following each line in this tutorial, you can learn basic R programming and can implement automated processing script in future analyses. Also, you can further use the code in `EFAshiny` within a script pupeline without launching the app. However, we still suggest you to use [`EFAshiny`](https://psychilin.shinyapps.io/EFAshiny/) APP :)) You can also have a look at our [tutorial](https://github.com/PsyChiLin/EFAshiny) of `EFAshiny` GUI to make an easy comparison while writing the code. Have fun with [`EFAshiny`](https://psychilin.shinyapps.io/EFAshiny/) and `R` !

### Preparation
First of all, we should load all the packages and fucntions in `EFAshiny` into `R` session. If you do not have these package installed, please use `install.package("packagename")` to perfrom installations.
```{r, message=FALSE, warning=FALSE}
require(ggplot2);require(psych);require(corrplot);require(reshape2);require(moments);require(gridExtra)
require(qgraph);require(bootnet);require(igraph);require(ggcorrplot);require(RCurl)
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/my_summary.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/faplot.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/bootEGA.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/bargraph.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/stackbar.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/printLoadings.R")
source("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/inst/efas/functions/theme_default.R")
```

### Data Input
Read the demonsration data [`RSE`](https://github.com/PsyChiLin/EFAshiny/blob/master/RSE/RSE.csv) into `R`. We assign it to a object called `dta`. Then using `head` to observe and explore this data. See our [tutorial](https://github.com/PsyChiLin/EFAshiny) for data description.
```{r}
dta <- read.csv(text=getURL("https://raw.githubusercontent.com/PsyChiLin/EFAshiny/master/RSE/RSE.csv"))
head(dta)
```

### Data Summary
Several kinds of data summary can be perform in `EFAshiny`. We demonstrate them using basic code. The package `ggplot2` is a useful package for plotting. We use it to plot the histograms, the density plots, and the correlation matrix. You can also check the documentation of [`ggplot2`](https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf).

##### Numeric Statistic
```{r}
NumericStatistic <- apply(dta,2,my_summary)
row.names(NumericStatistic) <- c("Mean","SD","Skewness","Kurtosis","Median","MAD")
NumericStatistic <- as.data.frame(t(NumericStatistic))
NumericStatistic <- round(NumericStatistic,3)
NumericStatistic
```

##### Histogram
```{r, message=FALSE}
dta_long <- melt(dta)
colnames(dta_long) <- c("Item", "Response")
Histogram <- ggplot(dta_long, aes(x = Response, fill = Item))+
        geom_histogram(bins = 10)+
        facet_wrap(~Item)+
        theme_default()
Histogram
```

##### Density Plot
```{r, message=FALSE}
DensityPlot <- ggplot(dta_long, aes(x = Response, fill = Item))+
        geom_density()+
        facet_wrap(~Item)+
        theme_default()
DensityPlot 
```

##### Correlation Matrix
```{r, fig.height=7, fig.width=7}
CorMat <- cor(as.matrix(dta))
corrplot(CorMat,order="hclust",type="upper",method="ellipse",
         tl.pos = "lt",mar = c(2,2,2,2))
corrplot(CorMat,order="hclust",type="lower",method="number",
         diag=FALSE,tl.pos="n", cl.pos="n",add=TRUE,mar = c(2,2,2,2))
```

##### ggcorplot
```{r}
ggcorrplot(CorMat, hc.order = T,type = "lower", lab = TRUE,
           colors = c("#E46726", "white", "#6D9EC2"))
```

### Factor Retention
We provide several factor retention methods in `EFAshiny`. Those analyses can also be performed using R code. You can adopt these methods for your own use. 

##### Scree Plot and Parallel Analysis
```{r, message=FALSE}
PA <- faplot(CorMat,n.obs = 256, quant = 0.95)
PA[[1]]
```

##### Numeric Rules
```{r}
NumericRule <- VSS(CorMat,n = 4, plot = F, n.obs = 256)
temp1 <- data.frame(nFactor = row.names(NumericRule$vss.stats), 
                     VSS1 = NumericRule$cfit.1, VSS2 = NumericRule$cfit.2, 
                     MAP = NumericRule$map)
temp2 <- NumericRule$vss.stats[,c(6:8,11)]
NumericRule <- cbind(temp1,temp2)
NumericRule
```

##### Exploratory Graph Analysis (EGA)
```{r}
EGArst <- bootEGA(data = dta, n = 10, medianStructure = TRUE, plot.MedianStructure = TRUE, 
                  ncores = 4, layout = "spring")
plot(EGArst$plot)
```

### Extarction and Rotation
The key analyses in EFA is factor extarction and rotation. We teach you to perform the analyses using `psych` package. Using these lines, you can obtain basic results of EFA. Can also see the code in `EFAshiny` server and `psych` package for details.  
```{r}
EFArst <- fa(CorMat,2,n.obs=256, rotate = "promax",fm = "pa", n.iter = 200)
EFArst
```

### Visualization
By passing the results of EFA from aforementioned analyses into the well-established functions, you can easily visualize these results from EFA. Definitely, `EFAshiny` can help you to do these automatically.

##### Diagram
```{r,message=FALSE}
fa.diagram(EFArst,simple = T,cut = 0.33,
           sort = T,errors = T,e.size = 0.05)
```

##### Bootstrapping Factor Loadings
```{r,message=FALSE}
order <- rev(row.names(as.data.frame(printLoadings(EFArst$cis$means,sort = T,cutoff = 0)))) # define the order of the variable
bargraph(EFArst,order = order,nf = 2,highcol = "firebrick",lowcol = "chartreuse4",ci = T)
```

##### Factor Loadings and Correlation Matrix
```{r,message=FALSE}
stackbar(CorMat,EFArst,order = order,highcol = "firebrick",lowcol = "chartreuse4")
```