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readme.rmd
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---
output:
md_document:
variant: markdown_github
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE, setting_options}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
Factor analysis visualization made easy with FAtools
=================
[](https://travis-ci.org/mattkcole/FAtools)
[](https://ci.appveyor.com/project/mattkcole/FAtools)
[](https://codecov.io/github/mattkcole/FAtools?branch=master)
### NOTE: THIS PACKAGE IS IN DEVELOPMENT
From choosing the numbers of factors to extract to inspecting loadings, factor analysis can be very visual in nature.
The FAtools R package aims to make this process easier by providing functions to do visualizations with ease.
### To Download:
```{r message=FALSE, loadingpackage, warning=FALSE}
library('devtools')
#devtools::install_github('mattkcole/FAtools')
library('FAtools')
```
### Examples:
We can first look at our data (here we are using the possibly cliche but familiar data, mtcars).
```{r loadingdata}
library(datasets)
summary(mtcars)
```
Let's first make our correlation matrix - we wont worry about scaling or investigating our data much for this demonstration (usually a bad idea).
```{r gettingcor}
corr.matrix <- cor(mtcars)
```
Let's load the packages we need for our analysis:
```{r lodingpackages2, cache=F, message=FALSE, warning=FALSE}
library('psych') # for statistical methods
library('FAtools') # for some plotting and EDA
library('dplyr') # for data wrangling
library('knitr') # for rmd help
```
Lets make and plot our scree plot to assess the number of factors present.
```{r scree_plot, cache=TRUE}
s.plot <- FAtools::scree_plot(corr.matrix, nrow(mtcars), ncol(mtcars))
plot(s.plot)
```
We can conduct our factor analysis with two factors using the psych package.
```{r loadings}
results <- psych::fa(corr.matrix, 2, rotate = "varimax")
results$loadings
```
The loadings look pretty good, but we can make them more interpretable by excluding low loadings (param: `cutoff`), rounding (param: `roundto`), incorporate a data dictionary, and include labels -- And we can use the knitr::kable() function for great looking tables in Rmarkdown documents.
```{r}
FAtools::loadings_table(results$loadings, 2, cutoff = 0.3, roundto = 2) %>%
kable()
```
Say we had more informative names than `colnames(mtcars)`.
```{r}
cool_names <- c("Miles Per Gallon", "Cylinders", "Displacement",
"Gross horsepower", "Rear Axle ratio", "Weight (1K lbs)",
"1/4 mile time", "V/S", "Manual", "Forward gears",
"Carburetors")
```
And say we wern't really all that interested in loadings with an absolute value less than 0.3.
```{r }
FAtools::loadings_table(loading_frame = results$loadings,
cutoff = 0.3, roundto = 2,
Name = colnames(mtcars),
description = cool_names) %>%
kable()
```
We could also display this graphically, which works well when we have more retained factors or many more variables. (let's say we have 5 factors extracted).
```{r factor_plot, fig.width=8, fig.height=4.5, cache=TRUE}
loadings5 <- cor(mtcars) %>%
psych::fa(2, rotate = "varimax")
FAtools::loadings_plot(loadings5$loadings,
colorbreaks = c(0, 0.2, 0.4, 0.6, 0.8, 1),
labRow = c("F1", "F2"),
columnlabels = cool_names)
```
Looks great!
Submit and issue with any concerns!
Credits:
Much of the scree plot functionality comes from code provided by:
[www.statmethods.net](http://www.statmethods.net/advstats/)