In mattkcole/FAtools: A Collection of Factor and Principle Component Analysis Visualization tools

knitr::opts_chunk$set(
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Factor analysis visualization made easy with FAtools

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:

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).

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).

corr.matrix <- cor(mtcars)

Let's load the packages we need for our analysis:

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.

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.

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.

FAtools::loadings_table(results$loadings, 2, cutoff = 0.3, roundto = 2) %>%
        kable()

Say we had more informative names than colnames(mtcars).

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.

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).

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

mattkcole/FAtools documentation built on May 21, 2019, 1:24 p.m.