To begin with, load the necessary packages:

> library(magrittr)

> install.packages("caret")

> install.packages("DataExplorer")

> install.packages("earth")

> install.packages("ggthemes")

> install.packages("psych")

> install.packages("tidyverse")

> options(scipen = 999)

Now, read the data into your environment:

> army_ansur <- readRDS("army_ansur.RData")

The feature names are fairly straightforward. Here, I just put in the last few features as output:

> colnames(army_ansur)
 [93] "wristcircumference"     "wristheight" 
 [95] "Gender"                 "Date" 
 [97] "Installation"           "Component" 
 [99] "Branch"                 "PrimaryMOS" 
[101] "SubjectsBirthLocation"  "SubjectNumericRace" 
[103] "Ethnicity"              "DODRace" 
[105] "Age"                    "Heightin" 
[107] "Weightlbs"              "WritingPreference" 
[109] "SubjectId"

I'm interested in looking at the breakdown of the "Component" and "Gender" columns:

> table(army_ansur$Component)

Army National Guard   Army Reserve   Regular Army 
               2708            220           3140 

> table(army_ansur$Gender)

Female   Male 
  1986   4082 

If we look at missing values, we can see something of interest. Here is the abbreviated output:

> sapply(army_ansur, function(x) sum(is.na(x)))
        PrimaryMOS   SubjectsBirthLocation   SubjectNumericRace 
                 0                       0                    0 
         Ethnicity                 DODRace                  Age 
              4647                       0                    0 
          Heightin               Weightlbs    WritingPreference 
                 0                       0                    0 
         SubjectId 
              4082 

We have a bunch of missing subject IDs. Fine, let's take care of that right now:

> army_ansur$subjectid <- seq(1:6068)

Since weight is what we will predict after we build our unsupervised model, let's have a look at it:

> sjmisc::descr(army_ansur$Weightlbs)

## Basic descriptive statistics
 var    type label    n NA.prc  mean   sd    se  md trimmed       range skew
  dd integer    dd 6068      0 174.8 33.69 0.43 173   173.4 321 (0-321) 0.39

Look at the range! We have someone who weighs zero. A plot of this data is in order, I believe:

> ggplot2::ggplot(army_ansur, ggplot2::aes(x = Weightlbs)) + 
    ggplot2::geom_density() +
    ggthemes::theme_wsj()

The output of the preceding code is as follows:

So, I would estimate we only have one or two observations of implausible weight values. Indeed, this code will confirm that assumption:

> dplyr::select(army_ansur, Weightlbs) %>%
    dplyr::arrange(Weightlbs)
# A tibble: 6,068 x 1
   Weightlbs
       <int>
 1         0
 2        86
 3        88
 4        90
 5        95
 6        95
 7        95
 8        96
 9        98
10       100
# ... with 6,058 more rows

Removing that observation is important:

> armyClean <- dplyr::filter(army_ansur, Weightlbs > 0)

We can now transition to bundling our features for PCA and creating training and testing dataframes.