Setting up and Reading the data

First let’s set the working directory.

#First of all let's load the Dplyr package

library(dplyr)

# Now let's set the working directory

setwd("/Users/fbeta/OneDrive/1-UMASS-DACSS/601/Week-4/HW-2")

# confirm the WD

print(getwd())

[1] "C:/Users/fbeta/OneDrive/1-UMASS-DACSS/601/Week-4/HW-2"

Load the data

# Reading the file

navy <- read.csv("navy_cleaned-navy_cleaned.csv")

Exploring the data

#let's have a preview of the data in the file

head(navy)

  enlisted pay_grade single.withoutchildren.male
1        E         1                        7820
2        E         2                       11198
3        E         3                       28163
4        E         4                       23285
5        E         5                       18856
6        E         6                        5917
  single.withoutchildren.female single.withoutchildren.total
1                          2275                        10095
2                          2718                        13916
3                          6396                        34559
4                          4266                        27551
5                          3649                        22505
6                          1429                         7346
  single.withchildren.male single.withchildren.female
1                      117                         34
2                      237                         87
3                      681                        788
4                      967                        949
5                     2837                       1787
6                     2725                       1284
  single.withchildren.total married.jointservice.male
1                       151                        30
2                       324                       113
3                      1469                       861
4                      1916                      1526
5                      4624                      2191
6                      4009                      1654
  married.jointservice.female married.jointservice.total
1                          57                         87
2                         164                        277
3                        1288                       2149
4                        1691                       3217
5                        2099                       4290
6                        1461                       3115
  married.civilian.female married.civilian.male
1                     806                   162
2                    2474                   388
3                   11297                  1791
4                   16277                  1768
5                   31491                  2272
6                   30404                  1561
  married.civilian.total married.male.total married.female.total
1                    968               8773                 2528
2                   2862              14022                 3357
3                  13088              41002                10263
4                  18045              42055                 8674
5                  33763              55375                 9807
6                  31965              40700                 5735
  married.total.total branch
1               11301   Navy
2               17379   Navy
3               51265   Navy
4               50729   Navy
5               65182   Navy
6               46435   Navy

By interpreting the data, seems that we have mainly 3 types of data among the variables:

Categorical:

enlisted (it is not clear what means each category, but seems type of enlistment)
branch

Ordinal:

pay_grade: seems that they show certain order, can’t assume equal distance between values, nor a true zero.

Discrete numerical:

all the rest of the variables seems are count of total number of people, there is no intermediate values between them which makes sense as we are talking about count of people.

However let’s check how R is reading the data.

# check if it is numeric value
is.numeric("single.withoutchildren.male")

[1] FALSE

is.numeric("enlisted")

[1] FALSE

is.numeric("pay_grade")

[1] FALSE

is.character("single.withoutchildren.male")

[1] TRUE

is.character("enlisted")

[1] TRUE

is.character("pay_grade")

[1] TRUE

Seems that any of the discrete numeric variables are being read as character.

Therefore we need to convert the Discrete numeric variable as numeric:

# converting a variable to numeric value
swochm2 <- as.numeric(as.character("single.withoutchildren.male"))

# check if it is numeric value
is.numeric(swochm2)

[1] TRUE

Let’s explore the data a bit more:

# Dimension of the table
dim(navy)

[1] 23 18

It has 23 rows and 18 columns/variables

#Get the col names of navy
colnames(navy)

 [1] "enlisted"                      "pay_grade"                    
 [3] "single.withoutchildren.male"   "single.withoutchildren.female"
 [5] "single.withoutchildren.total"  "single.withchildren.male"     
 [7] "single.withchildren.female"    "single.withchildren.total"    
 [9] "married.jointservice.male"     "married.jointservice.female"  
[11] "married.jointservice.total"    "married.civilian.female"      
[13] "married.civilian.male"         "married.civilian.total"       
[15] "married.male.total"            "married.female.total"         
[17] "married.total.total"           "branch"

Now let’s create some tables

Select specific columns

#Selecting Enlisted variable only
select(navy,1)

   enlisted
1         E
2         E
3         E
4         E
5         E
6         E
7         E
8         E
9         E
10        O
11        O
12        O
13        O
14        O
15        O
16        O
17        O
18        O
19        O
20        W
21        W
22        W
23        W

And create a table with specific variables

#Table for pay_grade and enlistment
table(select(navy,2,1))

         enlisted
pay_grade E O W
       1  1 1 0
       2  1 1 1
       3  1 1 1
       4  1 1 1
       5  1 1 1
       6  1 1 0
       7  1 1 0
       8  1 1 0
       9  1 1 0
       10 0 1 0