WWII Heros Over Time

This project will assess the popularity of names of World War II Medal of Honor recipients 10 years before and 10 years after WWII. My initial hypothesis is that the popularity of WWII veterans names would have spiked in America during and after times of war due to increased patriotism and to honor American war heros. The veterans that received the Medal of Honor in this data set were all male. For this project I will only be focusing on the male population with these names.

To asses this, I used names found from https://www.arlingtoncemetery.mil/Explore/Notable-Graves/Medal-of-Honor-Recipients/World-War-II-MoH-recipients and manually entered each name into an excel sheet.#After removing repeats, I was left with 39 names to examine the popularity of in 1935, 1945, and 1955.

But first, I needed to load the necessary packages along with my external data.

library(babynames)

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)
wwII <- read_excel("~/Desktop/wwII.xlsx",
                         col_types = c('text'))

colnames(wwII)[2] <- "name"

babynames |>
  inner_join(wwII, relationship = "many-to-many") -> merged

Joining with `by = join_by(name)`

I filtered my data through the babynames data set so I would only be seeing results from the years 1935 through 1955.

merged |> 
  filter(year %in% (1935:1955)) -> years

I then filtered the top 10 most popular names from my data set which is what I will primarily be working with.

years |> 
  group_by(name) |> 
  count(n) |> 
  arrange(desc(n)) |> 
  head(10) -> new

Storing counts in `nn`, as `n` already present in input
ℹ Use `name = "new_name"` to pick a new name.

I created a line graph to show the evolution of the popularity of the top 10 most popular baby names from the year 1935 until 1945, which is 10 years before the end of WWII. This graph shows a steady rate of names that neither fall or spike up.

years |> 
  filter(name %in% c("James", "John", "Joseph", "William", "Charles", "Henry", "Robert", "Thomas", "George", "Edward")) |> 
  filter(sex == "M") |> 
  filter(year %in% 1935:1945) |> 
  ggplot(aes(year, prop, color=name)) + geom_line() + ggtitle('Top 10 Male Names from 1935 to 1945')

Next I created a line graph to show the evolution of the popularity of the top 10 most popular baby names from the year 1945 until 1955, which is 10 years after the end of WWII. As we can see, most of the names were either on a slight or steady decline heading into 1955 which also happened to be the middle of the “baby boom”. This was surprising since I originally thought to blame the decrease in name popularity as a decrease in growing families, but this is not the case.

years |> 
  filter(name %in% c("James", "John", "Joseph", "William", "Charles", "Henry", "Robert", "Thomas", "George", "Edward")) |> 
  filter(sex == "M") |> 
  filter(year %in% 1945:1955) |> 
  ggplot(aes(year, prop, color=name)) + geom_line() + ggtitle('Top 10 Male Names from 1945 to 1955')

The final line graph I created shows the evolution of the popularity of the top 10 most popular baby names from the year 1935 until 1955. This helps summarize the previous two graphs into one.

years |> 
  filter(name %in% c("James", "John", "Joseph", "William", "Charles", "Henry", "Robert", "Thomas", "George", "Edward")) |> 
  filter(sex == "M") |> 
  ggplot(aes(year, prop, color=name)) + geom_line() + ggtitle('Top 10 Male Names from 1935 to 1955')

Findings

This graph disproves my hypothesis. Some of the names had small peaks after 1935 but hardly any peaked after 1945 which is when I assumed some of the WWII heroes might be honored.

I chose to make this next set of bar graphs to compare the top names each a decade a part so we can see the effects of the war, if any, on the names.

merged |> 
  filter(year == "1935") |> 
  filter(sex == 'M') |> 
  arrange(desc(prop)) |> 
  head(10) |> 
  ggplot(aes(reorder(name, prop), y = prop, fill = name)) + geom_col() + coord_flip() + ggtitle('Top 10 Male Names in 1935') + ylab('prop') + xlab("name")

Findings

As we can see Robert was the most popular name in 1935, followed closely by James. Note that David is the least popular of the top 10 names.

merged |> 
  filter(year == "1945") |> 
  filter(sex == 'M') |> 
  arrange(desc(prop)) |> 
  head(10) |> 
  ggplot(aes(reorder(name, prop), y = prop, fill = name)) + geom_col() + coord_flip() + ggtitle('Top 10 Male Names in 1945') + ylab('prop') + xlab("name")

Findings

In 1945 James took the spot for most popular name of the World War II veteran data set.

merged |> 
  filter(year == "1955") |> 
  filter(sex == 'M') |> 
  arrange(desc(prop)) |> 
  head(10) |> 
  ggplot(aes(reorder(name, prop), y = prop, fill = name)) + geom_col() + coord_flip() + ggtitle('Top 10 Male Names in 1955') + ylab('prop') + xlab("name")

Surprisingly David, which was the least popular of the 10 names in 1935, has risen to the top in 1955 passing James and Robert.

Conclusion

After viewing my visualizations, I would say my hypothesis is incorrect. This may be due to how tragic the events of World War II were and the lasting impact was too painful to remember. Therefore people may have strayed away from naming their children after these heroes. The WWII heroes that had the most popular names happened to also have fairly common and traditional names. Over the 20 years surrounding the war many social changes took place and some of these common names aged out.