How to use mse function - babynames example - r

Question

So I am using the 'babynames' package in rstudio and am trying to get the 35 most common unisex names. I am trying to rank the names based on the mean squared error from the 50-50 line (however, I am not sure how to do this). Any help would be greatly appreciated! (Also below my code I will put the 'reference code' we were given that includes what the top 35 unisex names are)
Reference Code:
actual_names <- c("Jessie", "Marion", "Jackie", "Alva", "Ollie",
"Jody", "Cleo", "Kerry", "Frankie", "Guadalupe",
"Carey", "Tommie", "Angel", "Hollis", "Sammie",
"Jamie", "Kris", "Robbie", "Tracy", "Merrill",
"Noel", "Rene", "Johnnie", "Ariel", "Jan",
"Devon", "Cruz", "Michel", "Gale", "Robin",
"Dorian", "Casey", "Dana", "Kim", "Shannon")

Answer 1

I think there are a few ways to answer the question as posed, since there's a tradeoff between "most popular" and "most unisex."
Here's a way to prep the data to collect some stats for each name.
library(babynames)
library(tidyverse)
babynames_share <-
babynames %>%
filter(year >= 1930, year <= 2012) %>%
count(name, sex, wt = n) %>%
spread(sex, n, fill = 0) %>%
mutate(Total = F + M,
F_share = F / Total,
MS_50 = ((F_share-0.5)^2 +
(0.5-F_share)^2) / 2)
It looks like around 100 names have perfect gender parity -- but they're all quite uncommon:
babynames_share %>%
filter(F == M) %>%
arrange(-Total)
# A tibble: 100 x 6
name F M Total F_share RMS_50
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Tyjae 157 157 314 0.5 0
2 Callaway 128 128 256 0.5 0
3 Avyn 100 100 200 0.5 0
4 Zarin 92 92 184 0.5 0
5 Tkai 72 72 144 0.5 0
6 Rayen 57 57 114 0.5 0
7 Meco 43 43 86 0.5 0
8 Pele 40 40 80 0.5 0
9 Nijay 35 35 70 0.5 0
10 Mako 27 27 54 0.5 0
# … with 90 more rows
Or we might pick some arbitrary threshold for what counts as unisex. In the example above, I've calculated the mean squared error for the female and male percent shares. We can plot that to see very gendered names on the top (MS_50 tops out at 0.25 by this measure), and unisex names toward the bottom. But it isn't obvious to me how far down we should go to count a name as unisex. Is Casey, which is 58.9% male, with therefore an 8.9%^2 = 0.79% squared error, unisex? Or do we need to further to Jessie, which is 50.8% male?
babynames_share %>%
ggplot(data = .,
aes(Total, MS_50, label = name)) +
geom_point(size = 0.2, alpha = 0.1, color = "gray30") +
geom_text(data = . %>% filter(Total > 10000),
check_overlap = TRUE, size = 3) +
scale_x_log10(breaks = c(10^(1:7)),
labels = scales::comma)
At the "Casey" level of gender parity, here are the top 35:
unisex_names <- babynames_share %>%
filter(MS_50 <= 0.00796) %>%
arrange(-Total) %>%
top_n(35, wt = Total)
It's also interesting to see the whole spectrum of names, with most male on the bottom, female on the top, and unisex in the middle:
babynames_share %>%
ggplot(data = .,
aes(Total, F_share, label = name)) +
geom_point(size = 0.2, alpha = 0.1, color = "gray30") +
geom_text(data = . %>% filter(Total > 10000),
check_overlap = TRUE, size = 2) +
scale_x_log10(breaks = c(10^(1:7)),
labels = scales::comma)