In ekothe/nzbabynames: Data on baby names in New Zealand

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

One popular way to understand baby names is to look at the most popular names in any given year.

Here let's look at the Top 10 names for each year in our data set.

Start by reading in the data and creating a rank for each name by Sex and Year.

library(nzbabynames)
library(dplyr)


names <- nzbabynames

names <- names %>% 
  group_by(Sex, Year) %>% 
  arrange(-Count) %>% 
  mutate(Rank = seq_along(Count))

names

Now let's filter the data set so we only have names in the Top 10 in any given year

names_top <- names %>%
  filter(Rank <= 10) 

tail(names_top)

We can find the most popular names in a given year

names_top %>% 
  filter(Year == 1985, Rank == 1)

I could also plot names over time. Here I'll use ggplot2 and gganimate to plot male and female names separately so it's easier to read.

library(ggplot2)
library(gganimate)

names_top_female <- names_top %>%
  filter(Sex == "Female") 

time <- ggplot(names_top_female, aes(x = Rank, y = Count, fill = Name)) +
  geom_col() + 
  facet_wrap("Sex") +
  geom_text(aes(label = Name), vjust = -0.5, size = 3) +
  theme_classic() +
  theme(legend.position="none") +
  labs(title="{current_frame}") +
  transition_manual(Year)

animate(time, duration=120)

library(ggplot2)
library(gganimate)

names_top_male <- names_top %>%
  filter(Sex == "Male") 

time <- ggplot(names_top_male, aes(x = Rank, y = Count, fill = Name)) +
  geom_col() + 
  facet_wrap("Sex") +
  geom_text(aes(label = Name), vjust = -0.5, size = 3) +
  theme_classic() +
  theme(legend.position="none") +
  labs(title="{current_frame}") +
  transition_manual(Year)

animate(time, duration=120)