Data Analysis

Load the required R packages:

library(babynames)
library(knitr)
library(dplyr)
library(ggplot2)
library(tidyr)
library(pheatmap)

Writing in Quarto part

Let’s have a look at the first couple of rows in the data:

head(babynames) |> kable()

year	sex	name	n	prop
1880	F	Mary	7065	0.0723836
1880	F	Anna	2604	0.0266790
1880	F	Emma	2003	0.0205215
1880	F	Elizabeth	1939	0.0198658
1880	F	Minnie	1746	0.0178884
1880	F	Margaret	1578	0.0161672

Let’s create functions to visualise some of the data according to sex:

Code

get_most_frequent <- function(babynames, select_sex, from = 1950) {
  most_freq <- babynames |>
    filter(sex == select_sex, year > from) |>
    group_by(name) |>
    summarise(average = mean(prop)) |>
    arrange(desc(average))
    
  return(list(
    babynames = babynames,
    most_frequent = most_freq,
    sex = select_sex,
    from = from))
}

plot_top <- function(x, top = 10) {
  topx <- x$most_frequent$name[1:top]
  
  p <- x$babynames |>
    filter(name %in% topx, sex == x$sex, year > x$from) |>
    ggplot(aes(x = year, y = prop, color = name)) +
    geom_line() +
    scale_color_brewer(palette = "Paired") +
    theme_classic()
  
  return(p)
}

We are going to look at the distribution of baby names over time. In Figure 1 we can see the ten most frequent names for girls. Likewise in Figure 2, we can see the same of boys.

Figure 1: Distribution of the top ten female names over time.

Figure 2: Distribution of the top ten male names over time.

git and github part

We want to plot multiple panels in a figure - an example for the most liked girl’s name can be seen in Figure 3.

# get most frequent girl names from 2010 onwards
from_year <- 2010
most_freq_girls <- get_most_frequent(babynames, select_sex = "F",
                                     from = from_year)

# plot top 5 girl names
most_freq_girls |>
  plot_top(top = 5)

# plot top 10 girl names
most_freq_girls |>
  plot_top(top = 10)

# get top 30 girl names in a matrix
# with names in rows and years in columns
prop_df <- babynames |> 
  filter(name %in% most_freq_girls$most_frequent$name[1:30] & sex == "F") |>
  filter(year >= from_year) |> 
  select(year, name, prop) |>
  pivot_wider(names_from = year,
              values_from = prop)

prop_mat <- as.matrix(prop_df[, 2:ncol(prop_df)])
rownames(prop_mat) <- prop_df$name

# create heatmap
pheatmap(prop_mat, cluster_cols = FALSE, scale = "row")