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2023

Source: vignettes/articles/2023.Rmd
2023.Rmd
library(babynamesIL)
library(tidyverse)
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#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

2023

Top 10 names 

babynamesIL %>%
    filter(year == 2023) %>%
    mutate(sector = factor(sector, levels = c("Jewish", "Muslim", "Christian", "Druze", "Other"))) %>%
    group_by(sector, sex) %>%
    slice_max(order_by = n, n = 20) %>%
    arrange(sector, sex, desc(n)) %>%
    mutate(name = forcats::fct_inorder(name)) %>%
    ggplot(aes(x = name, y = n)) +
    geom_col() +
    facet_wrap(sector ~ sex, scales = "free", ncol = 2) +
    ylab("total #") +
    xlab("") +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))

Names that changed the most in popularity 

babynamesIL %>%
    filter(year %in% c(2023, 2022), sector == "Jewish") %>%
    pivot_wider(names_from = year, values_from = c(prop, n)) %>%
    filter(!is.na(prop_2023) & !is.na(prop_2022)) %>%
    mutate(prop_diff = prop_2023 - prop_2022) %>%
    arrange(sex, desc(abs(prop_diff))) %>%
    group_by(sex) %>%
    slice(1:30) %>%
    ggplot(aes(x = n_2023, y = prop_diff, color = sex, label = name)) +
    geom_point() +
    theme_classic() +
    ggsci::scale_color_aaas() +
    ggrepel::geom_text_repel(size = 6) +
    scale_y_continuous(label = scales::percent) +
    geom_hline(yintercept = 0) +
    ylab("Difference in %") +
    xlab("# in 2023")
#> Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Named that shifted from ‘male’ to ‘female’ and vice versa 

unisex_data <- babynamesIL %>%
    filter(sector == "Jewish", year %in% c(2022, 2023)) %>%
    pivot_wider(names_from = "sex", values_from = c("n", "prop"), values_fill = 0) %>%
    filter(n_M > 0 & n_F > 0) %>%
    mutate(ratio = n_M / n_F) %>%
    group_by(name) %>%
    filter(abs(ratio[1] - ratio[2]) >= 0.2) %>%
    ungroup()
unisex_data %>%
    ggplot(aes(x = n_M, y = n_F, label = name, color = factor(year, levels = c(2023, 2022)), group = name)) +
    geom_point() +
    ggsci::scale_color_nejm(name = "year") +
    geom_line(color = "gray") +
    scale_x_log10() +
    scale_y_log10() +
    ggrepel::geom_text_repel() +
    geom_abline() +
    xlab("# male") +
    ylab("# female")

Only names that became more male:

unisex_data %>%
    group_by(name) %>%
    filter(ratio[1] > ratio[2]) %>%
    ggplot(aes(x = n_M, y = n_F, label = name, color = factor(year, levels = c(2023, 2022)), group = name)) +
    geom_point() +
    ggsci::scale_color_nejm(name = "year") +
    geom_line(color = "gray") +
    scale_x_log10() +
    scale_y_log10() +
    ggrepel::geom_text_repel() +
    geom_abline() +
    xlab("# male") +
    ylab("# female")

Only names that became more female:

unisex_data %>%
    group_by(name) %>%
    filter(ratio[2] > ratio[1]) %>%
    ggplot(aes(x = n_M, y = n_F, label = name, color = factor(year, levels = c(2023, 2022)), group = name)) +
    geom_point() +
    ggsci::scale_color_nejm(name = "year") +
    geom_line(color = "gray") +
    scale_x_log10() +
    scale_y_log10() +
    ggrepel::geom_text_repel() +
    geom_abline() +
    xlab("# male") +
    ylab("# female")