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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| --- | ||
| title: "2023" | ||
| --- | ||
|
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||
| ```{r, include = FALSE} | ||
| knitr::opts_chunk$set( | ||
| collapse = TRUE, | ||
| comment = "#>" | ||
| ) | ||
| ``` | ||
|
|
||
| ```{r setup} | ||
| library(babynamesIL) | ||
| library(tidyverse) | ||
| library(tgstat) | ||
| theme_set(theme_classic()) | ||
| ``` | ||
|
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| # 2023 | ||
|
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| ## Top 10 names | ||
|
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| ```{r top10, fig.width = 15, fig.height = 15} | ||
| 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)) | ||
| ``` | ||
|
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| ## Names that changed the most in popularity | ||
|
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| ```{r change, fig.width = 10, fig.height = 8} | ||
| 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") | ||
| ``` | ||
|
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| ## Named that shifted from 'male' to 'female' and vice versa | ||
|
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| ```{r change2, fig.width = 15, fig.height = 15} | ||
| 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") | ||
| ``` | ||
|
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||
| Only names that became more male: | ||
|
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| ```{r change3, fig.width = 15, fig.height = 15} | ||
| 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") | ||
| ``` | ||
|
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| Only names that became more female: | ||
|
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| ```{r change4, fig.width = 15, fig.height = 15} | ||
| 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") | ||
| ``` |