---
title: "%+%"
author: "Prof. Eric A. Suess"
format:
  html:
    self-contained: true
---

**Historical baby names**

See Section 3.3

```{r message = FALSE}
library(pacman)

p_load(tidyverse, babynames, mdsr, Hmisc)
```

This dataset was created in 2014.

```{r}
BabynamesDist <- make_babynames_dist()
head(BabynamesDist)
```

```{r}
BabynamesDist |>  
  filter(name == "Benjamin")
```

```{r}
joseph <- BabynamesDist  |> 
  filter(name == "Joseph" & sex == "M")

name_plot <- joseph  |>  ggplot(aes(x = year))

name_plot
```

In the Second edition of the book the authors changed to using geom_col().

```{r}
name_plot <- name_plot +
  geom_col(
    aes(y = count_thousands * alive_prob), 
    fill = "#b2d7e9", 
    color = "white",
    size = 0.1
  )

name_plot
```

```{r}
name_plot <- name_plot + 
  geom_line(aes(y = count_thousands), size = 2)
name_plot
```

```{r}
name_plot <- name_plot + 
  ylab("Number of People (thousands)") +
  xlab(NULL)

name_plot
```

```{r}
summary(name_plot)
```

```{r}
wtd_quantile <- Hmisc::wtd.quantile

median_yob <- joseph  |>  
  summarise(year = wtd_quantile(year, weights=est_alive_today, prob = 0.5))  |>  
  pull(year)

median_yob    

```
Again, in the new edition the authors us geom_col()

```{r}
name_plot <- name_plot +
  geom_col(
    color = "white", fill = "#008fd5", 
    aes(y = ifelse(year == median_yob, est_alive_today / 1000, 0))
  )

name_plot
```

```{r}
context <- tribble(
  ~year, ~num_people, ~label,
  1935, 40, "Number of Josephs\nborn each year",
  1915, 13, "Number of Josephs\nborn each year
  \nestimated to be alive\non 1/1/2014", 
  2003, 40, "The median\nliving Joseph\nis 37 years old", 
)

name_plot +
  ggtitle("Age Distribution of American Boys Named Joseph") + 
  geom_text(
    data = context, 
    aes(y = num_people, label = label, color = label)
  ) + 
  geom_curve(
    x = 1990, xend = 1974, y = 40, yend = 24, 
    arrow = arrow(length = unit(0.3, "cm")), curvature = 0.5
  ) + 
  scale_color_manual(
    guide = "none", 
    values = c("black", "#b2d7e9", "darkgray")
  ) + 
  ylim(0, 42)

```

Now replace the data in the ggplot and then make the new plot.

```{r}
name_plot %+% filter(
  BabynamesDist, 
  name == "Josephine" & sex == "F"
)
```

Faceting

Try the name Jessie

```{r}
names_plot <- name_plot + 
  facet_wrap(~sex)
names_plot %+% filter(BabynamesDist, name == "Jessie")
```

Try it with your own name.

```{r}
name_plot <- name_plot + 
  facet_wrap(~sex)
name_plot %+% filter(BabynamesDist, name == "Eric")
```

```{r}
many_names_plot <- name_plot + 
  facet_grid(name ~ sex)
mnp <- many_names_plot %+% filter(
  BabynamesDist, 
  name %in% c("Jessie", "Marion", "Jackie")
)
mnp
```

```{r}
mnp + facet_grid(sex ~ name)
```

**Most common women's names**

Thank you Hannah Kim!


```{r}
library(babynames)

com_fem <- BabynamesDist %>% 
  filter(sex == "F") %>%
  group_by(name) %>% 
  summarise(
    N = n(),
    est_num_alive = sum(est_alive_today)
  ) %>% 
  arrange(desc(est_num_alive)) %>%  
  head(25) %>%  
  select(name) %>%
  left_join(., BabynamesDist, by = "name") %>%  
  group_by(name) %>%  
  summarise(
    N = n(),
    est_num_alive = sum(est_alive_today),
    q1_age = wtd.quantile(age_today, weight = est_alive_today, probs = 0.25),
    median_age = wtd.quantile(age_today, weight = est_alive_today, probs = 0.5),
    q3_age = wtd.quantile(age_today, weight = est_alive_today, probs = 0.75) 
  )

w_plot <- ggplot(data = com_fem, aes(x = reorder(name, -median_age),
  y = median_age)) + xlab(NULL) + ylab("Age (in years)") +
  ggtitle("Median ages for females with the 25 most common names")

w_plot <- w_plot + geom_linerange(aes(ymin = q1_age, ymax = q3_age),
  color = "#f3d478", size = 10, alpha = 0.8)

w_plot <- w_plot +
  geom_point(fill = "#ed3324", colour = "white", size = 4, shape = 21)

w_plot +
  geom_point(aes(y = 55, x = 24), fill = "#ed3324", colour = "white",
             size = 4, shape = 21) +
  geom_text(aes(y = 58, x = 24, label = "   median")) +
  geom_text(aes(y = 26, x = 16, label = "   25th")) +
  geom_text(aes(y = 51, x = 16, label = "75th percentile      ")) +
  geom_point(aes(y = 24, x = 16), shape = 17) +
  geom_point(aes(y = 56, x = 16), shape = 17) +
  coord_flip()
```