---
title: '%+%'
output:
  word_document: default
  html_notebook: default
  pdf_document: default
---

**Historical baby names**

```{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
```

```{r}
name_plot <- name_plot +
  geom_bar(stat = "identity", aes(y = count_thousands*alive_prob),
           fill = "#b2d7e9", color = "white")

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

median_yob <- with(joseph, wtd.quantile(year, weights=est_alive_today, prob = 0.5))  # The pipe %>% does not work.

median_yob    
```

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

name_plot
```

```{r}
name_plot + ggtitle("Age Distribution of American Boys Named Joseph") +
  geom_text(x = 1935, y = 40, label = "Number of Josephs\nborn each year") +
  geom_text(x = 1915, y = 13, label = "Number of Josephs\nborn each year\nestimated to be alive\non 1/1/214",
          color = "#b2d7e9") +
  geom_text(x = 2003, y = 40, label = "The median\nliving Joseph\nis 37 years old", 
          color = "darkgrey") +
  geom_curve(x = 1995, xend = 1974, y = 40, yend = 24,
             arrow = arrow(length = unit(0.3, "cm")), curvature = 0.5) +
  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}
name_plot <- name_plot + facet_wrap(~sex)
name_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()
```