chapter_15_excercises.Rmd

---
title: "Chapter 15 excercises"
author: "Hugo Åkerstrand"  
date: "`r Sys.Date()`"
output: html_document
editor_options: 
  markdown: 
    wrap: 72
---

```{r setup, include=TRUE}
library(tidyverse)
library(babynames)
library(ggrepel)
```

### 15.2.4

---

1.

```{r}

exc_1_str <- c(R"(He said "That's amazing!")",
               r"(\a\b\c\d)",
               r"-(\\\\\\)-"
             )

exc_1_str

str_view(exc_1_str)

```

2.

`\u00a0` encodes a "non breaking space", preventing line breaks or that multiple  
whitespaces gets collapsed into one.

```{r}

x <- "This\u00a0is\u00a0tricky"

x

str_c(x)

```

### 15.3.4

---

1.

Tidyverse applies rules when it comes to handling NA and recycling, this results  
in differences in how the strings are handled: 

For the first string, the NA forces the output into NA. For the second, recycling  
is only used for single values and therefore throws an error.

```{r, error = TRUE}

str_c("hi ", NA)
str_c(letters[1:2], letters[1:3])

paste0("hi ", NA)
paste0(letters[1:2], letters[1:3])

```

3.

```{r}

food <- "pizza"
price <- 100
age <- 35
country <- "Sweden"
title <- "asskickery"


tribble(
  ~ input,                                               ~ output,
  str_c("The price of ", food, " is ", price),           str_glue("The price of {food} is {price}"),
  str_glue("I'm {age} years old and live in {country}"), str_c("I'm", age, "years old and live in", country, sep = " "),
  str_c("\\section{", title, "}"),                       str_glue("\\section{{{title}}}")
)

```

### 15.5.3

---

1.

The `babynames` data set reports the observed name for a given year, gender, 
and number of individuals with this specific name. As such, it is necessary  
to specify `wt = n` to get the sum of names with a specific length.

2.

```{r}

babynames |> 
  mutate(
    name_length = str_length(name),
    name_mid_position = if_else(
                          name_length %% 2 == 0, 
                          NA, 
                          ceiling(name_length / 2)
                          ),
    name_mid_letter = str_sub(
                        name, 
                        start = name_mid_position, 
                        end = name_mid_position
                      )
  )

```

3.

```{r}

#Add some summaries that will be interesting to analyze
babynames_analysis <- babynames |> 
  mutate(
    name_length = str_length(name),
    name_first_letter = str_sub(name, 1, 1),
    name_last_letter = str_sub(name, -1, -1),
    name_rank = dense_rank(desc(prop)),
    .by = c(year, sex)
  ) |> 
  arrange(year, name_rank)

#Box plot to identify top 3 popular names from oldest (1880) and newest (2017) year
babynames_analysis |> 
  filter(year %in% c(1880, max(year))) |> 
  ggplot(aes(y = prop, x = sex)) +
  geom_boxplot() +
  geom_text_repel(data = babynames_analysis |> filter(year %in% c(1880, max(year))) |> filter(name_rank <= 3), aes(label = name)) +
  facet_wrap( ~ year)
  
#Save the favorite names to a new object to call it for subsetting the line graph
favorite_names_old_modern <- babynames_analysis |> 
  filter(year %in% 1880 | year %in% 2017) |> 
  filter(name_rank <= 3) |> 
  select(year, name, sex)

#Draw the line graph to track popularity over years 
babynames_analysis |> 
  mutate(popularity = case_when( #Add new variable for facetting
    name %in% pull(filter(favorite_names_old_modern, year == 2017), name) ~ "Modern favorite",
    name %in% pull(filter(favorite_names_old_modern, year == 1880), name) ~ "Old school favorite"
  )) |> 
  filter(name %in% pull(filter(favorite_names_old_modern, sex == "F"), name) & sex == "F" |
         name %in% pull(filter(favorite_names_old_modern, sex == "M"), name) & sex == "M"
  ) |> #Filter out names given female or male sex
  ggplot(aes(x = year, y = prop, color = name)) +
  geom_line(aes(color = name)) +
  facet_grid(popularity ~ sex)
  
```