ex_01_ANSWERS.qmd

---
title: "Exercise 1. Your turn"
---

## Load library packages

```{r}
library(janeaustenr)
library(tidyverse)
library(tidytext)
library(wordcloud2)
```

## Your Turn. Exercise 1.

Goal: Make a basic word cloud for the novel, *Pride and Predjudice*, `pride_prej_novel`

a.  Prepare

```{r}
pride_prej_novel <-  tibble(text = prideprejudice) %>%
  mutate(line = row_number())
```

b.  Tokenize `sense_novel` with `unnest_tokens()`

```{r}
pride_prej_novel %>% 
  unnest_tokens(word, text)
```

c.  Remove stop-words

```{r}
pride_prej_novel %>% 
  unnest_tokens(word, text) %>% 
  anti_join(get_stopwords())
```

d.  calculate word fequency

```{r}
pride_prej_novel %>% 
  unnest_tokens(word, text) %>% 
  anti_join(get_stopwords()) %>% 
  count(word, sort = TRUE) 
```

e.  make a simple wordcloud

```{r}
pride_prej_novel %>% 
  unnest_tokens(word, text) %>% 
  anti_join(get_stopwords()) %>% 
  count(word, sort = TRUE) %>% 
  with(wordcloud::wordcloud(word, n, max.words = 100))
```

f.  Since "Friends don't let friends make word clouds", make a barplot of the word frequency.

```{r}
pride_prej_novel %>% 
  unnest_tokens(word, text) %>% 
  anti_join(get_stopwords(), by = "word") %>% 
  count(word, sort = TRUE) %>% 
  slice_head(n = 10) %>% 
  ggplot(aes(x = n, y = fct_reorder(word, n))) +
  geom_col() +
  labs(title = "Word Frequency",
       subtitle = "Jane Austen novel",
       x = "", y = "",
       caption = "Source: janeaustenr")
```