02_workbook.Rmd

---
title: "Grammar of Graphics"
author: "Ashrita Dara"
output:
  html_document:
    code_folding: hide
    toc: TRUE
    toc_float: TRUE
editor_options:
  chunk_output_type: console
---

```{r}
library(tidyverse)

```

## Exercise 1

Created a scatter plot that shows the relationship between pressure and wind.

```{r}
# YOUR WORK GOES HERE

# dots going negative slope diagonal scattered on outside
# aes is aesthetic mappings which gives labels,
# colors, transparency, etc..
# geom_point is geometric points

ggplot(data = storms) +
  geom_point(mapping = aes(x = pressure, y = wind, color = category))

```

## Exercise 2

```{r}
# YOUR WORK GOES HERE

# salmon color is default color

ggplot(data = storms) +
  geom_point(mapping = aes(x = pressure, y = wind, color = "green"))


# put the color outside of aes

ggplot(data = storms) +
  geom_point(mapping = aes(x = pressure, y = wind), color = "purple", alpha = 0.05)

# 1 / 0.05 is 20 so it takes 20 translucent dots layered on 
# top of each other to make it solid color
ggplot(data = storms) +
  geom_point(mapping = aes(x = pressure, y = wind), color = "purple", alpha = 0.05)


```

## Exercise 3

```{r}
# YOUR WORK GOES HERE

ggplot(data = msleep) +
  geom_point(mapping = aes(x = bodywt, y = sleep_total), 
             color = "navy", alpha = 0.4) +
  scale_x_log10() +
scale_y_continuous(limits = c(0, NA))

```

## Exercise 4

```{r}
adequate_sleep <- msleep |>
  mutate(sleep14 = if_else(
    condition = sleep_total > 16, 
    true = "Adequate sleep", 
    false = "Inadequate sleep")
  ) |>
  count(sleep14) |>
  mutate(prop = n / sum(n))

ggplot(data = adequate_sleep, aes(x = "", y = prop, fill = sleep14)) +
  geom_col() + 
  # YOUR WORK GOES HERE\
  coord_polar(theta = "y") +
  scale_fill_manual(values = c("Adequate sleep" = "lightgreen", "Inadequate sleep" = "salmon")) +
  theme_void() # gets rid of unneccesary labels


```

## Exercise 5

```{r}
# YOUR WORK GOES HERE

ggplot(data = storms) +
  geom_point(mapping = aes(x = pressure, y = wind), 
             color = "navy", alpha = 0.05) +
  facet_wrap(~ month)

```

## Exercise 6

```{r}
# YOUR WORK GOES HERE

# STATISTICAL TRANSFORMATIONS : transform data

ggplot(data = storms) +
  geom_bar(mapping = aes(x = category))

count(storms, category) %>%
  ggplot() +
  geom_col(mapping = aes(x = category, y = n))

```

## Exercise 7

```{r}
# YOUR WORK GOES HERE

ggplot(data = storms) +
  geom_bar(mapping = aes(x = category)) +
  theme_minimal() # makes white background instead of default grey

```

## Exercise 8

```{r}
# YOUR WORK GOES HERE

# example of layers

storms %>%
  filter(category > 0) %>% # gets storms greater than 0 because 0 is not a storm
  distinct(name, year) %>%
  count(year) %>%
  ggplot() +
  geom_line(mapping = aes(x = year, y = n), alpha = 0.5) +
  geom_point(mapping = aes(x = year, y = n))

```

## Exercise 9

```{r}
# YOUR WORK GOES HERE

ggplot(data = storms, mapping = aes(x = pressure, y = wind)) +
       geom_point(mapping = aes(color = category, alpha = 0.05)) +
       geom_smooth() # put line after the graph or else it will be covered

```

## Exercise 10

```{r}
# YOUR WORK GOES HERE
# from exercise 5

ggplot(data = storms) +
  geom_point(mapping = aes(x = pressure, y = wind), 
             color = "navy", alpha = 0.05) +
  facet_wrap(~ month)

ggsave(filename = "favorite-plot.png", width = 6.5, height = 4)


```

## Session Info

```{r}
sessionInfo()
```