---
title: "ExploratoryDataAnalysis2"
output:
  word_document:
    toc: yes
  pdf_document:
    toc: yes
  html_document:
    theme: cerulean
    toc: yes
    toc_float: yes
---

# Comparing Two Variables.  {.sidebar}

Today we will continue to discuss Exploratory Data Analysis (EDA).

1. Two categorical variables.
2. One categorical variable and one numeric variable.
3. Two numeric variables.

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


## Two categorical variables. {.sidebar}

```{r}
diamonds %>% ggplot(aes(x = cut, y = color)) +
  geom_count()
```

```{r}
diamonds %>% 
  count(color, cut) %>%  
  ggplot(mapping = aes(x = cut, y = color)) +
    geom_tile(mapping = aes(fill = n))
```


```{r}
diamonds %>% count(color, cut)
```

```{r}
diamonds %>% group_by(color, cut) %>%
  summarise(n=n())
```

## Contingency table. {.sidebar}

```{r}
diamonds %>% group_by(color, cut) %>%
  summarise(n=n()) %>%
  spread(cut, n)
```

Using the new *pivot_wider()* function, that replaces the *spread()*.  You will need to update the **tidyr** package to version 1.0.  The new function has a name that makes more sense and is more memorable.

```{r}
diamonds %>% group_by(color, cut) %>%
  summarise(n=n()) %>%
  pivot_wider(
    names_from = cut,
    values_from = n
  )
```


Export the data to an Excel file and try making this Pivot Table.

```{r}
write.csv(diamonds, file="~/diamonds.csv")
```


## One categorical variables and one numeric. {.sidebar}

```{r}
ggplot(data = diamonds, mapping = aes(x = price)) + 
  geom_freqpoly(mapping = aes(colour = cut), binwidth = 500)
```

```{r}
ggplot(data = diamonds, mapping = aes(x = price, y = ..density..)) + 
  geom_freqpoly(mapping = aes(colour = cut), binwidth = 500)
```

```{r}
ggplot(data = diamonds, mapping = aes(x = cut, y = price)) +
  geom_boxplot()
```



## Two numeric variables. {.sidebar}

```{r}
ggplot(data = diamonds) +
  geom_point(mapping = aes(x = carat, y = price))
```


```{r}
ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price), alpha = 1 / 100)
```



```{r}
smaller <- diamonds %>% 
  filter(carat < 3)

ggplot(data = smaller) +
  geom_bin2d(mapping = aes(x = carat, y = price))
```

```{r}
library(hexbin)

ggplot(data = smaller) +
  geom_hex(mapping = aes(x = carat, y = price))
```