06-Polishing-plots.Rmd

---
title: "Visualizing New House Price Index"
output: html_document
---

<!-- This file by Charlotte Wickham is licensed under a Creative Commons Attribution 4.0 International License. -->

```{r setup}
library(tidyverse)
housing <- read_csv("data/housing_bc.csv")

diamonds_plot <- ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = clarity)) 
```

## Your turn 1

**Discuss with your neighbours:**
What would you  want to change before publishing this plot?

```{r}
basic_plot <- housing %>%
  filter(geography %in% c("Canada", "British Columbia (12)")) %>%
  ggplot(mapping = aes(x = date, y = total)) +
    geom_line(aes(color = geography))
basic_plot
```

## Your turn 2

I've applied the diamond labels to the housing price plot.  Edit the code to add appropriate labelling to the house price plot.

```{r}
basic_plot + 
  labs(title = "Diamonds data",
       subtitle = "Most diamonds are Ideal cut",
       caption = "Data from ggplot2::diamonds",
       x = "Diamond Cut",
       fill = "Clarity"
  )
```

## Your turn 3

Add a theme to basic_plot. Try a few and pick one you like.

```{r}
basic_plot 
```


## Your turn 4

Add a brewer scale to the basic_plot.

```{r}
basic_plot 
```


## Your turn 5

Choose a better color palette for the brewer scale.

```{r}
basic_plot + scale_color_brewer()
```

## Your turn 6

Put the labels, theme and scale changes together for basic_plot.

What is left to change?

```{r}
basic_plot 
```

## Takeaways

Every visual element on a ggplot plot can be controlled.  The most frequent changes you want to make are to:

* Labels, `+ labs()`
* Themes, `+ theme_bw()`
* Scales `+ scale_*_*()`