04-highlighting.Rmd

---
title: "Data Storytelling 4 - Highlighting"
output: html_notebook
---

If you've skipped ahead (and not run Part 1), run this code chunk to load the relevant data and plot:

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(here)
source(here("R/init.R"))
```

## Changing the base plot (slightly)

Remember our original plot mapped color to `title`. There's also a variable called `status` in our data. `status` has two different values: a tv show can be a `riser` (positive trend in ratings), or be a `faller` (negative trend in ratings).

Let's modify the plot to use this variable to `color` our lines. We'll save it as a different object, this time called `my_new_plot`.

```{r}
my_new_plot <- 
  ggplot(tv_shows) + 
    aes(x = seasonNumber, 
        y= av_rating, 
        group=title, 
        color = status) +   #changing `color` to map to `status` here
    geom_line()

my_new_plot
```

## Highlighting part of your data

What if we only want to highlight one group in the data? In this case, maybe we want to highlight the *risers* in our dataset. If we color them blue, and leave the others as `grey` we can immediately highlight them as important and worth noticing in the context of the other data.

We can actually manually color our traces by using `scale_color_manual()`. This lets us manually map our values in our variable (`riser` and `faller`) to colors: (`blue` and `grey`).

```{r}
my_new_plot + 
  scale_color_manual(values=c("riser"="blue",
                              "faller"="grey")) +
  theme_minimal()
```

If you've mapped a variable to `fill`, you'll have to use `scale_fill_manual()` to map values to colors.

### Try out mapping colors

Try using different colors to contrast the lines in the `values` argument to `scale_color_manual()`. A small list of color names in R can be found here: https://www.r-graph-gallery.com/42-colors-names.html

```{r}
library(tidyverse)

my_new_plot + 
  scale_color_manual(values=c("riser"="blue", "faller"="grey"))
```

## Put it all together!

Cut and paste all your modifiers and make your final figure below!

```{r}
my_new_plot +
  theme_minimal()
```


# Saving High Quality Plots

We can use `ggsave()` to save our plot. By default, it will save the last plot we made. 

```{r eval=FALSE}
ggsave("movieplot.pdf")
```

# More Best Practices for figures:

Ten Simple Rules for Better Figures: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833


# More tips and tricks for using contrast and color

In `03-annotating`, we saw that we can specify the line type for a particular graph. We can also specify line type as an aesthetic. Be careful with line types - too many in a figure can obscure your point.

Shapes for points can also be helpful for highlighting particular data points. Here's a useful reference.

More information here:
http://www.cookbook-r.com/Graphs/Shapes_and_line_types/