01-starting-out.Rmd

---
title: "Data Storytelling 1 - Starting Out"
output: html_notebook
---
# Learning Objectives

- Learn about the `tv_shows` dataset
- Review `ggplot2` syntax
- Look at a simple visualization that we're going to improve

# RStudio Review (1 min)

- Script view 
- File Viewer

# Let's look at the basic dataset first (3 min)

We're going to look at a dataset about tv shows that was collected from ImDb (The internet movie database).

Run the code chunk below to load the data in and look at a table of the data.

```{r}
library(tidyverse)
library(here)

tv_shows <- read.csv(here("data/tv_shows.csv"))
DT::datatable(tv_shows)
```

# Part 1: Your Basic Plot (5 min)

We're going to start working with a basic visualization [define ggplot here]  A ggplot has the following recipe:

```
A dataset +
A mapping of visual aesthetics to variables +
A geom(etry)
```
Remember, we use `+` to add elements to a ggplot.

```{r}
library(ggplot2)

#A Dataset
ggplot(tv_shows) +

#A mapping of visual aesthetics to variables
  aes(x = seasonNumber, 
      y = av_rating, 
      group = title, 
      color = title) + 

#A geometry
  geom_line()
```

# Discussion (5 min)

While this visualization might be good for exploratory purposes, for explanatory purposes, it is a little too complicated to communicate our findings about the data. 

Before you move on, discuss on slack:

- What does this graph communicate?
- How could it be better?
- Are there any shows that stand out?

# What do we want to show?

We're going to highlight those shows that are `risers` in the data and make the point that shows that increase in ratings overall tend to stop early.

# Assigning our plot so we can reuse it (3 min).

Let's save our plot into an R object called a ggplot. We'll use the `<-` (left arrow) to assign it to the variable called `my_plot`:

```{r}
my_plot <- 
  ggplot(tv_shows) +
  aes(x = seasonNumber, 
      y = av_rating, 
      group = title, 
      color = title) + 
  geom_line()
```

Now, when we want to modify our plot, we can use `my_plot`. More on this in the next notebook. We're basically going to add commands to modify our plot. I like to think of it as a `paper doll` approach: we are dressing our plot in different clothes.

```{r}
my_plot
```

# What's next?

Open up `02-decluttering.Rmd`.