01-eda_hot_dogs.Rmd

---
title: "Lab 01: Nathan's Hot-Dog Eating Contest"
subtitle: "CS631"
author: "Alison Hill"
output:
  html_document:
    theme: flatly
    toc: TRUE
    toc_float: TRUE
    toc_depth: 2
    number_sections: TRUE
    code_folding: hide
---

```{r setup, include = FALSE, cache = FALSE}
knitr::opts_chunk$set(error = TRUE, comment = NA, warnings = FALSE, errors = FALSE, messages = FALSE, tidy = FALSE, cache = TRUE)
```

```{r load-packages, include = FALSE}
library(tidyverse)
library(extrafont)
```

# Goals for Lab 01

- Get your feet wet!
- Innoculate you against `ggplot2` errors- we all get them!
- Get exposed to the *range* of things you can do, before we go **deeep**...
- Develop your *own* **personal** preferences for data visualizations!
    - Do you like or hate gridlines?
    - What fonts do you find pleasant to read?
    - What kinds of colors do you like?
    - Are you team `theme_gray` or `theme_bw` (or `theme_minimal`)?
    
These are important questions, and I want you to develop (well-informed) opinions on these matters!
![](images/theme-team-tweets.png)


# Nathan's Hot Dog Eating Contest

![](https://i0.wp.com/flowingdata.com/wp-content/uploads/2009/06/hot-dogs1.gif?zoom=2&fit=900%2C423)

This includes a reconstruction of [Nathan Yau's hot dog contest example](http://flowingdata.com/2009/07/02/whos-going-to-win-nathans-hot-dog-eating-contest/hot-dogs-2/), as interpreted by Jackie Wirz, ported into R and `ggplot2` by Steven Bedrick for a workshop for the [OHSU Data Science Institute](https://ohsulibrary-datascienceinstitute.github.io), and finally adapted by Alison Hill for all you intrepid Data-Viz-onauts!


First, we load our packages: 


```{r eval=FALSE, message = FALSE, warning = FALSE}
library(tidyverse)
library(extrafont)
library(here)
```


# Read in and wrangle data

Next, we load some data. You can use the following chunk to load it in from a link:

```{r eval = FALSE}
hot_dogs <- read_csv("http://bit.ly/cs631-hotdog", 
    col_types = cols(
      gender = col_factor(levels = NULL)
    ))
```

Or you can save the file at the link to a local CSV file. I did this and saved my file in a folder called `data`, then built up the file path to the CSV using `here`:

```{r}
hot_dogs <- read_csv(here::here("data", "hot_dog_contest.csv"), 
    col_types = cols(
      gender = col_factor(levels = NULL)
    ))
```

Either way you do it, check it out once read in and make sure it looks like this!

```{r}
glimpse(hot_dogs)
hot_dogs
```

We'll be wanting to somehow include information about whether a given year was before or after the incorporation of the competitive eating league, so let's add an indicator field to the data using `mutate()`. Also, the data's a little sketchy pre-1981 and for our purposes today we'll be focusing on males only, so let's do some `filter`ing too:

```{r}
hot_dogs <- hot_dogs %>% 
  mutate(post_ifoce = year >= 1997) %>% 
  filter(year >= 1981 & gender == 'male')
hot_dogs
```



# Plot The Data

Now let's try making a first crack at a sketchy plot:

```{r}
ggplot(hot_dogs, aes(x = year, y = num_eaten)) + 
  geom_col()
```

Note that our data is already in "counted" form, so we're using `geom_col()` instead of `geom_bar()`.


# Add Axis Labels And Title


```{r}
ggplot(hot_dogs, aes(x = year, y = num_eaten)) + 
  geom_col() +
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017")
```

# Play With Colors

<div class="panel panel-success">
  <div class="panel-heading">Challenge #1:</div>
  <div class="panel-body">
Make 3 versions of the last plot we just made:

* __In the first,__ make all the columns outlined in "white".
* __In the second,__ make all the columns outlined in "white" and filled in "navyblue".
* __In the third,__ make all the columns outlined in "white" and filled in according to whether or not `post_ifoce` is TRUE or FALSE (use default colors for now).
  </div>
</div>



```{r}
ggplot(hot_dogs, aes(x = year, y = num_eaten)) + 
  geom_col(colour = "white") + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017")
```

```{r}
ggplot(hot_dogs, aes(x = year, y = num_eaten)) + 
  geom_col(colour = "white", fill = "navyblue") + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017")
```

```{r}
ggplot(hot_dogs, aes(x = year, y = num_eaten)) + 
  geom_col(aes(fill = post_ifoce), colour = "white") + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017")
```



<div class="panel panel-success">
  <div class="panel-heading">Challenge #2:</div>
  <div class="panel-body">
What if you want to change the legend in the last plot you made? Use google to figure out how to do the following:

* Delete the legend title
* Make the legend text either "Post-IFOCE" or "Pre-IFOCE".
  </div>
</div>

```{r}
ggplot(hot_dogs, aes(x = year, y = num_eaten)) + 
  geom_col(aes(fill = post_ifoce), colour = "white") + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017") +
  scale_fill_discrete(name = "",
                      labels=c("Pre-IFOCE", "Post-IFOCE"))
```

# Change The Dataset

Now, let's change the question a little bit. This looks at the _creation_ of the IFOCE. What about the _affiliation_ of the contestants? We'll need some different data for this. Through the _Magic Of Data Science™_, we have dug that information up and put it into an expanded version of our CSV file available at [http://bit.ly/cs631-hotdog-affiliated](http://bit.ly/cs631-hotdog-affiliated).


<div class="panel panel-success">
  <div class="panel-heading">Challenge #3:</div>
  <div class="panel-body">
Let's work with this new dataset! Do the following:

* Read in the "hot_dog_contest_with_affiliation.csv" data file, using `col_types` to read in `affiliated` and `gender` as factors. 
* Within a `mutate`, create a new variable called `post_ifoce` that is TRUE if `year` is greater than or equal to 1997. 
* Also `filter` the new data for only years 1981 and after, and only for male competitors.
  </div>
</div>

```{r eval = FALSE}
hdm_affil <- read_csv("http://bit.ly/cs631-hotdog-affiliated", 
    col_types = cols(
      affiliated = col_factor(levels = NULL), 
      gender = col_factor(levels = NULL)
      )) %>% 
  mutate(post_ifoce = year >= 1997) %>% 
  filter(year >= 1981 & gender == "male") 
```


```{r}
hdm_affil <- read_csv(here::here("data", "hot_dog_contest_with_affiliation.csv"), 
    col_types = cols(
      affiliated = col_factor(levels = NULL), 
      gender = col_factor(levels = NULL)
      )) %>% 
  mutate(post_ifoce = year >= 1997) %>% 
  filter(year >= 1981 & gender == "male") 
glimpse(hdm_affil)
```

<div class="panel panel-success">
  <div class="panel-heading">Challenge #4:</div>
  <div class="panel-body">
Let's do some basic EDA with this new dataset! Do the following:

* Use `dplyr::distinct` to figure out how many unique values there are of `affiliated`.
* Use `dplyr::count` to count the number of rows for each unique value of `affiliated`; use `?count` to figure out how to sort the counts in descending order.
  </div>
</div>

```{r}
hdm_affil %>% 
  distinct(affiliated)
hdm_affil %>% 
  count(affiliated, sort = TRUE)
```


Now let's plot this new data, and fill the columns according to our new `affiliated` column.

```{r}
ggplot(hdm_affil, aes(x = year, y = num_eaten)) + 
  geom_col(aes(fill = affiliated)) + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017")
```

<div class="panel panel-success">
  <div class="panel-heading">Challenge #5:</div>
  <div class="panel-body">
Do the following updates to the last plot we just made:

* Update the colors using hex colors: `c('#E9602B','#2277A0','#CCB683')`. 
* Change the legend title to "IFOCE-affiliation". 
* Save this plot object as "affil_plot".
  </div>
</div>

```{r}
affil_plot <- ggplot(hdm_affil, aes(x = year, y = num_eaten)) + 
  geom_col(aes(fill = affiliated)) + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017") +
  scale_fill_manual(values = c('#E9602B','#2277A0','#CCB683'),
                    name = "IFOCE-affiliation")
affil_plot
```

# Play With Scales & Coordinates

The spacing's a little funky down near the origin of the plot. The [documentation](http://ggplot2.tidyverse.org/reference/scale_continuous.html) tells us that the defaults are `c(0.05, 0)` for continuous variables. The first number is multiplicative and the second is additive. 

The default was that 1.8 ((2017-1981)*.05+0) was added to the right and left sides of the x-axis as padding, so the effective default limits were `c(1979, 2019)`.

Let's tighten that up with the `expand` property for the `scale_y_continuous` (we'll also change the breaks for y-axis tick marks here) and `scale_x_continuous` settings:

```{r}
affil_plot <- affil_plot + 
  scale_y_continuous(expand = c(0, 0),
                     breaks = seq(0, 70, 10)) +
  scale_x_continuous(expand = c(0, 0))
affil_plot
```



But now the plot looks like it is wearing tight pants.

![](https://media.giphy.com/media/xT1XH07no2wZSq4mnm/giphy.gif)

Let's loosen things up a bit by updating the plot coordinates. 

<div class="panel panel-success">
  <div class="panel-heading">Challenge #6:</div>
  <div class="panel-body">
Use `coord_cartesian` to:

* Set the x-axis range to 1980-2018
* Set the y-axis range to 0-80
  </div>
</div>

Using `coord_cartesian` is the preferred layer here because "setting limits on the coordinate system will zoom the plot (like you're looking at it with a magnifying glass), and will not change the underlying data like setting `limits` on a scale will."  

<div class="panel panel-info">
  <div class="panel-heading">Lesson:</div>
  <div class="panel-body">
Don't change `limits` unless you really know what you are doing! Most of the time, you want to change the coordinates instead.
  </div>
</div>

```{r}
affil_plot <- affil_plot + 
  coord_cartesian(xlim = c(1980, 2018), ylim = c(0, 80)) 
affil_plot
```


# Play With Theme Settings

Let's change some key theme settings:

```{r}
affil_plot +
  theme(plot.title = element_text(hjust = 0.5)) +
  theme(axis.text = element_text(size = 12)) +
  theme(panel.background = element_blank()) +
  theme(axis.line.x = element_line(color = "gray80", size = 0.5)) +
  theme(axis.ticks = element_line(color = "gray80", size = 0.5))
```

<div class="panel panel-info">
  <div class="panel-heading">Lesson:</div>
  <div class="panel-body">
You can change *almost anything* that your heart desires to change! 
  </div>
</div>


By default, plot titles in `ggplot2` are left-aligned. For `hjust`:

- `0` == left
- `0.5` == centered
- `1` == right

We could also save all these as a custom theme. We are not fans of the default font, so we are also going to change this. To do this, you need to install the (`extrafont` package)[https://github.com/wch/extrafont] and follow its setup instructions before doing this next step.

```{r}
hot_diggity <- theme(plot.title = element_text(hjust = 0.5),
                     axis.text = element_text(size = 12),
                     panel.background = element_blank(),
                     axis.line.x = element_line(color = "gray80", size = 0.5),
                     axis.ticks = element_line(color = "gray80", size = 0.5),
                     text = element_text(family = "Lato") # need extrafont for this
                     )
```



```{r}
affil_plot + hot_diggity 
```

We could also use someone else's theme:

```{r}
library(ggthemes)
affil_plot + theme_fivethirtyeight(base_family = "Lato")
affil_plot + theme_tufte(base_family = "Palatino")
```




The final thing we have to mess with is the x-axis ticks and labels. We'll do this in two steps, then override our previous layer `scale_x_continuous`.

```{r}
years_to_label <- seq(from = 1981, to = 2017, by = 4)
years_to_label
hd_years <- hdm_affil %>%
  distinct(year) %>% 
  mutate(year_lab = ifelse(year %in% years_to_label, year, ""))
```

```{r}
affil_plot + 
  hot_diggity +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = hd_years$year,
                     labels = hd_years$year_lab)
```

# Final (final, final) version

Don't name your files "final" :)

![](http://www.phdcomics.com/comics/archive/phd101212s.gif)

All together in one chunk, here is our final (for now) plot! I'm also adding some additional elements here to show you options:

```{r}
nathan_plot <- ggplot(hdm_affil, aes(x = year, y = num_eaten)) + 
  geom_col(aes(fill = affiliated)) + 
  labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
  ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017") +
  scale_fill_manual(values = c('#E9602B','#2277A0','#CCB683'),
                    name = "IFOCE-affiliation") + 
  hot_diggity +
  scale_y_continuous(expand = c(0, 0),
                     breaks = seq(0, 70, 10)) +
  scale_x_continuous(expand = c(0, 0), 
                     breaks = hd_years$year,
                     labels = hd_years$year_lab) + 
  coord_cartesian(xlim = c(1980, 2018), ylim = c(0, 80)) 
nathan_plot
```

Adding some plot annotations rather than having a fill legend:

```{r}
nathan_ann <- nathan_plot +
  guides(fill = FALSE) +
  coord_cartesian(xlim = c(1980, 2019), ylim = c(0, 85)) +
  annotate('segment', x=1980.75, xend=2000.25, y= 30, yend=30, size=0.5, color="#CCB683") +
  annotate('segment', x=1980.75, xend=1980.75, y= 30, yend=28, size=0.5, color="#CCB683") +
  annotate('segment', x=2000.25, xend=2000.25, y= 30, yend=28, size=0.5, color="#CCB683") +
  annotate('segment', x=1990, xend=1990, y= 33, yend=30, size=0.5, color="#CCB683") +
    annotate('text', x=1990, y=36, label="No MLE/IFOCE Affiliation", color="#CCB683", family="Lato", hjust=0.5, size = 3) + 

  
  
  annotate('segment', x=2000.75, xend=2006.25, y= 58, yend=58, size=0.5, color="#2277A0") +
  annotate('segment', x=2000.75, xend=2000.75, y= 58, yend=56, size=0.5, color="#2277A0") +
  annotate('segment', x=2006.25, xend=2006.25, y= 58, yend=56, size=0.5, color="#2277A0") +
  annotate('segment', x=2003.5, xend=2003.5, y= 61, yend=58, size=0.5, color="#2277A0") +
  annotate('text', x=2003.5, y=65, label="MLE/IFOCE\nFormer Member", color="#2277A0", family="Lato", hjust=0.5, size = 3) + 

  
  annotate('segment', x=2006.75, xend=2017.25, y= 76, yend=76, size=0.5, color="#E9602B") +
  annotate('segment', x=2006.75, xend=2006.75, y= 76, yend=74, size=0.5, color="#E9602B") +
  annotate('segment', x=2017.25, xend=2017.25, y= 76, yend=74, size=0.5, color="#E9602B") +
  annotate('segment', x=2012, xend=2012, y= 79, yend=76, size=0.5, color="#E9602B") +
  annotate('text', x=2012, y=82, label="MLE/IFOCE Current Member", color="#E9602B", family="Lato", hjust=0.5, size = 3) 
nathan_ann
```

Finally, adding in another layer of data from female contestants:

```{r}
hdm_females <- read_csv(here::here("data", "hot_dog_contest_with_affiliation.csv"), 
    col_types = cols(
      affiliated = col_factor(levels = NULL), 
      gender = col_factor(levels = NULL)
      )) %>% 
  mutate(post_ifoce = year >= 1997) %>% 
  filter(year >= 1981 & gender == "female") 
glimpse(hdm_females)
```

```{r}
nathan_w_females <- nathan_ann +
  # add in the female data, and manually set a fill color
  geom_col(data = hdm_females, 
           width = 0.75, 
           fill = "#F68A39") 
nathan_w_females
```

And adding a final caption:

```{r}
caption <- paste(strwrap("* From 2011 on, separate Men's and Women's prizes have been awarded. All female champions to date have been MLE/IFOCE-affiliated.", 70), collapse="\n")

nathan_w_females +
  # now an asterisk to set off the female scores, and a caption
  annotate('text', x = 2018.5, y = 39, label="*", family = "Lato", size = 8) +
  labs(caption = caption) +
  theme(plot.caption = element_text(family = "Lato", size=8, hjust=0, margin=margin(t=15)))
```