gganimate - Making a bar race

class: center, middle, inverse, title-slide

.title[
# <img src="./figures/bat-cartoon.png" /> gganimate - Making a bar race
]
.author[
### <font size="5"> Son Nguyen </font>
]

---

<style>

.remark-slide-content {
  background-color: #FFFFFF;
  border-top: 80px solid #F9C389;
  font-size: 17px;
  font-weight: 300;
  line-height: 1.5;
  padding: 1em 2em 1em 2em
}

.inverse {
  background-color: #696767;
  border-top: 80px solid #696767;
  text-shadow: none;
  background-image: url(https://github.com/goodekat/presentations/blob/master/2019-isugg-gganimate-spooky/figures/spider.png?raw=true);
	background-position: 50% 75%;
  background-size: 150px;
}

.your-turn{
  background-color: #8C7E95;
  border-top: 80px solid #F9C389;
  text-shadow: none;
  background-image: url(https://github.com/goodekat/presentations/blob/master/2019-isugg-gganimate-spooky/figures/spider.png?raw=true);
	background-position: 95% 90%;
  background-size: 75px;
}

.title-slide {
  background-color: #F9C389;
  border-top: 80px solid #F9C389;
  background-image: none;
}

.title-slide > h1  {
  color: #111111;
  font-size: 40px;
  text-shadow: none;
  font-weight: 400;
  text-align: left;
  margin-left: 15px;
  padding-top: 80px;
}
.title-slide > h2  {
  margin-top: -25px;
  padding-bottom: -20px;
  color: #111111;
  text-shadow: none;
  font-weight: 300;
  font-size: 35px;
  text-align: left;
  margin-left: 15px;
}
.title-slide > h3  {
  color: #111111;
  text-shadow: none;
  font-weight: 300;
  font-size: 25px;
  text-align: left;
  margin-left: 15px;
  margin-bottom: -30px;
}

</style>

---

class: inverse, middle, center

# Introduction

---

# What is gganimate?

- An extension to ggplot2 for creating animated graphics in R

- Originally written by David Robinson

- Updated by Thomas Lin Pedersen

- Code from a year(ish) ago will probably not work anymore

- Uses the R package [tweenr](https://github.com/thomasp85/tweenr) (also by Lin Pedersen)

- Pedersen describes it as...

> "a theoretical backbone of how to describe animation, and it's also an implementation of set theoretical backbones."

---

# gganimate Example

```r
# Load packages
library(gapminder)
library(gganimate)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(knitr)
g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = c("darkorchid4", "orange", 
                                 "purple", "darkorange", "black")) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  labs(title = 'Year: {frame_time}', 
       x = 'GDP per capita', 
       y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

animate(g, nframes = 400)
```

---

# gganimate Example

---

# gganimate Example

---
# How does it work?

From the documentation...

> gganimate provides "a range of new grammar classes that can be added to the plot object in order to customize how it should change with time."

<br>

---

# Transition: <font color="black"> States </font>

`transition_states`: Transition between *values* of a variable

Principles:

- `Step 1`: data is split in subsets according to the values of a variable

- `Step 2`: each subset generates a plot

- `Step 3`:	plots of subsets are combined into an animation

---

# Set up

Packages to load if you are following along

```r
# Loads the library
library(gganimate)
# Other libraries that will be used
library(ggplot2)
library(tidyverse)
library(knitr)
```

---

# Data

.left-code[

```r
# Create a dataset
data <- tibble(salary = c(1000, 2000), 
               year = c(2019, 2020))
kable(data)
```

]
.right-plot[

| salary| year|
|------:|----:|
|   1000| 2019|
|   2000| 2020|
]

---

# Static Plot

.left-code[

```r
*ggplot(data,
*      aes(x = year,
*          y = salary))
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-4-1.png" style="display: block; margin: auto;" />
]

---

# Static Plot

.left-code[

```r
ggplot(data, 
       aes(x = year, 
           y = salary))+ 
* geom_point(size=4)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-5-1.png" style="display: block; margin: auto;" />
]

---

# State 1

.left-code[

```r
ggplot(data %>% filter(year==2019), 
       aes(x = year, 
           y = salary))+ 
  geom_point(size=4)+
  xlim(2019, 2020)+
  ylim(1000, 2000)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
]

---

# State 2

.left-code[

```r
ggplot(data %>% filter(year==2020), 
       aes(x = year, 
           y = salary))+ 
  geom_point(size=4)+
  xlim(2019, 2020)+
  ylim(1000, 2000)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" />
]

---

# Combining States

.left-code[

```r
g <- ggplot(data, 
       aes(x = year, 
           y = salary))+
  geom_point(size=4) + 
* transition_states(year)
animate(g)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-8-1.gif" style="display: block; margin: auto;" />
]

---
# Static Plot

.left-code[

```r
*ggplot(data,
*      aes(x = year,
*          y = salary))
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-9-1.png" style="display: block; margin: auto;" />
]
---
# Static Plot

.left-code[

```r
ggplot(data, 
       aes(x = year, 
           y = salary))+ 
* geom_col()
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-10-1.png" style="display: block; margin: auto;" />
]

---
# State 1

.left-code[

```r
ggplot(data %>% filter(year==2019), 
       aes(x = year, 
           y = salary))+ 
  geom_col()+
  xlim(2018.5, 2020.5)+
  ylim(0, 2000)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-11-1.png" style="display: block; margin: auto;" />
]

---
# State 2

.left-code[

```r
ggplot(data %>% filter(year==2020), 
       aes(x = year, 
           y = salary))+ 
  geom_col()+
  xlim(2018.5, 2020.5)+
  ylim(0, 2000)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-12-1.png" style="display: block; margin: auto;" />
]

---
# Combining States

.left-code[

```r
g <- ggplot(data, 
       aes(x = year, 
           y = salary))+
  geom_col() + 
* transition_states(year)
animate(g)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-13-1.gif" style="display: block; margin: auto;" />
]

---
# Combining States

.left-code[

```r
g <- ggplot(data, 
       aes(x = year, 
           y = salary, 
*          fill = salary))+
  geom_col() + 
  transition_states(year) 
animate(g)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-14-1.gif" style="display: block; margin: auto;" />
]

---
# Second Data

.left-code[

```r
data <- data.frame(
  salary = c(1000, 2000, 5000, 20000), 
    year = c(2019, 2020, 2019, 2020), 
    Job=c('Part Time','Part Time',
          'Full-time','Full-time'))
kable(data)
```
]
.right-plot[

| salary| year|Job       |
|------:|----:|:---------|
|   1000| 2019|Part Time |
|   2000| 2020|Part Time |
|   5000| 2019|Full-time |
|  20000| 2020|Full-time |
]

---
# Static Plot

.left-code[

```r
ggplot(data, 
       aes(x=Job, 
           y=salary, 
           fill=salary))+ 
  geom_col()
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-16-1.png" style="display: block; margin: auto;" />
]

---
# State 1

.left-code[

```r
ggplot(data %>% filter(year==2019), 
       aes(x=Job, 
           y=salary, 
           fill=salary))+ 
  geom_col()+
  ylim(0, 25000)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
]

---
# State 2

.left-code[

```r
ggplot(data %>% 
         filter(year==2020), 
       aes(x=Job, 
           y=salary, 
           fill=salary)) + 
  geom_col()+
  ylim(0, 25000)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" />
]

---
# Combining States
.left-code[

```r
g <- ggplot(data, aes(x=Job, 
                 y=salary, 
                 fill=salary)) + 
  geom_col()+
  ylim(0, 25000)+
* transition_states(year)
animate(g)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-19-1.gif" style="display: block; margin: auto;" />
]

---
# Combining States
.left-code[

```r
g <- ggplot(data, aes(x=Job, 
                 y=salary, 
                 fill=salary)) + 
  geom_col()+
  ylim(0, 25000)+
  transition_states(year)+
* labs(title = 'Year: {closest_state}')
animate(g)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-20-1.gif" style="display: block; margin: auto;" />
]

---
# Compare NY and FL

Let's make an animation to compare the number of covid-19 cases in NY and FL from July to December 2020

- Step 1: Make the data for ploting:  Calculate the number of cases for these two states in these months

- Step 2: Make an animation with `transition_states` through variable `month`
---
# Step 1

```r
df <- read_csv('https://covidtracking.com/data/download/all-states-history.csv')
df$month <- month(df$date)
```

.left-code[

```r
d1 <- df %>% 
  filter(state %in% c('NY','FL'),
                      month>6)%>% 
  group_by(month, state) %>% 
  summarise(mean = mean(positive)) 
kable(d1)
```
]
.right-plot[

| month|state |      mean|
|-----:|:-----|---------:|
|     7|FL    |  309311.5|
|     7|NY    |  404663.0|
|     8|FL    |  556725.0|
|     8|NY    |  425254.1|
|     9|FL    |  662531.9|
|     9|NY    |  446471.9|
|    10|FL    |  741519.4|
|    10|NY    |  481957.9|
|    11|FL    |  881535.2|
|    11|NY    |  567379.9|
|    12|FL    | 1140378.6|
|    12|NY    |  807812.5|
]

---
# Step 2

.left-code[

```r
p1 <- d1 %>% 
  ggplot(aes(y=state, x=mean)) + 
  geom_col()+
  transition_states(month)+
  labs(x = 'Total Positive', 
       title = 'Month: {closest_state}')

animate(p1, nframes=400)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-23-1.gif" style="display: block; margin: auto;" />
]

---
# Make a bar race

- Position of states' bars are not fixed
- But the ranks are fixed. 
- We need to create the rank variable to rank the states in each month

---
# Craete the rank variable

.left-code[

```r
d2 <- d1 %>% 
  group_by(month) %>% 
  mutate(rank=rank(-mean))
kable(d2)
```
]
.right-plot[

| month|state |      mean| rank|
|-----:|:-----|---------:|----:|
|     7|FL    |  309311.5|    2|
|     7|NY    |  404663.0|    1|
|     8|FL    |  556725.0|    1|
|     8|NY    |  425254.1|    2|
|     9|FL    |  662531.9|    1|
|     9|NY    |  446471.9|    2|
|    10|FL    |  741519.4|    1|
|    10|NY    |  481957.9|    2|
|    11|FL    |  881535.2|    1|
|    11|NY    |  567379.9|    2|
|    12|FL    | 1140378.6|    1|
|    12|NY    |  807812.5|    2|
]

---
# Make the animation

.left-code[

```r
p1 <- d2 %>% 
  ggplot(aes(y=factor(rank), 
             x=mean, group=state, 
             fill=state)) + 
  geom_col()+
  transition_states(month)+
  labs(x = 'Total Positive', 
       title = 'Month: {closest_state}')

animate(p1, nframes=400)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-25-1.gif" style="display: block; margin: auto;" />
]

---
# Make it looks nicer

.left-code[

```r
p1 <- d2 %>% 
  ggplot(aes(x=rank, 
             y=mean, 
             group=state, 
             fill=state, 
             label=state))+ 
     geom_col()+
    geom_text(aes(y = mean, 
                  label = state), 
              hjust = 1.4)+ 
    coord_flip(clip = "off", 
               expand = FALSE)+
    scale_x_reverse()+
    labs(title='Month {closest_state}', 
         y='Total Positive', x='', fill='')+
    theme(plot.title = 
          element_text(hjust=1,size=22),
          axis.ticks.y=element_blank(),
          axis.text.y=element_blank())+ 
    transition_states(month)+
    ease_aes("cubic-in-out")

animate(p1, nframes=400)
```
]
.right-plot[
<img src="8_viz_bar_race_files/figure-html/unnamed-chunk-26-1.gif" style="display: block; margin: auto;" />
]

---
# Bar Race - Covid19

```r
df <- read_csv('https://covidtracking.com/data/download/all-states-history.csv')
df <- df %>% filter(deathIncrease>0, positiveIncrease>0)

library(lubridate)

df$week <- week(df$date)
d1 <- df %>% group_by(week, state) %>% summarise(mean = mean(positive))
d2 <- d1 %>% group_by(week) %>% mutate(rank=rank(-mean)) 
d3 <- d2 %>% filter(rank <= 10)

a1 <- d3 %>% ggplot(aes(x=rank, y=mean, group=state, fill=state, label=state)) + geom_col()+
    geom_text(aes(y = mean, label = state), hjust = 1.4)+ 
    coord_flip(clip = "off", expand = FALSE) +scale_x_reverse()+
    labs(title = 'Week {closest_state}', x='', y='Total Number of Positive Caeses', fill='state')+
    theme(plot.title = element_text(hjust = 1, size = 22),
          axis.ticks.y = element_blank(),
          axis.text.y  = element_blank()) + 
    transition_states(week)+
    ease_aes("cubic-in-out")

animate(a1, nframes = 400)
```

---
# Bar Race - Covid19