Trends

]

# Week 9: .fancy[Trends]

### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M243.4 2.587C251.4-.8625 260.6-.8625 268.6 2.587L492.6 98.59C506.6 104.6 514.4 119.6 511.3 134.4C508.3 149.3 495.2 159.1 479.1 160V168C479.1 181.3 469.3 192 455.1 192H55.1C42.74 192 31.1 181.3 31.1 168V160C16.81 159.1 3.708 149.3 .6528 134.4C-2.402 119.6 5.429 104.6 19.39 98.59L243.4 2.587zM256 128C273.7 128 288 113.7 288 96C288 78.33 273.7 64 256 64C238.3 64 224 78.33 224 96C224 113.7 238.3 128 256 128zM127.1 416H167.1V224H231.1V416H280V224H344V416H384V224H448V420.3C448.6 420.6 449.2 420.1 449.8 421.4L497.8 453.4C509.5 461.2 514.7 475.8 510.6 489.3C506.5 502.8 494.1 512 480 512H31.1C17.9 512 5.458 502.8 1.372 489.3C-2.715 475.8 2.515 461.2 14.25 453.4L62.25 421.4C62.82 420.1 63.41 420.6 63.1 420.3V224H127.1V416z"/></svg> EMSE 4572: Exploratory Data Analysis
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M224 256c70.7 0 128-57.31 128-128s-57.3-128-128-128C153.3 0 96 57.31 96 128S153.3 256 224 256zM274.7 304H173.3C77.61 304 0 381.6 0 477.3c0 19.14 15.52 34.67 34.66 34.67h378.7C432.5 512 448 496.5 448 477.3C448 381.6 370.4 304 274.7 304z"/></svg> John Paul Helveston
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M96 32C96 14.33 110.3 0 128 0C145.7 0 160 14.33 160 32V64H288V32C288 14.33 302.3 0 320 0C337.7 0 352 14.33 352 32V64H400C426.5 64 448 85.49 448 112V160H0V112C0 85.49 21.49 64 48 64H96V32zM448 464C448 490.5 426.5 512 400 512H48C21.49 512 0 490.5 0 464V192H448V464z"/></svg> October 26, 2022

]

---

# .fancy[.blue[Tip of the week]]

## Code outline in RStudio

---

## Today's data

Old:

```r
gapminder        <- read_csv(here::here('data', 'gapminder.csv'))
milk_production  <- read_csv(here::here('data', 'milk_production.csv'))
global_temps     <- read_csv(here::here('data', 'nasa_global_temps.csv'))
internet_region  <- read_csv(here::here('data', 'internet_users_region.csv'))
```

New:

```r
us_covid         <- read_csv(here::here('data', 'us_covid.csv'))
internet_country <- read_csv(here::here('data', 'internet_users_country.csv'))
hotdogs          <- read_csv(here::here('data', 'hot_dog_winners.csv'))
us_diseases      <- read_csv(here::here('data', 'us_contagious_diseases.csv'))
```

---

## New packages:

```r
install.packages('viridis')
install.packages('gganimate')
install.packages('magick')
```

---

# Week 9: .fancy[Trends]

## 1. Single Variables
## 2. Animations

## BREAK

## 3. Multiple Variables

---

# Week 9: .fancy[Trends]

## 1. .orange[Single Variables]
## 2. Animations

## BREAK

## 3. Multiple Variables

---

## Points

Plotting the data points is a good starting point for viewing trends

]

## Points + line

Adding lines between the points helps<br>see the overall trend

]

---

## Line

Omitting the points emphasizes the overall trend<br><br><br>

]

## Line + area

Filling area below line emphasizes cumulative over time

(y-axis should start at 0)

]

---

### If points are too sparse, a line can be misleading

]

]

---

## Smoothed line

Adding a "smoothed" line shows a modeled representation of the overall trend

]

## Smoothed line + points

Putting the smoothed line over the data points helps show whether **outliers** are driving the trend line

]

---

### Bars are useful when emphasizing the **data points**<br>rather than the **slope between them**

]

]

---

## How to: **Points + line**

Be sure to draw the line first,<br>then overlay the points

```r
ggplot(milk_ca,
*   aes(x = year, y = milk_produced)) +
*   geom_line(color = 'steelblue', size = 0.5) +
*   geom_point(color = 'steelblue', size = 2) +
    theme_half_open(font_size = 18) +
    labs(x = 'Year',
         y = 'Milk produced (billion lbs)',
         title = 'Milk production in California')
```

]]

]

---

## How to: **Line + area**

Likewise, draw the area first then overlay the line

```r
ggplot(milk_ca,
    aes(x = year, y = milk_produced)) +
*   geom_area(fill = 'steelblue', alpha = 0.5) +
*   geom_line(color = 'steelblue', size = 1) +
    scale_y_continuous(
        expand = expansion(mult = c(0, 0.05))) +
    theme_half_open(font_size = 18) +
    labs(x = 'Year',
         y = 'Milk produced (billion lbs)',
         title = 'Milk production in California')
```

]]

]

---

## How to: **Smoothed line + points**

Use `alpha` to make points slightly transparent

```r
ggplot(milk_ca,
    aes(x = year, y = milk_produced)) +
*   geom_point(color = 'grey',
*     size = 2, alpha = 0.9) +
*   geom_smooth(color = 'steelblue',
*     size = 1, se = FALSE) +
    theme_half_open(font_size = 18) +
    labs(
      x = 'Year',
      y = 'Milk produced (billion lbs)',
      title = 'Milk production in California')
```

]]

]

---

## Your turn

Use the `global_temps` data frame to explore ways to visualize the change in average global temperatures.

Consider using:

- points
- lines
- areas
- smoothed lines

]

```r
global_temps <- read_csv(here::here(
  'data', 'nasa_global_temps.csv'))

head(global_temps)
```

```
#> # A tibble: 6 × 3
#>    year meanTemp smoothTemp
#>   <dbl>    <dbl>      <dbl>
#> 1  1880    -0.15      -0.08
#> 2  1881    -0.07      -0.12
#> 3  1882    -0.1       -0.15
#> 4  1883    -0.16      -0.19
#> 5  1884    -0.27      -0.23
#> 6  1885    -0.32      -0.25
```
]

---

# Week 9: .fancy[Trends]

## 1. Single Variables
## 2. .orange[Animations]

## BREAK

## 3. Multiple Variables

---

### Animation adds emphasis to the **change over time**

### ...plus it's fun!

---

# Static chart

---

# Animated chart

---

### Animation is particularly helpful for the **time dimension**

---

### Animation is particularly helpful for the **time dimension**

---

### Animation is particularly helpful for the **time dimension**

---

### Animation is particularly helpful for the **time dimension**

---

## How to: **Animate a line plot**

Make a static plot w/labels for each year

```r
milk_region_anim_plot <- milk_region %>%
  ggplot(
    aes(x = year, y = milk_produced,
        color = region)) +
* geom_line(size = 1) +
* geom_point(size = 2) +
* geom_text_repel(
*   aes(label = region),
*   hjust = 0, nudge_x = 1, direction = "y",
*   size = 6, segment.color = NA) +
  scale_x_continuous(
    breaks = seq(1970, 2010, 10),
    expand = expansion(add = c(1, 13))) +
  scale_color_manual(values = c(
      'sienna', 'forestgreen', 'dodgerblue', 'orange')) +
  theme_half_open(font_size = 18) +
  theme(legend.position = 'none') +
  labs(x = 'Year',
       y = 'Milk produced (billion lbs)',
       title = 'Milk production in four US regions')

milk_region_anim_plot
```

]]

]

---

## How to: **Animate a line plot**

Now animate it

**Note the pause at the end!**

```r
*library(gganimate)

milk_region_anim <- milk_region_anim_plot +
*   transition_reveal(year)

# Render the animation
*animate(milk_region_anim,
*       end_pause = 15,
*       duration = 10,
*       width = 1100, height = 650, res = 150,
*       renderer = magick_renderer())

# Save last animation
anim_save(here::here(
  'figs', 'milk_region_animation.gif'))
```

]]

]

---

## How to: **Change label based on year**

First make a static plot

```r
gapminder_anim_plot <- ggplot(gapminder,
* aes(x = gdpPercap, y = lifeExp,
*     size = pop, color = continent)) +
  geom_point(alpha = 0.7) +
* scale_size_area(
*   guide = FALSE, max_size = 15) +
  scale_color_brewer(palette = 'Set2') +
  scale_x_log10() +
  theme_bw(base_size = 18) +
  theme(legend.position = c(0.85, 0.3)) +
  labs(x = 'GDP per capita',
       y = 'Life expectancy',
       color = 'Continent')

gapminder_anim_plot
```

]]

]

---

## How to: **Change label based on year**

Now animate it

**Note**: Year must be an integer!

```r
gapminder_anim <- gapminder_anim_plot +
* transition_time(year) +
* labs(title = "Year: {frame_time}")

# Render the animation
animate(gapminder_anim, end_pause = 10,
        width = 800, height = 600,
        res = 150,
        renderer = magick_renderer())
```

]]

]

---

```r
milk_race_anim <- milk_production %>%
    group_by(year) %>%
    mutate(
        rank = rank(-milk_produced),
        Value_rel = milk_produced / milk_produced[rank==1],
        Value_lbl = paste0(' ', round(milk_produced))) %>%
    group_by(state) %>%
    filter(rank <= 10) %>%
    ungroup() %>%
    mutate(year = as.integer(year)) %>%
    ggplot(aes(x = rank, group = state,
               fill = region, color = region)) +
*   geom_tile(aes(y = milk_produced / 2,
*                 height = milk_produced),
*             width = 0.9, alpha = 0.8, color = NA) +
    geom_text(aes(y = 0, label = paste(state, " ")),
              vjust = 0.2, hjust = 1) +
    geom_text(aes(y = milk_produced, label = Value_lbl),
              hjust = 0) +
    coord_flip(clip = 'off', expand = FALSE) +
    scale_y_continuous(labels = scales::comma) +
    scale_fill_viridis(discrete = TRUE) +
    scale_color_viridis(discrete = TRUE) +
    scale_x_reverse() +
    guides(color = FALSE) +
    theme_minimal_vgrid() +
    theme(
        axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        legend.position = c(0.7, 0.3),
        legend.background = element_rect(fill = 'white'),
        plot.title = element_text(
          size = 22, hjust = 0.5, face = 'bold',
          colour = 'grey', vjust = -1),
        plot.subtitle = element_text(
          size = 18, hjust = 0.5,
          face = 'italic', color = 'grey'),
        plot.caption = element_text(
          size = 8, hjust = 0.5,
          face = 'italic', color = 'grey'),
          plot.margin = margin(0.5, 2, 0.5, 3, 'cm')) +
*   transition_time(year) +
*   view_follow(fixed_x = TRUE) +
    labs(title    = 'Year : {frame_time}',
         subtitle = 'Top 10 states by milk produced',
         fill     = 'Region',
         caption  = 'Milk produced (billions lbs)')
```

]]

### .center[Making a bar chart race <br>[(tutorial here)](https://www.emilykuehler.com/portfolio/barchart-race/)]

```r
animate(milk_race_anim, duration = 17, end_pause = 15,
        width = 800, height = 700, res = 150,
        renderer = magick_renderer())
```

]

]

---

# Resources

### More animation options:

- [More on gapminder + line charts](https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/)
- [Customizing the animation](https://github.com/ropenscilabs/learngganimate/blob/2872425f08392f9f647005eb19a9d4afacd1ab44/animate.md)

---

## Your turn

Use the `global_temps` data frame to explore ways to _animate_ the change in average global temperatures.

Consider using:

- points
- lines
- areas

]

]

---

# Break!

## Stand up, Move around, Stretch!

---

# Week 9: .fancy[Trends]

## 1. Single Variables
## 2. Animations

## BREAK

## 3. .orange[Multiple Variables]

---

## With multiple categories,<br>points & lines can get messy

]

]

---

### **Better**: Lines alone makes distinguishing trends easier

]

### **Even better**: Directly label<br>lines to remove legend

]

---

### If goal is to communicate the **overall / total** trend,<br>consider a stacked area chart

### Highlights **regional** trends

]

### Highlights **overall / total** trend

]

---

### If you have **lots** of categories:<br>**1) Plot all the data with the average highlighted**

]

]

---

### If you have **lots** of categories:<br>1) Plot all the data with the average highlighted<br>**2) Plot all the data with a heat map**

---

## Heatmaps are great for multiple divisions of time

### My activity on Github:

###Check out this heat map on [Traffic fatalities](https://flowingdata.com/2017/04/27/traffic-fatalities-when-and-where/)

---

Make the basic line chart first

```r
# Format the data
milk_region <- milk_production %>%
  filter(region %in% c(
    'Pacific', 'Northeast', 'Lake States', 'Mountain')) %>%
  group_by(year, region) %>%
  summarise(milk_produced = sum(milk_produced)) %>%
  ungroup()

# Make the line chart
ggplot(milk_region,
* aes(x = year, y = milk_produced,
*     color = region)) +
* geom_line(size = 1) +
  scale_color_manual(values = c(
    'sienna', 'forestgreen', 'dodgerblue', 'orange')) +
  theme_half_open(font_size = 18) +
  labs(
    x     = 'Year',
    y     = 'Milk produced (billion lbs)',
    color = 'Region',
    title = 'Milk production in four US regions')
```

]]

## .center[How to:<br>**Directly label lines**]

<br>

]

---

# Make the line plot
ggplot(milk_region,
  aes(x = year, y = milk_produced,
      color = region)) +
  geom_line(size = 1) +
  # Add labels
* geom_text_repel(
*   data = milk_region %>%
*     filter(year == max(year)),
*   aes(label = region),
*     hjust = 0, nudge_x = 1, direction = "y",
*     size = 6, segment.color = NA) +
  # Create space for labels on right side
* scale_x_continuous(
*   breaks = seq(1970, 2010, 10),
*   expand = expansion(add = c(1, 13))) +
  scale_color_manual(values = c(
    'sienna', 'forestgreen', 'dodgerblue', 'orange')) +
  theme_half_open(font_size = 18) +
  # Remove legend
* theme(legend.position = 'none') +
  labs(x = 'Year',
       y = 'Milk produced (billion lbs)',
       title = 'Milk production in four US regions')
```

]]

## .center[How to:<br>**Directly label lines**]

<br>

]

---

## How to: **Stacked area**

```r
internet_region %>%
  mutate(numUsers = numUsers / 10^9) %>%
  ggplot() +
* geom_area(aes(x = year, y = numUsers,
*       fill = region)) +
  # Nice colors from "viridis" library:
* scale_fill_viridis(discrete = TRUE) +
  # Sort the legend into 3 rows
* guides(fill = guide_legend(
*   nrow = 3, byrow = FALSE)) +
  theme_minimal_grid(font_size = 15) +
  theme(legend.position = 'bottom') +
  labs(
    x = 'Year',
    y = NULL,
    fill = 'Region',
    title = 'Number of internet users (billions)')
```

]]

]

---

Format the data

```r
# Format the data
measles <- us_diseases %>%
  filter(
    disease == 'Measles',
    !state %in% c("Hawaii", "Alaska")) %>%
  mutate(
    rate = (count / population) * 10000,
    state = fct_reorder(state, rate)) %>%
  # Compute annual mean rate across all states
* group_by(year) %>%
* mutate(
*   mean_rate = sum(count) / sum(population) * 10000)
```

Make all the state lines in light grey color

```r
ggplot(measles) +
*   geom_line(aes(x = year, y = rate, group = state),
*             color = 'grey', alpha = 0.3) +
    # Add reference line & label:
    geom_vline(xintercept = 1963, col = 'blue',
               linetype = 'dashed') +
    annotate('text', x = 1964, y = 150, hjust = 0,
             label = 'Vaccine introduced in 1964',
             color = 'blue') +
    theme_minimal_grid(font_size = 18) +
    labs(y = 'Cases per 10,000 people')
```

]]

## How to:<br>**Average line overlay**

]

---

Now overlay the annual mean line

```r
ggplot(measles) +
  geom_line(
    aes(x = year, y = rate, group = state),
    color = 'grey', alpha = 0.3) +
* geom_line(
*   aes(x = year, y = mean_rate), size = 0.8) +
  # Add US mean label
* annotate(
*   'text', x = 1945, y = 55, hjust = 0,
*   label = 'US Mean') +
  # Add reference line & label
  geom_vline(xintercept = 1963, col = 'blue',
             linetype = 'dashed') +
  annotate('text', x = 1964, y = 150, hjust = 0,
           label = 'Vaccine introduced in 1964',
           color = 'blue') +
  theme_minimal_grid(font_size = 18) +
  labs(y = 'Cases per 10,000 people')
```

]]

## How to:<br>**Average line overlay**

]

---

Create main grid with `geom_tile()`

```r
ggplot(measles) +
* geom_tile(
*   aes(x = year, y = state, fill = rate),
*   color = 'grey80') +
  # Add reference line & label
  geom_vline(
    xintercept = 1963, col = 'blue') +
  annotate(
    'text', x = 1964, y = 50.5, hjust = 0,
    label = 'Vaccine introduced in 1964',
    color = 'blue')
```

]]

## How to: **Heat map**

]

---

Adjust scales and adjust theme

```r
ggplot(measles) +
  geom_tile(aes(x = year, y = state, fill = rate),
    color = 'grey80') +
  # Add reference line & label
  geom_vline(xintercept = 1963, col = 'blue') +
  annotate(
    'text', x = 1964, y = 50.5, hjust = 0,
    label = 'Vaccine introduced in 1964',
    color = 'blue') +
  # Adjust scales
* scale_x_continuous(expand = c(0, 0)) +
* scale_fill_viridis(
*   option = 'inferno', direction = -1) +
  # Adjust theme
* theme_minimal() +
* theme(
*   panel.grid = element_blank(),
*   legend.position = 'bottom',
*   text = element_text(size = 10)) +
* coord_cartesian(clip = 'off') +
  labs(
    x = NULL, y = NULL,
    fill = 'Cases per 10,000 people',
    title = 'Measles')
```

]]

#### .center[Color scale is linear in this chart]

]

---

Adjust scales and adjust theme

```r
ggplot(measles) +
  geom_tile(aes(x = year, y = state, fill = rate),
    color = 'grey80') +
  # Add reference line & label
  geom_vline(xintercept = 1963, col = 'blue') +
  annotate(
    'text', x = 1964, y = 50.5, hjust = 0,
    label = 'Vaccine introduced in 1964',
    color = 'blue') +
  # Adjust scales
  scale_x_continuous(expand = c(0, 0)) +
  scale_fill_viridis(
    option = 'inferno', direction = -1,
*   trans = 'sqrt') +
  # Modify legend color bar
* guides(fill = guide_colorbar(
*     title.position = 'top', reverse = TRUE)) +
  # Adjust theme
  theme_minimal() +
  theme(
    panel.grid = element_blank(),
    legend.position = 'bottom',
    text = element_text(size = 10)) +
  coord_cartesian(clip = 'off') +
  labs(
    x = NULL, y = NULL,
    fill = 'Cases per 10,000 people',
    title = 'Measles')
```

]]

#### .center[Non-linear color scale<br>helps with large variations]

]

---

## Your turn

Use the `us_covid` data frame to explore ways to visualize the number of daily cases using:

1. A labeled line chart
2. A stacked area chart
3. A heat map

]

```r
us_covid <- read_csv(here::here(
  'data', 'us_covid.csv'))

head(us_covid)
```

```
#> # A tibble: 6 × 7
#>   date         day state   cases_daily deaths_daily cases_total deaths_total
#>   <date>     <dbl> <chr>         <dbl>        <dbl>       <dbl>        <dbl>
#> 1 2020-01-23     1 Alabama           0            0           0            0
#> 2 2020-01-24     2 Alabama           0            0           0            0
#> 3 2020-01-25     3 Alabama           0            0           0            0
#> 4 2020-01-26     4 Alabama           0            0           0            0
#> 5 2020-01-27     5 Alabama           0            0           0            0
#> 6 2020-01-28     6 Alabama           0            0           0            0
```

]

---

# Two other examples for showing<br>change across mutliple categories

---

# Seasonal chart

.left[Source: http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#Seasonal%20Plot]

---

## Sankey chart

Source: https://flowcharts.llnl.gov/

---

Roberson, Laura A.  & Helveston, J.P. (2020) "Electric vehicle adoption: can short experiences lead to big change?," Environmental Research Letters. 15(0940c3).<br>Made using the [ggforce](https://www.data-imaginist.com/2019/the-ggforce-awakens-again/) package

]]