TidyTuesday_CDC.Rmd

---
title: "TidyTuesday: CDC"
output: html_document
---

# Load all packages
```{r}
# clear workspace
rm(list = ls())

# install necessary packages
library(install.load)
packages <-
  c(
    "tidylog",
    "dplyr",
    "tidyr",
    "magrittr",
    "stringr",
    "lubridate",
    "ggplot2",
    "openintro",
    "patchwork",
    "wesanderson",
    "gganimate",
    "forcats",
    "magick"
  )

install_load(packages)

# Install via
devtools::install_github("thebioengineer/tidytuesdayR")
library(tidytuesdayR)

# also, install the paletti package from github
devtools::install_github("edwinth/paletti")
library(paletti)
```

# Load the data
```{r}
# Get the Data
tbi_military <-
  readr::read_csv(
    'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-24/tbi_military.csv'
  )
```

# Theme for plot

For this week, we generated a color-matching theme for the CDC. We based the theme losely on the code by [Statworx](https://www.statworx.com/de/blog/custom-themes-in-ggplot2/).
```{r}
theme_new <- function(base_size = 15,
                      base_family = "",
                      base_line_size = base_size / 170,
                      base_rect_size = base_size / 170) {
  # using the theme_minimal as the baseline theme
  theme_minimal(
    base_size = base_size,
    base_family = base_family,
    base_line_size = base_line_size
  ) %+replace%
    
    # general theme
    theme(
      plot.title = element_text(
        color = "#E2E6E9",
        face = "bold",
        hjust = 0.5
      ),
      axis.title = element_text(color = "#E2E6E9",
                                size = rel(1)),
      axis.text = element_text(color = "#E2E6E9",
                               size = rel(1)),
      axis.title.x = element_blank(),
      axis.title.y = element_blank(),
      axis.ticks.y = element_blank(),
      axis.text.x = element_blank(),
      panel.background = element_rect(
        fill = "#3d3d3d",
        colour = "#3d3d3d",
        size = 0.5,
        linetype = "solid"
      ),
      
      # plot specifics
      plot.background = element_rect(fill = "#3d3d3d"),
      panel.grid.major = element_line(linetype = "blank", colour = "#728393"),
      panel.grid.minor = element_line(linetype = "blank", colour = "#728393"),
      panel.grid.major.x = element_blank(),
      
      # legend elements
      legend.text = element_text(color = "#E2E6E9", size = rel(1)),
      legend.title = element_text(
        color = "#E2E6E9",
        size = rel(1),
        hjust = 0
      ),
      legend.position = "none",
      
      plot.caption = element_text(
        size = 13,
        color = "#E2E6E9",
        hjust = 1
      ),
      complete = TRUE
    )
}

# We used https://html-color-codes.info/colors-from-image/ to identify the colors of CDCs graphs for TBI data
colors <- c(
  purple = "#7030A0",
  blue = "#2E75B6",
  green = "#548235",
  yellow = "#FFC001"
)

# Using the paletti package, we create our own color palette using the CDC colors
mycols_fill  <- get_scale_fill(get_pal(colors))
```

# Generate plots
We base our plots losely on [this code](https://medium.com/@korkmazarda1/creating-an-animated-bar-plot-in-r-de9200f57506) provided in Medium.

```{r}
total_army <- tbi_military %>%
  filter(severity != "Not Classifiable" & service=="Army") %>%
  group_by(year) %>% 
  summarise(total_number = sum(diagnosed, na.rm=TRUE)) %>% 
  ungroup() 
```

## Army
```{r}
army <- tbi_military %>%
  filter(severity != "Not Classifiable" & service == "Army") %>%
  left_join(total_army, by = "year") %>%
  group_by(severity, year) %>%
  dplyr::mutate(
    diagnosed_sum = sum(diagnosed, na.rm = TRUE),
    rel_number = (diagnosed_sum / total_number) * 100
  ) %>%
  ungroup() %>%
  mutate(severity = fct_rev(severity)) %>%
  ggplot(aes(x = severity, y = rel_number, fill = severity)) +
  geom_bar(stat = "identity") +
  labs(title = "\nArmy in {closest_state}") +
  geom_text(aes(y = rel_number, label = paste(round(rel_number, 2), "%")),
            hjust = 0,
            color = "#E2E6E9") +
  coord_flip() +
  theme_new() +
  mycols_fill() +
  labs(caption =
         " \n ") +
  transition_states(
    states = year,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

army2 <- animate(army, fps = 5, height=400, width=400)
```

## Navy
```{r}
navy <- tbi_military %>%
  filter(severity != "Not Classifiable" & service == "Navy") %>%
  left_join(total_army, by = "year") %>%
  group_by(severity, year) %>%
  dplyr::mutate(
    diagnosed_sum = sum(diagnosed, na.rm = TRUE),
    rel_number = (diagnosed_sum / total_number) * 100
  ) %>%
  ungroup() %>%
  mutate(severity = fct_rev(severity)) %>%
  ggplot(aes(x = severity, y = rel_number, fill = severity)) +
  geom_bar(stat = "identity") +
  labs(title = "Navy in {closest_state}") +
  geom_text(aes(y = rel_number, label = paste(round(rel_number, 2), "%")),
            hjust = 0,
            color = "#E2E6E9") +
  coord_flip() +
  theme_new() +
  theme(axis.text.y = element_blank(),
        axis.text.x = element_blank()) +
  mycols_fill() +
  labs(caption =
         " \n ") +
  transition_states(
    states = year,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

navy2 <- animate(navy, fps = 5, height=400, width=400)
```

## Marines
```{r}
marines <- tbi_military %>%
  filter(severity != "Not Classifiable" & service == "Marines") %>%
  left_join(total_army, by = "year") %>%
  group_by(severity, year) %>%
  dplyr::mutate(
    diagnosed_sum = sum(diagnosed, na.rm = TRUE),
    rel_number = (diagnosed_sum / total_number) * 100
  ) %>%
  ungroup() %>%
  mutate(severity = fct_rev(severity)) %>%
  ggplot(aes(x = severity, y = rel_number, fill = severity)) +
  geom_bar(stat = "identity") +
  labs(title = "Marines in {closest_state}") +
  geom_text(aes(y = rel_number, label = paste(round(rel_number, 2), "%")),
            hjust = 0,
            color = "#E2E6E9") +
  coord_flip() +
  theme_new() +
  theme(axis.text.y = element_blank(),
        axis.text.x = element_blank()) +
  mycols_fill() +
  labs(caption =
         " \n ") +
  transition_states(
    states = year,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

marines2 <- animate(marines, fps = 5, height=400, width=400)
```

## Air Force
```{r}
airforce <- tbi_military %>%
  filter(severity != "Not Classifiable" &
           service == "Air Force") %>%
  left_join(total_army, by = "year") %>%
  group_by(severity, year) %>%
  dplyr::mutate(
    diagnosed_sum = sum(diagnosed, na.rm = TRUE),
    rel_number = (diagnosed_sum / total_number) * 100
  ) %>%
  ungroup() %>%
  mutate(severity = fct_rev(severity)) %>%
  ggplot(aes(x = severity, y = rel_number, fill = severity)) +
  geom_bar(stat = "identity") +
  labs(title = "Air Force in {closest_state}") +
  geom_text(aes(y = rel_number, label = paste(round(rel_number, 2), "%")),
            hjust = 0,
            color = "#E2E6E9") +
  coord_flip() +
  theme_new() +
  theme(axis.text.y = element_blank(),
        axis.text.x = element_blank()) +
  mycols_fill() +
  labs(caption =
         "Mild But Serious: Visualizing TBI Data (2006-2014)\n Visualization: @cosima_meyer & @d_hammers") +
  transition_states(
    states = year,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')

airforce2 <- animate(airforce, fps = 5, height=400, width=400)
```

# Save the plot as a gif
To do so, we use the [code provided here](https://github.com/dariyasydykova/opencode/blob/master/animate_ROC.r) by [Dariya Sydykova](https://twitter.com/dariyasydykova).
```{r}
# read the first image (frame) of each animation
a <- image_read(army2[[1]])
b <- image_read(navy2[[1]])
c <- image_read(marines2[[1]])
d <- image_read(airforce2[[1]])

# combine the two images into a single image
combined1 <- image_append(c(a, b))
combined2 <- image_append(c(c, d))
combined <- image_append(c(combined1, combined2))
new_gif <- c(combined)

for (i in 2:100) {
  # combine images frame by frame
  a <- image_read(army2[[i]])
  b <- image_read(navy2[[i]])
  c <- image_read(marines2[[i]])
  d <- image_read(airforce2[[i]])
  combined1 <- image_append(c(a, b))
  combined2 <- image_append(c(c, d))
  combined <- image_append(c(combined1, combined2))
  new_gif <- c(new_gif, combined)
}

# make an animation of the combined images
combined_gif <- image_animate(new_gif)

# save as gif
image_write(combined_gif, "figures/TidyTuesday_CDC.gif")
```