Generate snowfall -- every snowflake scaled to a country population size ^1/3

countries-as-snowflakes.R

#===============================================================================
# 2019-12-12 -- twitter
# Generate snowfall -- every snowflake scaled to a country population size ^1/3
# Ilya Kashnitsky, ilya.kashnitsky@gmail.com
#===============================================================================

library(tidyverse)
library(magrittr)
library(gganimate)


# helper function to ensure the snowflakes don't disappear ----------------

# function for one snowflake
loop_single <- function(value){
    if(value %>% is_less_than(0)){value <- 1}
    if(value %>% is_greater_than(1)){value <- 0}
    return(value)
}

# vectorize
loop_multiple <- function(values){
    values %>% map_dbl(loop_single)
}

# create a larger dataframe with frames for snowflake movement ------------

# function to create smooth frames for snowflake falling
fun_framing <- function(df, n_frames = 100, wind = 0){
    
    # frames vector
    frames <- seq(1, n_frames)
    
    # empty list
    out <- list()
    
    # place the init df in the first element
    out[[1]] <- df
    
    # length of the df
    len <- nrow(df)
    
    
    for (i in seq_along(frames)) {
        
        # calculate x shift,  unique for each snowflake
        x_shift <- runif(len, -.5, .5) %>% add(wind)
        
        dfi <- out[[i]] %>% 
            mutate(
                x = x %>% 
                    add(x_shift %>% multiply_by(size/1e3)) %>% 
                    loop_multiple(),
                y = y %>% 
                    subtract(size/1e3) %>% 
                    loop_multiple()
            ) 
        out[[i+1]] <- dfi
    }
    
    # bind list
    out <- out %>% 
        bind_rows(.id = "frame") %>% 
        mutate(frame = frame %>% as.numeric)
    return(out)
}


# calculate dataset -------------------------------------------------------

# get countries' population size 
library(wpp2019)

data(pop)

set.seed(911)

df <- pop %>% 
    filter(
        country_code %>% paste %>% map_dbl(nchar) %>% equals(3),
        country_code %>% paste %>% str_sub(1,1) %>% equals(9) %>% not
    ) %>% 
    transmute(id = country_code, name, pop = `2020`) %>% 
    # calculate size as scaled log(pop)
    mutate(
        size = pop %>% raise_to_power(1/3) %>% scales::rescale(to = c(1, 12))
    ) %>% 
    # add random starting coordinates
    mutate(
        x  = runif(178), 
        y  = runif(178),
        angle  = runif(178) %>% scales::rescale(to = c(0, 60))
    )


# generate movement frames

set.seed(911)

# generate df for animation    
df_anim <- df %>% fun_framing(n_frames = 3e2, wind = -1)

save(df_anim, file = "1912-animated-snow/df_anim.rda")

load("1912-animated-snow/df_anim.rda")


# plot/animate ------------------------------------------------------------

library(ggimage)

svg_path <- "https://gist.githubusercontent.com/ikashnitsky/64ce22c6de6fdd5e1aac87fafe073453/raw/46c9908277330228515b57b7dafd532298ac5e18/snow-white.svg"

p <- df_anim %>% 
    ggplot(aes(x, y, size = I(size / 150))) + 
    geom_image(image = svg_path)+
    coord_cartesian(c(0, 1), c(0, 1), expand = FALSE)+
    theme_void()+
    theme(
        panel.background = element_rect(fill = "black"),
        plot.background = element_rect(fill = "black")
    )

# add transition
ani <- p + 
    transition_time(frame)+ 
    ease_aes('linear')+
    enter_fade()+
    exit_fade()

# define the number of data points
nfr <- df_anim %>% pull(frame) %>% unique() %>% length()

# animate
animate(
    ani, 
    nframes = nfr, fps = 30, 
    # device = "svg", renderer = magick_renderer(loop = TRUE),
    width = 500, height = 500
)

# save the output
anim_save("animated-snowflakes.gif")

snow-white.svg