Exploring Global Fertility: A Journey Across Borders

This is a test post. In this post, I try out different functionalities
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Second Tag
Author

Alex Labuda

Published

December 25, 2023

Let’s Explore the World Ferility Rates

In a world bustling with over seven billion people, each individual represents a unique thread in the intricate tapestry of humanity. But beyond the surface of bustling streets and crowded cities lies a story told not through words, but through numbers - the story of fertility rates. This vital statistic, often overlooked, serves as a silent narrator of our times, speaking volumes about societal trends, economic conditions, and cultural shifts.

As we embark on a journey across continents, we delve into the fascinating world of fertility rates. From the snow-capped villages of Scandinavia to the sun-drenched lands of Sub-Saharan Africa, we’ll explore how these numbers shape nations, influence policies, and reflect the diverse tapestry of human existence.

Join me as we unravel the tales hidden within these figures, discovering not just the “how” and “what,” but the profound “why” behind the fertility rates of various countries. We’ll look at how these rates are more than just numbers on a page; they are a reflection of healthcare access, gender equality, economic stability, and cultural norms.

Through this exploration, we’ll gain a deeper understanding of our world and its future, one number at a time.

Plot the US birthrates through time

Show the code 
# Create a function of the above plot code
plot_line <- function(data, iso2c = "US", title = "United States",
                      title_size = 10, hjust = 0,
                      dot_size = 0.6, linewidth = 0.2) {
  # Check if required columns exist in the data
  if (!("iso2c" %in% names(data)) || !("date" %in% names(data)) || !("SP.DYN.TFRT.IN" %in% names(data))) {
    stop("Data must contain 'iso2c', 'date', and 'SP.DYN.TFRT.IN' columns.")
  }

  # Filtering and plotting
  plot <- data %>%
    filter(iso2c == !!iso2c) %>%  # Correct filtering
    ggplot(aes(x = date, y = SP.DYN.TFRT.IN)) +
    geom_line(alpha = 0.7, linewidth = linewidth) +
    geom_point(size = dot_size, alpha = 0.7) +
    geom_smooth(
      span = 0.9,
      linewidth = linewidth,
      linetype = 2,
      se = FALSE,
      color = "#E91E63"
    ) +
    labs(
      x = "",
      y = "",
      title = title
    ) +
    theme(
      plot.title      = element_text(size = title_size, face = "bold",
                                     hjust = hjust),
      axis.title      = element_text(size = 8, face = "bold"),
      axis.text       = element_text(size = 8),
      legend.position = "none",
      panel.grid      = element_blank()
    )

  return(plot)
}

plot_line_small <- function(data, iso2c = "US", title = "United States",
                      title_size = 10, hjust = 0,
                      dot_size = 0.6, linewidth = 0.3) {
  # Check if required columns exist in the data
  if (!("iso2c" %in% names(data)) || !("date" %in% names(data)) || !("SP.DYN.TFRT.IN" %in% names(data))) {
    stop("Data must contain 'iso2c', 'date', and 'SP.DYN.TFRT.IN' columns.")
  }

  # Filtering and plotting
  plot <- data %>%
    filter(iso2c == !!iso2c) %>%  # Correct filtering
    ggplot(aes(x = date, y = SP.DYN.TFRT.IN)) +
    geom_line(alpha = 0.9, linewidth = linewidth) +
    # geom_point(size = dot_size, alpha = 0.7) +
    # geom_smooth(
    #   span = 0.9,
    #   linewidth = linewidth,
    #   linetype = 2,
    #   se = FALSE,
    #   color = "#E91E63"
    # ) +
    labs(
      x = "",
      y = "",
      title = title
    ) +
    theme(
      plot.title      = element_text(size = title_size, face = "bold",
                                     hjust = hjust),
      axis.title      = element_text(size = 8, face = "bold"),
      axis.text       = element_text(size = 7),
      legend.position = "none",
      panel.grid      = element_blank()
    )

  return(plot)
}
Show the code 
# United States
line_us <- plot_line_small(fert_tbl, "US", "United States",
                     hjust = 0.5)

# China
line_china <- plot_line_small(fert_tbl, "CN", "China",
                        hjust = 0.5)

# Japan
line_japan <- plot_line_small(fert_tbl, "JP", "Japan",
                        hjust = 0.5)

# Russia
line_russia <- plot_line_small(fert_tbl, "RU", "Russia",
                         hjust = 0.5)

# World
line_world <- plot_line(fert_tbl, "1W", "World",
                        title_size = 14, hjust = 0.5,
                        dot_size = 1.2, linewidth = 0.4)


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