Total Fertility Rate Definition and Calculation: Trendy Visualizations with R
It's been in the news and in articles a lot lately, but you may not be sure what it means or why it matters. Total Fertility Rate Meaningis Average number of children each fertile woman is expected to have in her lifetimewhich is more than just a statistic, it's an important indicator of the future of our society.
In this post, you'll learn what crude fertility rate is, calculate it with R code, and create a trendy visualization using the given data. Analyze the data yourself and see how the crude fertility rate changes over time. Note that the data is from Office for National Statistics Censusfrom the
What does Total Fertility Rate mean?
The total fertility rate is a metric calculated based on the number of live births and the number of women of childbearing age in a given year, and is an important indicator of a country's population growth or decline.
To make it easier to understand, here's an example of calculating the total fertility rate.
1. How the aggregate fertility rate is calculated
- The total fertility rate is the Number of birthsto Number of women of childbearing age (15-49)based on the value divided by
- However, this value is not just one year's worth of data, but the number of children a woman of a certain age is expected to have in her lifetime. Expected number of childrencalculated by the average of
2. simple example
For example, over the course of a year, an average of Assuming you have 1.5 childrenLet's do it.
Total fertility rate = (number of births) / (number of fertile women) = 150,000 / 100,000 = 1.5Therefore, the total fertility rate for the year is 1.5in the end.
3. Example with real data
- If in a particular year 250,000 women of childbearing ageand, 300,000 birthsWhat if?
- The total fertility rate is calculated like this
Total birth rate = 300,000 / 250,000 = 1.2 - This value is the average number of women of childbearing age in a given year who have a 1.2 childrenmeans.
- The total fertility rate is calculated like this
Meaning of the total fertility rate
A combined fertility rate of 2.1 or higher is the number of generations that can be sustained by the Replacement Fertility RateHowever, if the total fertility rate falls below 1.0, the population can shrink rapidly, resulting in a shrinking labor force and economic strain. This is why this data can be used to predict a country's demographic structure and future.
Visualize aggregate fertility rates in R
Now, let's use the data given to us to analyze and visualize the total fertility rate with R coding.
# Check and install required packages
if (!require(ggplot2)) install.packages("ggplot2")
if (!require(ggrepel)) install.packages("ggrepel")
if (!require(scales)) install.packages("scales")
# Load required libraries
library(ggplot2)
library(ggrepel)
library(scales)
# Create data
year <- 2000:2023
tfr <- c(1.480, 1.309, 1.178, 1.191, 1.164, 1.085, 1.132, 1.259, 1.192, 1.149,
1.226, 1.244, 1.297, 1.187, 1.205, 1.239, 1.172, 1.052, 0.977, 0.918,
0.837, 0.808, 0.778, 0.720)
# Create dataframe
tfr_df <- data.frame(Year = year, TFR = tfr)
# Create visualization
ggplot(tfr_df, aes(x = Year, y = TFR)) +
geom_line(color = "#3366CC", size = 1.2) +
geom_point(color = "#CC3366", size = 4, alpha = 0.7) +
geom_text_repel(aes(label = sprintf("%.3f", TFR)),
size = 3, color = "#333333",
box.padding = 0.5, point.padding = 0.5,
min.segment.length = 0, force = 2,
direction = "y", hjust = "left", nudge_x = 0.5) +
scale_x_continuous(breaks = seq(2000, 2023, by = 5)) +
scale_y_continuous(limits = c(0, 1.6),
breaks = seq(0, 1.6, by = 0.2),
labels = label_number(accuracy = 0.1)) +
labs(
title = "Total Fertility Rate in South Korea (2000-2023)",
subtitle = "Trend shows a significant decline over the years",
x = "Year",
y = "Total Fertility Rate",
caption = "Source: Statistics Korea (KOSTAT)"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
plot.subtitle = element_text(hjust = 0.5, size = 12, color = "#666666"),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "#EEEEEE")
)
Trends in aggregate fertility rates in R
When we visualize the data like this, we can see that the total fertility rate hovered around 1.4 in the early 2000s before steadily declining to 0.721 in 2023. The steep decline since 2013 in particular shows the severity of the decline.
Organize
Now you know not only what it means, but also how to calculate and visualize it yourself using data. Data has the power to tell a powerful story, and with R coding, you too can visualize important data and gain new insights!
The declining birthrate is more than just a number - it's a critical issue that has a direct bearing on the future of our society. Through data analysis and visualization, we can recognize the seriousness of the problem and contribute to effective policy formulation. This is an area that requires continued attention and research.
As a side note, if you're curious about the 2025 fertility rate projections, check out the Will Korea's fertility rate rebound after a decade? The Congressional Budget Office's projections for 2025 (feat. Python) Check out the post!
# Code Explained: Line by Line
# Check and install required packages if (!require(ggplot2)) install.packages("ggplot2") if (!require(ggrepel)) install.packages("ggrepel") if (!require(scales)) install.packages("scales")If a required package is not installed, it will be installed automatically.
# Load required libraries library(ggplot2) library(ggrepel) library(scales)Load the required libraries.
# Create data year <- 2000:2023 tfr <- c(1.480, 1.309, 1.178, 1.191, 1.164, 1.085, 1.132, 1.259, 1.192, 1.149, 1.226, 1.244, 1.297, 1.187, 1.205, 1.239, 1.172, 1.052, 0.977, 0.918, 0.837, 0.808, 0.778, 0.720)Create a vector of year and total fertility rate data.
# Create dataframe tfr_df <- data.frame(Year = year, TFR = tfr)Create a dataframe with year and total fertility rate data.
# Create visualization ggplot(tfr_df, aes(x = Year, y = TFR)) +Create a ggplot object and set the x-axis to Year and the y-axis to TFR.
geom_line(color = "#3366CC", size = 1.2) +The blue line shows the trend of the total fertility rate by year.
geom_point(color = "#CC3366", size = 4, alpha = 0.7) +The red dot shows the total fertility rate for each year.
geom_text_repel(aes(label = sprintf("%.3f", TFR)), size = 3, color = "#333333", box.padding = 0.5, point.padding = 0.5, min.segment.length = 0, force = 2, direction = "y", hjust = "left", nudge_x = 0.5) +Add TFR values to each data point as labels, adjusting them so they don't overlap.
scale_x_continuous(breaks = seq(2000, 2023, by = 5)) +Set the scale of the x-axis to 5-year intervals.
scale_y_continuous(limits = c(0, 1.6), breaks = seq(0, 1.6, by = 0.2), labels = label_number(accuracy = 0.1)) +Set the range, scale interval, and decimal display for the y-axis.
labs( title = "Total Fertility Rate in South Korea (2000-2023)", subtitle = "Trend shows a significant decline over the years", x = "Year", y = "Total Fertility Rate", caption = "Source: Statistics Korea (KOSTAT)" ) +Set the title, subtitle, axis names, and source for the graph.
theme_minimal() +Apply a minimalist theme.
theme( plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), plot.subtitle = element_text(hjust = 0.5, size = 12, color = "#666666"), axis.title = element_text(size = 12), axis.text = element_text(size = 10), panel.grid.minor = element_blank(), panel.grid.major = element_line(color = "#EEEEEE") )Adjust the detailed styling of the graph. Specify the position and style of titles and subheadings, the size of axis text, the style of grid lines, and more.
This code provides an effective visual representation of the change in South Korea's total fertility rate, clearly showing the value of each data point.
