Vietnam ’s fertility rate

Daily Graph Series

Economist-Style Plot using R

The graph was created by the Economist and can be replicated by using R as follows:

R Codes for Data Cleaning and Visualization

rm(list = ls())
# Package for data manipulation: 
library(tidyverse)

# Load wbstats package: 
library(wbstats)

# A list of indicators: 
my_indicator <- c("SP.DYN.TFRT.IN", "SP.POP.TOTL", "NY.GDP.PCAP.CD")

# Collect data for indicators: 

data <- wb(country = "all",
           indicator = my_indicator,
           startdate = 2017,
           enddate = 2017)

# Relabel: 

data %>% 
  select(iso3c, value, indicatorID, country) %>% 
  mutate(indicator_name = case_when(indicatorID == my_indicator[1] ~ "fertility", 
                                    indicatorID == my_indicator[2] ~ "population", 
                                    TRUE ~ "gdp")) -> data

# Country code: 

general_information <-  wb_cachelist
codes <- general_information[[1]]

codes %>% 
  select(iso3c, country, long, lat) %>% 
  filter(!is.na(long)) %>% 
  pull(iso3c) -> all_nations

# Convert to wide form for data set: 

data %>% 
  filter(iso3c %in% all_nations) %>% 
  select(-indicatorID) %>% 
  spread(key = "indicator_name", value = "value") %>% 
  na.omit() -> dfPlot

# https://www.economist.com/graphic-detail/2019/08/30/south-koreas-fertility-rate-falls-to-a-record-low

library(scales)
library(ggrepel)
library(ggthemes)

pointColor <- "#2fc1d3"
my_font <- "Ubuntu Condensed"
h1 <- 0.75
h2 <- 0.5
grey20 <- "grey20"


dfPlot %>% 
  ggplot(aes(gdp, fertility, size = population)) + 
  geom_point(show.legend = FALSE, color = pointColor, alpha = 0.5) + 
  scale_size(range = c(1, 25)) + 
  geom_hline(aes(yintercept = 2.1), color = "#e5001c", alpha = 0.6, linetype = "dashed", size = 0.6) + 
  theme_economist_white(base_family = my_font) + 
  scale_x_continuous(labels = dollar, limits = c(100, 110000), 
                     trans = "log10", expand = c(0.01, 0)) +
  scale_y_continuous(breaks = seq(0, 8, 2), limits = c(0, 9), 
                     position = "right", expand = c(0, 0)) + 
  # China + India: 
  geom_segment(data = dfPlot %>% filter(country %in% c("India", "China")), 
               aes(x = gdp, xend = gdp, y = fertility, yend = fertility - h1), 
               size = 0.5) + 
  geom_point(data = dfPlot %>% filter(country %in% c("India", "China")), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) + 
  geom_text_repel(data = dfPlot %>% filter(country %in% c("India", "China")), 
                  aes(x = gdp, y = fertility - h1, label = country), 
                  nudge_x = -0.3, direction = "x", size = 4.5, family = my_font) + 
  # Thailand: 
  geom_text_repel(data = dfPlot %>% filter(country == "Thailand"), 
                  aes(x = gdp, y = fertility, label = country), 
                  direction = "x", nudge_x = -0.25, size = 4.5, family = my_font) + 
  geom_point(data = dfPlot %>% filter(country == "Thailand"), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) + 
  # Vietnam: 
  geom_segment(data = dfPlot %>% filter(country == "Vietnam"),
               aes(x = gdp, xend = gdp, y = fertility, yend = fertility - 1.2),
               size = 0.5)  +
  geom_point(data = dfPlot %>% filter(country == "Vietnam"), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) + 
  geom_text_repel(data = dfPlot %>% filter(country == "Vietnam"),
                  aes(x = gdp, y = fertility - 1.1, label = country),
                  direction = "y", nudge_y = -0.05, size = 4.5, family = my_font, fontface = "bold") +
  # United States: 
  geom_segment(data = dfPlot %>% filter(country == "United States"), 
               aes(x = gdp, xend = gdp, y = fertility, yend = fertility + 0.3), 
               size = 0.5) + 
  geom_point(data = dfPlot %>% filter(country == "United States"), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) +
  geom_text_repel(data = dfPlot %>% filter(country == "United States"),
                  aes(x = gdp, y = fertility + 0.3, label = country),
                  nudge_y = 0.4, direction = "y", size = 4.5, family = my_font) +
  # Singapore: 
  geom_segment(data = dfPlot %>% filter(country == "Singapore"), 
               aes(x = gdp, xend = gdp, y = fertility, yend = fertility - h2), 
               size = 0.5) + 
  geom_point(data = dfPlot %>% filter(country == "Singapore"), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) + 
  geom_text_repel(data = dfPlot %>% filter(country == "Singapore"), 
                  aes(x = gdp, y = fertility - h2, label = country), 
                  nudge_x = -0.3, direction = "x", size = 4.5, family = my_font) + 
  # Japan: 
  geom_segment(data = dfPlot %>% filter(country == "Japan"), 
               aes(x = gdp, xend = gdp, y = fertility, yend = fertility + 1.3), 
               size = 0.5) + 
  geom_point(data = dfPlot %>% filter(country == "Japan"), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) + 
  geom_text_repel(data = dfPlot %>% filter(country == "Japan"), 
                  aes(x = gdp, y = fertility + 1.3, label = country), 
                  nudge_x = -0.2, direction = "x", size = 4.6, family = my_font) + 
  # Niger: 
  geom_text_repel(data = dfPlot %>% filter(country == "Niger"), 
                  aes(x = gdp, y = fertility, label = country), 
                  direction = "x", nudge_x = 0.2, size = 4.5, family = my_font) + 
  geom_point(data = dfPlot %>% filter(country == "Niger"), 
             aes(x = gdp, y = fertility, size = population), shape = 21, 
             fill = pointColor, show.legend = FALSE) + 
  theme(plot.background = element_rect(fill = "white")) + 
  theme(plot.margin = unit(c(1, 1, 1, 0.8), "cm")) + 
  theme(axis.text.y = element_blank()) + 
  theme(axis.title.y = element_blank()) + 
  labs(x = "GDP per capital, $, log scale", 
       title = "Gone baby gone", 
       subtitle = "GDP and fertility, 2017", 
       caption = "Source: United Nations; World Bank") + 
  theme(plot.title = element_text(size = 20), 
        plot.subtitle = element_text(size = 15), 
        plot.caption = element_text(size = 11, colour = "grey30")) + 
  theme(axis.ticks.length.x = unit(2, "mm")) + 
  theme(axis.title.x = element_text(size = 14, color = "grey10"), 
        axis.text.x = element_text(size = 14, color = "grey20")) + 
  annotate("text", x = 110000, y = seq(0, 8, 2) + 0.2, label = seq(0, 8, 2), family = my_font, size = 5, color = "grey20") + 
  annotate("text", x = 110000, y = 8.7, label = "Fertility rate", family = my_font, size = 5, color = "grey10", hjust = 1) + 
  annotate("text", x = 100, y = 2.35, label = "Replacement fertility level", 
           family = "Arial Narrow", size = 5, color = "#e5001c", hjust = -0.02, alpha = 0.7)


library(grid)
grid.rect(x = 1, y = 0.995, hjust = 0.97, vjust = 0, gp = gpar(fill = "#e5001c", lwd = 0))
grid.rect(x = 0.05, y = 0.97, hjust = 0.97, vjust = 0, gp = gpar(fill = "#e5001c", lwd = 0))