Create a plot of changes in African fertility rates (1950-2015)

fertility.R

library(tools)
library(dplyr)
library(readxl)
library(ggplot2)
library(countrycode)
library(ggthemes)

# Function to download and open Excel files
get_data <- function(url, ...) {

  tmp <- tempfile(fileext = paste0(".", file_ext(url)))
  download.file(url, tmp, mode = "wb")

  df <- read_excel(tmp, ...)
  names(df) <- make.names(tolower(names(df)))
  df
}

# Get fertility and population data
fertility_url <- paste0("http://www.un.org/en/development/desa/population/",
                   "publications/dataset/fertility/wfd2015/",
                   "UNPD_WFD_2015_FERTILITY.xlsx")

pop_url <- paste0("http://esa.un.org/unpd/wpp/DVD/Files/1_Indicators%20",
                  "(Standard)/EXCEL_FILES/1_Population/WPP2015_POP_F15_3_",
                  "ANNUAL_POPULATION_BY_AGE_FEMALE.XLS")

fertility <- get_data(fertility_url, sheet = "FERTILITY_INDICATORS", skip = 1)
pop <- get_data(pop_url, sheet = "ESTIMATES", skip = 16)

names(pop)[6] <- "year"

# Join together fertility and population data on country and year
df <- inner_join(fertility, select(pop, country.code, year, X0.4:X100.),
                 by = c("iso.code" = "country.code",
                        "yearstart" = "year"))

df$region <- countrycode(df$iso.code, "iso3n", "region")

plot <- df %>%
  filter(agegroup == "[15-19]") %>%
  filter(grepl("Africa", region)) %>%
  ggplot(aes(x = timemid, y = datavalue, color = region,
             weight = X15.19, size = X15.19)) +
  geom_point(alpha = 0.15, show.legend = FALSE) +
  geom_smooth(show.legend = FALSE) +
  facet_wrap(~region, scales = "free") +
  scale_color_few() +
  theme_light(9) +
  scale_x_continuous(limits = c(1950, NA)) +
  scale_y_continuous(limits = c(0, 350)) +
  labs(x = NULL, y = "# of births per 1,000 women\n",
       title = "Total Fertility Rates (Women Aged 15-19)\n")

# Save as SVG for post-production in Inkscape
ggsave(plot, file = "fertility.svg", width = 4, height = 3)