index.Rmd

---
title: "Gapminder Analysis"
author: "Joel Nitta"
date: "`r Sys.Date()`"
output: html_document
editor_options: 
  chunk_output_type: console
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
tar_load(c(
  gapminder,
  life_exp_mods
))
```

## Data

The `gapminder` dataset includes `r gapminder$country %>% n_distinct()` countries with per capita GDP, population, and life expectancy data from `r gapminder$year %>% min()` to `r gapminder$year %>% max()`.

## Methods

We fit a linear model of life expectancy by year for each country.

## Results

Asian countries showed the biggest gains in life expectancy over time, with European countries the least.
African and American countries have a wide spread of values.

```{r plot-mod, echo = FALSE, include = TRUE, fig.cap= "Results of modeling life expectancy over time by country. Only significant models shown (*p* < 0.05)."}
# Tidy modeling output data
life_exp_mods_for_plot <-
life_exp_mods %>%
  filter(term == "year_rel", p.value < 0.05) %>%
  left_join(
    unique(select(gapminder, country, continent)), 
    by = "country"
  ) %>%
  mutate(country = fct_reorder(country, estimate))

# Plot the modeling results
ggplot(
  life_exp_mods_for_plot,
  aes(x = estimate, y = country, fill = continent)) +
  geom_errorbarh(
    aes(xmax = estimate + std.error, xmin = estimate - std.error),
    color = "grey40") +
  geom_col() +
  scale_x_continuous(name = "Change in life expectancy per year") +
  scale_fill_brewer(palette = "Set1", name = "Continent") +
  theme_bw() +
  theme(
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_blank(),
    panel.grid.minor.y = element_blank()
  )
```