manuscript.Rmd

```{r setup, echo = FALSE, results='hide', message = FALSE, warning = FALSE}
opts_chunk$set(echo = FALSE)
invisible(sapply(list.files(path = "R", pattern = "R$", full.names = TRUE), source))
```

```{r author-info}
first_name <- "your first name"
last_name <- "your last name"
affiliation <- "your affiliation"
address <- "your address"
email <- "your email"
```

```{r manuscript_options}
break_year <- 1985
```

# Increase in life expectancy through time: a continental analysis

> By `r last_name`, `r first_name`
>
> Affiliation: `r affiliation`, `r address`
>
> Email: `r email`

In this paper, we will examine the change in life expectancy through time across
the five continents.

```{r summary}
gdp <- gather_gdp_data("data-raw")
latest_lifeExp <- get_latest_lifeExp(gdp)
mean_lifeExp <- get_mean_lifeExp(gdp)
coef_before_after <- get_coef_before_after(mean_lifeExp, break_year)
```


## The situation

There are important disparities in life expectancy across the five
continents. It ranges from `r min(latest_lifeExp[["latest_lifeExp"]])`
in `r latest_lifeExp[["continent"]][which.min(latest_lifeExp$latest_lifeExp)]`,
to `r max(latest_lifeExp[["latest_lifeExp"]])`
in `r latest_lifeExp[["continent"]][which.max(latest_lifeExp$latest_lifeExp)]`.

These disparities are also reflected in the increases in life expectancy between
`r min(gdp$year)` and `r max(gdp$year)`.

```{r lifeExp-trends-per-continent}
plot_summary_lifeExp_by_continent(mean_lifeExp)
```

To investigate these differences in more detail, we compared the rate of increase
for the periods `r min(gdp$year)`-`r manuscript_options()$year`
and `r manuscript_options()$year`-`r max(gdp$year)`

```{r change_trend}
plot_change_trend(mean_lifeExp, manuscript_options()$year)
```

```{r change-trend}
change_trend <- coef_before_after %>% group_by(continent) %>% summarize(diff = trend[2] - trend[1])
largest_neg_change_index <- which.min(change_trend$diff)
largest_pos_change_index <- which.max(change_trend$diff)
least_change_index <- which.min(abs(change_trend$diff))
```

The largest negative change is observed for `r change_trend$continent[largest_neg_change_index]` with a
decrease of `r change_trend$diff[largest_neg_change_index]`.year<sup>-1</sup>
between the two time periods; while `r change_trend$continent[largest_pos_change_index]`
experienced the largest increase with a difference of
`r change_trend$diff[largest_pos_change_index]`.year<sup>-1</sup> during the
same interval.
`r change_trend$continent[least_change_index]` stayed the most constant between the
two time periods.