report.Rmd

---
title: "Report: Fertility"
author: "YOUR NAME"
output: html_document
---

```{r pkgs, include = FALSE}
library(tidyverse)
library(readxl)
library(janitor)
library(broom)
```

Here we've used the fertility data from the [gapminder web site](https://www.gapminder.org/data/) which contains the number of babies
born per woman accross countries in the world. The documentation for this data is available [here](https://www.gapminder.org/data/documentation/gd008/)
.

First we read in the data and process it, but you will need to make some changes to do the full analysis.

```{r raw_data}
fertility_raw <- read_xlsx(
  "data/indicator-undata-total-fertility.xlsx"
  ) %>%
  clean_names() %>%
  rename(country = total_fertility_rate)

fertility_raw
```

We then pivot our data into long form and process our data

```{r fertility}
fertility <-
  fertility_raw %>%
  pivot_longer(cols = -c(country), # everything but country,
               # what is the name of the new variable we are changing the
               # column of names to?
               names_to = "year",
               # What is the name of the column we are changing the values to?
               values_to = "babies_per_woman") %>%
  # extract out the year information
  mutate(year = parse_number(year)) %>%
  # filter so we only look at years above 1950
  filter(year >= 1950 ) %>%
  # center year around 1950
  mutate(year1950 = year - min(year))
```


# Line plot for all countries

```{r line-plot-all}
ggplot(fertility, 
       aes(x = year, 
           y = babies_per_woman, 
           group = country)) +
  geom_line(alpha=0.1)
```


# How has fertility changed in Australia?

```{r aus-line-plot}
oz <- fertility %>% filter(country == "Australia")

ggplot(oz, 
       aes(x = year, 
           y = babies_per_woman, 
           group = country)) +
  geom_line()
```


## Fit a linear model to Australia

```{r oz_mod}
mod <- lm(babies_per_woman ~ year1950, data = oz)
```

## Plot the model fit against the data 

```{r oz_line_plot_mod_overlay}
mod %>% 
  augment_columns(oz) %>% 
  ggplot(aes(x = year, y = babies_per_woman)) +
  geom_line() +
  geom_point(aes(y = .fitted))
```

## Summarise the fit

```{r mod-summary}
glance(mod)
```