00_extract_e0.Rmd

---
title: "Data management"
output: html_notebook
---

# Intro
This script will aim to set up the project to use tidyverse packages, and use the hfdhmdplus library to download requisite data. To start with only life expectancy estimates will be required

# Load packages 


n.b. `pacman` has been installed already

```{r}
pacman::p_load(
  tidyverse,
  HMDHFDplus
)
```

# HMD 
 
Which countries are available?
 
```{r}
all_countries <- getHMDcountries() 
 
```

For each country, want to know which items are available 

But first the username and password need to be identified

```{r}
my_username <- userInput()
```
```{r}
my_password <- userInput()
```

```{r}
country_availability <- tibble(
  code = all_countries
) %>% 
  mutate(
    available_items = map(.x = code, .f = getHMDitemavail,
                          username = my_username, password = my_password
                          
  ),
  contains_e0 = map_lgl(available_items, ~`%in%`("E0per", .x))
) 

```



```{r}
country_availability
```

All countries have the item `E0per`.


Now we want to extract E0per for each country

```{r}
e0_data <- tibble(
  code = all_countries
) %>% 
  mutate(
    e0_df = map(code, readHMDweb, item = "E0per", username = my_username, password = my_password)
  ) %>% 
  unnest() %>% 
  gather(key = "sex", value = "e0", Female:Total) %>% 
  mutate(sex = tolower(sex)) %>% 
  rename(year = Year)


```


Now to write this out

```{r}
write_rds(e0_data, path = "tidy_data/e0_period.rds")

```



# Extract age-specific mortality rates too 

The main things to work out are whether there have been similar trends in infancy, young adulthood, and elderly trends in many countries.

Perhaps it would be good to get correlations between trends for these different age groups?

I think we should extract `Mx_1x1`

```{r}
Mx_data <- tibble(
  code = all_countries
) %>% 
  mutate(
    Mx_df = map(code, readHMDweb, item = "Mx_1x1", username = my_username, password = my_password)
  ) %>% 
  unnest() %>% 
  select(-OpenInterval) %>% 
  gather(key = "sex", value = "Mx", Female:Total) %>% 
  mutate(sex = tolower(sex)) %>% 
   rename(year = Year, age = Age)
```

Now write this 

```{r}
write_rds(Mx_data, "tidy_data/Mx_data.rds")

```