---
title: "Working with Geography"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Working with Geography}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
  
```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

## Understanding Geography Types

Nomis uses TYPE codes to organize geographies hierarchically:
  
```{r eval=FALSE}
library(nomisdata)

# Get all geography types
types <- get_codes("NM_1_1", "geography", "TYPE")
head(types)

# Common types:
# TYPE499 - Countries (UK, England, Wales, Scotland, NI)
# TYPE464 - Local authorities
# TYPE480 - Regions
# TYPE460 - Parliamentary constituencies
```

## Looking Up Geographies

```{r eval=FALSE}
# Search by name
manchester <- lookup_geography("Manchester")
print(manchester)

# With type filter
manchester_la <- lookup_geography("Manchester", type = "TYPE464")
```

## Fetching Data for Specific Areas

```{r eval=FALSE}
# Single area
london_data <- fetch_nomis(
  "NM_1_1",
  time = "latest",
  geography = "2013265927",  # London code
  measures = 20100
)

# Multiple areas
cities <- c("1879048226", "1879048225")  # Manchester, Liverpool
cities_data <- fetch_nomis(
  "NM_1_1",
  time = "latest",
  geography = cities,
  measures = 20100
)
```

## Geography Hierarchies

```{r eval=FALSE}
# Get all districts in a region
north_east <- get_codes(
  "NM_1_1",
  "geography",
  search = "*north east*"
)
```