---
title: "Getting Started"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r not-cran-check, include = FALSE}
# Skip code evaluation on CRAN (external API calls will fail)
RUN_VIGNETTE <- isTRUE(getOption("pkgdown.in_progress")) ||
  (identical(Sys.getenv("NOT_CRAN"), "true") &&
    !nzchar(Sys.getenv("_R_CHECK_PACKAGE_NAME_")))
knitr::opts_chunk$set(eval = RUN_VIGNETTE, purl = RUN_VIGNETTE)
```

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,        # standardized width that fits vignette text column
  fig.height = 5,       # default height, override in individual chunks as needed
  fig.dpi = 150,        # higher resolution for crisp rendering
  out.width = "100%",   # constrain to page width, prevents overflow
  warning = FALSE,
  message = FALSE
)
```

## Installation

Install from GitHub:

```{r install}
# install.packages("devtools")
# devtools::install_github("temuulene/mongolstats")
```

## Your First Analysis: Infant Mortality Trends

Let's walk through a complete workflow using infant mortality data—a key indicator of population health and health system performance.

### Step 1: Load Packages

```{r setup}
library(mongolstats)
library(dplyr)
library(ggplot2)

# Set language to English for readable output
nso_options(mongolstats.lang = "en")

# Set global ggplot2 theme with proper margins to prevent text cutoff
theme_set(
  theme_minimal(base_size = 11) +
    theme(
      plot.margin = margin(10, 10, 10, 10),
      plot.title = element_text(size = 13, face = "bold"),
      plot.subtitle = element_text(size = 10, color = "grey40"),
      legend.text = element_text(size = 9),
      legend.title = element_text(size = 10)
    )
)
```

### Step 2: Find the Right Table

Search for infant mortality data:

```{r search}
# Search by keyword
mortality_tables <- nso_itms_search("infant mortality")
mortality_tables |>
  select(tbl_id, tbl_eng_nm) |>
  head(5)
```

We'll use `DT_NSO_2100_015V1` - Infant Mortality Rate per 1,000 live births (Monthly).

### Step 3: Explore Table Metadata

Before fetching data, check what dimensions are available:

```{r metadata}
# View table structure
meta <- nso_table_meta("DT_NSO_2100_015V1")
meta

# Check available months
time_vals <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")
head(time_vals, 10)
```

### Step 4: Fetch Data

Get national infant mortality rates for the past two decades:

```{r fetch-data}
# Get all month codes
months <- nso_dim_values("DT_NSO_2100_015V1", "Month", labels = "en")

imr_national <- nso_data(
  tbl_id = "DT_NSO_2100_015V1",
  selections = list(
    "Region" = "0", # National level
    "Month" = months$code
  ),
  labels = "en" # Get English labels
)

# Preview
imr_national |>
  head(10)
```

### Step 5: Visualize the Trend

Create a publication-ready plot:

```{r plot-trend, fig.alt="Line plot showing decline in infant mortality rate from 2010 to 2015"}
# Prepare the data for visualization
# Step 1: Convert month strings to proper dates for time series plotting
# Step 2: Filter to recent decade (2015-2024) for clear trend visibility

p <- imr_national |>
  mutate(date = as.Date(paste0(Month_en, "-01"))) |>  # convert "YYYY-MM" string to Date
  filter(date >= as.Date("2015-01-01") & date <= as.Date("2024-12-31")) |>
  ggplot(aes(x = date, y = value, group = 1)) +
  geom_line(color = "#2c3e50", linewidth = 1, alpha = 0.3) +  # dim raw data so trend stands out
  geom_point(color = "#e74c3c", size = 3, shape = 21, fill = "white", stroke = 1.5, alpha = 0.6) +
  geom_smooth(method = "loess", se = TRUE, color = "#3498db", fill = "#3498db", alpha = 0.2, linewidth = 1.5) +  # LOESS reveals trend through monthly noise
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  labs(
    title = "Infant Mortality Rate in Mongolia (Monthly)",
    subtitle = "Deaths per 1,000 live births (National Trend)",
    x = NULL,
    y = "IMR (per 1,000)",
    caption = "Source: NSO Mongolia via mongolstats"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "grey40"),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()  # vertical gridlines clutter time series
  )

p  # print static ggplot
```

## Regional Comparison

Compare infant mortality across different regions:

```{r regional}
# Get all aimags for most recent year (2024)
# We'll take the average of monthly rates
months_2024 <- months |>
  filter(grepl("2024", label_en)) |>
  pull(code)

# Fetch IMR data for all regions in 2024
# We'll calculate the annual average from monthly data

imr_regional <- nso_data(
  tbl_id = "DT_NSO_2100_015V1",
  selections = list(
    "Region" = nso_dim_values("DT_NSO_2100_015V1", "Region")$code,
    "Month" = months_2024
  ),
  labels = "en"
) |>
  filter(nchar(Region) == 3) |> # Keep only Aimags and Ulaanbaatar (code length = 3)
  mutate(
    Region_en = trimws(Region_en),
    # Standardize region names to match geographic boundary data
    Region_en = dplyr::case_match(
      Region_en,
      "Bayan-Ulgii" ~ "Bayan-Ölgii",
      "Uvurkhangai" ~ "Övörkhangai",
      "Khuvsgul" ~ "Hovsgel",
      "Umnugovi" ~ "Ömnögovi",
      "Tuv" ~ "Töv",
      "Sukhbaatar" ~ "Sükhbaatar",
      .default = Region_en
    ),
    Type = ifelse(Region %in% c("1", "2", "3", "4"), "Region", "Aimag")
  ) |>
  # Calculate annual average IMR from monthly data
  group_by(Region_en, Type) |>
  summarise(value = mean(value, na.rm = TRUE), .groups = "drop")

# Top 10 highest IMR regions
imr_regional |>
  arrange(desc(value)) |>
  select(Region_en, value) |>
  head(10)
```

### Visualize Regional Disparities

```{r regional-plot, fig.alt="Bar chart comparing infant mortality rates across Mongolia's aimags", fig.height=7}
# Calculate national aimag average for reference line
aimag_mean <- mean(imr_regional$value[imr_regional$Type == "Aimag"], na.rm = TRUE)

p <- imr_regional |>
  filter(!is.na(value)) |>
  arrange(desc(value)) |>
  mutate(Region_en = forcats::fct_reorder(Region_en, value)) |>  # order bars by value, not alphabet
  ggplot(aes(x = value, y = Region_en)) +
  # Aimags get gradient fill to show relative severity
  geom_col(data = ~ subset(., Type == "Aimag"), aes(fill = value), width = 0.7) +
  # Regions (aggregates) get distinct dark color to differentiate
  geom_col(data = ~ subset(., Type == "Region"), fill = "#2c3e50", width = 0.7) +
  geom_text(aes(label = round(value, 1)), hjust = -0.2, color = "grey30", size = 3.5) +  # inline labels replace tooltips
  scale_fill_gradient2(
    low = "#27ae60",   # green = low mortality (good)
    mid = "#f39c12",   # yellow = average
    high = "#e74c3c",  # red = high mortality (concerning)
    midpoint = aimag_mean
  ) +
  geom_vline(xintercept = aimag_mean, linetype = "dashed", color = "grey50", linewidth = 0.5) +  # national average reference
  scale_x_continuous(expand = expansion(mult = c(0, 0.15))) +  # extra space for labels
  labs(
    title = "Infant Mortality by Aimag (2024 Average)",
    subtitle = "Dark bars represent Regional Averages; dashed line = national aimag average",
    x = "Deaths per 1,000 live births",
    y = NULL
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.major.y = element_blank(),  # horizontal gridlines clutter bar charts
    panel.grid.minor = element_blank(),
    axis.text.y = element_text(color = "black"),
    legend.position = "none"  # gradient is self-explanatory
  )

p  # print static ggplot
```

## Adding Geographic Context

Combine with mapping for spatial analysis:

```{r map-example, fig.alt="Choropleth map of infant mortality rates across Mongolia"}
library(sf)

# Get aimag boundaries
aimags <- mn_boundaries(level = "ADM1")

# Join IMR data to map
imr_map <- aimags |>
  left_join(imr_regional, by = c("shapeName" = "Region_en"))

# Create choropleth map
p <- imr_map |>
  ggplot() +
  geom_sf(aes(fill = value), color = "white", size = 0.2) +
  scale_fill_viridis_c(
    option = "magma",
    direction = -1,  # dark = high values (high mortality), light = low
    name = "IMR\n(per 1,000)",
    labels = scales::label_number()
  ) +
  labs(
    title = "Infant Mortality Geography (2024 Average)",
    subtitle = "Spatial distribution of mortality rates",
    caption = "Source: NSO Mongolia"
  ) +
  theme_void() +  # remove axes for cleaner map appearance
  theme(
    plot.title = element_text(face = "bold", size = 16),
    plot.subtitle = element_text(color = "grey40"),
    legend.position = "bottom",          # bottom legend maximizes map width
    legend.title = element_text(size = 10, face = "bold"),
    legend.key.width = unit(1.5, "cm")   # wider legend key for continuous scale
  )

p  # print static ggplot
```

## Key Functions Summary

| Function | Purpose | Example |
|----------|---------|---------|
| `nso_itms_search()` | Find tables by keyword | `nso_itms_search("mortality")` |
| `nso_table_meta()` | Get table dimensions | `nso_table_meta("DT_NSO_...")` |
| `nso_dim_values()` | List dimension values | `nso_dim_values(tbl, "Region")` |
| `nso_table_periods()` | Check time coverage | `nso_table_periods(tbl)` |
| `nso_data()` | Fetch data | `nso_data(tbl, selections, labels)` |
| `mn_boundaries()` | Get geographic boundaries | `mn_boundaries(level = "ADM1")` |

## Best Practices

1. **Always use labels**: Set `labels = "en"` in `nso_data()` for readable output
2. **Check metadata first**: Use `nso_table_meta()` to understand dimensions before fetching
3. **Use appropriate selections**: Specify dimensions by their English labels (e.g., `"Total"` not `"0"`)
4. **Filter carefully**: Exclude total rows (usually code `"0"`) when analyzing subgroups
5. **Clean labels**: Use `trimws()` to remove leading/trailing spaces from region names before joining

## Common Workflows

### Time Series Analysis
1. Search for table → Check periods → Fetch years → Plot trend

### Regional Comparison  
1. Search table → Get all regions → Fetch latest year → Compare rates

### Spatial Epidemiology
1. Fetch regional data → Get boundaries → Join → Create choropleth

## Next Steps

- **Discover More Data**: See the [Discovery Guide](discovery.html) for advanced search techniques
- **Create Maps**: Learn spatial analysis in the [Mapping Guide](mapping.html)  
- **Reference**: Browse all functions in the [Reference](https://temuulene.github.io/mongolstats/reference/index.html)

## Quick Reference: Common Health Tables

```{r health-tables, echo=FALSE}
tibble::tribble(
  ~Indicator, ~Table_ID,
  "Infant Mortality", "DT_NSO_2100_015V1",
  "Maternal Mortality", "DT_NSO_2100_050V1",
  "Under-5 Mortality", "DT_NSO_2100_030V2",
  "Cancer Incidence", "DT_NSO_2100_012V1",
  "TB Incidence", "DT_NSO_2800_026V1",
  "Communicable Diseases", "DT_NSO_2100_020V2"
) |>
  knitr::kable()
```