---
title: "PPM"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{PPM}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Overview
PPM (Pesquisa da Pecuária Municipal - Municipal Livestock Survey) is Brazil's comprehensive annual survey of livestock activities conducted by [IBGE](https://www.ibge.gov.br/). This dataset provides:
- **Livestock inventories**: Number of animals by species (cattle, pigs, poultry, sheep, horses, etc.)
- **Animal products**: Production volumes and values of milk, eggs, honey, wool, and other animal-origin products
- **Dairy farming**: Specialized data on milked cows, geographic distribution, productivity
- **Aquaculture**: Fish farming, shrimp farming, and mollusk farming activities
- **Sheep specialization**: Detailed shearing and wool production data
- **Multi-level geographic detail**: Country, region, state, and municipality levels
- **Long historical series**: Available from 1974 onwards
- **Economic value**: Both production quantities and market values
PPM is the primary data source for understanding Brazil's livestock sector, which is economically significant and globally important for beef, poultry, and dairy exports.
### Data Source and Methodology
PPM data is compiled from:
- Direct surveys of livestock producers
- Agricultural censuses and administrative records
- Municipal agriculture secretariats
- Processed and validated by IBGE
- Annual release with data for reference year
For more information, visit [IBGE Livestock Statistics](https://www.ibge.gov.br/en/statistics/economic/agriculture-forestry/).
***
## Available Datasets
### **1. ppm_livestock_inventory**
Total livestock herds disaggregated by animal species.
- **Coverage**: All livestock species across all Brazilian municipalities
- **Time period**: 1974 onwards
- **Geographic levels**: Country, Region, State, Municipality
- **Animal species**: Cattle, pigs, chickens, sheep, horses, goats, buffalo, others
- **Variables**: Number of animals by species, number of establishments
- **Use cases**:
- Identify regional livestock specialization
- Track herd size trends
- Analyze geographic concentration of livestock
- Understand animal agriculture structure
### **2. ppm_sheep_farming**
Specialized data on sheep production and wool/fleece harvest.
- **Coverage**: Sheep farming across Brazil
- **Time period**: 1974 onwards
- **Geographic levels**: Country, Region, State, Municipality
- **Variables**: Total sheep, sheared sheep, fleece weight, wool production
- **Use cases**:
- Analyze wool production and sheep farming specialization
- Track shearing practices and yields
- Regional wool industry assessment
### **3. ppm_animal_origin_production**
Production of animal-based products (milk, eggs, honey, wool, etc.).
- **Coverage**: All animal product production activities
- **Time period**: 1974 onwards
- **Geographic levels**: Country, Region, State, Municipality
- **Products included**: Cow milk, goat milk, chicken eggs, quail eggs, honey, wool, hides, wax
- **Variables**: Quantity produced and value of production
- **Use cases**:
- Track dairy and egg production
- Analyze honey and other bee products
- Economic analysis of animal product sectors
### **4. ppm_cow_farming**
Detailed dairy cow farming data with milking and productivity metrics.
- **Coverage**: Dairy cow operations
- **Time period**: 1974 onwards
- **Geographic levels**: Country, Region, State, Municipality
- **Variables**: Milked cows, milk production volume, productivity (liters per cow)
- **Use cases**:
- Dairy sector analysis
- Productivity assessment
- Geographic specialization in dairy
- Production trend analysis
### **5. ppm_aquaculture**
Aquaculture activities including fish, shrimp, and mollusk farming.
- **Coverage**: All aquaculture operations
- **Time period**: 1974 onwards (though aquaculture data more recent)
- **Geographic levels**: Country, Region, State, Municipality
- **Activities**: Fish farming, shrimp farming, mollusk/oyster farming, other aquaculture
- **Variables**: Quantity and value of aquaculture production by type
- **Use cases**:
- Aquaculture sector analysis
- Regional aquaculture potential
- Fish and seafood production trends
- Emerging aquaculture development
***
## Function Parameters
### 1. **dataset**
Selects which livestock/animal production dataset to download.
```r
dataset = "ppm_livestock_inventory" # Animal populations by species
dataset = "ppm_sheep_farming" # Sheep and wool production
dataset = "ppm_animal_origin_production" # Milk, eggs, honey, wool
dataset = "ppm_cow_farming" # Dairy cow productivity
dataset = "ppm_aquaculture" # Fish and aquaculture production
```
### 2. **raw_data**
Controls whether to download original or processed data.
- `TRUE`: Returns raw IBGE format
- `FALSE`: Returns treated data with English variable names and standardized units
```r
raw_data = FALSE # logical
```
### 3. **geo_level**
Specifies geographic aggregation level.
- `"country"`: National aggregate
- `"region"`: Brazilian geographic regions (5 regions)
- `"state"`: State-level data (27 units)
- `"municipality"`: All 5,570+ municipalities
```r
geo_level = "state" # character string
```
### 4. **time_period**
Specifies which year(s) to download.
```r
time_period = 2020 # single year
time_period = c(2010, 2020) # specific years
time_period = 2010:2020 # range of years
```
**Note**: All datasets available from 1974 onwards, though aquaculture more complete from 2000s.
### 5. **language**
Output language for variable names.
- `"pt"`: Portuguese
- `"eng"`: English
```r
language = "eng" # character string
```
***
## Examples
### Example 1: Livestock inventory by state
```{r eval=FALSE}
# download treated livestock inventory data at the state level for 2020
livestock <- load_ppm(
dataset = "ppm_livestock_inventory",
raw_data = FALSE,
geo_level = "state",
time_period = 2020,
language = "eng"
)
```
### Example 2: Dairy cow farming by state
```{r eval=FALSE}
# download treated dairy cow data at the state level for 2020
dairy <- load_ppm(
dataset = "ppm_cow_farming",
raw_data = FALSE,
geo_level = "state",
time_period = 2020,
language = "eng"
)
```
### Example 3: Animal origin production at the country level
```{r eval=FALSE}
# download treated animal origin production data at the country level for 2020
animal_products <- load_ppm(
dataset = "ppm_animal_origin_production",
raw_data = FALSE,
geo_level = "country",
time_period = 2020,
language = "eng"
)
```
### Example 4: Sheep farming by state
```{r eval=FALSE}
# download treated sheep farming data at the state level for 2020
sheep <- load_ppm(
dataset = "ppm_sheep_farming",
raw_data = FALSE,
geo_level = "state",
time_period = 2020,
language = "eng"
)
```
### Example 5: Aquaculture by state over time
```{r eval=FALSE}
# download treated aquaculture data at the state level for 2015 to 2020
aquaculture <- load_ppm(
dataset = "ppm_aquaculture",
raw_data = FALSE,
geo_level = "state",
time_period = 2015:2020,
language = "eng"
)
```
## Data Notes
### Data Structure
Each record typically contains:
- Geographic identifier (state or municipality)
- Year
- Animal species or product type
- Quantity (number of animals or production volume)
- Value (if applicable)
- Number of establishments
### Units of Measurement
- **Livestock counts**: Number of animals
- **Milk**: Liters
- **Eggs**: Dozens or units (verify in data)
- **Honey**: Kilograms
- **Wool**: Kilograms
- **Aquaculture**: Kilograms or tons
### Raw vs. Treated Data
- **Raw data**: IBGE original format, Portuguese names
- **Treated data**: English variable names, standardized units
### Important Limitations
1. **Survey-based data**: Subject to sampling and reporting error
2. **Informal operations**: May undercount small or informal livestock operations
3. **Data lag**: Published with delay; recent years may not be available
4. **Aquaculture newer**: Aquaculture data less complete for very early years
5. **Methodology changes**: Survey methods may evolve; can affect comparability
***