50-pnad.Rmd

# Pesquisa Nacional por Amostra de Domicilios (PNAD) {-}

[![Build Status](https://travis-ci.org/asdfree/pnad.svg?branch=master)](https://travis-ci.org/asdfree/pnad) [![Build status](https://ci.appveyor.com/api/projects/status/github/asdfree/pnad?svg=TRUE)](https://ci.appveyor.com/project/ajdamico/pnad)

*Contributed by Dr. Djalma Pessoa <<pessoad@gmail.com>>*

Brazil's previous principal household survey, the Pesquisa Nacional por Amostra de Domicilios (PNAD) measures general education, labor, income, and housing characteristics of the population.

* One table with one row per sampled household and a second table with one row per individual within each sampled household.

* A complex sample survey designed to generalize to the civilian non-institutional population of Brazil, although the rural north was not included prior to 2004.

* Released annually since 2001 except for years ending in zero, when the decennial census takes its place.

* Administered by the [Instituto Brasileiro de Geografia e Estatistica](http://www.ibge.gov.br/).

## Simplified Download and Importation {-}

The R `lodown` package easily downloads and imports all available PNAD microdata by simply specifying `"pnad"` with an `output_dir =` parameter in the `lodown()` function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

```{r eval = FALSE }
library(lodown)
lodown( "pnad" , output_dir = file.path( path.expand( "~" ) , "PNAD" ) )
```

`lodown` also provides a catalog of available microdata extracts with the `get_catalog()` function. After requesting the PNAD catalog, you could pass a subsetted catalog through the `lodown()` function in order to download and import specific extracts (rather than all available extracts).

```{r eval = FALSE , results = "hide" }
library(lodown)
# examine all available PNAD microdata files
pnad_cat <-
	get_catalog( "pnad" ,
		output_dir = file.path( path.expand( "~" ) , "PNAD" ) )

# 2011 only
pnad_cat <- subset( pnad_cat , year == 2011 )
# download the microdata to your local computer
pnad_cat <- lodown( "pnad" , pnad_cat )
```

## Analysis Examples with the `survey` library \ {-}

Construct a complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
options( survey.lonely.psu = "adjust" )

library(survey)

pnad_df <- readRDS( pnad_cat[ 1 , 'output_filename' ] )

pop_types <- 
	data.frame( 
		v4609 = unique( pnad_df$v4609 ) , 
		Freq = unique( pnad_df$v4609 )
	)

prestratified_design <-
	svydesign(
		id = ~ v4618 ,
		strata = ~ v4617 ,
		data = pnad_df ,
		weights = ~ pre_wgt ,
		nest = TRUE
	)
	
rm( pnad_df ) ; gc()

pnad_design <- 
	postStratify( 
		design = prestratified_design ,
		strata = ~ v4609 ,
		population = pop_types
	)
	
rm( prestratified_design ) ; gc()
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
pnad_design <- 
	update( 
		pnad_design , 
		age_categories = factor( 1 + findInterval( v8005 , seq( 5 , 60 , 5 ) ) ) ,
		male = as.numeric( v0302 == 2 ) ,
		teenagers = as.numeric( v8005 > 12 & v8005 < 20 ) ,
		started_working_before_thirteen = as.numeric( v9892 < 13 )
	)
```

### Unweighted Counts {-}

Count the unweighted number of records in the survey sample, overall and by groups:
```{r eval = FALSE , results = "hide" }
sum( weights( pnad_design , "sampling" ) != 0 )

svyby( ~ one , ~ region , pnad_design , unwtd.count )
```

### Weighted Counts {-}
Count the weighted size of the generalizable population, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ one , pnad_design )

svyby( ~ one , ~ region , pnad_design , svytotal )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ v4720 , pnad_design , na.rm = TRUE )

svyby( ~ v4720 , ~ region , pnad_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ age_categories , pnad_design )

svyby( ~ age_categories , ~ region , pnad_design , svymean )
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ v4720 , pnad_design , na.rm = TRUE )

svyby( ~ v4720 , ~ region , pnad_design , svytotal , na.rm = TRUE )
```

Calculate the weighted sum of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ age_categories , pnad_design )

svyby( ~ age_categories , ~ region , pnad_design , svytotal )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svyquantile( ~ v4720 , pnad_design , 0.5 , na.rm = TRUE )

svyby( 
	~ v4720 , 
	~ region , 
	pnad_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ started_working_before_thirteen , 
	denominator = ~ teenagers , 
	pnad_design ,
	na.rm = TRUE
)
```

### Subsetting {-}

Restrict the survey design to married persons:
```{r eval = FALSE , results = "hide" }
sub_pnad_design <- subset( pnad_design , v4011 == 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ v4720 , sub_pnad_design , na.rm = TRUE )
```

### Measures of Uncertainty {-}

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:
```{r eval = FALSE , results = "hide" }
this_result <- svymean( ~ v4720 , pnad_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
	svyby( 
		~ v4720 , 
		~ region , 
		pnad_design , 
		svymean ,
		na.rm = TRUE 
	)
	
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )
```

Calculate the degrees of freedom of any survey design object:
```{r eval = FALSE , results = "hide" }
degf( pnad_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ v4720 , pnad_design , na.rm = TRUE )
```

Include the complex sample design effect in the result for a specific statistic:
```{r eval = FALSE , results = "hide" }
# SRS without replacement
svymean( ~ v4720 , pnad_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ v4720 , pnad_design , na.rm = TRUE , deff = "replace" )
```

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See `?svyciprop` for alternatives:
```{r eval = FALSE , results = "hide" }
svyciprop( ~ male , pnad_design ,
	method = "likelihood" )
```

### Regression Models and Tests of Association {-}

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( v4720 ~ male , pnad_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
svychisq( 
	~ male + age_categories , 
	pnad_design 
)
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		v4720 ~ male + age_categories , 
		pnad_design 
	)

summary( glm_result )
```

## Poverty and Inequality Estimation with `convey` \ {-}

The R `convey` library estimates measures of income concentration, poverty, inequality, and wellbeing. [This textbook](https://guilhermejacob.github.io/context/) details the available features. As a starting point for PNAD users, this code calculates the gini coefficient on complex sample survey data:

```{r eval = FALSE , results = "hide" }
library(convey)
pnad_design <- convey_prep( pnad_design )

sub_pnad_design <- 
	subset( 
		pnad_design , 
		!is.na( v4720 ) & v4720 != 0 & v8005 >= 15
	)

svygini( ~ v4720 , sub_pnad_design , na.rm = TRUE )
```

---

## Replication Example {-}

```{r eval = FALSE , results = "hide" }
svytotal( ~one , pnad_design )
svytotal( ~factor( v0302 ) , pnad_design )
cv( svytotal( ~factor( v0302 ) , pnad_design ) )
```