10-ahs.Rmd

# American Housing Survey (AHS) {-}

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The American Housing Survey tracks housing structures across the United States.

* A collection of tables, most with one row per housing unit.

* A complex sample survey designed to generalize to both occupied and vacant housing units across the United States and also for about twenty-five metropolitan areas.

* Released more or less biennially since 1973.

* Sponsored by [the Department of Housing and Urban Development (HUD)](https://www.hud.gov/) and conducted by the [U.S. Census Bureau](https://www.census.gov/).

## Simplified Download and Importation {-}

The R `lodown` package easily downloads and imports all available AHS microdata by simply specifying `"ahs"` 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( "ahs" , output_dir = file.path( path.expand( "~" ) , "AHS" ) )
```

`lodown` also provides a catalog of available microdata extracts with the `get_catalog()` function. After requesting the AHS 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 AHS microdata files
ahs_cat <-
	get_catalog( "ahs" ,
		output_dir = file.path( path.expand( "~" ) , "AHS" ) )

# 2013 only
ahs_cat <- subset( ahs_cat , year == 2013 )
# download the microdata to your local computer
ahs_cat <- lodown( "ahs" , ahs_cat )
```

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

Construct a complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
library(survey)

ahs_df <- 
	readRDS( 
		file.path( path.expand( "~" ) , "AHS" , 
			"2013/national_v1.2/newhouse_repwgt.rds" 
		) 
	)

ahs_design <- 
	svrepdesign(
		weights = ~ wgt90geo ,
		repweights = "repwgt[1-9]" ,
		type = "Fay" ,
		rho = ( 1 - 1 / sqrt( 4 ) ) ,
		mse = TRUE ,
		data = ahs_df
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
ahs_design <- 
	update( 
		ahs_design , 

		tenure = 
			factor( 
				ifelse( is.na( tenure ) , 4 , tenure ) , 
				levels = 1:4 , 
				labels = 
					c( 'Owned or being bought' ,
					'Rented for cash rent' ,
					'Occupied without payment of cash rent' ,
					'Not occupied' )
			) ,
			
			
		lotsize =
			factor( 
				1 + findInterval( lot ,
					c( 5500 , 11000 , 22000 , 
					44000 , 220000 , 440000 ) ) , 
				levels = 1:7 ,
				labels = c( "Less then 1/8 acre" , 
				"1/8 up to 1/4 acre" , "1/4 up to 1/2 acre" ,
				"1/2 up to 1 acre" , "1 up to 5 acres" , 
				"5 up to 10 acres" , "10 acres or more" ) ) ,
				
				
		below_poverty = as.numeric( poor < 100 )
				
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ tenure , ahs_design , unwtd.count )
```

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

svyby( ~ one , ~ tenure , ahs_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ rooms , ~ tenure , ahs_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ lotsize , ahs_design , na.rm = TRUE )

svyby( ~ lotsize , ~ tenure , ahs_design , svymean , na.rm = TRUE )
```

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

svyby( ~ rooms , ~ tenure , ahs_design , svytotal , na.rm = TRUE )
```

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

svyby( ~ lotsize , ~ tenure , ahs_design , svytotal , na.rm = TRUE )
```

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

svyby( 
	~ rooms , 
	~ tenure , 
	ahs_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

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

### Subsetting {-}

Restrict the survey design to homes with a garage or carport:
```{r eval = FALSE , results = "hide" }
sub_ahs_design <- subset( ahs_design , garage == 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ rooms , sub_ahs_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( ~ rooms , ahs_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ rooms , 
		~ tenure , 
		ahs_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( ahs_design )
```

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

# SRS with replacement
svymean( ~ rooms , ahs_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( ~ below_poverty , ahs_design ,
	method = "likelihood" , na.rm = TRUE )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( rooms ~ below_poverty , ahs_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		rooms ~ below_poverty + lotsize , 
		ahs_design 
	)

summary( glm_result )
```

## Analysis Examples with `srvyr` \ {-}

The R `srvyr` library calculates summary statistics from survey data, such as the mean, total or quantile using [dplyr](https://github.com/tidyverse/dplyr/)-like syntax. [srvyr](https://github.com/gergness/srvyr) allows for the use of many verbs, such as `summarize`, `group_by`, and `mutate`, the convenience of pipe-able functions, the `tidyverse` style of non-standard evaluation and more consistent return types than the `survey` package. [This vignette](https://cran.r-project.org/web/packages/srvyr/vignettes/srvyr-vs-survey.html) details the available features. As a starting point for AHS users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
ahs_srvyr_design <- as_survey( ahs_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
ahs_srvyr_design %>%
	summarize( mean = survey_mean( rooms , na.rm = TRUE ) )

ahs_srvyr_design %>%
	group_by( tenure ) %>%
	summarize( mean = survey_mean( rooms , na.rm = TRUE ) )
```

---

## Replication Example {-}

The example below matches statistics and standard errors from this table pulled from the US Census Bureau's [Quick Guide to Estimating Variance Using Replicate Weights](https://www.census.gov/content/dam/Census/programs-surveys/ahs/tech-documentation/2015/Quick%20Guide%20to%20Estimating%20Variance%20Using%20Replicate%20Weights%202009%20to%20Current.pdf):

`r knitr::include_graphics("images/ahs2013.png")`
	
Compute the statistics and standard errors for monthly housing costs by owner/renter status of the unit:

```{r eval = FALSE , results = "hide" }
means <- c( 1241.8890 , 972.6051 , 170.0121 )
std_err <- c( 7.3613 , 5.6956 , 6.1586 )
ci_lb <- c( 1227.3511 , 961.3569 , 157.8495 )
ci_ub <- c( 1256.4270 , 983.8532 , 182.1747 )

results <- 
	svyby( 
		~ zsmhc , 
		~ tenure , 
		ahs_design , 
		svymean , 
		na.rm = TRUE , 
		na.rm.all = TRUE 
	)

ci_res <- 
	confint( results , df = degf( ahs_design ) + 1 )

stopifnot( all( round( coef( results ) , 4 ) == means ) )

stopifnot( all( round( SE( results ) , 4 ) == std_err ) )

stopifnot( all( round( ci_res[ , 1 ] , 4 ) == ci_lb ) )

stopifnot( all( round( ci_res[ , 2 ] , 4 ) == ci_ub ) )
```