43-nychvs.Rmd

# New York City Housing and Vacancy Survey (NYCHVS) {-}

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

The New York City Housing and Vacancy Survey (NYCHVS) covers the city-wide rental vacancy rate and other characteristics like neighborhood housing stock.

* One table with one record per occupied housing unit, a second table with one record per person inside each occupied housing unit, and a third table with one record per unoccupied housing unit.

* A complex sample survey designed to generalize to all occupied and unoccupied housing units in the five boroughs of New York City.

* Released triennially since 1998.

* Funded by the [New York City Department of Housing Preservation and Development](www.nyc.gov/hpd) and conducted by the [United States Census Bureau](http://www.census.gov/).

## Simplified Download and Importation {-}

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

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

# 2014 only
nychvs_cat <- subset( nychvs_cat , year == 2014 )
# download the microdata to your local computer
nychvs_cat <- lodown( "nychvs" , nychvs_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)

# load the occupied units table
nychvs_df <- readRDS( file.path( path.expand( "~" ) , "NYCHVS" , "2014/occ.rds" ) )

nychvs_design <- 
	svydesign( ~ 1 , data = nychvs_df , weights = ~ fw )
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
nychvs_design <- 
	update( 
		nychvs_design , 
		
		one = 1 ,
		
		home_owners = as.numeric( sc115 == 1 ) ,

		yearly_household_income = ifelse( uf42 == 9999999 , 0 , as.numeric( uf42 ) ) ,
		
		gross_monthly_rent = ifelse( uf17 == 99999 , NA , as.numeric( uf17 ) ) ,
		
		borough =
			factor( boro , levels = 1:5 , labels =
				c( 'Bronx' , 'Brooklyn' , 'Manhattan' , 
				'Queens' , 'Staten Island' )
			) ,
			
		householder_sex = factor( hhr2 , labels = c( 'male' , 'female' ) )
			
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ borough , nychvs_design , unwtd.count )
```

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

svyby( ~ one , ~ borough , nychvs_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ yearly_household_income , ~ borough , nychvs_design , svymean , na.rm = TRUE )
```

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

svyby( ~ householder_sex , ~ borough , nychvs_design , svymean )
```

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

svyby( ~ yearly_household_income , ~ borough , nychvs_design , svytotal , na.rm = TRUE )
```

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

svyby( ~ householder_sex , ~ borough , nychvs_design , svytotal )
```

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

svyby( 
	~ yearly_household_income , 
	~ borough , 
	nychvs_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

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

### Subsetting {-}

Restrict the survey design to Manhattan:
```{r eval = FALSE , results = "hide" }
sub_nychvs_design <- subset( nychvs_design , boro == 3 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ yearly_household_income , sub_nychvs_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( ~ yearly_household_income , nychvs_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ yearly_household_income , 
		~ borough , 
		nychvs_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( nychvs_design )
```

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

# SRS with replacement
svymean( ~ yearly_household_income , nychvs_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( ~ home_owners , nychvs_design ,
	method = "likelihood" )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( yearly_household_income ~ home_owners , nychvs_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		yearly_household_income ~ home_owners + householder_sex , 
		nychvs_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 NYCHVS users, this code replicates previously-presented examples:

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

nychvs_srvyr_design %>%
	group_by( borough ) %>%
	summarize( mean = survey_mean( yearly_household_income , na.rm = TRUE ) )
```

---

## Replication Example {-}

```{r eval = FALSE , results = "hide" }

```