40-nsduh.Rmd

# National Study on Drug Use and Health (NSDUH) {-}

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

The National Study on Drug Use and Health measures the prevalence and correlates of drug use in the United States.

* One table with one row per sampled respondent.

* A complex sample survey designed to generalize to the civilian, noninstitutionalized population of the United States aged 12 and older.

* Released periodically since 1979 and annually since 1990.

* Administered by the [Substance Abuse and Mental Health Services Administration](http://www.samhsa.gov/).

## Simplified Download and Importation {-}

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

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

# 2016 only
nsduh_cat <- subset( nsduh_cat , year == 2016 )
# download the microdata to your local computer
nsduh_cat <- lodown( "nsduh" , nsduh_cat )
```

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

Construct a complex sample survey design:

```{r eval = FALSE }

```

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

nsduh_df <- 
	readRDS( file.path( path.expand( "~" ) , "NSDUH" , "2016 main.rds" ) )

variables_to_keep <-
	c( 'verep' , 'vestr' , 'analwt_c' , 'health' , 'cigtry' , 'cocage' ,
		'mjever' , 'coutyp4' , 'preg' )
	
nsduh_df <- nsduh_df[ variables_to_keep ] ; gc()
	
nsduh_design <- 
	svydesign( 
		id = ~ verep , 
		strata = ~ vestr , 
		data = nsduh_df , 
		weights = ~ analwt_c , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
nsduh_design <- 
	update( 
		nsduh_design , 
		
		one = 1 ,
		
		health = 
			factor( 
				health , 
				levels = 1:5 , 
				labels = c( "excellent" , "very good" , "good" ,
					"fair" , "poor" )
			) ,
			
		age_tried_first_cigarette = ifelse( cigtry > 99 , NA , cigtry ) ,
		
		age_tried_cocaine = ifelse( cocage > 99 , NA , cocage ) ,

		ever_used_marijuana = as.numeric( mjever == 1 ) ,
		
		county_type =
			factor(
				coutyp4 ,
				levels = 1:3 ,
				labels = c( "large metro" , "small metro" , "nonmetro" )
			)
			
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ county_type , nsduh_design , unwtd.count )
```

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

svyby( ~ one , ~ county_type , nsduh_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ age_tried_first_cigarette , ~ county_type , nsduh_design , svymean , na.rm = TRUE )
```

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

svyby( ~ health , ~ county_type , nsduh_design , svymean , na.rm = TRUE )
```

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

svyby( ~ age_tried_first_cigarette , ~ county_type , nsduh_design , svytotal , na.rm = TRUE )
```

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

svyby( ~ health , ~ county_type , nsduh_design , svytotal , na.rm = TRUE )
```

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

svyby( 
	~ age_tried_first_cigarette , 
	~ county_type , 
	nsduh_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

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

### Subsetting {-}

Restrict the survey design to individuals who are pregnant:
```{r eval = FALSE , results = "hide" }
sub_nsduh_design <- subset( nsduh_design , preg == 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ age_tried_first_cigarette , sub_nsduh_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( ~ age_tried_first_cigarette , nsduh_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ age_tried_first_cigarette , 
		~ county_type , 
		nsduh_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( nsduh_design )
```

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

# SRS with replacement
svymean( ~ age_tried_first_cigarette , nsduh_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( ~ ever_used_marijuana , nsduh_design ,
	method = "likelihood" , na.rm = TRUE )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( age_tried_first_cigarette ~ ever_used_marijuana , nsduh_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		age_tried_first_cigarette ~ ever_used_marijuana + health , 
		nsduh_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 NSDUH users, this code replicates previously-presented examples:

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

nsduh_srvyr_design %>%
	group_by( county_type ) %>%
	summarize( mean = survey_mean( age_tried_first_cigarette , na.rm = TRUE ) )
```

---

## Replication Example {-}

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

```