31-ncvs.Rmd

# Youth Risk Behavior Surveillance System (NCVS) {-}

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

The Youth Risk Behavior Surveillance System is the high school edition of the Behavioral Risk Factor Surveillance System (BRFSS), a scientific study of good kids who do bad things.

* One table with one row per sampled youth respondent.

* A complex sample survey designed to generalize to all public and private school students in grades 9-12 in the United States.

* Released biennially since 1993.

* Administered by the [Centers for Disease Control and Prevention](http://www.cdc.gov/).

## Simplified Download and Importation {-}

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

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

# 2015 only
ncvs_cat <- subset( ncvs_cat , year == 2015 )
# download the microdata to your local computer
ncvs_cat <- lodown( "ncvs" , ncvs_cat )
```

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

Construct a complex sample survey design:

```{r eval = FALSE }

```

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

ncvs_df <- readRDS( file.path( path.expand( "~" ) , "NCVS" , "2015 main.rds" ) )

ncvs_design <- 
	svydesign( 
		~ psu , 
		strata = ~ stratum , 
		data = ncvs_df , 
		weights = ~ weight , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
ncvs_design <- 
	update( 
		ncvs_design , 
		q2 = q2 ,
		never_rarely_wore_bike_helmet = as.numeric( qn8 == 1 ) ,
		ever_smoked_marijuana = as.numeric( qn47 == 1 ) ,
		ever_tried_to_quit_cigarettes = as.numeric( q36 > 2 ) ,
		smoked_cigarettes_past_year = as.numeric( q36 > 1 )
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ ever_smoked_marijuana , ncvs_design , unwtd.count )
```

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

svyby( ~ one , ~ ever_smoked_marijuana , ncvs_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ bmipct , ~ ever_smoked_marijuana , ncvs_design , svymean , na.rm = TRUE )
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ q2 , ncvs_design , na.rm = TRUE )

svyby( ~ q2 , ~ ever_smoked_marijuana , ncvs_design , svymean , na.rm = TRUE )
```

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

svyby( ~ bmipct , ~ ever_smoked_marijuana , ncvs_design , svytotal , na.rm = TRUE )
```

Calculate the weighted sum of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ q2 , ncvs_design , na.rm = TRUE )

svyby( ~ q2 , ~ ever_smoked_marijuana , ncvs_design , svytotal , na.rm = TRUE )
```

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

svyby( 
	~ bmipct , 
	~ ever_smoked_marijuana , 
	ncvs_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

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

### Subsetting {-}

Restrict the survey design to youths who ever drank alcohol:
```{r eval = FALSE , results = "hide" }
sub_ncvs_design <- subset( ncvs_design , qn41 == 1 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ bmipct , sub_ncvs_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( ~ bmipct , ncvs_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ bmipct , 
		~ ever_smoked_marijuana , 
		ncvs_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( ncvs_design )
```

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

# SRS with replacement
svymean( ~ bmipct , ncvs_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( ~ never_rarely_wore_bike_helmet , ncvs_design ,
	method = "likelihood" , na.rm = TRUE )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( bmipct ~ never_rarely_wore_bike_helmet , ncvs_design )
```

Perform a chi-squared test of association for survey data:
```{r eval = FALSE , results = "hide" }
svychisq( 
	~ never_rarely_wore_bike_helmet + q2 , 
	ncvs_design 
)
```

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		bmipct ~ never_rarely_wore_bike_helmet + q2 , 
		ncvs_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 NCVS users, this code replicates previously-presented examples:

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

ncvs_srvyr_design %>%
	group_by( ever_smoked_marijuana ) %>%
	summarize( mean = survey_mean( bmipct , na.rm = TRUE ) )
```

---

## Replication Example {-}

This snippet replicates the "never/rarely wore bicycle helmet" row of [PDF page 29 of this CDC analysis software document](https://www.cdc.gov/healthyyouth/data/yrbs/pdf/2015/2015_yrbs_analysis_software.pdf#page=29).

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

unwtd.count( ~ never_rarely_wore_bike_helmet , yrbss_design )

svytotal( ~ one , subset( yrbss_design , !is.na( never_rarely_wore_bike_helmet ) ) )
 
svymean( ~ never_rarely_wore_bike_helmet , yrbss_design , na.rm = TRUE )

svyciprop( ~ never_rarely_wore_bike_helmet , yrbss_design , na.rm = TRUE , method = "beta" )

```