rss.Rmd

# Rapid Surveys System (RSS) {-}

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) <a href="https://github.com/asdfree/rss/actions"><img src="https://github.com/asdfree/rss/actions/workflows/r.yml/badge.svg" alt="Github Actions Badge"></a> 

The standardized platform to answer time-sensitive questions about emerging and priority health issues.

* One table with one row per [AmeriSpeak](https://amerispeak.norc.org/) or [KnowledgePanel](https://www.ipsos.com/en-us/solutions/public-affairs/knowledgepanel) respondent.

* A cross-sectional survey generalizing to the noninstitutionalized adult population of the U.S.

* Releases expected four times per year.

* Conducted by the [National Center for Health Statistics](https://www.cdc.gov/nchs/) at the [Centers for Disease Control](http://www.cdc.gov/).

---

Please skim before you begin:

1. [NCHS Rapid Surveys System (RSS): Round 1 Survey Description](https://www.cdc.gov/nchs/data/rss/survey-description.pdf)

2. [Quality Profile, Rapid Surveys System Round 1](https://www.cdc.gov/nchs/data/rss/quality-profile.pdf)

3. A haiku regarding this microdata:

```{r}
# first response heroes
# question design thru publish
# time 'doxed by zeno
```

---

## Download, Import, Preparation {-}

Download and import the first round:
```{r eval = FALSE , results = "hide" }
library(haven)

sas_url <- "https://www.cdc.gov/nchs/data/rss/rss1_puf_t1.sas7bdat"
	
rss_tbl <- read_sas( sas_url )

rss_df <- data.frame( rss_tbl )

names( rss_df ) <- tolower( names( rss_df ) )

rss_df[ , 'one' ] <- 1
```

### Save Locally \ {-}

Save the object at any point:

```{r eval = FALSE , results = "hide" }
# rss_fn <- file.path( path.expand( "~" ) , "RSS" , "this_file.rds" )
# saveRDS( rss_df , file = rss_fn , compress = FALSE )
```

Load the same object:

```{r eval = FALSE , results = "hide" }
# rss_df <- readRDS( rss_fn )
```

### Survey Design Definition {-}
Construct a complex sample survey design:

```{r eval = FALSE , results = "hide" }
library(survey)

options( survey.lonely.psu = "adjust" )

rss_design <- 
	svydesign( 
		~ p_psu , 
		strata = ~ p_strata , 
		data = rss_df , 
		weights = ~ weight_m1 , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE , results = "hide" }
rss_design <- 
	
	update( 
		
		rss_design , 
		
		how_often_use_cleaner_purifier =
			factor(
				ven_use ,
				levels = c( -9:-6 , 0:3 ) ,
				labels = 
					c( "Don't Know" , "Question not asked" , "Explicit refusal/REF" , 
					"Skipped/Implied refusal" , "Never" , "Rarely" , "Sometimes" , "Always" )
			) ,
		
		has_health_insurance = ifelse( p_insur >= 0 , p_insur , NA ) ,
		
		metropolitan = 
			factor( as.numeric( p_metro_r == 1 ) , levels = 0:1 , labels = c( 'No' , 'Yes' ) )
		
	)
```

---

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

### Unweighted Counts {-}

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

svyby( ~ one , ~ metropolitan , rss_design , unwtd.count )
```

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

svyby( ~ one , ~ metropolitan , rss_design , svytotal )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svymean( ~ p_hhsize_r , rss_design )

svyby( ~ p_hhsize_r , ~ metropolitan , rss_design , svymean )
```

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

svyby( ~ how_often_use_cleaner_purifier , ~ metropolitan , rss_design , svymean )
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svytotal( ~ p_hhsize_r , rss_design )

svyby( ~ p_hhsize_r , ~ metropolitan , rss_design , svytotal )
```

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

svyby( ~ how_often_use_cleaner_purifier , ~ metropolitan , rss_design , svytotal )
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
svyquantile( ~ p_hhsize_r , rss_design , 0.5 )

svyby( 
	~ p_hhsize_r , 
	~ metropolitan , 
	rss_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE 
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ p_agec_r , 
	denominator = ~ p_hhsize_r , 
	rss_design 
)
```

### Subsetting {-}

Restrict the survey design to adults that most of the time or always wear sunscreen:
```{r eval = FALSE , results = "hide" }
sub_rss_design <- subset( rss_design , sun_useface >= 3 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ p_hhsize_r , sub_rss_design )
```

### 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( ~ p_hhsize_r , rss_design )

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

grouped_result <-
	svyby( 
		~ p_hhsize_r , 
		~ metropolitan , 
		rss_design , 
		svymean 
	)
	
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( rss_design )
```

Calculate the complex sample survey-adjusted variance of any statistic:
```{r eval = FALSE , results = "hide" }
svyvar( ~ p_hhsize_r , rss_design )
```

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

# SRS with replacement
svymean( ~ p_hhsize_r , rss_design , 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( ~ has_health_insurance , rss_design ,
	method = "likelihood" , na.rm = TRUE )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( p_hhsize_r ~ has_health_insurance , rss_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		p_hhsize_r ~ has_health_insurance + how_often_use_cleaner_purifier , 
		rss_design 
	)

summary( glm_result )
```

---

## Replication Example {-}

This example matches the statistic and confidence intervals from the "Ever uses a portable air cleaner or purifier in home" page of the [Air cleaners and purifiers dashboard](https://www.cdc.gov/nchs/rss/round1/air-purifiers.html):

```{r eval = FALSE , results = "hide" }
result <-
	svymean(
		~ as.numeric( ven_use > 0 ) ,
		subset( rss_design , ven_use >= 0 )
	)

stopifnot( round( coef( result ) , 3 ) == .379 )

stopifnot( round( confint( result )[1] , 3 ) == 0.366 )

stopifnot( round( confint( result )[2] , 3 ) == 0.393 )
```

---

## 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 RSS users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(srvyr)
rss_srvyr_design <- as_survey( rss_design )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
rss_srvyr_design %>%
	summarize( mean = survey_mean( p_hhsize_r ) )

rss_srvyr_design %>%
	group_by( metropolitan ) %>%
	summarize( mean = survey_mean( p_hhsize_r ) )
```