nsfg.Rmd

# National Survey of Family Growth (NSFG) {-}

[![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/nsfg/actions"><img src="https://github.com/asdfree/nsfg/actions/workflows/r.yml/badge.svg" alt="Github Actions Badge"></a> 

The principal survey to measure reproductive behavior in the United States population.

* Multiple tables with one row per respondent for the female and male tables, then a separate table with one row per pregnancy.

* A complex sample survey designed to generalize to the 15-49 year old population of the United States, by gender.

* Released every couple of years since 1973.

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

---

Please skim before you begin:

1. [Sample Design Documentation](https://www.cdc.gov/nchs/data/nsfg/NSFG-2017-2019-Sample-Design-Documentation-508.pdf)

2. [Wikipedia Entry](https://en.wikipedia.org/wiki/National_Survey_of_Family_Growth)

3. A haiku regarding this microdata:

```{r}
# family structure
# questions cuz radar fails at
# storks with bassinets
```

---

## Download, Import, Preparation {-}

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

dat_url <-
	"https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NSFG/2017_2019_FemRespData.dat"

sas_url <-
	file.path( dirname( dat_url ) , "sas/2017_2019_FemRespSetup.sas" )
	
sas_positions <-
	parse.SAScii( sas_url )

sas_positions[ , 'varname' ] <-
	tolower( sas_positions[ , 'varname' ] )

sas_positions[ , 'column_types' ] <-
		ifelse( sas_positions[ , 'char' ] , "c" , "d" )

nsfg_tbl <-
	read_fwf(
		dat_url ,
		fwf_widths( 
			abs( sas_positions[ , 'width' ] ) , 
			col_names = sas_positions[ , 'varname' ] 
		) ,
		col_types = paste0( sas_positions[ , 'column_types' ] , collapse = "" ) ,
		na = c( "" , "." )
	)
	
nsfg_df <- data.frame( nsfg_tbl )
```

### Save Locally \ {-}

Save the object at any point:

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

Load the same object:

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

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

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

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

nsfg_design <- 
	svydesign( 
		id = ~ secu , 
		strata = ~ sest , 
		data = nsfg_df , 
		weights = ~ wgt2017_2019 , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

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

		one = 1 ,
		
		birth_control_pill = as.numeric( constat1 == 6 ) ,
		
		age_categories = 
			factor( findInterval( ager , c( 15 , 20 , 25 , 30 , 35 , 40 ) ) ,
				labels = c( '15-19' , '20-24' , '25-29' , '30-34' , '35-39' , '40-49' ) ) ,
		
		marstat =
			factor( marstat , levels = c( 1:6 , 8:9 ) ,
				labels = c(
					"Married to a person of the opposite sex" ,
					"Not married but living together with a partner of the opposite sex" ,
					"Widowed" ,
					"Divorced or annulled" ,
					"Separated, because you and your spouse are not getting along" ,
					"Never been married" ,
					"Refused" ,
					"Don't know" )
			)
	)
```

---

## 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( nsfg_design , "sampling" ) != 0 )

svyby( ~ one , ~ age_categories , nsfg_design , unwtd.count )
```

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

svyby( ~ one , ~ age_categories , nsfg_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ pregnum , ~ age_categories , nsfg_design , svymean , na.rm = TRUE )
```

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

svyby( ~ marstat , ~ age_categories , nsfg_design , svymean )
```

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

svyby( ~ pregnum , ~ age_categories , nsfg_design , svytotal , na.rm = TRUE )
```

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

svyby( ~ marstat , ~ age_categories , nsfg_design , svytotal )
```

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

svyby( 
	~ pregnum , 
	~ age_categories , 
	nsfg_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE , na.rm = TRUE
)
```

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

### Subsetting {-}

Restrict the survey design to ever cohabited:
```{r eval = FALSE , results = "hide" }
sub_nsfg_design <- subset( nsfg_design , timescoh > 0 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ pregnum , sub_nsfg_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( ~ pregnum , nsfg_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ pregnum , 
		~ age_categories , 
		nsfg_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( nsfg_design )
```

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

# SRS with replacement
svymean( ~ pregnum , nsfg_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( ~ birth_control_pill , nsfg_design ,
	method = "likelihood" , na.rm = TRUE )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( pregnum ~ birth_control_pill , nsfg_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		pregnum ~ birth_control_pill + marstat , 
		nsfg_design 
	)

summary( glm_result )
```

---

## Replication Example {-}

This example matches the [Variance Estimates for Percentages using SAS (9.4) and STATA (14)](https://www.cdc.gov/nchs/data/nsfg/NSFG-2017-2019-VarEst-Ex1-508.pdf):

Match the sum of the weights:

```{r eval = FALSE , results = "hide" }
result <- svytotal( ~ one , nsfg_design )

stopifnot( round( coef( result ) , 0 ) == 72671926 )

stopifnot( round( SE( result ) , 0 ) == 3521465 )
```
	
Match row percentages of women currently using the pill by age:

```{r eval = FALSE , results = "hide" }
row_percents <- c( 19.5112 , 23.7833 , 19.6916 , 15.2800 , 6.4965 , 6.5215 )

std_err_row_percents <- c( 1.8670 , 2.1713 , 2.2773 , 1.7551 , 0.9895 , 1.0029 )

results <- svyby( ~ birth_control_pill , ~ age_categories , nsfg_design , svymean )

stopifnot( all( round( coef( results ) * 100 , 4 ) == row_percents ) )

stopifnot( all( round( SE( results ) * 100 , 4 ) == std_err_row_percents ) )
```

---

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

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

nsfg_srvyr_design %>%
	group_by( age_categories ) %>%
	summarize( mean = survey_mean( pregnum , na.rm = TRUE ) )
```