41-nsfg.Rmd

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

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

The National Survey of Family Growth (NSFG) is 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 15-44 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/).

## Simplified Download and Importation {-}

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

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

# 2013-2015 only
nsfg_cat <- subset( nsfg_cat , grepl( "2013_2015" , full_url ) )
# download the microdata to your local computer
nsfg_cat <- lodown( "nsfg" , nsfg_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)

nsfg_df <- readRDS( file.path( path.expand( "~" ) , "NSFG" , "2013_2015_FemRespData.rds" ) )

nsfg_design <- 
	svydesign( 
		id = ~ secu , 
		strata = ~ sest , 
		data = nsfg_df , 
		weights = ~ wgt2013_2015 , 
		nest = TRUE 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
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' , '40-44' ) ) ,
		
		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" )
			)
	)
```

### 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( ~ npregs_s , nsfg_design , na.rm = TRUE )

svyby( ~ npregs_s , ~ 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( ~ npregs_s , nsfg_design , na.rm = TRUE )

svyby( ~ npregs_s , ~ 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( ~ npregs_s , nsfg_design , 0.5 , na.rm = TRUE )

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

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

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

grouped_result <-
	svyby( 
		~ npregs_s , 
		~ 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( ~ npregs_s , 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( ~ npregs_s , nsfg_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ npregs_s , 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( npregs_s ~ 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( 
		npregs_s ~ birth_control_pill + marstat , 
		nsfg_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 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( npregs_s , na.rm = TRUE ) )

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

---

## Replication Example {-}

The example below matches statistics and standard errors from this table pulled from the [CDC](https://www.cdc.gov/nchs/data/nsfg/NSFG_2013_2015_VarEst_Ex1.pdf) variance estimation examples:

`r knitr::include_graphics("images/nsfg_2013_2015_varest_ex1.png")`

Match the sum of the weights:

```{r eval = FALSE , results = "hide" }
stopifnot( round( coef( svytotal( ~ one , nsfg_design ) ) , 0 ) == 61491766 )
```
	
Compute the statistics and standard errors for percentage of women currently using the pill by age:

```{r eval = FALSE , results = "hide" }
row_percents <- c( 16.1723 , 27.8173 , 18.7197 , 13.4367 , 9.0851 , 7.7224 )

std_err_row_percents <- c( 1.5414 , 2.4162 , 1.9890 , 1.5074 , 1.5222 , 1.6053 )

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 ) )
```