61-ssa.Rmd

# Social Security Administration Public Use Microdata (SSA) {-}

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

Research extracts provided by the Social Security Administration.

* Tables contain either one record per person or one record per person per year.

* The entire population of either social security number holders (most of the country) or social security recipients (just beneficiaries). One-percent samples should be multiplied by 100 to get accurate nationwide count statistics, five-percent samples by 20.

* No expected release timeline.

* Released by the United States [Social Security Administration (SSA)](http://www.ssa.gov/).

## Simplified Download and Importation {-}

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

## Analysis Examples with base R \ {-}

Load a data frame:

```{r eval = FALSE }
ssa_df <- readRDS( file.path( path.expand( "~" ) , "SSA" , "ssr_data/SSIPUF.rds" ) )
```

```{r eval = FALSE }

```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
ssa_df <- 
	transform( 
		ssa_df , 
		
		mental_disorder = as.numeric( diag %in% 1:2 ) ,
		
		program_eligibility =
			factor( 
				prel , 
				
				levels = 0:5 , 
				
				labels =
					c( "Unspecified" ,
					"Aged individual" ,
					"Aged spouse" ,
					"Disabled or blind individual" ,
					"Disabled or blind spouse" ,
					"Disabled or blind child" )
			)
			
	)
	
```

### Unweighted Counts {-}

Count the unweighted number of records in the table, overall and by groups:
```{r eval = FALSE , results = "hide" }
nrow( ssa_df )

table( ssa_df[ , "stat" ] , useNA = "always" )
```

### Descriptive Statistics {-}

Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
mean( ssa_df[ , "fpmt" ] )

tapply(
	ssa_df[ , "fpmt" ] ,
	ssa_df[ , "stat" ] ,
	mean 
)
```

Calculate the distribution of a categorical variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
prop.table( table( ssa_df[ , "program_eligibility" ] ) )

prop.table(
	table( ssa_df[ , c( "program_eligibility" , "stat" ) ] ) ,
	margin = 2
)
```

Calculate the sum of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
sum( ssa_df[ , "fpmt" ] )

tapply(
	ssa_df[ , "fpmt" ] ,
	ssa_df[ , "stat" ] ,
	sum 
)
```

Calculate the median (50th percentile) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
quantile( ssa_df[ , "fpmt" ] , 0.5 )

tapply(
	ssa_df[ , "fpmt" ] ,
	ssa_df[ , "stat" ] ,
	quantile ,
	0.5 
)
```

### Subsetting {-}

Limit your `data.frame` to females:
```{r eval = FALSE , results = "hide" }
sub_ssa_df <- subset( ssa_df , sex == "F" )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
mean( sub_ssa_df[ , "fpmt" ] )
```

### Measures of Uncertainty {-}

Calculate the variance, overall and by groups:
```{r eval = FALSE , results = "hide" }
var( ssa_df[ , "fpmt" ] )

tapply(
	ssa_df[ , "fpmt" ] ,
	ssa_df[ , "stat" ] ,
	var 
)
```

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

Perform a t-test:
```{r eval = FALSE , results = "hide" }
t.test( fpmt ~ mental_disorder , ssa_df )
```

Perform a chi-squared test of association:
```{r eval = FALSE , results = "hide" }
this_table <- table( ssa_df[ , c( "mental_disorder" , "program_eligibility" ) ] )

chisq.test( this_table )
```

Perform a generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	glm( 
		fpmt ~ mental_disorder + program_eligibility , 
		data = ssa_df
	)

summary( glm_result )
```

## Analysis Examples with `dplyr` \ {-}

The R `dplyr` library offers an alternative grammar of data manipulation to base R and SQL syntax. [dplyr](https://github.com/tidyverse/dplyr/) offers many verbs, such as `summarize`, `group_by`, and `mutate`, the convenience of pipe-able functions, and the `tidyverse` style of non-standard evaluation. [This vignette](https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html) details the available features. As a starting point for SSA users, this code replicates previously-presented examples:

```{r eval = FALSE , results = "hide" }
library(dplyr)
ssa_tbl <- tbl_df( ssa_df )
```
Calculate the mean (average) of a linear variable, overall and by groups:
```{r eval = FALSE , results = "hide" }
ssa_tbl %>%
	summarize( mean = mean( fpmt ) )

ssa_tbl %>%
	group_by( stat ) %>%
	summarize( mean = mean( fpmt ) )
```