44-pew.Rmd

# Pew Research Center (PEW) {-}

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

The Pew Research Center releases its survey microdata on U.S. Politics & Policy, Journalism & Media, Internet, Science & Tech, Religion & Public Life, Hispanic Trends, Global Attitudes & Trends, and Social & Demographic Trends.

* Generally one table per survey, with one row per sampled respondent.

* Complex sample surveys, often designed to generalize to the U.S. adult population or the adult populations of the nations surveyed.

* Administered by the [Pew Research Center](http://www.pewresearch.org/).

## Simplified Download and Importation {-}

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

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

# spring 2015 only
pew_cat <- subset( pew_cat , name == "Spring 2015 Survey Data" )
# download the microdata to your local computer
pew_cat <- lodown( "pew" , pew_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)

pew_df <- 
	readRDS( 
		file.path( path.expand( "~" ) , "PEW" , 
		"Global Attitudes & Trends/2015/Spring 2015 Survey Data" ,
		"Pew Research Global Attitudes Spring 2015 Dataset for Web FINAL.rds" )
	)

# limit the global attitudes data set to just israel
israel_df <- subset( pew_df , country == 14 )
	
pew_design <- 
	svydesign(
		id = ~ psu , 
		strata = ~ stratum , 
		weight = ~ weight , 
		data = israel_df 
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
pew_design <- 
	update( 
		pew_design , 
		
		one = 1 ,
		
		your_day_today =
			factor( 
				q1 , 
				levels = 1:3 ,
				labels = 
					c( 
						'a typical day' , 
						'a particularly good day' , 
						'a particularly bad day' 
					)
			) ,

		school_years = ifelse( q163b %in% 98:99 , NA , q163b ) ,
		
		age_in_years = ifelse( q146 %in% 98:99 , NA , q146 ) ,

		climate_change_concern = ifelse( q13a %in% 1:5 , as.numeric( q13a < 3 ) , NA ) ,
		
		country_economic_situation =
			factor(
				q3 ,
				levels = 1:4 ,
				labels = 
					c( 
						'very good' , 
						'somewhat good' , 
						'somewhat bad' , 
						'very bad' 
					)
			)
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ your_day_today , pew_design , unwtd.count )
```

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

svyby( ~ one , ~ your_day_today , pew_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ school_years , ~ your_day_today , pew_design , svymean , na.rm = TRUE )
```

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

svyby( ~ country_economic_situation , ~ your_day_today , pew_design , svymean , na.rm = TRUE )
```

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

svyby( ~ school_years , ~ your_day_today , pew_design , svytotal , na.rm = TRUE )
```

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

svyby( ~ country_economic_situation , ~ your_day_today , pew_design , svytotal , na.rm = TRUE )
```

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

svyby( 
	~ school_years , 
	~ your_day_today , 
	pew_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE ,
	na.rm = TRUE
)
```

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

### Subsetting {-}

Restrict the survey design to seniors:
```{r eval = FALSE , results = "hide" }
sub_pew_design <- subset( pew_design , q146 >= 65 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ school_years , sub_pew_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( ~ school_years , pew_design , na.rm = TRUE )

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

grouped_result <-
	svyby( 
		~ school_years , 
		~ your_day_today , 
		pew_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( pew_design )
```

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

# SRS with replacement
svymean( ~ school_years , pew_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( ~ climate_change_concern , pew_design ,
	method = "likelihood" , na.rm = TRUE )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( school_years ~ climate_change_concern , pew_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		school_years ~ climate_change_concern + country_economic_situation , 
		pew_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 PEW users, this code replicates previously-presented examples:

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

pew_srvyr_design %>%
	group_by( your_day_today ) %>%
	summarize( mean = survey_mean( school_years , na.rm = TRUE ) )
```

---

## Replication Example {-}

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

```