20-dhs.Rmd

# Demographic and Health Surveys (DHS) {-}

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The Demographic and Health Surveys collect data on population, health, HIV, and nutrition in over 90 countries.

* Many tables, often with one row per male, per female, or per responding household.

* A complex sample survey designed to generalize to the residents of various countries.

* Many releases for different countries annually, since 1984.

* Administered by the [ICF International](http://www.icfi.com/) and funded by the [US Agency for International Development](http://www.usaid.gov/).

## Simplified Download and Importation {-}

The R `lodown` package easily downloads and imports all available DHS microdata by simply specifying `"dhs"` 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( "dhs" , output_dir = file.path( path.expand( "~" ) , "DHS" ) , 
	your_email = "email@address.com" , 
	your_password = "password" , 
	your_project = "project" )
```

`lodown` also provides a catalog of available microdata extracts with the `get_catalog()` function. After requesting the DHS 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 DHS microdata files
dhs_cat <-
	get_catalog( "dhs" ,
		output_dir = file.path( path.expand( "~" ) , "DHS" ) , 
		your_email = "email@address.com" , 
		your_password = "password" , 
		your_project = "project" )

# malawi 2004 only
dhs_cat <- subset( dhs_cat , country == 'Malawi' & year == 2004 )
# download the microdata to your local computer
dhs_cat <- lodown( "dhs" , dhs_cat , 
	your_email = "email@address.com" , 
	your_password = "password" , 
	your_project = "project" )
```

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

Construct a complex sample survey design:

```{r eval = FALSE }

```

```{r eval = FALSE }
library(survey)

dhs_df <- 
	readRDS( 
		file.path( path.expand( "~" ) , "DHS" , 
		"Malawi/Standard DHS 2004/MWIR4EFL.rds" )
	)

# convert the weight column to a numeric type
dhs_df$weight <- as.numeric( dhs_df$v005 )

# paste the `sdist` and `v025` columns together
# into a single strata variable
dhs_df$strata <- do.call( paste , dhs_df[ , c( 'sdist' , 'v025' ) ] )
# as shown at
# http://userforum.dhsprogram.com/index.php?t=rview&goto=2154#msg_2154

dhs_design <- 
	svydesign( 
		~ v021 , 
		strata = ~strata , 
		data = dhs_df , 
		weights = ~weight
	)
```

### Variable Recoding {-}

Add new columns to the data set:
```{r eval = FALSE }
dhs_design <- 
	update( 
		dhs_design , 
		
		one = 1 ,
		
		total_children_ever_born = v201 ,
		
		surviving_children = v201 - v206 - v207 ,
		
		urban_rural = factor( v025 , labels = c( 'urban' , 'rural' ) ) ,
		
		ethnicity =
			factor( v131 , levels = c( 1:8 , 96 ) , labels =
				c( "Chewa" , "Tumbuka" , "Lomwe" , "Tonga" , 
				"Yao" , "Sena" , "Nkonde" , "Ngoni" , "Other" ) ) ,
				
		no_formal_education = as.numeric( v149 == 0 )
		
	)
```

### Unweighted Counts {-}

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

svyby( ~ one , ~ urban_rural , dhs_design , unwtd.count )
```

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

svyby( ~ one , ~ urban_rural , dhs_design , svytotal )
```

### Descriptive Statistics {-}

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

svyby( ~ surviving_children , ~ urban_rural , dhs_design , svymean )
```

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

svyby( ~ ethnicity , ~ urban_rural , dhs_design , svymean , na.rm = TRUE )
```

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

svyby( ~ surviving_children , ~ urban_rural , dhs_design , svytotal )
```

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

svyby( ~ ethnicity , ~ urban_rural , dhs_design , svytotal , na.rm = TRUE )
```

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

svyby( 
	~ surviving_children , 
	~ urban_rural , 
	dhs_design , 
	svyquantile , 
	0.5 ,
	ci = TRUE ,
	keep.var = TRUE 
)
```

Estimate a ratio:
```{r eval = FALSE , results = "hide" }
svyratio( 
	numerator = ~ surviving_children , 
	denominator = ~ total_children_ever_born , 
	dhs_design 
)
```

### Subsetting {-}

Restrict the survey design to 40-49 year old females only:
```{r eval = FALSE , results = "hide" }
sub_dhs_design <- subset( dhs_design , v447a %in% 40:49 )
```
Calculate the mean (average) of this subset:
```{r eval = FALSE , results = "hide" }
svymean( ~ surviving_children , sub_dhs_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( ~ surviving_children , dhs_design )

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

grouped_result <-
	svyby( 
		~ surviving_children , 
		~ urban_rural , 
		dhs_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( dhs_design )
```

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

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

# SRS with replacement
svymean( ~ surviving_children , dhs_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( ~ no_formal_education , dhs_design ,
	method = "likelihood" )
```

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

Perform a design-based t-test:
```{r eval = FALSE , results = "hide" }
svyttest( surviving_children ~ no_formal_education , dhs_design )
```

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

Perform a survey-weighted generalized linear model:
```{r eval = FALSE , results = "hide" }
glm_result <- 
	svyglm( 
		surviving_children ~ no_formal_education + ethnicity , 
		dhs_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 DHS users, this code replicates previously-presented examples:

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

dhs_srvyr_design %>%
	group_by( urban_rural ) %>%
	summarize( mean = survey_mean( surviving_children ) )
```

---

## Replication Example {-}

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

```