`## Loading required package: convey`

`## Loading required package: vardpoor`

`## Loading required package: data.table`

`## Loading required package: laeken`

`## Warning: package 'laeken' was built under R version 3.5.2`

`## Loading required package: stringr`

`## Loading required package: ggplot2`

`## Loading required package: pracma`

I don’t expect this vignette to be help for most srvyr users, it is instead intended for other package developers. An exciting new feature that is easier now that I have reworked srvyr’s non-standard evaluation to match dplyr 0.7+ is that it is now possible for non-srvyr functions to be called from within `summarize`

. This vignette describes some of the inner-workings of summarize so that others can extend srvyr. This is kind of a fiddly part of srvyr, and I don’t expect that many people will want or need to understand it, so this guide is mostly aimed at package authors who already have an understanding of how survey objects work. If you’d like more explanation, please let me know on github!

srvyr implements the “survey tatistics” functions from the survey package. Some examples are the svymean, svytotal, svyciprop, svyquantile and svyratio all return a `svystat`

object which usually prints out the estimate and its standard error and other estimates of the variance can be calculated from it. In srvyr, these estimates are created inside of a summarize call and the variance estimates are specified at the same time.

The combination of srvyr’s group_by and summarize is analagous to the `svyby`

function that performs one of the survey statistic function and performs it on multiple groups.

Note that srvyr does not implement many other types of calculations that the survey package can (notably the regressions). While some of these could be shoehorned into srvyr, I feel that they are outside the scope of what people usually expect to have in a data.frame. In general, I think the broom package is better for tidying these kinds of calculations.

srvyr’s summarize expects that the survey statistics functions will return objects that are formatted in a particular way. Below, I’ll explain some of the functions that will help create these objects for you in most cases, but the return should be:

- A
`data.frame`

object - If ungrouped, 1 row long, otherwise 1 row per group
- If grouped, include both the grouping variables and the estimates
- The names are based on the argument name from the summarize call but this name can’t provided to the functions. Instead, summarize does the renaming and your function should name the variables “__SRVYR_COEF__” (which is renamed to the parameter name) or with a suffix that will be appended after the parameter name.

srvyr now exports several functions that can help convert functions designed for the survey package to this format.

`current_svy()`

- This function, modelled after`dplyr::current_vars()`

, is a hidden way to send the survey object to the object (by hidden, I mean that the user doesn’t have to specify the survey in the arguments of their function call). To use it, have an argument in your functions that defaults to current_svy().`set_survey_vars()`

- Many survey functions have limited support for both supplying a formula indicating the variables to calculate a statistic on as well as a vector. However, oftentimes the vector version is less well supported than the formula version. Since srvyr uses dplyr semantics, it ends up returning the values as vectors. This function will add on the variable to the survey, defaulting to having the name “__SRVYR_TEMP_VAR__”.`get_var_est()`

- A helper function that calculates variance estimates like standard error (se), confidence interval (ci), variance (var), or coefficient of variance (cv). For functions that support it, there is a separate argument for design effects (to match survey’s conventions).

Note that these functions may not work in all cases. In srvyr, I’ve actually had to write 3 versions of `get_var_est()`

because of minor differences in the way survey objects are returned. Hopefully they will help in most situations, or at least give you a good place to start.

Two less important conventions that srvyr functions follow are:

- snake_case function names (to better match the tidyverse)
- Multiple choice arguments that default to the first (so for var_type, if no parameters are specified, use only “se” not all of them).

That was just a lot of text, but I think it’s probably easiest just to provide an example. The convey package provides several methods for analysis of inequality using survey data. The svygini function calculates the gini coefficient. Here, we’ll write functions that make a srvyr version `survey_gini`

.

To distinguish between ungrouped and grouped survey objects, we’ll make a generic. Also note the use of `.svy = current_svy()`

to get the survey object from the current summarize context.

```
# S3 generic function
survey_gini <- function(
x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
) {
UseMethod("survey_gini", .svy)
}
```

And here’s the ungrouped version, which uses `set_survey_vars()`

, `convey::svygini()`

and `get_var_est()`

.

```
survey_gini.tbl_svy <- function(
x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
) {
if (missing(vartype)) vartype <- "se"
vartype <- match.arg(vartype, several.ok = TRUE)
.svy <- srvyr::set_survey_vars(.svy, x)
out <- convey::svygini(~`__SRVYR_TEMP_VAR__`, na.rm = na.rm, design = .svy)
out <- srvyr::get_var_est(out, vartype)
out
}
```

Finally, the grouped version which uses the above functions plus `survey::svyby()`

.

```
survey_gini.grouped_svy <- function(
x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"), .svy = current_svy(), ...
) {
if (missing(vartype)) vartype <- "se"
vartype <- match.arg(vartype, several.ok = TRUE)
.svy <- srvyr::set_survey_vars(.svy, x)
grps_formula <- survey::make.formula(group_vars(.svy))
out <- survey::svyby(
~`__SRVYR_TEMP_VAR__`, grps_formula, convey::svygini, na.rm = na.rm, design = .svy
)
out <- srvyr::get_var_est(out, vartype, grps = group_vars(.svy))
out
}
```

And here’s what these functions look like in practice:

```
# Example from ?convey::svygini
suppressPackageStartupMessages({
library(srvyr)
library(survey)
library(convey)
library(vardpoor)
})
data(eusilc) ; names( eusilc ) <- tolower( names( eusilc ) )
# Setup for survey package
des_eusilc <- svydesign(
ids = ~rb030,
strata = ~db040,
weights = ~rb050,
data = eusilc
)
des_eusilc <- convey_prep(des_eusilc)
# Setup for srvyr package
srvyr_eusilc <- eusilc %>%
as_survey(
ids = rb030,
strata = db040,
weights = rb050
) %>%
convey_prep()
## Ungrouped
# Calculate ungrouped for survey package
svygini(~eqincome, design = des_eusilc)
#> gini SE
#> eqincome 0.26497 0.0019
# With our new function
survey_gini(srvyr_eusilc$variables$eqincome, .svy = srvyr_eusilc)
#> __SRVYR_COEF__ _se
#> 1 0.2649652 0.001946982
# And finally, the more typical way through summarize
srvyr_eusilc %>%
summarize(eqincome = survey_gini(eqincome))
#> # A tibble: 1 x 2
#> eqincome eqincome_se
#> <dbl> <dbl>
#> 1 0.265 0.00195
## Groups
# Calculate by groups for survey
survey::svyby(~eqincome, ~rb090, des_eusilc, convey::svygini)
#> rb090 eqincome se
#> male male 0.2578983 0.002617279
#> female female 0.2702080 0.002892713
# With our new function
survey_gini(srvyr_eusilc$variables$eqincome, .svy = group_by(srvyr_eusilc, rb090))
#> rb090 __SRVYR_COEF__ _se
#> 1 male 0.2578983 0.002617279
#> 2 female 0.2702080 0.002892713
# And finally, the more typical way through summarize
srvyr_eusilc %>%
group_by(rb090) %>%
summarize(eqincome = survey_gini(eqincome))
#> # A tibble: 2 x 3
#> rb090 eqincome eqincome_se
#> <fct> <dbl> <dbl>
#> 1 male 0.258 0.00262
#> 2 female 0.270 0.00289
```