srvyr brings parts of dplyr’s syntax to survey analysis, using the survey package.
srvyr focuses on calculating summary statistics from survey data, such as the mean, total or quantile. It allows for the use of many dplyr verbs, such as summarize
, group_by
, and mutate
, the convenience of pipe-able functions, rlang’s style of non-standard evaluation and more consistent return types than the survey package.
You can try it out:
install.packages("srvyr")
# or for development version
# devtools::install_github("gergness/srvyr")
First, describe the variables that define the survey’s stucture with the function as_survey()
with the bare column names of the names that you would use in functions from the survey package like survey::svydesign()
, survey::svrepdesign()
or survey::twophase()
.
library(srvyr, warn.conflicts = FALSE)
data(api, package = "survey")
dstrata <- apistrat %>%
as_survey_design(strata = stype, weights = pw)
Now many of the dplyr verbs are available.
mutate()
adds or modifies a variable.dstrata <- dstrata %>%
mutate(api_diff = api00 - api99)
summarise()
calculates summary statistics such as mean, total, quantile or ratio.dstrata %>%
summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
#> # A tibble: 1 x 3
#> api_diff api_diff_low api_diff_upp
#> <dbl> <dbl> <dbl>
#> 1 32.9 28.8 37.0
group_by()
and then summarise()
creates summaries by groups.dstrata %>%
group_by(stype) %>%
summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
#> # A tibble: 3 x 4
#> stype api_diff api_diff_low api_diff_upp
#> <fct> <dbl> <dbl> <dbl>
#> 1 E 38.6 33.1 44.0
#> 2 H 8.46 1.74 15.2
#> 3 M 26.4 20.4 32.4
my_model <- survey::svyglm(api99 ~ stype, dstrata)
summary(my_model)
#>
#> Call:
#> svyglm(formula = api99 ~ stype, dstrata)
#>
#> Survey design:
#> Called via srvyr
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 635.87 13.34 47.669 <2e-16 ***
#> stypeH -18.51 20.68 -0.895 0.372
#> stypeM -25.67 21.42 -1.198 0.232
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for gaussian family taken to be 16409.56)
#>
#> Number of Fisher Scoring iterations: 2
[srvyr] lets us use the survey library’s functions within a data analysis pipeline in a familiar way.
– Kieran Healy, in Data Visualization: A practical introduction
- Yay!
–Thomas Lumley, in the Biased and Inefficent blog
I do appreciate bug reports, suggestions and pull requests! I started this as a way to learn about R package development, and am still learning, so you’ll have to bear with me. Please review the Contributor Code of Conduct, as all participants are required to abide by its terms.
If you’re unfamiliar with contributing to an R package, I recommend the guides provided by Rstudio’s tidyverse team, such as Jim Hester’s blog post or Hadley Wickham’s R packages book.