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
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 structure with the function
as_survey()with the bare column names of the names that you would use in functions from the survey package like
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")) #> api_diff api_diff_low api_diff_upp #> 1 32.89252 28.79413 36.99091
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, design = 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
–Thomas Lumley, in the Biased and Inefficient blog
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