srvyr

https://gdfe.co/srvyr

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srvyr brings dplyr-style syntax and verbs to survey analysis in R, allowing users to calculate...

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### Install srvyr

Source: https://gdfe.co/srvyr

Installation commands for the srvyr package from CRAN or GitHub.

```R
install.packages("srvyr")
# or for development version
# remotes::install_github("gergness/srvyr")
```

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### Initialize Survey Design

Source: https://gdfe.co/srvyr

Define survey structure using as_survey_design with bare column names.

```R
library(srvyr, warn.conflicts = FALSE)
data(api, package = "survey")

dstrata <- apistrat %>%
   as_survey_design(strata = stype, weights = pw)
```

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### Use survey Package Functions

Source: https://gdfe.co/srvyr

Directly apply functions from the survey package to srvyr objects.

```R
my_model <- survey::svyglm(api99 ~ stype, dstrata)
summary(my_model)
```

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### Grouped Summaries

Source: https://gdfe.co/srvyr

Calculate summary statistics by groups using group_by and summarise.

```R
dstrata %>% 
  group_by(stype) %>%
  summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
```

--------------------------------

### Summarize Survey Data

Source: https://gdfe.co/srvyr

Calculate summary statistics like mean, total, or quantile using summarise.

```R
dstrata %>% 
  summarise(api_diff = survey_mean(api_diff, vartype = "ci"))
```

--------------------------------

### Mutate Survey Data

Source: https://gdfe.co/srvyr

Modify or add variables to the survey design object using mutate.

```R
dstrata <- dstrata %>%
  mutate(api_diff = api00 - api99)
```

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