# survey_mean

##### Calculate the mean and its variation using survey methods

Calculate means and proportions from complex survey data. A wrapper
around `svymean`

, or if `proportion = TRUE`

,
`svyciprop`

. `survey_mean`

should always be
called from `summarise`

.

##### Usage

```
survey_mean(x, na.rm = FALSE, vartype = c("se", "ci", "var", "cv"),
level = 0.95, proportion = FALSE, prop_method = c("logit",
"likelihood", "asin", "beta", "mean"), deff = FALSE, df = NULL,
.svy = current_svy(), ...)
```

##### Arguments

- x
A variable or expression, or empty

- na.rm
A logical value to indicate whether missing values should be dropped

- vartype
Report variability as one or more of: standard error ("se", default), confidence interval ("ci"), variance ("var") or coefficient of variation ("cv").

- level
(For vartype = "ci" only) A single number or vector of numbers indicating the confidence level

- proportion
Use methods to calculate the proportion that may have more accurate confidence intervals near 0 and 1. Based on

`svyciprop`

.- prop_method
Type of proportion method to use if proportion is

`TRUE`

. See`svyciprop`

for details.- deff
A logical value to indicate whether the design effect should be returned.

- df
(For vartype = "ci" only) A numeric value indicating the degrees of freedom for t-distribution. The default (NULL) uses

`degf`

, but Inf is the usual survey package's default (except in`svyciprop`

.- .svy
A

`tbl_svy`

object. When called from inside a summarize function the default automatically sets the survey to the current survey.- ...
Ignored

##### Examples

```
# NOT RUN {
library(survey)
data(api)
dstrata <- apistrat %>%
as_survey_design(strata = stype, weights = pw)
dstrata %>%
summarise(api99 = survey_mean(api99),
api_diff = survey_mean(api00 - api99, vartype = c("ci", "cv")))
dstrata %>%
group_by(awards) %>%
summarise(api00 = survey_mean(api00))
# Leave x empty to calculate the proportion in each group
dstrata %>%
group_by(awards) %>%
summarise(pct = survey_mean())
# Setting proportion = TRUE uses a different method for calculating confidence intervals
dstrata %>%
summarise(high_api = survey_mean(api00 > 875, proportion = TRUE, vartype = "ci"))
# level takes a vector for multiple levels of confidence intervals
dstrata %>%
summarise(api99 = survey_mean(api99, vartype = "ci", level = c(0.95, 0.65)))
# Note that the default degrees of freedom in srvyr is different from
# survey, so your confidence intervals might not be exact matches. To
# Replicate survey's behavior, use df = Inf
dstrata %>%
summarise(srvyr_default = survey_mean(api99, vartype = "ci"),
survey_defualt = survey_mean(api99, vartype = "ci", df = Inf))
comparison <- survey::svymean(~api99, dstrata)
confint(comparison) # survey's default
confint(comparison, df = survey::degf(dstrata)) # srvyr's default
# }
```

*Documentation reproduced from package srvyr, version 0.3.5, License: GPL-2 | GPL-3*