Of the entire population

srvyr also provides summarize() and several survey-specific functions that calculate summary statistics on numeric variables: survey_mean(), survey_total(), survey_quantile() and survey_ratio(). These functions differ from their counterparts in survey because they always return a data.frame in a consistent format. As such, they do not return the variance-covariance matrix, and so are not as flexible.

# Using srvyr
out <- strat_design_srvyr %>%
  summarize(api_diff = survey_mean(api_diff, vartype = "ci"))

out

## # A tibble: 1 × 3
##   api_diff api_diff_low api_diff_upp
##      <dbl>        <dbl>        <dbl>
## 1     32.9         28.8         36.9

# Using survey
out <- svymean(~api_diff, strat_design_survey)

out

##            mean     SE
## api_diff 32.893 2.0511

confint(out)

##             2.5 %   97.5 %
## api_diff 28.87241 36.91262

By group

srvyr also allows you to calculate statistics on numeric variables by group, using group_by().

# Using srvyr
strat_design_srvyr %>%
  group_by(stype) %>%
  summarize(api_increase = survey_total(api_diff >= 0),
            api_decrease = survey_total(api_diff < 0))

## # A tibble: 3 × 5
##   stype api_increase api_increase_se api_decrease api_decrease_se
##   <fct>        <dbl>           <dbl>        <dbl>           <dbl>
## 1 E            4067.           119.         354.            119. 
## 2 H             498.            49.4        257.             49.4
## 3 M             998.            19.9         20.4            19.9

# Using survey
svyby(~api_diff >= 0, ~stype, strat_design_survey, svytotal)

##   stype api_diff >= 0FALSE api_diff >= 0TRUE se.api_diff >= 0FALSE
## E     E             353.68           4067.32             119.17185
## H     H             256.70            498.30              49.37208
## M     M              20.36            997.64              19.85371
##   se.api_diff >= 0TRUE
## E            119.17185
## H             49.37208
## M             19.85371

Proportions by group

You can also calculate the proportion or count in each group of a factor or character variable by leaving x empty in survey_mean() or survey_total().

# Using srvyr
srs_design_srvyr %>%
  group_by(awards) %>%
  summarize(proportion = survey_mean(),
            total = survey_total())

## # A tibble: 2 × 5
##   awards proportion proportion_se total total_se
##   <fct>       <dbl>         <dbl> <dbl>    <dbl>
## 1 No           0.38        0.0338 2354.     210.
## 2 Yes          0.62        0.0338 3840.     210.

# Using survey
svymean(~awards, srs_design_survey)

##           mean     SE
## awardsNo  0.38 0.0338
## awardsYes 0.62 0.0338

svytotal(~awards, srs_design_survey)

##            total     SE
## awardsNo  2353.7 209.65
## awardsYes 3840.3 209.65

Unweighted calculations

Finally, the unweighted() function can act as an escape hatch to calculate unweighted calculations on the dataset.

# Using srvyr
strat_design_srvyr %>%
  group_by(stype) %>%
  summarize(n = unweighted(n()))

## # A tibble: 3 × 2
##   stype     n
##   <fct> <int>
## 1 E       100
## 2 H        50
## 3 M        50

# Using survey
svyby(~api99, ~stype, strat_design_survey, unwtd.count)

##   stype counts se
## E     E    100  0
## H     H     50  0
## M     M     50  0

Using survey functions on srvyr objects

Because srvyr objects are just survey objects with some extra structure, all of the functions from survey will still work with them. If you need to calculate something beyond simple summary statistics, you can use survey functions.

glm <- svyglm(api00 ~ ell + meals + mobility, design = strat_design)
summary(glm)

## 
## Call:
## svyglm(formula = api00 ~ ell + meals + mobility, design = strat_design)
## 
## Survey design:
## Called via srvyr
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 820.8873    10.0777  81.456   <2e-16 ***
## ell          -0.4806     0.3920  -1.226    0.222    
## meals        -3.1415     0.2839 -11.064   <2e-16 ***
## mobility      0.2257     0.3932   0.574    0.567    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 5171.966)
## 
## Number of Fisher Scoring iterations: 2

Using expressions to create variables on the fly

Like dplyr, srvyr allows you to use expressions in the arguments, allowing you to create variables in a single step. For example, you can use expressions:

1. as the arguments inside the survey statistic functions like survey_mean

strat_design %>%
  summarize(prop_api99_over_700 = survey_mean(api99 > 700))

## # A tibble: 1 × 2
##   prop_api99_over_700 prop_api99_over_700_se
##                 <dbl>                  <dbl>
## 1               0.306                 0.0356

2. as an argument to summarize

strat_design %>%
  group_by(awards) %>%
  summarize(percentage = 100 * survey_mean())

## # A tibble: 2 × 3
##   awards percentage percentage_se
##   <fct>       <dbl>         <dbl>
## 1 No           36.1          3.44
## 2 Yes          63.9          3.44

3. and you can even create variables inside of group_by

strat_design %>%
  group_by(api99_above_700 = api99 > 700) %>%
  summarize(api00_mn = survey_mean(api00))

## # A tibble: 2 × 3
##   api99_above_700 api00_mn api00_mn_se
##   <lgl>              <dbl>       <dbl>
## 1 FALSE               599.        7.88
## 2 TRUE                805.        7.15

Though on-the-fly expressions are syntactically valid, it is possible to make statistically invalid numbers from them. For example, though the standard error and confidence intervals can be multiplied by a scalar (like 100), the variance does not scale the same way, so the following is invalid:

# BAD DON'T DO THIS!
strat_design %>%
  group_by(awards) %>%
  summarize(percentage = 100 * survey_mean(vartype = "var"))
# VARIANCE IS WRONG

Non-Standard evaluation

Srvyr supports the non-standard evaluation conventions that dplyr uses. If you’d like to use a function programmatically, you can use the functions from rlang like the {{ operator (aka “curly curly”) from rlang.

Here’s a quick example, but please see the dplyr vignette vignette("programming", package = "dplyr") for more details.

mean_with_ci <- function(.data, var) {
  summarize(.data, mean = survey_mean({{var}}, vartype = "ci"))
}

srs_design_srvyr <- apisrs %>% as_survey_design(fpc = fpc)

mean_with_ci(srs_design_srvyr, api99)

## # A tibble: 1 × 3
##    mean mean_low mean_upp
##   <dbl>    <dbl>    <dbl>
## 1  625.     606.     643.

Srvyr will also follow dplyr’s lead on deprecating the old methods of NSE, such as rlang::quo, and !!, in addition to the so-called “underscore functions” (like summarize_). Currently, they have been soft-deprecated, they may be removed altogether in some future version of srvyr.

Working column-wise

As of version 1.0 of srvyr, it supports dplyr’s across function, so when you want to calculate a statistic on more than one variable, it is easy to do so. See vignette("colwise", package = "dplyr") for more details, but here is another quick example:

# Calculate survey mean for all variables that have names starting with "api"
strat_design %>%
  summarize(across(starts_with("api"), survey_mean))

## # A tibble: 1 × 6
##   api00 api00_se api99 api99_se api.stu api.stu_se
##   <dbl>    <dbl> <dbl>    <dbl>   <dbl>      <dbl>
## 1  662.     9.41  629.     9.96    498.       16.1

Srvyr also supports older methods of working column-wise, the “scoped variants”, such as summarize_at, summarize_if, summarize_all and summarize_each. Again, these are maintained for backwards compatibility, matching what the tidyverse team has done, but may be removed from a future version.

Calculating proportions in groups

You can calculate the weighted proportion that falls into a group using the survey_prop() function (or the survey_mean() function with no x argument). The proportion is calculated by “unpeeling” the last variable used in group_by() and then calculating the proportion within the other groups that fall into the last group (so that the proportion within each group that was unpeeled sums to 100%).

# Calculate the proportion that falls into each category of `awards` per `stype`
strat_design %>%
  group_by(stype, awards) %>%
  summarize(prop = survey_prop())

## When `proportion` is unspecified, `survey_prop()` now defaults to `proportion = TRUE`.
## ℹ This should improve confidence interval coverage.
## This message is displayed once per session.

## # A tibble: 6 × 4
## # Groups:   stype [3]
##   stype awards  prop prop_se
##   <fct> <fct>  <dbl>   <dbl>
## 1 E     No     0.270  0.0441
## 2 E     Yes    0.730  0.0441
## 3 H     No     0.680  0.0644
## 4 H     Yes    0.320  0.0644
## 5 M     No     0.520  0.0696
## 6 M     Yes    0.480  0.0696

If you want to calculate the proportion for groups from multiple variables at the same time that add up to 100%, the interact function can help. The interact function creates a variable that is automatically split apart so that more than one variable can be unpeeled.

# Calculate the proportion that falls into each category of both `awards` and `stype`
strat_design %>%
  group_by(interact(stype, awards)) %>%
  summarize(prop = survey_prop())

## # A tibble: 6 × 4
##   stype awards   prop prop_se
##   <fct> <fct>   <dbl>   <dbl>
## 1 E     No     0.193  0.0315 
## 2 E     Yes    0.521  0.0315 
## 3 H     No     0.0829 0.00785
## 4 H     Yes    0.0390 0.00785
## 5 M     No     0.0855 0.0114 
## 6 M     Yes    0.0789 0.0114