Workflow to estimate proportion, total population estimates and variability from survey designs

Create tibble output with proportion (0-1 range) total estimates and coefficient of variation

srvyr_prop_step_01(design, numerator, denominator)

srvyr_prop_step_02(design, numerator, denominator, numerator_level)

srvyr_prop_step_03(design, numerator, denominator)

serosvy_proportion(design, numerator, denominator)

Arguments

design	srvyr design object
numerator	variable assigned as numerator
denominator	variable assigned as denominator
numerator_level	category inside the variable assigned as numerator

Functions

• srvyr_prop_step_01: step 01

• srvyr_prop_step_02: step 02

• srvyr_prop_step_03: step 03

• serosvy_proportion: gather all steps in one

References

Greg Freedman Ellis and Ben Schneider (2020). srvyr: 'dplyr'-Like Syntax for Summary Statistics of Survey Data. http://gdfe.co/srvyr, https://github.com/gergness/srvyr.

Examples

if (FALSE) {

# inspiracion

# https://github.com/gergness/srvyr/issues/13
# solution: https://github.com/gergness/srvyr/issues/13#issuecomment-321407979


# 00 ----------------------------------------------------------------------


library(tidyverse)
library(srvyr)
library(survey)
data(api)
dstrata <- apistrat %>% as_survey_design(strata = stype, weights = pw)
dstrata2 <- apistrat %>%
  mutate(pw2=1) %>%
  as_survey_design(strata = stype, weights = pw2)
dstrata %>%
  summarise(pct = survey_mean(awards=="Yes",proportion = TRUE))
dstrata2 %>%
  summarise(pct = survey_mean(awards=="Yes",proportion = TRUE))


# 01 ----------------------------------------------------------------------

srvyr_prop_step_01(design = dstrata,
                     numerator = awards,
                     denominator = stype)

dstrata %>%
  srvyr_prop_step_01(numerator = awards,
                     denominator = stype)


# 01 + 02 -----------------------------------------------------------------

srvyr_prop_step_01(design = dstrata,
                     numerator = awards,
                     denominator = stype) %>%
  mutate(resultado=pmap(.l = select(.,design=design,
                                    numerator = numerator,
                                    denominator = denominator,
                                    numerator_level=numerator_level),
                       .f = srvyr_prop_step_02)) %>%
  unnest(resultado)

# 01 + 02 + 03 ------------------------------------------------------------

srvyr_prop_step_01(design = dstrata,
                     numerator = awards,
                     denominator = stype) %>%
  mutate(resultado=pmap(.l = select(.,design=design,
                                    numerator = numerator,
                                    denominator = denominator,
                                    numerator_level=numerator_level),
                       .f = srvyr_prop_step_02)) %>%
  unnest(resultado) %>%
  mutate(crudo=pmap(.l = select(.,design=design,
                                numerator=numerator,
                                denominator=denominator),
                    .f = srvyr_prop_step_03)) %>%
  unnest(crudo) %>%
  select(-design:-numerator) %>%
  filter(numerator_level==awards & denominator_level==stype)

# one function ------------------------------------------------------------

serosvy_proportion(design = dstrata,
                      numerator = awards,
                      denominator = stype)

}