Calculate a ratio from a survey
create_analysis_ratio.RdThe function will calculate the ratio between 2 variables, the numerator and denominator. The numerator : By default, it will change all NAs to 0. If numerator_NA_to_0 is set to FALSE, rows with missing values will be filtered out.
The denominator : All rows with missing value will be filtered out (cannot be changed). In addition, by default, all rows with a value equal to 0 will be filtered out, if filter_denominator_0 is set to TRUE, they will be kept.
Usage
create_analysis_ratio(
design,
group_var = NA,
analysis_var_numerator,
analysis_var_denominator,
numerator_NA_to_0 = TRUE,
filter_denominator_0 = TRUE,
level = 0.95
)Arguments
- design
design survey
- group_var
dependent variable(s), variable to group by. If no dependent variable, it should be NA or empty string. If more than one variable, it should be one string with each variable separated by comma, e.g. "groupa, groupb" to group for groupa and groupb. NA is default for no grouping.
- analysis_var_numerator
character string with the numerator column name.
- analysis_var_denominator
character string with the denominator column name.
- numerator_NA_to_0
Will turn all NA of the numerator into 0's, default TRUE.
- filter_denominator_0
Will remove all rows with 0's in the denominator, default TRUE.
- level
the confidence level. 0.95 is default
Examples
school_ex <- data.frame(
hh = c("hh1", "hh2", "hh3", "hh4"),
num_children = c(3, 0, 2, NA),
num_enrolled = c(3, NA, 0, NA),
num_attending = c(1, NA, NA, NA),
group = c("a", "a", "b", "b")
)
me_design <- srvyr::as_survey(school_ex)
# Default value will give a ratio of 0.2 as there are 1 child out of 5 attending school.
# numerator: 1 child from hh1 and 0 from hh3. denominator: 3 from hh1 and 2 from hh3. In the
# hh3, the num_attending is NA because there is a skip logic, there cannot be a child attending as
# none are enrolled. By default, the function has the argument numerator_NA_to_0 set to TRUE to
# turn that NA into a 0.
# n and n_total are 2 as 2 households were included in the calculation. hh2 was not included in
# the calculation of totals. The argument filter_denominator_0 set to TRUE removes that row.
create_analysis_ratio(me_design,
analysis_var_numerator = "num_attending",
analysis_var_denominator = "num_children"
)
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio num_attendin… NA %/% NA NA NA 0.2
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
# If numerator_NA_to_0 is set to FALSE, ratio will be 1/3, as hh3 with 2 children and NA for
# attending will be removed with the na.rm = T inside the survey_ratio calculation.
# n and n_total is 1 as only 1 household was used.
create_analysis_ratio(me_design,
analysis_var_numerator = "num_attending",
analysis_var_denominator = "num_children",
numerator_NA_to_0 = FALSE
)
#> Warning: There were 2 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `srvyr::survey_ratio(...)`.
#> Caused by warning in `qt()`:
#> ! NaNs produced
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio num_attendin… NA %/% NA NA NA 0.333
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
# If filter_denominator_0 is set to FALSE, ratio will be 0.2 as there are 1 child out of 5
# attending school.
# The number of household counted, n and n_total, is equal to 3 instead 2. The household with 0
# child is counted in the totals.
# (01 + 0 + 0) / (3 + 0 + 2)
create_analysis_ratio(me_design,
analysis_var_numerator = "num_attending",
analysis_var_denominator = "num_children",
filter_denominator_0 = FALSE
)
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio num_attendin… NA %/% NA NA NA 0.2
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
# For weights and group:
set.seed(8988)
somedata <- data.frame(
groups = rep(c("a", "b"), 50),
children_518 = sample(0:5, 100, replace = TRUE),
children_enrolled = sample(0:5, 100, replace = TRUE)
) %>%
dplyr::mutate(children_enrolled = ifelse(children_enrolled > children_518,
children_518,
children_enrolled
))
somedata[["weights"]] <- ifelse(somedata$groups == "a", 1.33, .67)
create_analysis_ratio(srvyr::as_survey(somedata, weights = weights, strata = groups),
group_var = NA,
analysis_var_numerator = "children_enrolled",
analysis_var_denominator = "children_518",
level = 0.95
)
#> # A tibble: 1 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio children_enr… NA %/% NA NA NA 0.639
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>
create_analysis_ratio(srvyr::as_survey(somedata, weights = weights, strata = groups),
group_var = "groups",
analysis_var_numerator = "children_enrolled",
analysis_var_denominator = "children_518",
level = 0.95
)
#> # A tibble: 2 × 13
#> analysis_type analysis_var analysis_var_value group_var group_var_value stat
#> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 ratio children_enr… NA %/% NA groups a 0.670
#> 2 ratio children_enr… NA %/% NA groups b 0.578
#> # ℹ 7 more variables: stat_low <dbl>, stat_upp <dbl>, n <int>, n_total <dbl>,
#> # n_w <dbl>, n_w_total <dbl>, analysis_key <chr>