scaleup_part: Scale segmented participation counts to total (if needed)

In southwick-associates/salic: Prepare Agency Dashboard Data

Description

This scaling accounts for missing values in segments, scaling up all counts to ensure the sum matches the total count. It expects 2 tables as input, both produced by est_part. If no scaling is needed (i.e., sum(part_segment$part) == sum(part_total$part)) the function will simply return the input df.

Usage

scaleup_part(part_segment, part_total, test_threshold = 10,
  show_test_stat = FALSE, outvar = "participants")

scaleup_recruit(part_segment, part_total, test_threshold = 10,
  show_test_stat = FALSE, outvar = "recruits")

Arguments

part_segment	data frame: A segmented participation table produced by est_part (e.g., with segment argument set to "res")
part_total	data frame: An overall participation table produced by est_part
test_threshold	numeric: threshold in whole number percentage points which defines the upper limit of acceptable proportion of missing values for the segment. The function will stop with an error if this threshold is exceeded. Relaxing the threshold can allow the check to pass, but use this with caution since a high percentage of missing values might suggests that the breakouts aren't representative (e.g., if not missing at random).
show_test_stat	logical: If TRUE, the output table will include a variable holding the test statistic for each row.
outvar	character: name of variable that stores metric

See Also

Salic Function Reference: salic

Other dashboard functions: check_threshold, est_churn, est_part, format_result

Examples

library(dplyr)
data(history)
history <- label_categories(history)

# demonstrate the need for scaling
part_total <- est_part(history)
part_segment <- est_part(history, "sex", test_threshold = 40)
left_join(
    select(part_total, year, part_tot = participants),
    group_by(part_segment, year) %>% summarise(part_seg = sum(participants)),
)

# perform scaling
part_segment <- scaleup_part(part_segment, part_total)
left_join(
    select(part_total, year, part_tot = participants),
    group_by(part_segment, year) %>% summarise(part_seg = sum(participants)),
)

# new recruits - unscaled
history_new <- filter(history, R3 == "Recruit")
part_total <- est_recruit(history_new, "tot")
part_segment <- est_recruit(history_new, "sex")
part_segment

# new recruits - scaled
scaleup_recruit(part_segment, part_total)

southwick-associates/salic documentation built on Nov. 5, 2019, 9:13 a.m.