#' jrt_table()
#'
#' This function creates a summary table of JRT results.
#' @param enroll_data dataframe: a dataframe containing enrollment data."[Admin] raw_enrollment_report"
#' @param eto_programs character vector: a vector of character containing the name of ETO programs to keep for analysis.
#' @param tp_data dataframe: a dataframe containing touchpoint data."[Admin] raw_touchpoint_report_detailed"
#' @return dataframe
#' @export
#' @examples
#' enroll <- laycUtils::load_txt('./my_data_folder/enrollment.txt')
#' enroll <- laycUtils::format_data(enroll)
#' tp <- laycUtils::load_txt('./my_data_folder/touchpoints.txt')
#' tp <- laycUtils::format_data(tp)
#'
#' jrt_table(enroll_data = enroll, tp_data = tp)
jrt_table <- function(enroll_data,
tp_data,
measure = c("enrolled in JRT", "assessed on soft skills", "gained soft-skills"),
target = c(enrolled = .75, assessed = .90, gained = .70),
eto_programs = c("ss - job readiness", "pg - employment job training", "dc - wise job training"),
workforce_programs = c("ss - ccorps projects", "ss - counseling",
"ss - ged", "ss - job placement", "ss - job readiness",
"pg - employment case management", "pg - employment ged",
"pg - employment in school", "pg - employment job placement",
"pg - employment job training", "dc - wise ged", "dc - wise job placement",
"dc - wise job training"),
for_bullet = 'no')
{
# Get workforce enrollment
workforce <- laycEnrollment::get_enroll(enroll, eto_programs = workforce_programs)
# Get JRT enrollment
enrollment <- jrt_enroll(enroll_data)
# Get JRT assessed on skills
assessed <- jrt_gains(tp_data)[['total']]
# Get JRT gained in skills
gained <- jrt_gains(tp_data)[['positive']]
# Create data frame
n <- c(workforce, enrollment, assessed)
value <- c(enrollment, assessed, gained)
df <- data.frame(measure, target, value, n)
# Format data frame
df$value_scaled <- round(df$value / df$n, 3)
df$y_title <- paste('%', df$measure, '\nn =', df$n)
df$target_met[df$value_scaled < df$target / 2] <- 'low'
df$target_met[df$value_scaled >= df$target / 2 & df$value_scaled < df$target] <- 'medium'
df$target_met[df$value_scaled >= df$target] <- 'high'
df$target_met <- ordered(df$target_met)
df$target_met <- ordered(df$target_met, levels = c("low", "medium", "high"))
# Return dataframe
return(df)
}
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