Colorado_SGP_2025.R

#+ include = FALSE, purl = FALSE, eval = FALSE
##############################################################################
###                                                                        ###
###              Colorado 2025 CMAS and PSAT-SAT SGP analyses              ###
###                                                                        ###
##############################################################################

###   Load packages
require(SGP)
require(data.table)

###   Load 2024 growth data and results
load("Data/Colorado_SGP.Rdata")

###   Load current year, formatted data
load("Data/Colorado_CMAS_Data_2025.Rdata")
load("Data/Colorado_PSAT_Data_2025.Rdata")

# ###   Read in SGP configuration scripts and combine
# source("SGP_CONFIG/2025/ELA.R")
# source("SGP_CONFIG/2025/MATHEMATICS.R")
# source("SGP_CONFIG/2025/CO_Proj_Sequences_2025.R")

CO_Config_2025 <- c(
    ELA_2025.config,
    ELA_PSAT_9_2025.config,
    ELA_PSAT_10_2025.config,
    ELA_SAT_2025.config,

    MATHEMATICS_2025.config,
    MATHEMATICS_PSAT_9_2025.config,
    MATHEMATICS_PSAT_10_2025.config,
    MATHEMATICS_SAT_2025.config
)

###   Parallel Config
parallel.config <- list(
        BACKEND = "FOREACH",
        TYPE = "doParallel",
        # BACKEND = "PARALLEL",
        WORKERS = list(    
            PERCENTILES = 10,
            PROJECTIONS = 8,
            LAGGED_PROJECTIONS = 8,
            SGP_SCALE_SCORE_TARGETS = 8
        ))

###   Run updateSGP analysis
Colorado_SGP <-
    updateSGP(
        what_sgp_object = Colorado_SGP,
        with_sgp_data_LONG = rbindlist(list(
            Colorado_CMAS_Data_2025,
            Colorado_PSAT_Data_2025),
            use.names = TRUE, fill = TRUE
        ),
        steps = c("prepareSGP", "analyzeSGP", "combineSGP", "outputSGP"),
        sgp.config = CO_Config_2025,
        sgp.percentiles = TRUE,
        sgp.projections = TRUE,
        sgp.projections.lagged = TRUE,
        sgp.percentiles.baseline = FALSE,
        sgp.projections.baseline = FALSE,
        sgp.projections.lagged.baseline = FALSE,
        sgp.target.scale.scores = TRUE,
        outputSGP.output.type = c("LONG_Data", "LONG_FINAL_YEAR_Data"),
        save.intermediate.results = FALSE,
        parallel.config = parallel.config
    )


###   Add R session Info & Save results (`cfaDocs` version 0.0-1.12 or later)
source(
    system.file(
        "rmarkdown", "shared_resources", "rmd", "R_Session_Info.R",
        package = "cfaDocs"
    )
)
##   Need to fix `prepareSGP` to allow for these features
Colorado_SGP@Version[["session_platform"]][["2025"]] <- session_platform
Colorado_SGP@Version[["attached_pkgs"]][["2025"]]    <- attached_pkgs
Colorado_SGP@Version[["namespace_pkgs"]][["2025"]]   <- namespace_pkgs

###   Save results with all CMAS and PSAT/SAT results
save(Colorado_SGP, file = "Data/Colorado_SGP.Rdata")


#' ### Conduct SGP analyses
#'
#' Cohort-referenced SGPs were calculated concurrently using
#' the [`R` Software Environment](http://www.r-project.org)
#' in conjunction with the [`SGP` package](http://sgp.io).
#' Broadly, the Colorado CMAS analyses were completed in five steps.
#'
#' 1. `prepareSGP`
#' 2. `analyzeSGP`
#' 3. `combineSGP`
#' 4. `outputSGP`
#' 5. `visualizeSGP`
#' 
#' Because these steps are almost always conducted simultaneously, the `SGP`
#' package has "wrapper" functions, `abcSGP` and `updateSGP`, that combine
#' the above steps into a single function call and simplify the source code
#' associated with the data analysis. Documentation for all SGP functions are
#' [available online.](https://cran.r-project.org/web/packages/SGP/SGP.pdf)
#' 
#' We use the [`updateSGP`](https://www.rdocumentation.org/packages/SGP/versions/2.0-1.0/topics/updateSGP)
#' function to ***a)*** do the final preparation and addition of the cleaned and
#' formatted new annual data,
#' ([`prepareSGP`](https://www.rdocumentation.org/packages/SGP/versions/2.0-1.0/topics/prepareSGP)
#' step), ***b)*** calculate SGP estimates
#' ([`analyzeSGP`](https://www.rdocumentation.org/packages/SGP/versions/2.0-1.0/topics/analyzeSGP)
#' step), ***c)*** merge the results into the master longitudinal data set
#' ([`combineSGP`](https://www.rdocumentation.org/packages/SGP/versions/2.0-1.0/topics/combineSGP)
#' step) and ***d)*** output a pipe delimited version of the complete long data
#' ([`outputSGP`](https://www.rdocumentation.org/packages/SGP/versions/2.0-1.0/topics/outputSGP)
#' step).
#' 
#' #### Visualize results
#' 
#' Once all analyses were completed via `updateSGP`, individual student growth
#' reports were produced using the [`visualizeSGP`](https://www.rdocumentation.org/packages/SGP/versions/2.0-1.0/topics/visualizeSGP)
#' function and a custom template designed for Colorado. English and Spanish
#' language versions of these reports were created, and the individual reports
#' and school level catalogs were bundled according to CDE specifications.