---
title: "SGP Data Analysis"
author: "Damian W Betebenner & Adam R Van Iwaarden"
date: "February 20th, 2019"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{SGP Data Analysis}
  %\VignetteEngine{knitr::rmarkdown}
  %\usepackage[utf8]{inputenc}
---

```{r include = FALSE}
    library(SGP)
    library(SGPdata)

    is_html_output = function() {
        knitr::opts_knit$get("rmarkdown.pandoc.to")=="html"
    }

    knitr::opts_chunk$set(
        collapse=TRUE,
        comment="",
        prompt=TRUE,
        fig.dpi=96)

    if (is_html_output()) {
        options(width=1000)
    }
```

# Introduction

SGP analyses can be conducted in many ways with several variations. In general, one can always conduct the analyses using the *lower level functions*
[```studentGrowthPercentiles```](https://sgp.io/reference/studentGrowthPercentiles.html) and [```studentGrowthProjections```](https://sgp.io/reference/studentGrowthProjections.html).
However, it is often easier to use *higher level functions* (wrapper functions) for the lower level functions, particularly for operational analyses.
Both approaches to running SGP analyses are discussed below.


# SGP analysis using lower level functions




# SGP analysis using higher level functions

## SGP Analysis in 6 Steps

1. [```prepareSGP```](https://sgp.io/reference/prepareSGP.html)
2. [```analyzeSGP```](https://sgp.io/reference/analyzeSGP.html)
3. [```combineSGP```](https://sgp.io/reference/combineSGP.html)
4. [```summarizeSGP```](https://sgp.io/reference/summarizeSGP.html)
5. [```visualizeSGP```](https://sgp.io/reference/visualizeSGP.html)
6. [```outputSGP```](https://sgp.io/reference/outputSGP.html)


## SGP Analysis in 1 Step

Because these steps are almost always conducted simultaneously in operational SGP analyses, the SGP Package has wrapper functions
[```abcSGP```](https://sgp.io/reference/abcSGP.html) and [```updateSGP```](https://sgp.io/reference/updateSGP.html)
that "wrap" the above 6 steps into a single function call, simplifying the source code associated with the analysis.


```{r eval=FALSE, prompt=FALSE}
#####################################################################################
###
### R Script for Demonstration SGP analyses
###
#####################################################################################

### Load SGP & SGPdata packages

require(SGP)
require(SGPdata)


### abcSGP

Demonstration_SGP <- abcSGP(
	sgp_object=sgpData_LONG,
	data_supplementary=list(INSTRUCTOR_NUMBER=sgpData_INSTRUCTOR_NUMBER),
	parallel.config=list(BACKEND="PARALLEL", WORKERS=list(PERCENTILES=4, BASELINE_PERCENTILES=4, PROJECTIONS=4, LAGGED_PROJECTIONS=4, SGP_SCALE_SCORE_TARGETS=4, SUMMARY=4, GA_PLOTS=4, SG_PLOTS=1)))


### Save results

save(Demonstration_SGP, file="Data/Demonstration_SGP.Rdata")
```

Running this script will run the six steps mentioned earlier by performing the following:

1. [```prepareSGP```](https://sgp.io/reference/prepareSGP.html) Take exemplar LONG data, *sgpData_LONG*, and INSTRUCTOR-STUDENT lookup files, *sgpData_INSTRUCTOR_NUMBER* and create the SGP object *Demonstration_SGP* that includes them.
2. [```analyzeSGP```](https://sgp.io/reference/analyzeSGP.html) Performs SGP analyses for all years and content areas including: student growth percentiles, baseline student growth percentiles, student growth projections, baseline student growth projections, lagged student growth projections, lagged baseline student growth projections.
3. [```combineSGP```](https://sgp.io/reference/combineSGP.html) Merges results derived from *analyzeSGP* back into the master longitudinal record, *Demonstration_SGP@Data*, and calculates/creates scale scores associated with SGP targets.
4. [```summarizeSGP```](https://sgp.io/reference/summarizeSGP.html) Calculates summary tables and puts them results into the *Demonstration_SGP@Summary* slot.
5. [```visualizeSGP```](https://sgp.io/reference/visualizeSGP.html) Creates state level summary bubble plots, growth achievement plots (wave charts), and demonstration student growth plots.
6. [```outputSGP```](https://sgp.io/reference/outputSGP.html) Exports data in multiple formats:
  * *Demonstration_SGP_Data_LONG.txt.zip* a pipe delimited copy of *Demonstration_SGP@Data*, the complete master longitudinal record.
  * *Demonstration_SGP_Data_LONG_2020.txt.zip* a pipe delimited copy of the 2020 longitudinal record subsetted from *Demonstration_SGP@Data*.
  * *Demonstration_SGPData_WIDE.txt.zip* a pipe delimited WIDE version of the master longitudinal record, *Demonstration_SGP@Data*.
  * *Demonstration_SGP_INSTRUCTOR_Data.txt.zip* a pipe delimited LONG version of the INSTRUCTOR_NUMBER file merged with the master longitudinal data record.

The *parallel.config* argument is used to take advantage of computing resources with multiple processors/cores. In general, parallel computation works better on
Linux/OSX than on Windows, though the package is tested on all three operating systems. Removing the *parallel.config* argument will result in the analyses being run
sequentially --- same results, just takes longer.


# Contributions & Requests

If you have a contribution or topic request for this vignette, don't hesitate to write or set up
an [issue on GitHub](https://github.com/CenterForAssessment/SGP/issues).