CConley.ca

“There’s no crying in baseball “

There is a big learning curve to R.
It might make you cry.
It will probably make you angry.
It will definitely make you frustrated. And it is all worth it because:

• It documents your work which supports transparency and accountability. This is an important step to building trust both inside and outside an organization.
• It supports iterative and collaborative development. You can walk away from your work and pick it back up to continue or improve your work quickly.
• It increases efficiency and timeliness of reporting/analysis. Once developed, R code can reduce the time for analysis and reporting from days and weeks to minutes and hours. In one project, the development of my code took weeks (same amount of time as it would have taken with traditional workflows) but in the next reporting period the time to produce those same updated reports (over 300 pdf’s) was just over 1 hour. That’s more time to devote to other important projects.

As summer arrives and the summer blockbuster movies become released, here are 5 blockbuster reasons to get started with R and consider creating an R package for your organization:

5: Managing file locations

“There’s no place like home.”

Having your network drive remapped is a pain in the neck. I’ve lived through the months of error messages that are discovered in code that needs to be updated with a new path. One of the first functions I created for our department was assigning the network drive locations for our common data sets. When the O drive is suddenly switched to the P drive and the drive hierarchy has a new departmental subdirectory, a quick update to the package results in scripts that continue to run without missing a beat. This is much faster than having to review every script to confirm that a file path is current or needs updating.

In this function (below), the network directory is returned when “P” is used and returns my local directory when any other character is used.

Function: datadrive

datadrive <- function(drive) {

datadrive.string <- ifelse(drive == “P”, return(“P:/Org/Department/MasterData/ “),

return(“C:/Temp/LocalData/”))

}

Use: datapath <- datadrive(“P”)

4. Corporate colours and aesthetic

“Here’s lookin at you kid .”

Having the hex codes (a six digit code the defines the red, green and blue combinations for each colour) for your organization’s colour scheme/brand available is incredibly handy for corporately aligned, aesthetically pleasing visualizations. The colour palettes I included in my organization’s package were built following a blog post by @drsimonj who walks through all steps required to build your own palettes: https://drsimonj.svbtle.com/creating-corporate-colour-palettes-for-ggplot2

Useful tools: Hex codes are used in R as a string with the prefix #.  For example, black is “#000000”, white is “#FFFFFF”.

Considering colour combinations: ColorBrewer2 is the go-to site for considering colour combinations for all kinds of data (sequential, diverging, qualitative): http://colorbrewer2.org/#type=sequential&scheme=BuGn&n=3

Finding a colour code: If you want to find the hex code for a colour, Adobe’s Color website is an excellent resource: https://color.adobe.com

Finding a tint or shade: If you would like to find shades and tints for a specific colour (useful for graphs) High Integrity Design has a useful online tool: https://highintegritydesign.com/tools/tinter-shader/

3: Standardizing your data extracts

“I’ll be back.”

Although dashboards are fantastic for monitoring systems, there is still a need for milestone reporting (i.e. a year-end report summarizing your key metrics). Having your RSQL bundled in a package streamlines the process and ensures that when you return to run the report the next month or year, the data structure will be the same.

Good: an .R file with code that extracts data from your information systems.

Better: turning the code into a function so that the process is easy to run.

Best: add the function to a package so you don’ t worry about where the source file is or which version is the most recent to use.

2: Keep your key metrics on hand at all times

“May the force be with you.”

If you have a set of key metrics it can be included as a table in your package.  This makes them portable and convenient to access when you are working offline.

1: Availability of definitions and keys

“Are you the Key Master?”

Working with data sets from several different information systems can result in many different naming conventions.  Including a master table in the package that aligns all the identifiers in the package is a big time saver and makes analysis much more straightforward.

• Famous Movie Lines
  • “If you build it, they will come.” – Field of Dreams
  • “There’s no crying in baseball.” – A League of their own”
  • “There’s no place like home.” – Wizard of Oz
  • “Here’s lookin at you kid.” – Casablanca
  • “I’ll be back.” – Terminator
  • “May the Force be with you.” – Star Wars
  • “Are you the Key Master?” – Ghostbusters

Introducing REQAO, an R Package for those who work with EQAO data files. This package is a collection of functions to assist in the loading of files and the relabeling of values.  As additional functions are added and expanded, the most up-to-date version will be found at https://github.com/cconley/REQAO

What is EQAO?

EQAO (Education Quality and Accountability Office) is an independent agency that develops, administers, analyses and reports on student achievement across Ontario, Canada.  EQAO coordinates the administration of Reading, Writing and Mathematics assessments for students in Grades 3 and 6, Grade 9 Mathematics assessments for students enrolled in Grade 9 Academic and Applied Mathematics courses and an Ontario Secondary School Literacy Test (OSSLT) for students in Grade 10 English. EQAO also supports the administration of national and international assessments (TIMSS, PIRLS, PISA, PCAP, ICILS) in Ontario.

Each September, staff in school boards run analyses and produce public reports on the achievement of their students on these assessments.  This package supports the preparation of data for analysis.

This package currently has three functions:

SchoolLoad(grade, year, bident, board, datadir)

What it does: creates a tibble (previously known as a dataframe) of school summary achievement data on EQAO assessments.

Merge all the data files from an EQAO administration year into a single tibble.

Fill in the missing board name and bident (missing in the EQAO files) with the arguments that are passed to the function).

Create a new column with the year of the assessment. This makes it easier to work with multiple years of assessments.

Note: in EQAO files, the year as of June is used as the EQAO naming convention for each school-year.

How it is used:

grade: this is a numeric value that can be 3, 6, 9 or 10. This is used to construct the names of the data files.

year: this is a numeric value in the form YYYY. This is used to construct the names of the data files.

bident: this is the 5-digit number assigned by the Ministry of Education. This is used to construct the names of the data files.

board: this can be any string you choose in the form “ABCDEFG”. This is used to fill in the blank SchoolName value for the board summary row.

datadir: this is your data directory as a strong in the form “C:/directory/subdirectory”. This can be passed as a vector and is used to construct the string identifying the file and location.

Example:

SchoolTibble <- SchoolLoad(3, 2017, 12345, “Random DSB”, “C:/temp/data/”)

StudentLoad(grade, year, bident datadir)

What it does: creates a tibble of student achievement on EQAO assessments

Merge all the data files from an EQAO administration year into a single tibble.

Compile and relabel the IEP variables into a single, new, readable column (IEPcode).

Relabel values of demographic variables to a readable form (i.e. Gender, ELL, Eligibility, Program, FI etc. as applicable).

How it is used:

grade: this is a numeric value that can be 3, 6, 9 or 10. This is used to construct the names of the data files.

year: this is a numeric value in the form YYYY. This is used to construct the names of the data files.

bident: this is the 5-digit number assigned by the Ministry of Education. This is used to construct the names of the data files.

datadir: this is your data directory, as a string, in the form “C:/directory/subdirectory”. This can be passed as a vector and is used to construct the string identifying the file and location.

Example:

NewTbl <- StudentLoad(6, 2017, 12345, “C:/temp/data/”)

AchieveLabel(x, grade, type)

What it does: Modifies tibbles made using the StudentLoad() function:

Relabel values of all achievement variables contained in the assessment file (i.e. ROverallLevel, Prior_G6_MOverallLevel, OSSLTOutcome, etc.) as character labels (i.e. “Level 1”, “Level 3”, “Exempt”) or numeric labels (i.e. 0, 1, 2, 3, 4, NA). Note, when values are relabeled as numeric, all non-level values are relabeled as NA

How it is used: Arguments used in the function

x: the name of the tibble that was created using the StudentLoad() function.

grade: this is a numeric value that can be 3, 6, 9 or 10. This is used to identify naming conventions used by each version of the EQAO assessments.

type: this is one of two strings:

“char”: this will change the values to more meaningful, readable labels (i.e. “Level 1”, “Exempt”, “Pending”

“num”: this will change the values to numeric where Levels 0 to 4 are coded as 0, 1, 2, 3 and 4 and all other versions of no-data (withheld, pending, absent, exempt etc.) are relabeled as NA. This is equivalent to calculating “Fully Participating”.

Example:

NewStdntTbl <- AchieveLabel(OldStdntTbl, 6, “char”)

Installation and use

The REQAO package can be installed using the following function from the devtools package:

devtools::install_github(“cconley/REQAO“)

Once installed, REQAO can be used like any other package:

library(REQAO)