Managing ASQ Data: a Guide for Relief Nursery Administrative Assistants

MANAGING ASQ DATA
A guide for relief nursery administrative assistants

USINGTHE DATABASE
 ASQ data is stored in an Access database
 Open Access and select the database
 Open the ASQ table
 Go to External Data tab on the Ribbon
 Export Data as “ASQ.csv” into the working directory (see next slide)

USING AWORKING DIRECTORY
 Choose a fairly top-level directory in which to store “ASQ.csv”
 “Desktop” can be a good choice
 You will refer to this directory when you run the analysis in R.
 You will also store the R code file here.This file is provided along with this
presentation.
 It’s possible to run this analysis without storing the code file, but you will need to
cut and paste into an editor.That’s is also easy, and will be explained later in this
presentation.

WORKING DIRECTORY CONTENTS
 You should have the following files in your working directory before proceeding:
 “ASQ.csv” (you generate this from Access, as discussed earlier)
 “ASQ_Progress_Matrix.csv” (table of reference values for ASQ categories, provided
along with this presentation)
 “ASQEvaluation.R” (R code provided along with this presentation)

USING R
 R is very simple to use in cases like this where you just need to run some code.You
don’t need to know how to program, or how to do statistical analyses. Just run the
code and let it do it’s thing.
 Start the program “RStudio.”
 Load the program file into RStudio.You can do this in one of two ways:
 Use CTRL-O to open a file like you would withWord or Excel.Then navigate to the
working directory and select the R code file “ASQEvaluation.R”
 Cut and paste code directly from these slides or from the code file itself, which is
basically just a text file (with R magic written in!).You will first need to open a new code
window (i.e. “source file”) in RStudio by using CTRL-Shift-N.

SETTING WORKING DIRECTORY IN R
 In the command line in RStudio, enter
the command to the right.
 Replace the part that says
“yourname” with your name as it
appears on the network.
 Keep those quotation marks you see
in the parentheses (they are part of
the syntax).
 This assumes you used “Desktop” as
your working directory. Change if
necessary.
setwd(“C:/Users/yourname/Desktop”)

THE CODE
 The actual code consists of two functions: one which imports and “cleans” the
data, and another which sorts the data to produce the key figures required by
stakeholders such as PCL.
 The code is explained in the following slides, but it is not necessary to understand
it.You may wish to experiment with it if you understand programming concepts,
or if you wish to learn these concepts by messing around with the code and seeing
how the results change.
 If you intend to play with the code for learning purposes, just be sure to edit under
a new file name so the original remains intact. Remember to actually use the
original code for reporting.

READING DATA INTO R
 The code to the right reads the two
CSV files into R, and determines
the number of rows of data (i.e.
how many total ASQ records you
have).
 After running the code, you can
invoke “index” in the command line
if you just want to know how much
data you have.
progress <- read.csv("ASQ_Progress_Matrix.csv")
asq <- read.csv("ASQ.csv“)
index <- nrow(asq)

CALCULATE MARGINALVALUES
 Loops through the ASQs and
calculates the marginal values
(i.e. how far above or below
benchmark in each of 5
categories)
mycollection <- data.frame()
for (i in 1:index){
myasqtype <- asq[i,4]
mysample <- asq[i,c(5:9)]
mystandard <-progress[progress$ASQType == myasqtype, c(2:6)]
myscore <- mysample - mystandard
mycollection <- rbind(mycollection, myscore)
}

CREATINGTHE FINALTABLE
 Produces the final table upon
which we run the analytical
code.
 The table can be invoked in the
command line as “allmeas” if
you want to review the data.
allmeas <- cbind (asq[,5:9], mycollection) firstfour <- asq[,1:4]
allmeas[, seq(1,9,2)] <- asq[,5:9]
allmeas[, seq(2,10,2)] <- mycollection
allmeas <- cbind(firstfour, allmeas) colnames(allmeas)<-
c(colnames(allmeas[,1:4]),
c("COM","margCOM", "GROS", "margGROS",
"FINE", "margFINE", "PROB", "margPROB",
"PERS", "margPERS"))

WRITINGTHE FINALTABLETOWORKING
DIRECTORY
 This line of code writes the final table
to a csv file which you can open in
Excel.
 The file gets saved to your working
directory.
 The code makes this file
automatically once it is run.
write.csv(allmeas, "ASQ_final.csv")

ANALYTICAL FUNCTIONS
 The second part of the code is the
function “pclreport.”
 After the whole code file is sourced,
you will need to invoke this function
by typing the following into the
command line:
 pclreport()
 The results will print in the console.
 pclreport() consists of a number of
helper functions which work together
to answer questions posed in the PCL
report.
 These helper functions are explained
individually ahead.

IDENTIFYINGTHEVALID SUBSET
 Within any span of time under
consideration (fiscal year, fiscal quarter,
etc), PCL wants to assess students in
program 6 + months.
 This function identifies students with at
least 2 ASQs conducted, with the first
and last ASQ on record being at least 6
months apart.
valid <- function(student=""){
x <- subset(allmeas, StudentID==student)
if (nrow(x)==1) {
return (FALSE)
}
else if ((diff(x$ASQType)) >= 6){
return(TRUE)
} else{
return (FALSE)
}
}

IDENTIFYINGTHE SUBSET WITH DELAYS
 PCL assesses program effectiveness in
impacting developmental progress.
 This function identifies students from
the prior subset whose scores gave
indication of delays in at least one
category.
delay <- function (student=""){
x <- subset(allmeas, StudentID==student,
select=c(seq(6,14,2)))
xlogical <- x >= 0
if (FALSE %in% xlogical){
return(TRUE)
} else{
return (FALSE)
}
}

IDENTIFYINGTHE SUBSET OF SUCCESS
CASES
 This function identifies students
from the delays-indicated subset
whose scores on their last ASQ
indicated no delays present.
success <- function(student=""){
x <- subset(allmeas, StudentID==student,
select=c(2,3, seq(4, 14, 2)))
x$ASQDate <- as.Date(x$ASQDate,
"%m/%d/%Y")
lastASQ <- max(x$ASQDate)
data <- x[x$ASQDate==lastASQ, ]
obs <- data[4:8]
logicval <- -1 %in% sign(obs)
if (logicval == FALSE){
return(TRUE)
} else {
return(FALSE)
}
}

GENERIC SORTING CODE
 This function applies the other
functions within the larger function.
 It’s kind of like the circulation that
makes the other functions (muscles)
able to process data (oxygen, blood,
whatever) within the body of the larger
function.
getsubsets <- function(myvector=c(), myfunct=""){
a <- lapply(myvector, myfunct)
b <- which(a==TRUE)
c <- length(b)
d <- myvector[b]
return(d)
}

PUTTING IT ALLTOGETHER
 Generic sorting code is invoked to get
the data needed
 Display variables are created to store
the system responses
 System responds to user through the
print command at the end
 Results are written to a CSV file named
“ASQPCL.csv” in working directory
roster <- unique(allmeas$StudentID)
validIDs <- getsubsets(roster, valid)
delayedIDs <- getsubsets(validIDs, delay)
successfulIDs <- getsubsets(delayedIDs, success)
disprost <- paste("There are", length(roster), "students in the file who
have recieved ASQs.")
dispvalid <- paste ("Of these,", length(validIDs),
"students received a 6 month follow up.")
dispdelay <- paste(length(delayedIDs), "of these students were
identified as having delays in at least one ASQ.")
dispsuccess <- paste(length(successfulIDs), "students with delays were
found to have no delays after 6 months in program.")
paragraph <- paste(c(disprost, dispvalid,
dispdelay, dispsuccess))
print(paragraph)
write.csv(paragraph, “ASQPCL.csv”)

REVIEW
 Get the stuff into your working directory
 Set your working directory in RStudio to be the same one that has the R code and the CSV
files using setwd(“C:blahblahblah”)
 Open the R code file in RStudio
 Source the code into the working environment by pressing the “Source” button in the
upper right hand corner of the code window.
 Type “pclreport()”, all lowercase, without the quotation marks, into the R console.
 At this point, you will have answers on the screen and two new CSV files with the data
table and the results of the PCL function preserved. Remember that R will save over these
files every time you run this code, so if you want to preserve any files for records, move it
into another directory and rename them with an appropriately identifying name.

Managing ASQ Data: a Guide for Relief Nursery Administrative Assistants

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Managing ASQ Data: a Guide for Relief Nursery Administrative Assistants