Tools for Education Data in R
In eeptools: Convenience Functions for Education Data

Introduction

eeptools is an R package that makes it easier for analysts at state and local education agencies to work with administrative records on students, schools, and districts. It focuses on the tasks that are specific to education unit-record data -- calculating ages and grade retention, identifying student mobility, checking unique identifiers, and exploring example datasets -- and tries to make those tasks simpler and less error-prone.

knitr::opts_chunk$set(
  cache=FALSE,
  comment="#>",
  collapse=TRUE,
  echo=TRUE
)
library(knitr); library(eeptools)

Administrative Data Functions

For analysts using unit-record data of some type, there are several calc functions which automate common tasks including calculating ages (age_calc), grade retention (retained_calc), and student mobility (moves_calc).

age_calc(dob = as.Date('1995-01-15'), enddate = as.Date('2003-02-16'),
         units = "years")
age_calc(dob = as.Date('1995-01-15'), enddate = as.Date('2003-02-16'),
         units = "months")
age_calc(dob = as.Date('1995-01-15'), enddate = as.Date('2003-02-16'),
         units = "days")

age_calc also now properly accounts for leap years and leap seconds by default. age_calc can be passed a vector of dates of birth and a vector of end dates or a single end-date and produce a vector of ages as well -- suitable for computing student age on the fly from date-of-birth records.

retained_calc takes a vector of student identifiers and a vector of grades and checks whether or not the student was retained in the grade level specified by the user. It returns a data.frame of all students who could have been retained and a yes or no indicator of whether they were retained.

x <- data.frame(sid = c(101, 101, 102, 103, 103, 103, 104, 105, 105, 106, 106),
                 grade = c(9, 10, 9, 9, 9, 10, 10, 8, 9, 7, 7))
retained_calc(df = x, sid = "sid", grade = "grade", grade_val = 9)

retained_calc is intended to be used after you have processed your data as it does not take into account time or sequence other than the order in which the data is passed to it.

moves_calc is intended to identify based on enrollment dates whether a student experienced a school move within a school year.

df <- data.frame(sid = c(rep(1,3), rep(2,4), 3, rep(4,2)),
                   schid = c(1, 2, 2, 2, 3, 1, 1, 1, 3, 1),
                   enroll_date = as.Date(c('2004-08-26',
                   '2004-10-01', '2005-05-01', '2004-09-01',
                   '2004-11-03', '2005-01-11', '2005-04-02',
                   '2004-09-26', '2004-09-01','2005-02-02'), format='%Y-%m-%d'),
                   exit_date = as.Date(c('2004-08-26', '2005-04-10',
                    '2005-06-15', '2004-11-02', '2005-01-10',
                    '2005-03-01', '2005-06-15', '2005-05-30',
                    NA, '2005-06-15'), format='%Y-%m-%d'))

moves <- moves_calc(df, sid = "sid", schid = "schid", enroll_date = "enroll_date",
                    exit_date = "exit_date")
moves

Manipulating and Cleaning Data

Another set of key functions in the package make basic data manipulation easier. One thing users of other statistical packages may miss when using R is a convenient function for determining the mode of a vector. The statamode function is designed to do just that. statamode works with numeric, character, and factor data types. It also includes various options for how to deal with a tie demonstrated below.

vecA <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
statamode(vecA, method = "stata")
vecB <- c(1, 1, 1, 3:10)
statamode(vecB, method = "last")
vecC <- c(1, 1, 1, NA, NA, 5:10)
statamode(vecC, method = "last")
vecA <- c(LETTERS[1:10]); vecA <- factor(vecA)
statamode(vecA, method = "last")
vecB <- c("A", "A", "A", LETTERS[3:10]); vecB <- factor(vecB)
statamode(vecB, method = "last")
vecA <- c(LETTERS[1:10])
statamode(vecA, method = "sample")
vecB <- c("A", "A", "A", LETTERS[3:10])
statamode(vecB, method = "stata")
vecC <- c("A", "A", "A", NA, NA, LETTERS[5:10])
statamode(vecC, method = "stata")

Administrative extracts are rarely clean. remove_char strips a specific character -- such as the * often used to mark redacted cells -- out of a column and returns the result, replacing the marker with NA so the column can be coerced to numeric.

scores <- c(1, 5, 3, 6, "*", 2, 5, "*", "*")
remove_char(scores, "*")

Fixed-width identifier codes (school numbers, district codes, FIPS codes) often lose their leading zeroes when read in as numbers. leading_zero pads them back to a fixed width.

leading_zero(c(1, 23, 7, 105), digits = 4)

When you summarize arbitrary subsets of data, some subsets may contain only missing values. Taking max() of such a subset returns -Inf and a warning; max_mis returns NA instead, which is convenient inside apply or do.call constructions. nth_max returns the 2nd, 3rd, etc. largest value in a vector.

max_mis(c(7, NA, 3, 2, 0))
max_mis(c(NA, NA, NA, NA))
nth_max(c(1:20, 20:1), n = 2)

Checking and Summarizing Data

Before aggregating unit-record data it is good practice to confirm which variables uniquely identify a row. isid (named after the Stata command) checks whether a set of variables forms a unique key.

data(stuatt)
isid(stuatt, vars = c("sid"))
isid(stuatt, vars = c("sid", "school_year"))

cutoff and thresh help you understand how concentrated a quantity is. After sorting a vector in descending order, cutoff returns the number of elements needed to reach a given proportion of the total, while thresh returns the proportion of the total reached after a given number of elements.

set.seed(442)
enrollment <- rpois(100, lambda = 50)
cutoff(enrollment, 0.5)  # elements needed to reach 50% of the total
thresh(enrollment, 10)   # proportion of the total in the 10 largest

Datasets

eeptools provides three new datasets of interest to education researchers. These datasets are also used in the R Bootcamp for Education Analysts

data("stuatt")
head(stuatt)

The stuatt, student attributes, dataset is provided from the Strategic Data Project Toolkit for Effective Data Use. This dataset is useful for learning how to clean data in R and how to aggregate and summarize individual unit-record data into group-level data.

data(stulevel)
head(stulevel)

The stulevel dataset is a simulated student-level longitudinal record. It contains student and school level attributes and is useful for practicing evaluating longitudinal analyses of student unit-record data.

data("midsch")
head(midsch)

The midsch dataset contains an analysis on abnormality in school average assessment scores. It contains observed and predicted values of aggregated test scores at the school level for a large midwestern state.

Deprecated functions

As of version 1.3.0 several older convenience functions are deprecated and will be removed in a future release. These include defac(), makenum(), decomma(), cleanTex(), lag_data(), gelmansim(), autoplot.lm(), crosstabs(), crosstabplot(), profpoly(), and profpoly.data(), along with the now-defunct theme_dpi*() themes. Where a modern replacement exists it is named in each function's help page and in NEWS.md; for example, use as.character() in place of defac(), readr::parse_number() in place of decomma(), and dplyr::lag() in place of lag_data().