Author: Jared E. Knowles
Date: 2026-06-22

# Tools for Education Data in R

#### Jared E. Knowles

#### 2026-06-22

# 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.

# 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")
    #> [1] 8.087671
    age_calc(dob = as.Date('1995-01-15'), enddate = as.Date('2003-02-16'),
             units = "months")
    #> [1] 97.03571
    age_calc(dob = as.Date('1995-01-15'), enddate = as.Date('2003-02-16'),
             units = "days")
    #> Time difference of 2954 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)
    #>   sid retained
    #> 1 101        N
    #> 2 102        N
    #> 3 103        Y
    #> 4 105        N

`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
    #>   sid moves
    #> 1   1     4
    #> 2   2     4
    #> 3   3     2
    #> 4   4    NA

# 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")
    #> [1] "."
    vecB <- c(1, 1, 1, 3:10)
    statamode(vecB, method = "last")
    #> [1] 1
    vecC <- c(1, 1, 1, NA, NA, 5:10)
    statamode(vecC, method = "last")
    #> [1] 1
    vecA <- c(LETTERS[1:10]); vecA <- factor(vecA)
    statamode(vecA, method = "last")
    #> [1] J
    #> Levels: J
    vecB <- c("A", "A", "A", LETTERS[3:10]); vecB <- factor(vecB)
    statamode(vecB, method = "last")
    #> [1] A
    #> Levels: A
    vecA <- c(LETTERS[1:10])
    statamode(vecA, method = "sample")
    #> [1] "J"
    vecB <- c("A", "A", "A", LETTERS[3:10])
    statamode(vecB, method = "stata")
    #> [1] "A"
    vecC <- c("A", "A", "A", NA, NA, LETTERS[5:10])
    statamode(vecC, method = "stata")
    #> [1] "A"

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)
    #> [1] "0001" "0023" "0007" "0105"

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))
    #> [1] 7
    max_mis(c(NA, NA, NA, NA))
    #> [1] NA
    nth_max(c(1:20, 20:1), n = 2)
    #> [1] 19

# 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"))
    #> [1] FALSE
    isid(stuatt, vars = c("sid", "school_year"))
    #> [1] FALSE

`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
    #> [1] 44
    thresh(enrollment, 10)   # proportion of the total in the 10 largest
    #> [1] 0.1254708

# 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)
    #>   sid school_year male race_ethnicity birth_date first_9th_school_year_reported
    #> 1   1        2004    1              B      10869                           2004
    #> 2   1        2005    1              H      10869                           2004
    #> 3   1        2006    1              H      10869                           2004
    #> 4   1        2007    1              H      10869                           2004
    #> 5   2        2006    0              W      11948                             NA
    #> 6   2        2007    0              B      11948                             NA
    #>   hs_diploma      hs_diploma_type hs_diploma_date
    #> 1          0
    #> 2          0
    #> 3          0
    #> 4          0
    #> 5          1     Standard Diploma        6/5/2008
    #> 6          1 College Prep Diploma       5/24/2009

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)
    #>     X school  stuid grade schid dist white black hisp indian asian econ female
    #> 1  44      1 149995     3   495  105     0     1    0      0     0    0      0
    #> 2  53      1  13495     3   495   45     0     1    0      0     0    1      0
    #> 3 116      1 106495     3   495   45     0     1    0      0     0    1      0
    #> 4 244      1  45205     3   205   15     0     1    0      0     0    1      0
    #> 5 274      1 142705     3   205   75     0     1    0      0     0    1      0
    #> 6 276      1  14995     3   495  105     0     1    0      0     0    1      0
    #>   ell disab sch_fay dist_fay luck   ability    measerr      teachq year attday
    #> 1   0     0       0        0    0  87.85405  11.133264 39.09024712 2000    180
    #> 2   0     0       0        0    1  97.78756   6.822394  0.09848192 2000    180
    #> 3   0     0       0        0    0 104.49303  -7.856159 39.53885270 2000    160
    #> 4   0     0       0        0    1 111.67151 -17.574152 24.11612277 2000    168
    #> 5   0     0       0        0    0  81.92539  52.983338 56.68061304 2000    156
    #> 6   0     0       0        0    0 101.92904  22.604145 71.62196655 2000    157
    #>   schoolscore district schoolhigh schoolavg schoollow   readSS   mathSS
    #> 1    29.22427        3          0         1         0 357.2865 387.2803
    #> 2    55.96326        3          0         1         0 263.9046 302.5724
    #> 3    55.96326        3          0         1         0 369.6722 365.4614
    #> 4    55.96326        3          0         1         0 346.5957 344.4964
    #> 5    55.96326        3          0         1         0 373.1254 441.1581
    #> 6    55.96326        3          0         1         0 436.7607 463.4033
    #>       proflvl race
    #> 1       basic    B
    #> 2 below basic    B
    #> 3       basic    B
    #> 4       basic    B
    #> 5       basic    B
    #> 6  proficient    B

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)
    #>   district_id school_id subject grade n1   ss1 n2   ss2 predicted  residuals
    #> 1          14       130    math     4 44 433.1 40 463.0  468.7446 -5.7445937
    #> 2          70        20    math     4 18 443.0 20 477.2  476.4765  0.7235053
    #> 3         112        80    math     4 86 445.4 94 472.6  478.3509 -5.7508949
    #> 4         119        50    math     4 95 427.1 94 460.7  464.0586 -3.3585931
    #> 5         147        60    math     4 27 424.2 27 458.7  461.7937 -3.0936928
    #> 6         147       125    math     4 17 423.5 26 463.1  461.2470  1.8530072
    #>       resid_z     resid_t       cooks test_year     tprob flagged_t95
    #> 1 -0.59189645 -0.59170988 0.000171271      2007 0.2787298           0
    #> 2  0.07455731  0.07452135 0.000003510      2007 0.4706873           0
    #> 3 -0.59266905 -0.59248250 0.000244921      2007 0.2774827           0
    #> 4 -0.34605798 -0.34591020 0.000059900      2007 0.3650957           0
    #> 5 -0.31877383 -0.31863490 0.000054100      2007 0.3762745           0
    #> 6  0.19093568  0.19084643 0.000019800      2007 0.4250936           0

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()`.