`eeptools`

is an R package that seeks to make it easier
for analysts at state and local education agencies to analyze and
visualize their data on student, school, and district performance. By
putting simple wrappers around a number of R functions,
`eeptools`

strives to make many common tasks simpler and less
prone to error specific to analysis of education data.

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.

```
<- data.frame(sid = c(101, 101, 102, 103, 103, 103, 104, 105, 105, 106, 106),
x 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.

```
<- data.frame(sid = c(rep(1,3), rep(2,4), 3, rep(4,2)),
df 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_calc(df, sid = "sid", schid = "schid", enroll_date = "enroll_date",
moves exit_date = "exit_date")
moves#> sid moves
#> 1 1 4
#> 2 2 4
#> 3 3 2
#> 4 4 NA
```

Another set of key functions in the package are to make basic data
manipulation easier. One thing users of other statistical packaegs 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.

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

There are a number of functions to save you keystrokes like
`defac`

for converting a factor to a character,
`makenum`

for turning a factor variable into a numeric
variable, `max_mis`

for taking the maximum of a vector of
numerics and ignoring any NAs (useful for inclusion in
`do.call`

or `apply`

constructions).
`remove_char`

allows you to quickly `gsub`

out a
specific character from a string vector such as an `*`

or
`...`

. `decomma`

is a somewhat specialized version
of this for processing data where numerics are written with commas.
`nth_max`

allows you to identify the 2nd, 3rd, etc. maximum
value in a vector.

`eeptools`

includes ways to simplify the use of regression
analyses tools recommended by Gelman and Hill 2006 through the
`gelmansim`

function, which itself is a wrapper for the
`arm::sim()`

function. This function allows the distribution
of predicted values to be generated automatically which is useful for
gauging uncertainty in a statistical model and also to compare
predictions from multiple models on the same case data to see if the
values of those models overlap or are distinct from one another.

```
library(MASS)
#> Warning: package 'MASS' was built under R version 4.2.3
#Examples of "sim"
set.seed (1)
<- 15
J <- J*(J+1)/2
n <- rep (1:J, 1:J)
group <- 5
mu.a <- 2
sigma.a <- rnorm (J, mu.a, sigma.a)
a <- -3
b <- rnorm (n, 2, 1)
x <- 6
sigma.y <- rnorm (n, a[group] + b*x, sigma.y)
y <- runif (J, 0, 3)
u <- cbind (y, x, group)
dat # Linear regression
<- as.data.frame(dat)
dat $group <- factor(dat$group)
dat<- glm (y ~ x + group, data=dat)
M3 <- expand.grid(x = seq(-2, 2, by=0.1),
cases group=seq(1, 14, by=2))
$group <- factor(cases$group)
cases<- gelmansim(mod = M3, newdata = cases, n.sims=200, na.omit=TRUE)
sim.results head(sim.results)
#> x group yhats yhatMin yhatMax
#> 1 -2.0 1 1.1736195 -6.264184 8.243267
#> 2 -1.9 1 0.7390300 -7.376271 8.587548
#> 3 -1.8 1 1.1866869 -6.256829 7.846494
#> 4 -1.7 1 -0.3616534 -8.355161 7.494966
#> 5 -1.6 1 0.1931550 -7.104866 8.648023
#> 6 -1.5 1 -0.6293359 -7.803899 6.654324
```

There is also a `ggplot2`

version of `plot.lm`

included:

```
data(mpg)
<- lm(cty~displ + cyl + drv, data=mpg)
mymod autoplot(mymod)
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
```

Finally, there is a convenient method for creating labeled mosaic plots.

```
<- data.frame(cbind(x=seq(1,3,by=1), y=sample(LETTERS[6:8], 60,
sampDat replace=TRUE)),
fac=sample(LETTERS[1:4], 60, replace=TRUE))
<-c('Quality','Grade')
varnamescrosstabplot(sampDat, "y", "fac", varnames = varnames, label = TRUE,
title = "Crosstab Plot", shade = FALSE)
```

And without labels:

```
crosstabplot(sampDat, "y", "fac", varnames = varnames, label = FALSE,
title = "Crosstab Plot", shade = TRUE)
```

`eeptools`

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

```
library(eeptools)
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.