Introduction
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.
Datasets
eeptools provides three new datasets of interest to education researchers. These datasets are also used in the R Bootcamp for Education Analysts
library(eeptools)
#> Loading required package: ggplot2
data("stuatt")
head(stuatt)
#> sid school_year male race_ethnicity birth_date
#> 1 1 2004 1 B 10869
#> 2 1 2005 1 H 10869
#> 3 1 2006 1 H 10869
#> 4 1 2007 1 H 10869
#> 5 2 2006 0 W 11948
#> 6 2 2007 0 B 11948
#> first_9th_school_year_reported hs_diploma hs_diploma_type
#> 1 2004 0
#> 2 2004 0
#> 3 2004 0
#> 4 2004 0
#> 5 NA 1 Standard Diploma
#> 6 NA 1 College Prep Diploma
#> hs_diploma_date
#> 1
#> 2
#> 3
#> 4
#> 5 6/5/2008
#> 6 5/24/2009The 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
#> 1 44 1 149995 3 495 105 0 1 0 0 0 0
#> 2 53 1 13495 3 495 45 0 1 0 0 0 1
#> 3 116 1 106495 3 495 45 0 1 0 0 0 1
#> 4 244 1 45205 3 205 15 0 1 0 0 0 1
#> 5 274 1 142705 3 205 75 0 1 0 0 0 1
#> 6 276 1 14995 3 495 105 0 1 0 0 0 1
#> female ell disab sch_fay dist_fay luck ability measerr teachq
#> 1 0 0 0 0 0 0 87.85405 11.133264 39.09024712
#> 2 0 0 0 0 0 1 97.78756 6.822394 0.09848192
#> 3 0 0 0 0 0 0 104.49303 -7.856159 39.53885270
#> 4 0 0 0 0 0 1 111.67151 -17.574152 24.11612277
#> 5 0 0 0 0 0 0 81.92539 52.983338 56.68061304
#> 6 0 0 0 0 0 0 101.92904 22.604145 71.62196655
#> year attday schoolscore district schoolhigh schoolavg schoollow readSS
#> 1 2000 180 29.22427 3 0 1 0 357.2865
#> 2 2000 180 55.96326 3 0 1 0 263.9046
#> 3 2000 160 55.96326 3 0 1 0 369.6722
#> 4 2000 168 55.96326 3 0 1 0 346.5957
#> 5 2000 156 55.96326 3 0 1 0 373.1254
#> 6 2000 157 55.96326 3 0 1 0 436.7607
#> mathSS proflvl race
#> 1 387.2803 basic B
#> 2 302.5724 below basic B
#> 3 365.4614 basic B
#> 4 344.4964 basic B
#> 5 441.1581 basic B
#> 6 463.4033 proficient BThe 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
#> 1 14 130 math 4 44 433.1 40 463.0 468.7446
#> 2 70 20 math 4 18 443.0 20 477.2 476.4765
#> 3 112 80 math 4 86 445.4 94 472.6 478.3509
#> 4 119 50 math 4 95 427.1 94 460.7 464.0586
#> 5 147 60 math 4 27 424.2 27 458.7 461.7937
#> 6 147 125 math 4 17 423.5 26 463.1 461.2470
#> residuals resid_z resid_t cooks test_year tprob
#> 1 -5.7445937 -0.59189645 -0.59170988 0.000171271 2007 0.2787298
#> 2 0.7235053 0.07455731 0.07452135 0.000003510 2007 0.4706873
#> 3 -5.7508949 -0.59266905 -0.59248250 0.000244921 2007 0.2774827
#> 4 -3.3585931 -0.34605798 -0.34591020 0.000059900 2007 0.3650957
#> 5 -3.0936928 -0.31877383 -0.31863490 0.000054100 2007 0.3762745
#> 6 1.8530072 0.19093568 0.19084643 0.000019800 2007 0.4250936
#> flagged_t95
#> 1 0
#> 2 0
#> 3 0
#> 4 0
#> 5 0
#> 6 0The 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.
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 daysage_calc also now properly accounts for leap years and leap seconds by default.
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 Nretained_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 NAManipulate Data
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.
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] "G"
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"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.
Regression Models
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.
require(MASS)
#> Loading required package: MASS
#> Warning: package 'MASS' was built under R version 3.2.2
#Examples of "sim"
set.seed (1)
J <- 15
n <- J*(J+1)/2
group <- rep (1:J, 1:J)
mu.a <- 5
sigma.a <- 2
a <- rnorm (J, mu.a, sigma.a)
b <- -3
x <- rnorm (n, 2, 1)
sigma.y <- 6
y <- rnorm (n, a[group] + b*x, sigma.y)
u <- runif (J, 0, 3)
dat <- cbind (y, x, group)
# Linear regression
dat <- as.data.frame(dat)
dat$group <- factor(dat$group)
M3 <- glm (y ~ x + group, data=dat)
cases <- expand.grid(x = seq(-2, 2, by=0.1),
group=seq(1, 14, by=2))
cases$group <- factor(cases$group)
sim.results <- gelmansim(mod = M3, newdata = cases, n.sims=200, na.omit=TRUE)
head(sim.results)
#> x group yhats yhatMin yhatMax
#> 1 -2.0 1 1.49216152 -5.317070 8.826878
#> 2 -1.9 1 1.05530376 -6.141328 9.084779
#> 3 -1.8 1 1.50069226 -5.341525 8.256558
#> 4 -1.7 1 -0.04991641 -7.027672 6.982415
#> 5 -1.6 1 0.50262366 -6.315685 7.944253
#> 6 -1.5 1 -0.32213552 -7.343319 6.737021Plotting Themes
The package includes a number of themes for ggplot2 named theme_dpi to reflect their internal use at the Wisconsin Department of Public Instruction. These themes are very similar to the theme_bw() within ggplot2 but with font sizes optimized for presentations and publications.
crimes <- data.frame(state = tolower(rownames(USArrests)), USArrests)
require(reshape) # for melt
#> Loading required package: reshape
crimesm <- melt(crimes, id = 1)
states_map <- map_data("state")
p1 <- ggplot(crimes, aes(map_id = state)) + geom_map(aes(fill = Murder),
linetype = 1, map = states_map) +
expand_limits(x = states_map$long, y = states_map$lat) + labs(title="USA Crime")
p1 <- p1 + coord_map()
p1 + theme_dpi_map()There is also a ggplot2 version of plot.lm included:
data(mpg)
mymod <- lm(cty~displ + cyl + drv, data=mpg)
autoplot(mymod)Finally, there is a convenient method for creating labeled mosaic plots.
sampDat <- data.frame(cbind(x=seq(1,3,by=1), y=sample(LETTERS[6:8], 60,
replace=TRUE)),
fac=sample(LETTERS[1:4], 60, replace=TRUE))
varnames<-c('Quality','Grade')
crosstabplot(sampDat, "y", "fac", varnames = varnames, label = TRUE,
title = "Crosstab Plot", shade = FALSE)Helping Out
eeptools is intended to be a useful project for the education analytics community. Contributions are welcomed. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.