Data Quality Analysis • txschooldata

Overview

This vignette performs quality assurance checks on Texas public school enrollment data from the Texas Education Agency (TEA). We analyze statewide trends and examine data for major districts to identify any anomalies or data quality issues.

Data Coverage

TEA enrollment data is available through the TAPR (Texas Academic Performance Reports) system from 2020 onwards via the current download interface.

library(txschooldata)
library(dplyr)
library(ggplot2)
library(scales)

Statewide Enrollment Analysis

Multi-Year Download

# Download enrollment data for available years
years <- 2020:2024
enr_multi <- fetch_enr_multi(years, use_cache = TRUE)

# State-level total enrollment by year
state_totals <- enr_multi %>%
  filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
  select(end_year, n_students) %>%
  arrange(end_year)

stopifnot(nrow(state_totals) > 0)
print(state_totals)

##   end_year n_students
## 1     2020    5479173
## 2     2021    5359040
## 3     2022    5402928
## 4     2023    5504150
## 5     2024    5517464

Statewide Enrollment Trend

print(state_totals)

##   end_year n_students
## 1     2020    5479173
## 2     2021    5359040
## 3     2022    5402928
## 4     2023    5504150
## 5     2024    5517464

state_totals %>%
  ggplot(aes(x = end_year, y = n_students)) +
  geom_line(linewidth = 1.2, color = "#003366") +
  geom_point(size = 3, color = "#003366") +
  geom_vline(xintercept = 2020.5, linetype = "dotted", color = "gray50") +
  scale_y_continuous(labels = comma, limits = c(0, NA)) +
  labs(
    title = "Texas Public School Enrollment",
    subtitle = "Total K-12 enrollment, 2020-2024",
    x = "School Year (End)",
    y = "Total Students",
    caption = "Source: Texas Education Agency TAPR"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.minor = element_blank()
  )

Year-over-Year Changes

# Calculate year-over-year changes
state_changes <- state_totals %>%
  mutate(
    prev_year = lag(n_students),
    change = n_students - prev_year,
    pct_change = (change / prev_year) * 100
  ) %>%
  filter(!is.na(prev_year))

print(state_changes)

##   end_year n_students prev_year  change pct_change
## 1     2021    5359040   5479173 -120133 -2.1925389
## 2     2022    5402928   5359040   43888  0.8189526
## 3     2023    5504150   5402928  101222  1.8734656
## 4     2024    5517464   5504150   13314  0.2418902

# Flag any jumps greater than 5%
large_changes <- state_changes %>%
  filter(abs(pct_change) > 5)

if (nrow(large_changes) > 0) {
  cat("\n** DATA QUALITY ALERT: Year-over-year changes exceeding 5%:\n")
  print(large_changes)
} else {
  cat("\n** All year-over-year changes are within +/- 5% threshold.\n")
}

## 
## ** All year-over-year changes are within +/- 5% threshold.

Major District Analysis

Texas has over 1,200 school districts. We focus on the 5 largest urban districts for detailed quality checks:

Houston ISD (101912) - Largest district in Texas
Dallas ISD (057905) - Second largest
San Antonio ISD (015907) - Major urban district
Fort Worth ISD (220905) - Major urban district
Austin ISD (227901) - State capital

# Define major districts to analyze
major_districts <- tibble(
  district_id = c("101912", "057905", "015907", "220905", "227901"),
  district_label = c("Houston ISD", "Dallas ISD", "San Antonio ISD",
                     "Fort Worth ISD", "Austin ISD")
)

# Extract enrollment for major districts
district_trends <- enr_multi %>%
  filter(
    is_district,
    subgroup == "total_enrollment",
    grade_level == "TOTAL",
    district_id %in% major_districts$district_id
  ) %>%
  left_join(major_districts, by = "district_id") %>%
  select(end_year, district_id, district_label, n_students) %>%
  arrange(district_label, end_year)

# Display current enrollment
current_enrollment <- district_trends %>%
  filter(end_year == max(end_year)) %>%
  arrange(desc(n_students))

stopifnot(nrow(current_enrollment) > 0)
print(current_enrollment)

##   end_year district_id  district_label n_students
## 1     2024      101912     Houston ISD     183603
## 2     2024      057905      Dallas ISD     139096
## 3     2024      227901      Austin ISD      72739
## 4     2024      220905  Fort Worth ISD      70903
## 5     2024      015907 San Antonio ISD      44635

District Enrollment Trends

print(district_trends %>% select(end_year, district_label, n_students))

##    end_year  district_label n_students
## 1      2020      Austin ISD      80718
## 2      2021      Austin ISD      74725
## 3      2022      Austin ISD      71883
## 4      2023      Austin ISD      73198
## 5      2024      Austin ISD      72739
## 6      2020      Dallas ISD     153784
## 7      2021      Dallas ISD     145105
## 8      2022      Dallas ISD     143430
## 9      2023      Dallas ISD     141042
## 10     2024      Dallas ISD     139096
## 11     2020  Fort Worth ISD      82704
## 12     2021  Fort Worth ISD      76754
## 13     2022  Fort Worth ISD      74326
## 14     2023  Fort Worth ISD      72637
## 15     2024  Fort Worth ISD      70903
## 16     2020     Houston ISD     209309
## 17     2021     Houston ISD     196550
## 18     2022     Houston ISD     193727
## 19     2023     Houston ISD     189290
## 20     2024     Houston ISD     183603
## 21     2020 San Antonio ISD      48495
## 22     2021 San Antonio ISD      45780
## 23     2022 San Antonio ISD      44568
## 24     2023 San Antonio ISD      45212
## 25     2024 San Antonio ISD      44635

district_trends %>%
  ggplot(aes(x = end_year, y = n_students, color = district_label)) +
  geom_line(linewidth = 1) +
  geom_point(size = 2) +
  geom_vline(xintercept = 2020.5, linetype = "dotted", color = "gray50") +
  scale_y_continuous(labels = comma) +
  scale_color_brewer(palette = "Set1") +
  labs(
    title = "Major Texas District Enrollment Trends",
    subtitle = "2020-2024 school years",
    x = "School Year (End)",
    y = "Total Students",
    color = "District",
    caption = "Source: Texas Education Agency TAPR"
  ) +
  theme_minimal() +
  theme(
    legend.position = "right",
    plot.title = element_text(face = "bold", size = 14),
    panel.grid.minor = element_blank()
  )

District Year-over-Year Changes

district_changes <- district_trends %>%
  group_by(district_label) %>%
  mutate(
    prev_year = lag(n_students),
    change = n_students - prev_year,
    pct_change = round((change / prev_year) * 100, 2)
  ) %>%
  filter(!is.na(prev_year)) %>%
  ungroup()

# Pivot to show changes by year
district_change_summary <- district_changes %>%
  select(district_label, end_year, pct_change) %>%
  tidyr::pivot_wider(names_from = end_year, values_from = pct_change)

print(district_change_summary)

## # A tibble: 5 × 5
##   district_label  `2021` `2022` `2023` `2024`
##   <chr>            <dbl>  <dbl>  <dbl>  <dbl>
## 1 Austin ISD       -7.42  -3.8    1.83  -0.63
## 2 Dallas ISD       -5.64  -1.15  -1.66  -1.38
## 3 Fort Worth ISD   -7.19  -3.16  -2.27  -2.39
## 4 Houston ISD      -6.1   -1.44  -2.29  -3   
## 5 San Antonio ISD  -5.6   -2.65   1.44  -1.28

# Flag large changes
large_district_changes <- district_changes %>%
  filter(abs(pct_change) > 5)

if (nrow(large_district_changes) > 0) {
  cat("\n** DATA QUALITY ALERT: District changes exceeding 5%:\n")
  print(large_district_changes %>%
        select(district_label, end_year, n_students, change, pct_change))
} else {
  cat("\n** All major district changes are within +/- 5% threshold.\n")
}

## 
## ** DATA QUALITY ALERT: District changes exceeding 5%:
## # A tibble: 5 × 5
##   district_label  end_year n_students change pct_change
##   <chr>              <dbl>      <dbl>  <dbl>      <dbl>
## 1 Austin ISD          2021      74725  -5993      -7.42
## 2 Dallas ISD          2021     145105  -8679      -5.64
## 3 Fort Worth ISD      2021      76754  -5950      -7.19
## 4 Houston ISD         2021     196550 -12759      -6.1 
## 5 San Antonio ISD     2021      45780  -2715      -5.6

Demographic Breakdown Validation

Check that demographic subgroups sum approximately to total enrollment:

# Get 2024 state demographic data
state_2024 <- enr_multi %>%
  filter(is_state, end_year == 2024, grade_level == "TOTAL") %>%
  select(subgroup, n_students)

# Total enrollment
total <- state_2024 %>%
  filter(subgroup == "total_enrollment") %>%
  pull(n_students)

# Race/ethnicity subgroups
race_groups <- c("white", "black", "hispanic", "asian",
                 "native_american", "pacific_islander", "multiracial")
race_sum <- state_2024 %>%
  filter(subgroup %in% race_groups) %>%
  summarize(sum = sum(n_students, na.rm = TRUE)) %>%
  pull(sum)

cat("Total Enrollment:", comma(total), "\n")

## Total Enrollment: 5,517,464

cat("Sum of Race/Ethnicity Groups:", comma(race_sum), "\n")

## Sum of Race/Ethnicity Groups: 5,517,464

cat("Difference:", comma(total - race_sum),
    "(", round((total - race_sum) / total * 100, 2), "%)\n")

## Difference: 0 ( 0 %)

# This difference may be due to students with unknown race/ethnicity
if (abs(total - race_sum) / total > 0.05) {
  cat("\n** DATA QUALITY NOTE: Race/ethnicity categories don't sum to total.\n")
  cat("   This may indicate missing demographic data or students with unknown race.\n")
}

Grade Level Validation

Check that individual grade enrollments sum to approximately total enrollment:

# Get grade-level enrollment for 2024 state
grades <- c("EE", "PK", "K", "01", "02", "03", "04", "05",
            "06", "07", "08", "09", "10", "11", "12")

grade_sum <- enr_multi %>%
  filter(is_state, end_year == 2024,
         subgroup == "total_enrollment",
         grade_level %in% grades) %>%
  summarize(sum = sum(n_students, na.rm = TRUE)) %>%
  pull(sum)

cat("Total Enrollment:", comma(total), "\n")

## Total Enrollment: 5,517,464

cat("Sum of Grade Levels:", comma(grade_sum), "\n")

## Sum of Grade Levels: 5,517,464

cat("Difference:", comma(total - grade_sum),
    "(", round((total - grade_sum) / total * 100, 2), "%)\n")

## Difference: 0 ( 0 %)

if (abs(total - grade_sum) / total > 0.05) {
  cat("\n** DATA QUALITY NOTE: Grade levels don't sum to total.\n")
  cat("   This may indicate ungraded students or data reporting differences.\n")
}

Data Quality Summary

Issues Identified

issues <- character()

# Check for large state-level changes
if (nrow(large_changes) > 0) {
  issues <- c(issues, paste(
    "Statewide enrollment changes >5% in:",
    paste(large_changes$end_year, collapse = ", ")
  ))
}

# Check for large district changes
if (nrow(large_district_changes) > 0) {
  issues <- c(issues, paste(
    "Major district changes >5%:",
    nrow(large_district_changes), "instances"
  ))
}

# Print summary
cat("=== Data Quality Summary ===\n\n")

## === Data Quality Summary ===

cat("Years Analyzed:", min(years), "-", max(years), "\n")

## Years Analyzed: 2020 - 2024

cat("Total State Records:", comma(nrow(enr_multi)), "\n")

## Total State Records: 1,281,090

cat("Districts in Dataset:",
    enr_multi %>% filter(is_district) %>% distinct(district_id) %>% nrow(), "\n")

## Districts in Dataset: 1216

cat("Campuses in Dataset:",
    enr_multi %>% filter(is_campus) %>% distinct(campus_id) %>% nrow(), "\n\n")

## Campuses in Dataset: 9540

if (length(issues) > 0) {
  cat("Issues Found:\n")
  for (issue in issues) {
    cat("  -", issue, "\n")
  }
} else {
  cat("No major data quality issues identified.\n")
}

## Issues Found:
##   - Major district changes >5%: 5 instances

Notes

Data Source

All data comes from the Texas Education Agency’s TAPR (Texas Academic Performance Reports) system: https://rptsvr1.tea.texas.gov/perfreport/tapr/

Known Limitations

Historical Data: The current download interface only supports years 2020 and later. Earlier years may require different data sources.
COVID-19 Impact: The 2020-2021 school year saw significant enrollment disruptions due to the COVID-19 pandemic. Any unusual patterns in this period should be interpreted in that context.
Charter Schools: Charter school data is included in district totals. The is_charter flag can be used to separate traditional public schools from charter operators.
Data Masking: TEA applies data masking to protect student privacy for small subgroups. Masked values appear as NA in the data.

Update Schedule

TEA releases TAPR data annually, typically in the fall following the end of a school year. The 2023-24 data (end_year = 2024) represents the most recent complete school year.

Session Info

sessionInfo()

## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] scales_1.4.0       ggplot2_4.0.2      dplyr_1.2.0        txschooldata_0.1.0
## 
## loaded via a namespace (and not attached):
##  [1] gtable_0.3.6       jsonlite_2.0.0     compiler_4.5.2     tidyselect_1.2.1  
##  [5] tidyr_1.3.2        jquerylib_0.1.4    systemfonts_1.3.2  textshaping_1.0.5 
##  [9] yaml_2.3.12        fastmap_1.2.0      R6_2.6.1           labeling_0.4.3    
## [13] generics_0.1.4     knitr_1.51         tibble_3.3.1       desc_1.4.3        
## [17] bslib_0.10.0       pillar_1.11.1      RColorBrewer_1.1-3 rlang_1.1.7       
## [21] utf8_1.2.6         cachem_1.1.0       xfun_0.56          fs_1.6.7          
## [25] sass_0.4.10        S7_0.2.1           cli_3.6.5          pkgdown_2.2.0     
## [29] withr_3.0.2        magrittr_2.0.4     digest_0.6.39      grid_4.5.2        
## [33] rappdirs_0.3.4     lifecycle_1.0.5    vctrs_0.7.1        evaluate_1.0.5    
## [37] glue_1.8.0         farver_2.1.2       codetools_0.2-20   ragg_1.5.1        
## [41] purrr_1.2.1        rmarkdown_2.30     tools_4.5.2        pkgconfig_2.0.3   
## [45] htmltools_0.5.9