mapvizieR provides a comprehensive suite of visualization and analysis tools for NWEA MAP assessment data. It helps educators and analysts explore student growth, achievement, and progress through intuitive visualizations and data transformations.
# Install from GitHub (recommended)
# install.packages("pak")
pak::pak("almartin82/mapvizieR")
# Or use devtools
# install.packages("devtools")
devtools::install_github("almartin82/mapvizieR")mapvizieR works with two primary data inputs from NWEA MAP:
- CDF (Comprehensive Data File): Student-level test results
- Roster: Student demographic and enrollment information
library(mapvizieR)
# Read your MAP data files
cdf <- read_cdf("path/to/your/cdf_file.csv")
roster <- read_roster("path/to/your/roster_file.csv")# Create the main mapvizieR object
mapviz <- mapvizieR(
cdf = cdf,
roster = roster,
growth_norms = 2015 # Use 2015 NWEA growth norms
)# Summary statistics
summary(mapviz)
# Get student growth data
growth_df <- mapviz$growth_df
# Filter by specific criteria
filtered <- mv_filter(
mapviz,
roster_filter = quote(schoolname == "My School")
)mapvizieR includes many visualization functions for exploring MAP data:
# Becca Plot: Student-level growth visualization
becca_plot(
mapvizieR_obj = mapviz,
studentids = students$studentid,
measurementscale = "Mathematics",
start_fws = "Fall",
start_year = 2023,
end_fws = "Spring",
end_year = 2024
)# Galloping Elephants: Distribution over time
galloping_elephants(
mapvizieR_obj = mapviz,
studentids = students$studentid,
measurementscale = "Reading",
first_and_spring_only = FALSE
)# HAID Plot: Historical achievement and growth
haid_plot(
mapvizieR_obj = mapviz,
studentids = students$studentid,
measurementscale = "Mathematics",
start_fws = "Fall",
start_year = 2023,
end_fws = "Spring",
end_year = 2024
)mapvizieR 0.4.0 introduces consistent theming for visualizations:
library(ggplot2)
# Use the mapvizieR theme
ggplot(data, aes(x, y)) +
geom_point() +
theme_mapvizier()
# Quartile color scales
ggplot(data, aes(x, y, fill = quartile)) +
geom_col() +
scale_fill_quartile()
# Get color palettes
mapvizier_quartile_colors()
mapvizier_growth_colors()- R >= 4.1.0
- ggplot2 >= 3.4.0
- dplyr >= 1.1.0
mapvizieR includes NWEA norms data for percentile calculations:
| Norm Year | Student Status | School Status | Student Growth | School Growth |
|---|---|---|---|---|
| 2011 | ✅ | - | ✅ | - |
| 2015 | ✅ | ✅ | ✅ | ✅ |
| 2020 | ✅ | ✅ | - | ✅ |
| 2025 | ✅ | ✅ | - | ❌ |
Note: NWEA has not published detailed school conditional growth norms for 2025. The 2025 Technical Manual only provides aggregate growth statistics by grade, not the RIT-conditional tables needed for school growth percentile calculations. Use 2020 school growth norms as the most recent detailed option.
- Package website - Full documentation and vignettes
- Function reference - Complete API documentation
Internal development documentation and analysis is maintained in a private repository. Contributors with repository access can request an invite to mapvizieR-analysis for:
- Architecture documentation
- Code audits and modernization notes
- Test regression analysis
- Implementation planning documents
Contact the maintainers for access.
Contributions are welcome! Please see our contributing guidelines for details.
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
MIT License. See LICENSE for details.
If you use mapvizieR in your research or reporting, please cite it:
@software{mapvizieR,
author = {Martin, Andrew},
title = {mapvizieR: Visualizations and Analysis for NWEA MAP Data},
url = {https://github.com/almartin82/mapvizieR},
year = {2024}
}
- NWEA MAP Growth - The assessment system
- tidyverse - R packages for data science