In MIDFIELDR/midfieldr: Tools and Methods for Working with MIDFIELD Data in 'R'
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knitr::opts_chunk$set(fig.path = "man/figures/README-")
midfieldr is an R package that provides tools and methods for studying undergraduate student-level records from the MIDFIELD database.
#| echo: false
#| out.width: "15%"
knitr::include_graphics("man/figures/logo.png")
Introduction
midfieldr provides these functions for manipulating student-level data:
add_completion_status() Determine completion status for every studentadd_data_sufficiency() Determine data sufficiency for every studentadd_timely_term() Calculate a timely completion term for every studentfilter_cip() Filter CIP data to match search stringsprep_fye_mice() Prepare FYE data for multiple imputationselect_required() Select required midfieldr variables
Additional functions for processing intermediate results:
order_multiway() Order categorical variables of multiway data
R packages in examples and vignettes
- Data preparation. We use the data.table system and some base R for data manipulation [@Dowle+Srinivasan:2022:data.table]. To assist users who might prefer other systems, the MIDFIELD Institute website [@midfieldinstitute:2024] includes tutorials providing side-by-side base R, data.table, and dplyr solutions to common data shaping tasks using MIDFIELD practice data.
- Charts. Our preferred package for charts is ggplot2 [@Wickham:2016:ggplot2]. The lattice package [@Sarkar:2008] also offers users comprehensive control over graphical elements (though our lattice experience is no longer current).
Usage
In this example, we gather all students ever enrolled in Engineering and summarize their graduation status (in any major), grouping by race/ethnicity and sex. If you are writing your own script to follow along, we use these packages in this example:
library(midfieldr)
library(midfielddata)
library(data.table)
Load the practice data. Reduce initial dimensions of data tables.
# Load the practice data
data(student, term, degree)
# Reduce dimensions of source data tables
student <- select_required(student)
term <- select_required(term)
degree <- select_required(degree)
# View example result
term
Filter for data sufficiency.
# Initialize the working data frame
DT <- term[, .(mcid, cip6)]
# Filter observations for data sufficiency
DT <- add_timely_term(DT, term)
DT <- add_data_sufficiency(DT, term)
DT <- DT[data_sufficiency == "include"]
DT
Filter for degree-seeking students ever enrolled in Engineering.
# Inner join to filter observations for degree-seeking
cols_we_want <- student[, .(mcid)]
DT <- cols_we_want[DT, on = c("mcid"), nomatch = NULL]
# Filter observations for engineering programs
DT <- DT[cip6 %like% "^14"]
# Filter observations for unique students (first instance)
DT <- DT[, .SD[1], by = c("mcid")]
DT
Determine completion status.
# Add completion status variable
DT <- add_completion_status(DT, degree)
DT
Aggregate observations by groupings.
# Left join to add race/ethnicity and sex variables (omit unknowns)
cols_we_want <- student[, .(mcid, race, sex)]
DT <- student[DT, on = c("mcid")]
DT <- DT[!(race %ilike% "unknown" | sex %ilike% "unknown")]
# Create a variable combining race/ethnicity and sex
DT[, people := paste(race, sex)]
# Aggregate observations by groupings
DT_display <- DT[, .N, by = c("completion_status", "people")]
setorderv(DT_display, c("completion_status", "people"))
DT_display
Reshape and display results.
# Transform to row-record form
DT_display <- dcast(DT_display, people ~ completion_status, value.var = "N", fill = 0)
# Prepare the table for display
setcolorder(DT_display, c("people", "timely", "late"))
setkeyv(DT_display, c("people"))
setnames(DT_display,
old = c("people", "timely", "late", "NA"),
new = c("People", "Timely completion", "Late completion", "Did not complete")
)
#| echo: false
library(gt)
DT_display |>
gt() |>
tab_caption("Table 1: Completion status of engineering undergraduates in the practice data") |>
tab_options(table.font.size = "small") |>
opt_stylize(style = 1, color = "gray") |>
tab_style(
style = list(cell_fill(color = "#c7eae5")),
locations = cells_column_labels(columns = everything())
)
"Timely completion" is the count of graduates completing their programs in no more than 6 years; "Late completion" is the count of those graduating in more than 6 years; "Did not complete" is the count of non-graduates.
Installation
Install from CRAN with:
#| eval: false
install.packages("midfieldr")
Install latest development version from GitHub with:
#| eval: false
install.packages("pak")
pak::pkg_install("MIDFIELDR/midfieldr")
midfieldr interacts with practice data provided in the midfielddata data package. midfielddata is too large for CRAN so we deploy it to a drat package repository. Install midfielddata using:
#| eval: false
install.packages("midfielddata",
repos = "https://MIDFIELDR.github.io/drat/",
type = "source"
)
The installed size of midfielddata is about 24 Mb, so the installation takes some time.
More information
Acknowledgments
References
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.
#| include: false knitr::opts_chunk$set(fig.path = "man/figures/README-")
midfieldr is an R package that provides tools and methods for studying undergraduate student-level records from the MIDFIELD database.
#| echo: false #| out.width: "15%" knitr::include_graphics("man/figures/logo.png")
Introduction
midfieldr provides these functions for manipulating student-level data:
add_completion_status()Determine completion status for every studentadd_data_sufficiency()Determine data sufficiency for every studentadd_timely_term()Calculate a timely completion term for every studentfilter_cip()Filter CIP data to match search stringsprep_fye_mice()Prepare FYE data for multiple imputationselect_required()Select required midfieldr variables
Additional functions for processing intermediate results:
order_multiway()Order categorical variables of multiway data
R packages in examples and vignettes
- Data preparation. We use the data.table system and some base R for data manipulation [@Dowle+Srinivasan:2022:data.table]. To assist users who might prefer other systems, the MIDFIELD Institute website [@midfieldinstitute:2024] includes tutorials providing side-by-side base R, data.table, and dplyr solutions to common data shaping tasks using MIDFIELD practice data.
- Charts. Our preferred package for charts is ggplot2 [@Wickham:2016:ggplot2]. The lattice package [@Sarkar:2008] also offers users comprehensive control over graphical elements (though our lattice experience is no longer current).
Usage
In this example, we gather all students ever enrolled in Engineering and summarize their graduation status (in any major), grouping by race/ethnicity and sex. If you are writing your own script to follow along, we use these packages in this example:
library(midfieldr) library(midfielddata) library(data.table)
Load the practice data. Reduce initial dimensions of data tables.
# Load the practice data data(student, term, degree) # Reduce dimensions of source data tables student <- select_required(student) term <- select_required(term) degree <- select_required(degree) # View example result term
Filter for data sufficiency.
# Initialize the working data frame DT <- term[, .(mcid, cip6)] # Filter observations for data sufficiency DT <- add_timely_term(DT, term) DT <- add_data_sufficiency(DT, term) DT <- DT[data_sufficiency == "include"] DT
Filter for degree-seeking students ever enrolled in Engineering.
# Inner join to filter observations for degree-seeking cols_we_want <- student[, .(mcid)] DT <- cols_we_want[DT, on = c("mcid"), nomatch = NULL] # Filter observations for engineering programs DT <- DT[cip6 %like% "^14"] # Filter observations for unique students (first instance) DT <- DT[, .SD[1], by = c("mcid")] DT
Determine completion status.
# Add completion status variable DT <- add_completion_status(DT, degree) DT
Aggregate observations by groupings.
# Left join to add race/ethnicity and sex variables (omit unknowns) cols_we_want <- student[, .(mcid, race, sex)] DT <- student[DT, on = c("mcid")] DT <- DT[!(race %ilike% "unknown" | sex %ilike% "unknown")] # Create a variable combining race/ethnicity and sex DT[, people := paste(race, sex)] # Aggregate observations by groupings DT_display <- DT[, .N, by = c("completion_status", "people")] setorderv(DT_display, c("completion_status", "people")) DT_display
Reshape and display results.
# Transform to row-record form DT_display <- dcast(DT_display, people ~ completion_status, value.var = "N", fill = 0) # Prepare the table for display setcolorder(DT_display, c("people", "timely", "late")) setkeyv(DT_display, c("people")) setnames(DT_display, old = c("people", "timely", "late", "NA"), new = c("People", "Timely completion", "Late completion", "Did not complete") )
#| echo: false library(gt) DT_display |> gt() |> tab_caption("Table 1: Completion status of engineering undergraduates in the practice data") |> tab_options(table.font.size = "small") |> opt_stylize(style = 1, color = "gray") |> tab_style( style = list(cell_fill(color = "#c7eae5")), locations = cells_column_labels(columns = everything()) )
"Timely completion" is the count of graduates completing their programs in no more than 6 years; "Late completion" is the count of those graduating in more than 6 years; "Did not complete" is the count of non-graduates.
Installation
Install from CRAN with:
#| eval: false install.packages("midfieldr")
Install latest development version from GitHub with:
#| eval: false install.packages("pak") pak::pkg_install("MIDFIELDR/midfieldr")
midfieldr interacts with practice data provided in the midfielddata data package. midfielddata is too large for CRAN so we deploy it to a drat package repository. Install midfielddata using:
#| eval: false install.packages("midfielddata", repos = "https://MIDFIELDR.github.io/drat/", type = "source" )
The installed size of midfielddata is about 24 Mb, so the installation takes some time.
More information
Acknowledgments
References
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.