btskinner/crosswalkr

Rename and encode variables using external crosswalk files

Packages

• [R] rscorecard: R wrapper for U.S. Department of Education College Scorecard Data API
• [R] crosswalkr: Rename and encode variables using external crosswalk files
• [R] duawranglr: R Package to Securely Wrangle Dataset According to Data Usage Agreement
• [R] distRcpp: Compute Geodesic Distance and Distance-Weighted Measures Using Rcpp
• [Python] grm: GitHub CLI classroom manager

Course/lessons focused on coding/quantitative research

• [R] A Gentle Introduction to Bayesian Analysis with Applications to QuantCrit
• [R] EDH7916: Contemporary research in higher education
• [R] Workshop on R
• [Stata] LPO9951: PhD student research practicum

Replication files

Each paper is linked in the repository README or on my webpage (contact me if you need access)

• Skinner, B.T., Burtch, T., and Levy, H. (forthcoming) Variation in broadband access among undergraduate populations across the United States. Research in Higher Education
• Skinner, B.T. & Doyle, W.R. (forthcoming) Predicting postsecondary attendance by family income in the United States using multilevel regression with poststratification. Economics of Education Review
• Skinner, B.T., Levy, H., and Burtch, T. (forthcoming) Digital redlining: the relevance of 20th century housing policy to 21st century broadband access and education. Educational Policy
• Ortagus, J.C., Skinner, B.T. & Tanner, M. (2021). Investigating Why Academically Successful Community College Students Leave College Without a Degree. AERA Open, 7(1), 1-17.
• Skinner, B.T. & Doyle, W.R. (2021). Are civic returns to higher education different across subpopulations? An analysis using propensity forests. Journal of Education Finance, 46(4), 519–562.
• Skinner, B.T. (2019). Making the connection: Broadband access and online course enrollment at public open admissions institutions. Research in Higher Education, 60(7), 960–999.
• Skinner, B.T. (2019). Choosing College in the 2000s: An Updated Analysis Using the Conditional Logistic Choice Model. Research in Higher Education, 60(2), 153–183.
• Doyle, W.R., & Skinner, B.T. (2017). Does Postsecondary Education Result in Civic Benefits? The Journal of Higher Education, 88(6), 863–893.
• Doyle, W.R., & Skinner, B.T. (2016). Estimating the education-earnings equation using geographic variation. Economics of Education Review, 53, 254–267

General

• Pronouns: he/him/his
• Website: btskinner.io

crosswalkr's Issues

Bug in renamefrom() resulting in an error with labelled version 2.1.0

Dear @btskinner

Labelled version 2.1.0 will be released soon. In this new version, additional checking have been added to var_label(). The examples provided with renamefrom() with now produce an error.

It seems that the problem is

crosswalkr/R/renamefrom.R

Line 170 in 56c5c2f

labelled::var_label(.data) <- null_label_list

When exploring the problem with the debugger, it appeared that the value of null_label_list was

$<NA>
NULL

Best regards

Test failure with dev version of haven

   > test_check("crosswalkr")
    1. Failure: Failed to assign proper values (@test-encodefrom.R#67) 
    haven::zap_labels(vec) not equal to `act_vals`.
    Attributes: < Modes: list, NULL >
    Attributes: < Lengths: 2, 0 >
    Attributes: < names for target but not for current >
    Attributes: < current is not list-like >
    target is labelled, current is character

I'm not sure exactly what is causing this, but it's probably related to the changes to the labelled class.

Could you please take a look? I'm planning on submitting haven 2.0 to CRAN on November 7

Drop of columns with upper-case names

Dear @btskinner, first of all, thank you so much for the package!

There is a question relating to the case_ignore and drop_extra arguments. It seems that if both of them are TRUE, it leads to an undeliberate drop is the upper-case names of the .data data frame, as reported in this question.

As I can see, that is connected with the following renamefrom_( ) code chunk

names_ <- names(.data)
if (case_ignore) {
    names_ <- tolower(names_)
}
if (drop_extra) {
    .data <- .data[names_ %in% cw[[raw]]]
    names_ <- names(.data)
    if (case_ignore) {
        names_ <- tolower(names_)
    }
}

So, if case_ignore == TRUE, we first change all the column names to the lower case. Then if drop_extra == TRUE we compare these lower-cased names with the raw ones, which leads to the drop of the columns which names we have just modified ourselves.

Probably, it makes sense to add an additional case_ignore -check into the drop_extra-statement? Something like this:

if (drop_extra) {
    if (case_ignore) {
        .data <- .data[names_ %in% tolower(cw[[raw]])]
    } else {
        .data <- .data[names_ %in% cw[[raw]]]
    }
    names_ <- names(.data)
}  

Best regards