fanwangecon/recontools

R tools related to panel data analysis and optimization

A Collection of R Functions

This is a work-in-progress website consisting of files for doing multi-dimensional data analysis and other things. Materials gathered from various projects in which R code is used. Files are from Fan's REconTools repository.

From Fan's other repositories: For dynamic borrowing and savings problems, see MEconTools and Dynamic Asset Repository; For code examples, see also Python Example Code (bookdown site), R Example Code (bookdown site), Matlab Example Code and Stata Example Code; For intro econ with Matlab, see Intro Mathematics for Economists, and for intro stat with R, see Intro Statistics for Undergraduates (bookdown site). See here for all of Fan's public repositories.

From other repositories: For dynamic borrowing and savings problems, see MEconTools and Dynamic Asset Repository; For code examples, see also R Code Examples, Matlab Example Code, Stata Example Code, Python Example Code; For intro econ with Matlab, see Intro Mathematics for Economists, and for intro stat with R, see Intro Statistics for Undergraduates. See here for all of Fan's public repositories.

Please contact FanWangEcon for issues or problems.

Installation

This package contains tools used by various projects that use R. To run code from various projects, need to install this package first.

# To Install the Programs in the R folder of the REconTools Repository
devtools::install_github("fanwangecon/REconTools")

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Please contact for issues or problems.

recontools's Issues

Panel Long Attendance Roster to Wide

Following R4Econ, generate function for Panel Long Attendance Roster to Wide.

There are $N$ students in class, but only a subset of them attend class each day. If student $id_i$ is in class on day $Q$, the teacher records on a sheet the date and the student ID. So if the student has been in class 10 times, the teacher has ten rows of recorded data for the student with two columns: column one is the student ID, and column two is the date on which the student was in class. Suppose there were 50 students, who on average attended exactly 10 classes each during the semester, this means we have $10 \cdot 50$ rows of data, with differing numbers of rows for each student.

Now we want to generate a new dataframe, where each row is a date, and each column is a student. The values in the new dataframe shows, at the $Q^{th}$ day, how many classes student $i$ has attended so far.