Tidying and Analyzing 'Coursera' Research Export Data
c(person(given = "Aboozar",
family = "Hadavand", email ="hadavand.a@gmail.com",
role = c("aut", "cre")), person(given = "Jeff",
family = "Leek", role = "aut"),
person(given = "John", family = "Muschelli", role = "aut"))
Tidies and performs preliminary analysis of 'Coursera' research
export data. These export data can be downloaded by anyone who has classes
on Coursera and wants to analyze the data. Coursera is one of the leading
providers of MOOCs and was launched in January 2012. With over 25 million
learners, Coursera is the most popular provider in the world being followed
by EdX, the MOOC provider that was a result of a collaboration between
Harvard University and MIT, with over 10 million users. Coursera has over
150 university partners from 29 countries and offers a total of 2000+
courses from computer science to philosophy. Besides, Coursera offers 180+
specialization, Coursera's credential system, and four fully online Masters
degrees. For more information about Coursera check Coursera's
About page on <https://blog.coursera.org/about/>.
dplyr, purrr, readr, digest, tidytext, tibble, rcorpora, knitr
Aboozar Hadavand [aut, cre],
Jeff Leek [aut],
John Muschelli [aut]

Obtain Suggested Coordinate Reference System Information for
Spatial Data
c(person(given = "Kyle", family = "Walker",
email="kyle@walker-data.com", role=c("aut", "cre")))
Uses data from the 'EPSG' Registry to look up suitable coordinate reference
system transformations for spatial datasets in R.  Returns a data frame with 'CRS' codes
that can be used for 'CRS' transformation and mapping projects.  Please see
the 'EPSG' Dataset Terms of Use at <https://epsg.org/terms-of-use.html> for more information.
sf (>= 0.9), units, dplyr, purrr, mapview

person("Sjoerd", "van der Spoel", , "R@sjoerdvanderspoel.nl", role = c("aut", "cre"))
Provides wrapper functions for running classification and
regression tests using different machine learning techniques, such as Random
Forests and decision trees. The package provides standardized methods for
preparing data to suit the algorithm's needs, training a model, making
predictions, and evaluating results. Also, some functions are provided to run
multiple instances of a test.
testthat, knitr, e1071, randomForest, rpart, adabag,
rmarkdown
Sjoerd van der Spoel <R@sjoerdvanderspoel.nl>